ANALYSIS OF SATELLITE RAINFALL DATA AND GLOBAL RUNOFF
PROCESS TO IMPROVE GLOBAL RUNOFF MODELING
by
WooSuk Han
A dissertation submitted to the faculty of
The University of Utah
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Civil and Environmental Engineering
The University of Utah
August 2010
Copyright © WooSuk Han 2010
All Rights Reserved
The University of Utah Graduate School
STATEMENT OF DISSERTATION APPROVAL
WooSukHan
The dissertation of
has been approved by the following supervisory committee members:
StevenJ. Burian
and by
, Chair
Ch ristine A. Pomeroy
, Member
Elizabeth A. Dudley-Murphy
, Member
Gregory D. Nash
, Member
Rick Forster
, Member
P a lJ. T ika Isk
y
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_______
_
the Department of
6/4/10
bateApproved
6/4/10
Date Approved
6/4/10
DateApproved
6/4/10
DateApproved
6/4/10
Date Approved
_________
Civil and Environmental Engineering
and by Charles A. Wight, Dean of The Graduate School.
'
Chair of
ABSTRACT
Global hydrologic modeling is advancing in response to the needs of global
change studies, water conflict resolution, global hazard forecasting, and more. There
remain many challenges limiting continued advancement. This dissertation describes
research addressing two of the challenges: (1) accuracy of satellite rainfall data and (2)
quantifying factors influencing the rainfall-runoff process in global hydrologic models.
The assessment of satellite rainfall data accuracy is accomplished by comparing
the 3B42 satellite rainfall product from NASA’s Tropical Rainfall Measuring Mission
(TRMM) Multisatellite Precipitation Analysis (TMPA) to rain gage observations in semiarid to humid climatic regions. Although TMPA matches well with rain gage observation
at all locations, TMPA was slightly underestimated for semiarid regions and
overestimated for humid regions. The relative magnitude of TMPA was smaller for
urbanized watersheds and higher intensity events. Based on the analysis of TMPA
accuracy by season and the correlation with temperature and relative humidity, TMPA
was concluded to be more accurate for convective rainfall events.
To study the influence of land slope, land use/cover, and drainage size on the
global rainfall-runoff process, a new global runoff model was developed implementing
the Curve Number (CN) approach. The land slope was found to have a significant
influence. The simulated runoff was consistently overestimated for flat river-basins, but
underestimated for steep river-basins. In addition, river basins with greater human impact
were found to have rainfall-runoff relationships more sensitive to slope.
The last phase of the dissertation research involved the development of a new
relationship to incorporate a slope effect into the global runoff model. Land-slope effect
was accounted for in the model using a land-slope correction computed through a trendline analysis of simulated and observed runoff. The correction was found to provide
improved runoff volume estimates in more than 40% of the river basins. Overall, the
mean absolute error of the runoff estimate was reduced by 33%.
iv
TABLE OF CONTENTS
ABSTRACT…………………………………………………………………………
iii
LIST OF FIGURES…………………………………………………………………
viii
LIST OF TABLES…………………………………………………………………..
xi
ACKNOWLEDGEMENTS…………………………………………………………
xiii
Chapters
1.
2.
3.
INTRODUCTION……………………………………………………………
1
1.1
1.2
1.3
1.4
1.5
1
2
4
6
8
8
Global Water Challenges…………………………………………….
Global Change………………………………………………………..
Global Hazards……………………………………………………….
Global Water Scarcity and Conflict………………………………….
Problem Statement and Research Objectives………………………...
1.5.1
Problem Statement…………………………………………
1.5.2
Research Goal and
Hypotheses…………………………………………………
1.5.3
Contribution………………………………………………..
11
12
LITERATURE REVIEW…………………………………………………….
13
2.1
2.2
Global Satellite Precipitation Data…………………………………...
Global Hydrologic Modeling………………………………………...
13
18
ASSESSMENT OF ACCURACY OF SATELLITE-BASED RAINFALL
ESTIMATES…………………………………………………………………
25
3.1
3.2
3.3
Introduction…………………………………………………………..
Methods………………………………………………………………
3.2.1
Case Study Locations………………………………………
3.2.2
Data………………………………………………………...
3.2.3
Analysis Methods…………………………………………..
Results………………………………………………………………..
3.3.1
Storm Event Comparison…………………………………..
25
27
27
29
31
36
36
3.3.2
Higher Intensity Storm Event Comparison………………...
3.3.3
Hurricanes and Tropical Storm Event Comparison………..
3.3.4
Storm Event Monthly Comparison………………………...
3.3.5
Accuracy Related to Temperature and Relative Humidity...
Conclusion……………………………………………………………
38
38
41
44
47
FACTORS INFLUENCING RAINFALL-RUNOFF IN GLOBAL
RUNOFF MODELS………………………………………………………….
49
3.4
4.
4.1
4.2
Introduction…………………………………………………………..
Methods………………………………………………………………
4.2.1
Global Runoff Model………………………………………
4.2.1.1 Global Satellite Rainfall Dataset………………..
4.2.1.2 Land Use/Cover Dataset………………………….
4.2.1.3 Soil Type Dataset………………………………...
4.2.1.4 Antecedent Moisture Condition (AMC)…………
4.2.1.5 CN Lookup Table………………………………...
4.2.1.6 Land Surface Slope Data…………………………
4.2.2
Case Study Areas…………………………………………..
4.2.3
Base Flow Separation………………………………………
4.2.3.1 Storm Runoff Event Definition…………………..
4.2.3.2 Monthly Runoff Calculation for WMO River
Basins…………………………………………….
4.2.4
Analysis Overview…………………………………………
4.2.4.1 Studying the Rainfall-Runoff Process Using the
CN………………………………………………..
4.2.4.2 Comparative Statistics……………………………
4.2.4.3 Hydrologic Effective Factors…………………….
Results………………………………………………………………..
4.3.1
Land Slope Effect…………………………………………..
4.3.1.1 Case Study Watersheds…………………………..
4.3.1.2 WMO River Basins………………………………
4.3.2
Land Use/Cover Effect……………………………………..
4.3.2.1 Case Study Watersheds…………………………..
4.3.2.2 WMO River Basins………………………………
4.3.3
Drainage Size Effect………………………………………..
4.3.3.1 Case Study Watersheds…………………………..
4.3.3.2 WMO River Basins………………………………
Conclusion……………………………………………………………
72
78
79
81
81
81
84
86
86
87
89
89
89
91
INTRODUCING A LAND SURFACE SLOPE CORRECTION FACTOR
INTO A GLOBAL RUNOFF MODEL……………………………………...
93
5.1
5.2
5.3
93
94
94
4.3
4.4
5.
Introduction…………………………………………………………..
Methods………………………………………………………………
Results………………………………………………………………..
vi
49
52
52
53
55
56
57
59
60
63
66
71
72
72
5.3.1
Land Surface Slope Correction…………………………….
5.3.2
Testing the CN – Land Surface Slope Correction………….
5.3.3
Land Use/Cover Effect……………………………………..
5.3.4
Drainage Size Effect……………………………………….
5.3.5
Global Model Runoff Analysis…………………………….
Conclusion……………………………………………………………
94
102
103
107
107
109
CONCLUSION………………………………………………………………
113
5.4
6.
Appendices
A.
SATELLITE PRECIPITATION DATA…………………………………...
115
B.
LAND USE/COVER DATA (MODIS)……………………………………...
120
C.
SOIL DATA (TERRASTAT) ……………………………………………….
123
D.
GLOBAL SCS CN LOOKUP TABLE………………………………………
126
E.
SNOW-COVERED MONTH…………..…………………………………...
128
REFERENCES.……………………………………………………………………...
vii
131
LIST OF FIGURES
Figure
3.1
Page
Case study areas (U.S. and Cheongju, Korea) (Grids shown are TMPA
grids)..…………………………………………………………………..
28
3.2
Theissen polygons for Houston and Cheongju…………………………
33
3.3
Comparison of spatial average of TMPA and rain gage daily
accumulations for the four case study locations………………………..
34
Scatter plots of TMPA versus rain gage estimates of rainfall
accumulation for all storm events for the four case study areas (in each
plot, the upper left equation represents the linear trendline for the
urban watershed and the lower right equation represents the nonurban
watershed).……………………………………………………………...
37
Scatter plots of TMPA versus rain gage estimates of rainfall
accumulation for the 20% highest intensity (mm/day) events for the
four case study areas (in each plot, the upper left equation represents
the linear trendline for the urban watershed and the lower right
equation represents the nonurban watershed)…………………..............
39
3.6
Storm-based scatter plot graphs (Tropical Storms and Hurricanes)……
42
3.7
Monthly MAE distributions for four case study locations……………..
43
3.8
Average monthly temperature and relative humidity in the urbanized
and nonurbanized river basins for the four case study locations…….....
45
Flowchart outlining the investigation of hydrologic effective factors
on the global rainfall-runoff process…………………………………...
51
4.2
Approach taken by GRM to estimate runoff…………………………...
54
4.3
Slope calculation……………………………………………………….
61
4.4
Approach to calculate the rate of change of surface elevation in the
horizontal and vertical direction (a to i: elevation)…………………….
62
3.4
3.5
4.1
4.5
Case study areas (Grid is TRMM grid cell)……………………………
64
4.6
WMO river basins and GRDC streamflow gage stations………………
67
4.7
The scatter graphs of (a) runoff volume and (b) runoff depth………….
73
4.8
Sensitivity analysis for CN calculation………………………………...
75
4.9
Percentage of human impacted land area in the selected WMO river
basins…………………………………………………………………...
81
Scatter plots of observed and simulated runoff volume for storm
events in the study period for the selected case study watersheds in the
(a) Las Vegas, (b) Houston, (c) Atlanta, and (d) Cheongju regions
(Urban: Upper-left equation and R-squared value, Nonurban: Lowerright equation and R-squared value)……………………………………
83
Plot of the relationship between runoff estimation bias and average
land surface slope………………………………………………………
85
4.12
Relationship between watershed average slope and CN difference……
86
4.13
The relationship between slopes and two human activated groups
(HIR is categorized by higher than the percentage of human activation
land 10% (a), 20% (b), 30% (c), and 40% (d).(HIR: Upper-left
equation and R-squared value, NIR: Lower-right equation and Rsquared value) ………………………………………………………….
88
Relationship between drainage size and storm-by-storm metrics (a)
MAE and Eff, (b) Bias………………………………………………….
90
4.15
CN difference by drainage size distribution……………………………
91
5.1
Flowchart for adjusting the land slope and analyzing method…………
95
5.2
Selected rivers basins used for developing and testing the CN – land
surface slope correction………………………………………………...
96
Scatter plots of (a) developing river-basins group and (b) testing riverbasins group…………………………………………………………….
101
The relationship between slopes and two human impacted groups after
slope-effect adjustment (HIR is categorized by higher than the
percentage of human impacted land 10% (a), 20% (b), 30% (c), and
40% (d).(HIR: Upper-left equation and R-squared value, NIR: Lowerright equation and R-squared value)……………………………………
105
4.10
4.11
4.14
5.3
5.4
ix
5.5
CN difference by drainage size distribution (after implementing the
CN –slope adjustment)…………………………………………………
108
Comparison by latitude (a) model runoff (before and after adjustment),
(b) the percentage of land (Human activation land: 12 to 14 categories
of MODIS, Urban: 13 category of MODIS), (c) the percentage of land
slope, and (d) the world population density (persons/km2(Source:
www.ciesin.org)).....................................................................................
110
TRMM V6 data processing overview
(Source: http://disc.sci.gsfc.nasa.gov/precipitation/TRMM_v6.shtml)..
118
B.1
MODIS land use/cover map……………………………………………
122
C.1
Soil Data ((a) Soil Data from TERRASTAT database, (b) Reclassified
soil data)………………………………………………………………..
125
5.6
A.1
x
LIST OF TABLES
Table
1.1
1.2
Water withdrawal and consumption estimates and projections in km3
(Shiklomanov, 1998) …………………………………………………..
Page
7
The number of countries that have International River Basins (IRB)
(Source: Trans-boundary Freshwater Dispute Database at Oregon
State University (http://www.transboundarywaters.orst.edu))................
9
3.1
Characteristics of case study locations…………………………………
30
3.2
Historical monthly storm types (source: www.myforecast.com and
WAMIS) ……………………………………………………………….
30
Comparison statistics of TMPA and rain gage rainfall estimates for all
storm events…………………………………………………………….
38
Comparison statistics of TMPA and rain gage rainfall estimates for
higher intensity storm events…………………………………………...
40
Characteristics and rainfall accumulations for selected tropical storm
and hurricane events in the Houston case study region (1998 to 2007)..
40
Multiple linear correlation coefficients of climatological parameters (T
and RH) versus storm-by-storm metric (MAE and Eff) for case study
areas…………………………………………………………………….
47
4.1
IGBP (Type 1) land use/cover classification…………………………...
56
4.2
Hydrological soil group (HSG) derived from soil properties (Source:
Hong and Adler, 2008)…………………………………………………
57
CN derived from MODIS land cover classification and hydrological
soil groups (HSGs) under fair hydrological conditions (Source: Hong
and Adler, 2008) ……………………………………………………….
59
Characteristics of case study areas……………………………………..
65
3.3
3.4
3.5
3.6
4.3
4.4
4.5
WMO regions and the number of WMO river basins………………….
68
4.6
The characteristics of selected WMO river basins……………………..
69
4.7
Calculated runoff and runoff ratio……………………………………...
74
4.8
Comparison of CN values calculated using the approach based on
annual data and the average of storm event CN values for 2000………
79
Calculated statistics of the comparison of observed and simulated
storm event runoff volume……………………………………………..
82
The characteristics of the development and testing groups of river
basins (Dev.: relationship development group, Test: testing group)…...
97
The characteristics of the river basins in the development and testing
groups…………………………………………………………………..
98
5.3
Comparison of Observed, Table, and Adjusted CN values…………….
102
5.4
Statistics of model performance for the WMO river basins for the year
2000 based on the before and after CN adjustment comparison……….
103
Slope and R-squared values of trend-line from changed HIR and NIR
groups before and after adjustment (a) HIR (b) NIR…………………..
106
5.6
Statistic on HIR (30%) and NIR for before and after adjustment……...
106
5.7
Statistics for large and small river basin groups before and after
implementing the CN – slope adjustment……………………………...
108
B.1
Land Cover Types of MODIS land use/cover data…………………….
121
C.1
Infiltration rate for Hydrologic Soil Group (Source: McCuen, 1998)…
124
C.2
Characteristics of Soils Assigned to Soil Groups (Source: McCuen,
1998) …………………………………………………………………...
124
Reclassified soil groups from TERRASTAT database to Hydrological
soil group (HSG)……………………………………………………….
124
D.1
Global SCS CN lookup table…………………………………………..
127
E.1
Snow-covered months………………………………………………….
129
4.9
5.1
5.2
5.5
C.3
xii
ACKNOWLEDGEMENTS
I would like to express my appreciation to my research advisor, Dr. Steven J.
Burian, for his constant and invaluable support and advice. He is more than just a
research advisor for me. He gave me countless opportunities to develop myself
academically and advised me through my whole life in the U.S.A. Without his help, this
research would not have been finished. Also, I would like to acknowledge all my
committee members for their time and support. Always, they gave me advice, support,
and encouragement. I would want to thank all my friends, past and present, with whom I
worked in this lab. Also, I would want to thank NASA for their support to conduct this
research.
I would like to appreciate my parents, my grandmother, and my brother for their
constant support and love during my entire life.
Finally, I would like to express my endless thanks to my family: JooSun, my
daughter, JeeMin, and my new child, SooMin. They always encouraged me with constant
support and smiles. Without their support and trust, it is impossible to achieve my goals.
CHAPTER 1
INTRODUCTION
1.1 Global Water Challenges
Water has always been one of the major challenges facing civilization and is
anticipated to be among the most difficult problems in the 21st century (Mays, 2007).
Numerous studies have investigated why ancient civilizations failed (Tainter, 1990;
Alley, 2000; Diamond, 2005; and Linden, 2006) and different theories have been
presented. Several of the proposed theories of reasons for ancient societies disappearing
have highlighted water issues as a main component. Specific water issues identified
include misuses of land and forestry, population growth, and climatic change.
Furthermore, the water problems experienced by ancient civilizations led to ensuing
problems such as invasion and migration that could be attributed to societal weaknesses
induced by water problems.
Looking to the remainder of the 21st century and beyond, the challenges once
faced by individual civilizations in isolated regions of the world are now faced by the
collective population of the world. Climate variability (e.g., drought) and land use change
once caused civilizations such as the Maya civilization to disappear (Peterson and Haug,
2005). Now concerns over these same issues at the global scale in the form of global
2
change are concerns for the entire world’s population. Water management conflicts
between neighboring countries and natural hazards that once affected only local to
regional scales are becoming global issues because of the increased interconnectedness of
those affected regions to the world.
1.2 Global Change
Population growth has driven a significant global change problem in the form of
urbanization and other land use changes. According to the United Nations Population
Fund Association (UNPFA, 1999), approximately 2% of midlatitude land area is covered
by urban areas. Urbanization is accompanied by a range of impacts to the land surface
and surrounding ecosystems. The impacts of the land surface alteration during
urbanization include changes to the local microclimatic affecting temperature, relative
humidity, and near-surface winds (Lin et al., 2007; Um and Ha, 2007). Temperature and
relative humidity changes caused by urbanization contribute to the creation of the wellknown urban heat island (UHI) effect (Oke, 1982). Indirect changes to the urban
microclimate and input of aerosols and air pollutants lead to additional modified local
and regional atmospheric radiation and precipitation (Jin et al., 2005). In hydrologic
terms, urbanization is defined primarily as the increase in impervious areas (e.g., streets,
parking lots, roofs, sidewalks, etc.) that results from urban and residential development
(Dow and DeWalle, 2000). A great number of studies have documented the possible
impacts of land use change on the hydrologic cycle (Klige, 1990; Nijssen et al., 2001;
Groisman and Soja, 2007; Batra et al., 2008). The studies in general concluded that
urbanization contributed to hydrologic cycle modifications such as increased runoff,
3
decreased lag time between precipitation and a runoff, and increased peak flow volume
and magnitude.
In addition and in many ways linked to land use change is climate change
(Barlage et al., 2002; Levy et al., 2004; Li et al., 2009). Modifications to the hydrologic
cycle caused by land use change may in turn cause local to mesoscale climate changes.
And the accumulation of the local to mesoscale changes may affect the regional or global
scale climate. In 2001, Nijssen et al. estimated the hydrologic sensitivity of global rivers
(Amazon, Amur, Mackenzie, Mekong, Mississippi, Severnaya Dvina, Xi, Yellow, and
Yenisei) by climate change using General Circulation Models (GCMs). Climatic change
was simulated by representing greenhouse gas concentrations and modern land surface
parameterizations. They found that the largest hydrologic cycle changes are predicted on
the snow-dominated basins of mid to higher latitudes.
In addition to the documented hydrologic impact of climate change on river
basins, there has been much interest on the impacts to water management policy and
system design and performance. Water-related policy, systems, and infrastructure such as
water resource management, storm-water drainage systems, reservoirs, dams, bridges,
and other hydraulic structures are designed to a specified rainfall rate recurrence based on
frequency analysis of historical data (Anon et al., 1997; He et al., 2006). If climate
change occurs, those systems would be affected, potentially leading to increased failures.
According to Lehner et al. (2006), the frequency of extreme natural disasters such as 100year return period droughts or floods will be higher, leading to a currently defined 100year return period event to be classified in the future (2070 for example) as a 50-year, 25year, or smaller return period event. Recently, studies have also focused on global climate
4
change impacts on hydrologic applications such as water management (Anon, 1997; Lins
and Stakhiv, 1998; Vanrheenen et al., 2004; Purkey et al., 2007) and water-related
infrastructure design (Simonovic and Lanhai, 2004; Arisz and Burrell, 2006; He et al.,
2006). Quantifying the global change impacts on the hydrologic cycle requires a global
hydrology model with enough detail to represent local scale land surface features.
1.3 Global Hazards
Recently, a trend in natural disasters seems to be emerging with droughts and
floods becoming more frequent and impacting larger areas with indirect effects now felt
around the world (Schreider et al., 2000; Brissette et al., 2003). This trend is expected to
continue and perhaps intensify as populations continue to expand into hazard-prone
regions and global change continues to modify the pattern, potential, and severity of
hazards such as floods (Pielke and Downton, 2000; Intergovernmental Panel on Climate
Change, 2001). One of the most serious natural disasters in terms of human impact is
flooding. Compared to other natural disasters, flooding occurs more frequently and has
the potential to cause the greatest loss of life and damage to property (Kim, 2004).
According to the World Disasters Report of 2003, between 1993 and 2002, over 1000 of
the more than 2900 natural disasters were floods and the impacts were 90,000 deaths, 1.4
billion people affected, and approximately $210 billion in damages (US Dollars in 2002).
The rapid increase in global population over the past 50 years has resulted in massive
population migration into flood-prone regions of the world. This is especially true in less
developed countries in East Asia and Africa. Population growth in flood-prone areas in
5
less developed countries is a major problem because flood protection strategies and
control and warning systems typically do not exist.
Although natural disasters such as flood and drought are phenomena that are not
restricted by river basin extent and countries boundaries, most past studies have focused
on the river-basin scale in part because of the limitations of models and data. However,
recently, the importance of global scale natural disasters have been highlighted in Europe,
especially the linkage to global land use change and climatic change (Jones, 1996;
Watson et al., 1997; EEA, 1999; Arnell et al., 2000; Parry, 2000; IPCC, 2001; Voss et al.,
2002). Research has indicated the northern regions of Europe may experience a higher
flood risk due to increased average rainfall, while southern areas may experience
prolonged dry periods. Furthermore, as noted above, the frequency of extreme drought
and flood events may recur more frequently in the future (Lehner et al., 2006). The
important Asia monsoon season may also be impacted, leading to large-scale hazards.
Dairaku et al. (2008), for example, estimated global climatic change effects on the
hydrologic cycle changes associated with the Asian monsoon and found potentially
significant increases in precipitation in the western part of India and the southern edge of
the Tibetan Plateau. Furthermore, the timing of precipitation in some regions may be
altered, leading to less precipitation during the growing season.
In addition to climatic change, land use change, in particular urbanization, can
exacerbate hazards and the consequences of hazards. Urbanization is well-known to
modify the rainfall-runoff response, leading to flashier runoff with higher peak discharges
and volumes. Recently, there have been major flood inundation events impacting major
urban centers. For example, Hurricane Katrina in 2005 caused the death of at least 1,836
6
people and caused $81.2 billion in property damage. There have been many studies
documenting urbanization effects on flood damages (Brown, 1988; Ng and Marsalek,
1989; Kang et al., 1998; Gremilion et al., 2000). Conceptually, urbanization affects
flooding characteristics and increases damage by an increased proportion of precipitation
to runoff, a decreased lag time between precipitation and a runoff, and an increased peak
flow volume and magnitude (Shaw, 1994). Some studies mentioned the relationship
between the urbanization of a watershed and the increase in urban flooding. Brun and
Band (2000) found that there is a threshold of percent impervious cover (approximately
20 – 25%) above which the runoff ratio increases. While it is generally agreed that
flooding volume and damage is changed by watershed urbanization, the specific form of
this relationship is still unclear (Morgan et al., 2004). Morgan et al. (2004) also
mentioned that systematic changes in regional climate enhance or obscure changes in
basin hydrology affected by urbanization and the increase in impervious cover.
Furthermore, there are many studies that reveal evidence of human activities causing
regional or global climatic change and this change will continue into the future
(Intergovernmental Panel on Climate Change, 2001).
Assessing global hydrologic
hazards and the role of urban areas may be facilitated by global hydrology models.
1.4 Global Water Scarcity and Conflict
According to the World Meteorological Organization (WMO) of the United
Nations, between 1967 and 1992, more than half of 2.8 billion people who suffered from
weather-related disasters were affected by drought (Obasi, 1994). The return period of
extreme droughts is projected to be shorter under future global climate conditions
7
(Lehner et al., 2006). The increased risk of drought will be compounded by the increased
water demand of growing populations (Shiklomanov, 1998).
A driving force behind the expanding water management problems is the rapidly
growing demand for water. The United Nations (UN) estimated global population in
2000 to be 6.2 billion and projected population to be 9.2 billion by 2050
(http://www.un.org). Based on current water use rates, the increasing population and
industrial development is projected to increase water demands substantially. Also, many
project current water use rates to further increase as those in developing countries raise
their standard of living expectations. Shiklomanov (1998) estimated the time trends of
global water withdrawal and consumption, as shown in Table 1.1. According to his
estimation, water withdrawal in the world increased continuously from 579 km3 in 1900
to 3765 km3 in 1995 and will be increased to 5137 km3 in 2025. Furthermore, global
water consumption has increased from 415 km3 in 1900 to 2265 km3 in 1995.
Table 1.1 Water withdrawal and consumption estimates and projections in km3
(Shiklomanov, 1998)
Continent
Europe
North
America
Africa
Asia
South
America
Australia
Oceania
Total
(rounded)c
a
1900
37.5a
17.6b
70
29.2
41
34
414
322
15.2
11.3
1.6
0.6
579
415
1940
71
29.8
221
83.8
49
39
689
528
27.7
20.6
6.8
3.4
1065
704
Historical estimates of use
1950 1960 1970 1980
93.8
185
294
445
38.4
53.9
81.8
158
286
410
555
677
104
138
181
221
56
86
116
168
44
66
88
129
860
1222 1499 1784
654
932
1116 1324
59.4
68.5
85.2
111
41.7
44.4
57.8
71
10.3
17.4
23.3
29.4
5.1
9
11.9
14.6
1366 1989 2573 3214
887
1243 1536 1918
1990
491
183
652
221
199
151
2067
1529
152
91.4
28.5
16.4
3590
2192
1995
511
187
685
238
215
160
2157
1565
166
97.7
30.5
17.6
3765
2265
Forecasted use
2000 2010 2025
534
578
619
191
202
217
705
744
786
243
255
269
230
270
331
169
190
216
2245 2483 3104
1603 1721 1971
180
213
257
104
112
122
32.6
35.6
39.6
18.9
21
23.1
3927 4324 5137
2329 2501 2818
Underlined numbers show water withdrawal.
Italic numbers show water consumption.
c
Includes about 270 cubic kilometers in water losses from reservoirs for 2025.
b
8
Water management in the past was a local issue with limited impacts on other
parts of the world. Now, local water management decisions may have implications
regionally and compounding effects globally. The history of water conflict is rich with
numerous short-term and long-term examples (Marty, 2001; Chatterji et al., 2002; Wolf,
2002). Recently, the potential for analyzing water conflicts from a regional to global
scale has emerged (Yoffe et al., 2004; Al Jayyousi, 2007) driven by the tension in
international river basins (IRBs). Around the world, 145 counties have IRBs in their
territories (see Table 1.2). Regional or global scale hydrologic models can play an
important part in the assessment and mitigation of water conflict in IRBs (Katiyar and
Hossain, 2007; Zhao, 2009).
1.5 Problem Statement and Research Objectives
1.5.1 Problem Statement
Water challenges are expanding from a historically local issue to a global scale.
Climate variability, land use change, hazards, and water conflict are historical problems
that have caused ancient civilizations to migrate, collapse, and disappear. The extent of
these problems has increased rapidly from local to global. The rapid growth in scale of
these problems has driven the recent emergence of techniques to analyze water problems
at the global scale. New questions regarding water at the global scale are being posed by
scientists and policy makers (Alcamo, 2003). On one hand, scientists have keen interests
in the large-scale impacts of climate changes, land cover changes, and other global
changes on water (Arnell, 1996). On the other hand, governments are interested in
assessing and setting global priorities to support development of water resources and
9
Table 1.2 The number of countries that have International River Basins (IRB) (Source:
Trans-boundary Freshwater Dispute Database at Oregon State University
(http://www.transboundarywaters.orst.edu))
Percentage within IRBs
Number of countries
90 – 100 %
39
80 – 90 %
11
70 – 80 %
14
60 – 70 %
11
50 – 60 %
17
40 – 50 %
10
30 – 40 %
10
20 – 30 %
13
10 – 20 %
9
0 – 10 %
11
Total Countries
145
protect against potential hazards. Global hydrologic models have emerged as a tool to
address these research and policy questions. Based on increased interest in regional and
global scale water issues, there have been new global hydrologic studies such as the
“World Water Vision” exercise of the World Water Commission (Cosgrove and
Rijsberman, 2000; World Water Commission, 2000), the “Comprehensive Assessment of
Freshwater Resources of the World” supported by a consortium of UN organizations
(Raskin et al., 1997), the World Resources Institute (e.g. WRI, 2000), the United Nations
Environment Programme (UNEP, 2000), and so on (Alcamo et al., 2003). Furthermore,
several global scale hydrology models have been developed (Vorosmarty et al., 1989;
Yates, 1997; Klepper and van Drecht, 1998; Arnell, 1999 a,b; Doll et al., 2003).
A major transformation in the approach to global hydrologic modeling has
recently occurred with the advances in remote sensing technology, data management, and
computational power, permitting global hydrologic modeling to operate with global
databases and to simulate longer periods of study in a relatively short time interval.
Global precipitation data, for example, are now available at near real-time at
10
hydrologically-relevant spatiotemporal scales (tens of kilometers and sub-daily) (Hong et
al., 2005; Hong et al., 2007b; Huffman et al., 2007). Also global hydrologic models are
providing output at the relevant spatial and temporal scales to be used in local to regional
assessments.
Satellite precipitation data have emerged recently as a potential data source to use
in global hydrologic modeling studies, but questions related to accuracy remain to be
answered (Artan et al., 2007). Studies of satellite rainfall data accuracy have concluded
that the accuracy is greatly influenced by geographical and climatic characteristics
(Sharma et al., 2007; McCollum et al., 2000; Curtis et al., 2007). Of the satellite rainfall
data studies few have focused on studies of flood magnitude and fewer have assessed the
impact of urban areas on the accuracy.
Although there have been several advances in the use of satellite precipitation
data in global scale water models (e.g., Vorosmarty et al., 1989; Arnell, 1996; Yates,
1997; Klepper and Van Drecht, 1998; Arnell, 1999a,b; Alcamo et al., 2003; Doll et al.,
2003; Hong et al., 2007a,b; Hong et al., 2008)), these models are operating without an
accounting of factors affecting the rainfall-runoff process at the river-basin scale. An area
of particular need is the rainfall-runoff transformation that has been relatively neglected
in the analysis of global climate change that has focused on land-atmosphere fluxes.
There is a need to analyze the foundation of the rainfall-runoff transformation and the
influence of factors on the process. Once the factors have been elucidated, their impact
must be incorporated into global runoff models.
11
1.5.2 Research Goal and Hypotheses
The goal of this research is to improve the accuracy and increase the confidence
in the output of global hydrology models. Two key areas are addressed. First, an
assessment of the accuracy of global satellite rainfall datasets for urban areas and flood
scale events, two areas of known need, is presented. Second, factors influencing the
global rainfall-runoff process are analyzed and corrections are introduced and tested for
use in a global runoff model. This research was guided by three hypotheses:
•
Hypothesis 1
o Statement: Satellite precipitation data accuracy is different in urban and
nonurban areas and is influenced by climatic factors.
o Research Question: Are satellite precipitation data affected by geographic
(urban and nonurban) and climatic (semi-arid and humid) factors?
•
Hypothesis 2
o Statement: There are several factors influencing the rainfall-runoff process
at the global scale and they affect the accuracy and confidence in output of
global hydrology models.
o Research Question: What factors affect the rainfall-runoff process at the
global scale and what is the effect of those factors on the rainfall-runoff
process?
•
Hypothesis 3
o Statement: Global runoff estimation can be improved by accounting for
hydrologic influencing factors in the model.
12
o Hypothesis Question: Can hydrologic important factors be adjusted to
improve global runoff model results?
1.5.3 Contribution
The research contributions of this dissertation are expected to advance the use of
global satellite rainfall data sources for use in global hydrologic modeling and global
hydrologic studies linked to global change, hazard planning and forecasting, and water
management.
CHAPTER 2
LITERATURE REVIEW
As the interest in studying water issues at the global scale has increased, global
hydrology models have emerged (e.g., Vorosmarty et al., 1989; Arnell, 1996; Yates,
1997; Klepper and Van Drecht, 1998; Arnell, 1999a,b; Alcamo et al., 2003; Doll et al.,
2003; Hong et al., 2007a,b; Hong et al., 2008). There are many current challenges facing
global hydrologic modeling including the large scale, input data uncertainty, and complex
hydrologic processing. This chapter presents a summary of the relevant literature on two
topics relevant to global hydrologic modeling and this dissertation: (1) global satellite
precipitation data and (2) global hydrologic models and factors impacting rainfall-runoff.
The objective is to highlight past research accomplishments, establish the current state,
identify the key areas of research need, and tie them to the dissertation research. To
accomplish this objective, the chapter is divided into two broad sections, one on global
satellite precipitation data and the other on global hydrologic models.
2.1 Global Satellite Precipitation Data
Precipitation is the driving input for hydrologic modeling. It is known to vary
temporally and spatially (Artan et al., 2007). One of the most important tasks to build
reliable hydrologic models is to obtain high accuracy precipitation data in the necessary
14
temporal and spatial resolutions. Largely, three methods have been used to obtain the
precipitation data: rain gages, ground-based radar, and satellites.
Data from individual rain gages and rain gage networks have been used for more
than a hundred years to analyze rainfall-runoff relationships and more recently, to drive
hydrologic models. At the global scale, rain gages present numerous challenges because
they are point estimates of rainfall. Dense networks are possible in very small areas for
specific reasons (e.g., flood warning), but are prohibitively expensive and logistically
challenging for dense coverage of larger areas. Global rain gage coverage at the spatial
and temporal resolution necessary for global hydrologic studies and modeling is thus not
currently considered feasible. Even at the regional and local scale, rain gage coverage is
simply not logistically possible or economically feasible. For example, numerous
countries in East Asia and Africa lack even basic rainfall data infrastructure and services
(Shrestha, 2008).
Another precipitation observation technology is ground-based radar. Groundbased radars have been in service for nearly 50 years in parts of the world. Their limits
for global application are similar to the rain gages – inability for global coverage because
of sparse data coverage and expense and logistical constraints to extend current coverage.
In addition, ground-based radar instruments have constraints imposed in extreme
topographical areas (mountains and inaccessible areas) (Yilmaz, 2005) and with
convective clouds and thunderstorm systems (Nirala and Cracknell, 1998). For large
storm events such as hurricanes, both radar and rain gage observations can have low
accuracy caused by inappropriate Z-R relationships and gage under-catch, and potential
data loss (Curtis, 2007).
15
Recently, advanced remote sensing technology has provided near real-time
rainfall observations at hydrologically-relevant spatiotemporal scales (tens of kilometers
and sub-daily) (Hong et al., 2005; Hong et al., 2007b; Huffman et al., 2007). Also
satellite-based rainfall data is available for near global coverage with a level of
consistency not provided by the merged rain gage and ground-based data products
available. Although several satellite-based rainfall data products have been developed,
the recently developed passive microwave is hypothesized to more reliably capture
rainfall estimation than the earlier developed infrared-based sensor, leading to potential
advances in applications of satellite rainfall data, including flood forecasting (Hossain
and Anagnostou, 2004).
Satellite rainfall data have many advantages such as being efficient and cost
effective for large areas and having continuous and consistent coverage of large areas.
Yilmaz (2005) mentioned that the use of satellite rainfall estimates is more useful than
the use of surface or low altitude precipitation platforms for global hydrology studies.
When spatial resolution increases, physical factors are less complex. Fortunately, low
spatial resolution for large scale study has still considerable scientific and economic value
(Yilmaz, 2005).
In previous research, satellite rainfall data were validated using ground rainfall
data (Ohsaki et al., 1999; Bolen and Chandrasekar, 2003; Datta et al., 2003; Wolff et al.,
2005). In general, satellite rainfall data validation research has found the accuracy can be
affected by many factors such as location, climate, period, and rainfall type (Nicholson et
al., 2003; Barros et al., 2000; Sharma et al., 2007; McCollum et al., 2000; Schumacher
and Houze, 2003; Bowman, 2005; Shin et al., 2001; Zhou et al., 2008). Sharma et al.
16
(2007), for example, compared Tropical Rainfall Measuring Mission (TRMM) 3B42 with
ground-based rain gage data in Nepal. In their study, they mentioned that even very close
locations have very different precipitation volume by geographical characteristics.
However, they found that the TRMM grid value was closely associated with the average
rainfall recorded by rain gages in the TRMM grid area. Also, they found TRMM slightly
overestimated in arid areas and underestimated in humid areas during peak monsoon
season. Therefore, they concluded that TRMM is an acceptable rainfall data product to
use for flood estimation in the Nepal area.
Another study of satellite rainfall data accuracy was archived by Islam and Uyeda
(2007). They analyzed several satellite rainfall data sources (TRMM 3B42 version 5
(V5), 3B42 V5, 3B42 version 6 (V6), 3B43 V6) and the Bangladesh Meteorological
Department rain-gage network in the Bangladesh area during premonsoon to postmonsoon periods. Comparing satellite data with rain-gages, they concluded that satellite
rainfall data is overestimated during the premonsoon season and in arid regions, but
underestimated during the monsoon season and in humid regions. They asserted that the
reason for the differences according to season and location is considered to be the vertical
cross section of convection.
Satellite rainfall data can be influenced by topographical characteristics, too.
Dinku et al. (2008) evaluated the high-resolution satellite rainfall data such as the
NOAA-CPC African rainfall estimation algorithm (RFE), TRMM 3B42, the CPC
morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratory’s
blended product using station networks. They selected two locations: Ethiopia with a
very complex terrain and Zimbabwe with a less rugged topography. They found that
17
although satellites can detect the occurrence of rainfall well, the amount of rainfall in
each pixel was more poorly estimated. A relatively flat area, Zimbabwe, has better
accuracy than the area with complex terrain, Ethiopia. Furthermore, McCollum et al.
(2000) evaluated satellite-based rainfall data from the Global Precipitation Climatology
Project (GPCP). They suggested that GPCP satellite estimates in Equatorial Africa are
reliable, because of two possible explanations related to the physical properties of the air
masses in this region: an abundance of aerosols in central Africa and precipitation that is
associated with convection.
Previous research studies have mentioned that the accuracy of satellite rainfall
data can be effected by several factors such as seasonal trend, rain types (i.e., convective,
stratiform), and influence of climatological factors (Steiner and Smith, 1998; Schumacher
and Houze, 2003; Jiang et al., 2008). In previous research, Jiang et al. (2008) noted the
influence of climatological moisture on tropical cyclones (TCs) using multiple linear
correlation coefficients. The highest multiple correlation coefficient value was 0.7 in
volumetric rain versus maximum wind, total precipitable water (TPW), horizontal
moisture convergence (HMC), and ocean surface flux (OSF) in land and ocean areas.
The accuracy of the satellite rainfall data can be different by geographical or
climatic characteristics. Rapidly increased urban area extents can be new factors affecting
the accuracy of satellite rainfall data, because climatic characteristics can be changed by
the urbanization. According to previous research, satellite rainfall data provide reasonable
rainfall data. However, the validation of accuracy for satellite rainfall data is lacking in
urban areas, which is one of the most important places for water management and flood
control because of dense population and property areas. Urban areas are also important
18
places for validation of satellite rainfall data because of precipitation uncertainty given
potential urban effects (Schreider et al., 2000; Burian and Shepherd, 2005; Shepherd et
al., 2009) and urban-influenced climatic variables, e.g., temperature and relative humidity
(Lin et al., 2007; Um and Ha, 2007).
Although satellite rainfall data offers an effective and economical method for
observing rainfall rates and amounts over large areas, the use of satellite rainfall data in
global hydrologic studies and modeling remains limited partly because data accuracy
remains in question, the datasets are large and cumbersome to use in modeling, and the
impact on hydrologic model errors is uncertain. There have been specific questions raised
about the accuracy of satellite rainfall data and how it is influenced by topographical
characteristics, time, rainfall and cloud types, and so on (Artan et al., 2007).
2.2 Global Hydrologic Modeling
In the past, the predominant spatial unit of hydrologic analysis has been defined
using surface and/or subsurface hydrologic units. For example, the drainage catchment is
used in urban hydrologic studies, the river basin is used in water management (Alcoma et
al. 2003), and the aquifer extent is used in well hydraulics or subsurface contaminant
transport studies. However, recently, the demand for global scale hydrologic studies has
increased to address global change, hazard planning and forecasting, conflict resolution,
and water management needs (Vorosmarty et al., 1989; Arnell, 1996; Yates, 1997;
Klepper and Van Drecht, 1998; Arnell, 1999a,b; Alcamo et al., 2003; Doll et al., 2003).
Those interests have encouraged global scale hydrology studies such as the “World Water
Vision” exercise of the World Water Commission (World Water Commission, 2000;
19
Cosgrove and Rijsberman, 2000), the “Comprehensive Assessment of Freshwater
Resources of the World” supported by a consortium of UN organizations (Raskin et al.,
1997), the World Resources Institute (e.g. WRI, 2000), the United Nations Environment
Programme (UNEP, 2000), and so on (Alcamo et al., 2003).
Based on the increased interest of global scale studies, several global hydrologic
models have been developed (Vorosmarty et al., 1989; Yates, 1997; Klepper and van
Drecht, 1998; Arnell, 1999a,b; Doll et al., 2003). The development of the global
hydrologic model has presented many difficulties such as the complexity of the processes
and the large scale and the limited quality of the input data. Spatial resolution is
especially important for global scale hydrologic models. In global hydrologic modeling,
high spatial resolution can cause the model to be burdened with unnecessary complexity
and detail, while low spatial resolution can cause the model to not capture important
processes. The spatial scale of most global hydrologic models is 0.5º latitude and
longitude because this spatial resolution is the highest resolution for feasible climatic
input parameters (Doll et al., 2003). A 0.5º grid cell has length dimensions of
approximately 1800 to 2700 km2, depending on geographic location (Arnell, 1999a).
Based on the 0.5º latitude and longitude spatial resolution, several global water
models have been developed. Yates (1997) developed a simple global hydrology model,
which computed the monthly water balance without the use of independent data sets like
soil water storage capacity or land cover, but derived the necessary input data from a
climate-vegetation classification dataset. Klepper and van Drecht (1998) developed a
global hydrology model, which works on a daily time step based on the pseudo daily
precipitation data distributed equally over all days of the month. Their model uses a
20
number of independent data sets, including soil, vegetation and other geographical
information, and the model contains a heuristic algorithm to partition total runoff into
surface runoff and groundwater recharge.
Another large scale model was developed by Arnell (1999 a, b). He developed a
macro-scale hydrological model, which can be applied repeatedly over a large geographic
domain, without the need for calibration at the catchment scale. A global scale hydrology
model was also developed by Vorosmarty et al. (1989) (Schloss et al., 1996). The
WaterGAP 2 model considers the human activities using a global water use model, which
is one of two main components, global water use and global hydrology (Alcamo et al.,
2003; Doll et al., 2003). Those macro-scale hydrology models (Vorosmarty et al., 1989;
Arnell, 1999 a,b;; Alcamo et al., 2003; Doll et al., 2003) apply the actual soil moisture
dynamics using pseudo-precipitation from monthly accumulated precipitation data and
the number of wet days in each month. However, many regional or global scale models
need to calibrate using observed data, because of complex hydrological processing, input
data limitation, large scale, and so on.
Although 0.5º latitude and longitude spatial resolution is the highest resolution for
feasible climatic input parameters, this spatial resolution is too coarse to quantify and
adjust factors influencing hydrologic response in global scale hydrologic models,
especially if the model output is to be applied in regional to local scale assessments.
Furthermore, the use of complicated physically-based hydrologic models in regional or
global-scale studies is not efficient because data limitations and complex physical
processing can introduce uncertainties into the model results. In response, relatively
simple approaches have been implemented in global runoff models.
21
Katiyar and Hossain (2007), for example, used an open-book watershed model for
monitoring floods in international river basins. NASA’s real-time satellite (IR-3B41RT)
was used for rainfall data. They concluded that simple conceptual models can reliably
produce results for flood monitoring in nations that have IRBs. Another simple
conceptual rainfall-runoff model that has been applied in global runoff estimation is the
Soil Conservation Service (now called the Natural Resources Conservation Service)
(NRCS) Curve Number (CN) method. Harris and Hossain (2008) suggest it can be used
effectively for global scale modeling, but it has inherent limitations in its applicability
because the method is based on research in much smaller watersheds. CN values are
chosen by land use/cover, soil type, and soil moisture condition. Using CN values and
rainfall depth, runoff depth can be calculated. Although the CN method is simple and
widely used, it has limitations, too. The CN method is a conceptual model. Therefore, it
is impossible to account for retention, detention, ice, snow, and other important
influences on rainfall-runoff. Another limitation is drainage size. Although the strict
limitation of drainage size for the CN method is not mentioned in the National
Engineering Handbook (NEH), NEH 4 suggested that drainage size should be no greater
than 20 mi2 (52 km2), because watersheds for the first CN table construction were
between 0.001 to 186 km2 with most watersheds smaller than 52 km2.
A first attempt at global runoff modeling using global satellite precipitation data
was introduced by Hong et al. (2007). Hong et al. (2007) presented a global runoff model
applying the well-known SCS CN method. The model result is a daily-based 1 km spatial
resolution runoff response, because the finest spatial resolution data is land use and it has
1 km spatial resolution. Therefore, CN values can be specified at 1 km spatial resolution.
22
Fine spatial and temporal resolution has many advantages to characterize and quantify the
factors that influence hydrologic response, including climate factors (e.g., rain type,
intensity, seasonal effect, etc.), topological factors (land slope and drainage size), and
human impact factors (land use/cover change and urbanization).
The global runoff model of Hong et al., (2007) uses three remote sensing global
datasets: rainfall data (TRMM 3B42), land use/cover (Moderate Resolution Imaging
Spectroradiometer (MODIS) land classification data), and soils data (TERRASTAT
database (2003) (http://www.fao.org/AG/agl/agll/dsmw.htm)). The model calculates
pixel-based CN values using land use/cover, soil data, and soil moisture conditions.
Using calculated CN values, runoff depth is calculated using the standard approach
practiced in hydrology for decades.
There is one major advantage to the SCS CN approach in global hydrology
models: avoiding the representation of complex physical processes and yet providing a
reasonable result. However, according to previous micro-scale watershed research, a
number of influencing factors on hydrologic response have been identified as important
for the CN approach, including land slope, land use, and catchment size. Calculated
runoff volume by CN method has been found to be larger than observed runoff for the
flat and larger watersheds. The impact of land use type on CN method results is variable
by location and climatic condition.
Land slope is an important factor in the SWAT (Soil and Water Assessment Tool;
Nietsch et al., 2002), EPIC (Erosion-Productivity Impact Calculator; Williams, 1995),
and SWRRB (Simulator for Water Resources in Rural Basins; Arnold and Williams,
1995) models. According to previous research, the land slope effect is positive; as slope
23
increases, the rainfall-to-runoff coefficient increases. However, research has also found a
negative effect for slope. Garg et al. (2003) mentioned a negative relationship between
slope and CN in the semi-arid region of southwest Oklahoma. Also, in their research,
they found that watersheds with extensively higher or lower average CN and slope
conditions in their landscape should not be used for calibrating the Agricultural Nonpoint
Source Pollution Model (AGNPS) model. Furthermore, VerWeire et al. (2005) found a
divergent negative relationship between CN and average land slope. According to this
previous research, the influence of slope on the CN approach remains uncertain. Garg et
al. (2003) and VerWeire et al. (2005) both used GIS techniques and different pixel sizes
for the GIS analyses, which can have an influence on the results (Hawkins et al., 2009).
Basically, CN values can be determined by land use/cover, soil type, and soil
moisture. However, research has revealed that CN values can be affected by land use in
different locations and under different conditions (Simanton et al., 1977; Hawkins and
Ward, 1998; Rietz, 1999; Rietz and Hawkins, 2000). CN values of some land use types
can be affected by seasonal effects. Price (1998) found distinguishable seasonal patterns
for CN values from several forested watersheds in humid climates. However, in the rainfed agricultural, rangelands, urban, and desert watershed, the seasonal patterns were
shown to be weak.
Drainage size is one factor that can influence the hydrologic response. Although
the strict limitation of drainage size for the CN method is not defined in NEH 4, NEH 4
does suggest that drainage size should be no greater than 20 mi2 (52 km2 ), because
watersheds for the original CN table construction were between 0.001 to 186 km2 with
most watersheds smaller than 52 km2. Research has investigated the drainage size effect
24
on the CN value (Osborn et al., 1980; Osborn, 1983; Simanton et al., 1996). They found
that the CN decreased with increasing drainage area in semi-arid regions, suspected
because of channel transmission losses.
Research has also been focused on climatic and topographical influences on the
CN. There was no restriction for using CN method for storm size, but Chapter 21 of the
NEH4 recommended smaller CN values for 10-day storms for use in designing flood
control structures. However, research has revealed that data-defined CN values have
negative relationships with storm intensity and duration (Van Mullem, 1997; Hawkins
and VerWeire, 2005).
Although extensively studied at the smaller scales, studies of factors influencing
hydrologic response using the CN method have not been adequately studied at the global
scale. In this dissertation research, selected factors that are known to influence the CN are
investigated in the context of a global runoff model. The study includes an assessment of
the influence of slope, land use, and drainage size on the CN and develops a correction
for slope effect for use in a global runoff model.
CHAPTER 3
ASSESSMENT OF ACCURACY OF SATELLITEBASED RAINFALL ESTIMATES
3.1 Introduction
Regional-to-global scale hydrologic and flood modeling is an essential tool
needed to plan for water supply management and flood control (Hossain and Katiyar,
2006; Hossain and Lettenmaier, 2006; Hossain et al., 2007, Katiyar and Hossain, 2007;
Hossain, 2009; Bakker, 2009). Although critical, the application of regional-to-global
scale hydrologic and flood models faces numerous challenges, including model
uncertainty, input data uncertainty and availability, data management constraints,
computational requirements, and incorporation of results into decision making. The major
challenge being addressed in this chapter is the uncertain accuracy of global precipitation
observations.
Rainfall data are among the most critical inputs for global scale hydrology
models. For local-to-regional scale water modeling applications, rain gage networks and
ground-based radar observation systems are the standard rainfall data sources. However,
the development of more advanced satellite technologies has produced near-real time
global rainfall data coverages at a fine enough spatial (tens of kilometers) and temporal
(sub-daily) resolution to serve regional and global hydrology modeling needs (Hossain
26
and Anagnostou, 2004; Hong et al., 2005; Hong et al., 2007b; Huffman et al., 2007; Kidd
et al., 2009). Indeed, satellite rainfall observations have emerged in the past decade as a
viable data source for a wide range of hydrologic applications at the global scale,
including flood modeling and forecasting (Artan et al., 2007; Sharma et al., 2007), water
management (Lakshmi, 2004), hydrologic science (Hong et al., 2006; Shrestha et al.,
2008), and landslide prediction (Hong et al., 2007b).
Previous research has validated satellite rainfall estimates for hydrologic studies
(Ohsaki et al., 1999; Bolen and Chandrasekar, 2003; Datta et al., 2003; Wolff et al., 2005;
Ebert et al. 2007; Shepherd et al., 2007; Hand and Shepherd 2009), although the
influence of location, climate, topography, time period, cloud types, and rainfall types
were found to be important factors affecting accuracy (Dinku et al., 2008; Zhou et al.,
2008; Artan et al., 2007; Islam and Uyeda, 2007; Sharma et al., 2007; Bowman, 2005;
Nicholson et al., 2003; Schumacher and Houze, 2003; Shin et al., 2001; Barros et al.,
2000; McCollum et al., 2000). A small number of these investigations have focused on
validating satellite rainfall observations for use in flood studies and modeling
applications. Sharma et al. (2007), for example, found Tropical Rainfall Measuring
Mission (TRMM) 3B42 RT observations of flood magnitude events in Nepal to be close
to rain gage observations, except during the peak monsoon season when overestimations
were observed in semi-arid areas and underestimations observed in humid areas. Curtis et
al. (2007) assessed the use of TRMM rainfall observations for representing the 1999
Hurricane Floyd. Their volumetric rainfall comparison showed TRMM observations to
have slightly higher rainfall volume estimates compared to ground-based radar and rain
gage observations for the one event.
27
Although conclusions from previous studies have suggested TRMM provides
reasonable estimates of high rainfall intensities, more studies are needed to continue to
corroborate these conclusions for a wider range of case studies consisting of different
climate and landscape regimes (Hong et al., 2007a, b), especially to quantify the
influence of microclimate variables (e.g., temperature and relative humidity). Moreover,
studies of TRMM accuracy for flood scale events are lacking in urban areas, which are
highly susceptible to flood impacts because of proximity to people, high-value property,
and critical infrastructure. Urban areas are also important locations for precipitation
observation uncertainty given potential urban effects (Shepherd et al., 2009; Burian and
Shepherd, 2005; Schreider et al., 2000) and urban-influenced climatic variables, e.g.,
temperature and relative humidity (Um and Ha, 2007; Lin et al., 2007). The study
presented in this chapter was designed to contribute to these areas of need by focusing on
storm events, especially high intensity events, in urban and nonurban areas and
investigating the correlation of accuracy to possibly urban influenced micrometeorological variables.
3.2. Methods
3.2.1 Case Study Locations
For this study, watersheds were selected in the Las Vegas, Houston, Atlanta, and
Cheongju, Korea (Figure 3.1) metropolitan areas. Las Vegas was selected to represent a
semi-arid region exposed to thunderstorms, especially during the North American
monsoon period. Atlanta was selected as an inland, humid city. Houston was selected to
represent a humid, coastal region exposed to high intensity thunderstorm, tropical storm,
Las Vegas
Cheongju
•
•
•
•
•
•
Legend
•
_
o
o
Rain Gauge
Urban Area
Urban Watershed
Nonurban Watershed
,N
o
25 50
100 Kill
~~~
Figure 3.1 Case study areas (U.S. and Cheongju, Korea) (Grids shown are TMPA grids).
_I
29
and hurricane events. Cheongju, Korea was selected to represent a humid city with a cold
season in an area surrounded on three sides by sea, different than the U.S. case studies. In
each case study region, one urbanized river basin and one nonurbanized river basin were
selected for comparison. Criteria for selecting the river basins included (1) being as close
as possible to the headwaters of the larger river basin, (2) having sufficient rain gage and
stream gage observations (specifically having data coverage from 1998 to 2007 to
coincide with the satellite rainfall observations), and (3) not having significant reservoir
or lake storage. Criteria (1) and (3) are essential for subsequent studies related to
hydrologic modeling accuracy to be performed in the future. Relevant characteristics of
the selected case study areas are summarized in Table 3.1.
Historical monthly storm types for the case study watersheds are summarized in
Table 3.2. Convective storm events that tend to have higher rainfall intensity generally
occurred during the summer seasons. In the case study period, more than 70% of all
storm events recorded occurred during periods of convective storm event predominance.
3.2.2 Data
Satellite rainfall estimates from 1998 to 2007 were acquired from the high
spatiotemporal resolution TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42
research version dataset (Huffman et al., 2004, 2007) for the four case study locations.
The TMPA product includes available microwave (e.g., TRMM microwave imager,
Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer
(AMSR) and Advanced Microwave Sounding Unit (AMSU)), and calibrated infrared(IR)
estimates. For comparison to the TMPA rainfall estimates, rain gage data were obtained
30
Table 3.1 Characteristics of case study locations
Area
Urban*
City
Land Use
(km2)
(Km2)
Urban
4000
524
Las Vegas
Nonurban
17550
50
Urban
1450
806
Houston
Nonurban
1400
240
Urban
2700
1642
Atlanta
Nonurban
1800
40
Annual Average
Rainfall** (mm)
110
150
1200
1200
1240
1310
Number of
Rain Gages
7
17
8
9
6
8
Urban
1425
250
1530
17
Nonurban
620
10
1570
14
*Estimated from Moderated Resolution Imaging Spectroradiometer (MODIS) land
classification map (Urban and Built-up lands index)
**Annual Average Rainfall from TMPA from 1998 to 2007
Cheongju
Table 3.2 Historical monthly storm types (source: www.myforecast.com and WAMIS)
Las Vegas
Houston
Atlanta
Cheongju
Jan
F
F
F
F
Feb
F
F
F
F
Mar
F
F
F
F
F: Frontal storm types
C: Convective storm types
Apr
F
F
F
F
May
C
C
C
F
Jun
C
C
C
C
Jul
C
C
C
C
Aug
C
C
C
C
Sep
C
C
C
C
Oct
C
C
F
F
Nov
F
F
F
F
Dec
F
F
F
F
31
from the National Climatic Data Center (NCDC) (www.ncdc.noaa.gov/oa/ncdc.html) for
the three U.S. case study locations and from the Korean Water Resources Management
Information System (WAMIS) (www.wimis.go.kr) for the one Korean case study
location.
Although both rain gage and ground-based radar can be used to compare with
TMPA, in this research, only rain gage data were used because of limitations of groundbased radar data. Ground-based radar instruments have constraints imposed in extreme
topographical areas (mountains and inaccessible areas) (Yilmaz, 2005), convective
clouds and thunderstorm systems (Nirala and Cracknell, 1998), and large storm events
such as hurricanes (Curtis, 2007). Cheongju is in a mountainous area and one of the
important analyses of this research is TMPA accuracy for convective and higher intensity
storms. Therefore, rain gage data were used to investigate TMPA accuracy.
3.2.3 Analysis Methods
The challenge faced in this study, similar to previous TMPA validation studies, is
the disparity between the spatial nature of the satellite-based rainfall measurement and
the point measurement of the rain gage. Previous studies have concluded TRMM data can
be successfully validated if average values are compared (Hand and Shepherd, 2009).
This approach may be manifested as using the average rainfall rate from rain gages
within a TMPA grid or average of rates from several TMPA grids to the average of rain
gages covering the TMPA grids (Sharma et al., 2007). In the study presented herein, the
point measurements from the rain gages are first area-averaged over the river basins
using the Theissen Polygon approach (Bedient and Huber, 2002). The TMPA rainfall
32
rates for the river basins are determined by finding the area-weighted average of the
TMPA grids covering the basin. Figure 3.2 displays the TMPA grid cells and Theissen
Polygon coverage for two of the case study locations.
Previous studies have validated TMPA rainfall rates using daily (Islam and
Uyeda, 2007) and monthly comparisons (Dinku et al., 2007). For most hydrologic
modeling purposes, at the river basin scale, monthly comparisons are too coarse, and
daily data resolution creates a problem because many rainfall events can be longer than
one day and, in some cases, the rainfall event can begin one day and end the subsequent
day. This disparity prevents direct comparison of the events in their entirety. A
preliminary comparison at the daily level was performed to test this potential problem. As
seen in Figure 3.3, the data did not show a clear relationship likely due to time of
measurement (rain gages) and integration (TMPA) differences. Therefore, in this study,
an alternative approach is taken to define the temporal resolution of the analysis. The
rainfall values based on the rain gage records and the TMPA data are first separated into
individual storm events. The division is not made based on a climatological analysis, but
rather using an interevent dry period of one day as the divider. In addition, only storm
events where both TMPA and the rain gages registered rainfall were included to avoid
using days when TMPA registers rainfall, but the storm in the grid cell does not extend
over the rain gage locations (which would not be appropriate for assessing accuracy of
satellite rainfall estimates). Also, it avoids the small intensity storm events that may not
be recorded in the TMPA, a known limitation related to satellite rainfall rate estimation
but not relevant for this study because those smaller events would likely generate
minimal rainfall.
33
Figure 3.2 Theissen polygons for Houston and Cheongju
34
Figure 3.3 Comparison of spatial average of TMPA and rain gage daily accumulations for
the four case study locations.
The study includes several comparisons. First, all storm events are compared for
the urbanized and nonurbanized river basins in the four case study locations. The main
purpose of this comparison is estimation of overall correlation between TMPA and rain
gage data by climatic (humid or semiarid regions) and geographical (urbanized or nonurbanized regions) attributes. Second, the largest 20% of events based on intensity are
used to assess TMPA for higher intensity events of potential importance for flooding.
35
Third, to study extreme rainfall, selected tropical storm and hurricane events are analyzed
for the Houston location. And fourth, storm events are stratified by month to investigate
seasonal trends, effect of rain type (i.e., convective, stratiform), and influence of
climatological parameters (Steiner and Smith, 1998; Schumacher and Houze, 2003; Jiang
et al., 2008) on TMPA data accuracy. Given the accuracy of satellite rainfall estimates
may be influenced by climatological conditions such as moisture availability and vertical
wind shear (Jiang et al., 2008) and urban areas are known to modify moisture in the form
of relative humidity (RH) and temperature (T) (Lin et al., 2007; Um and Ha 2007), the
relative accuracy of TMPA was assessed as a function of local T and RH using 10-yr
monthly average data from the NCDC and Korean Meteorological Administration
(KMA).
Using the rain gage data as the standard, the fit of the TMPA data for the storm
events is assessed based on the scatter plots. In addition, the summary statistics of bias,
mean absolute error (MAE), and efficiency (Eff) are determined based on the storm event
accumulated depths:
Bias =
1
N
∑ (S − G)
1
MAE = N
Eff
(3-1)
∑ (S − G)
G
∑ (S − G)
= 1−
∑ (G − G )
(3-2)
2
2
(3-3)
where G is the average storm event accumulated rainfall depth based on the gages in the
study area, G is gage average for all storm events included in the comparison, S is
36
average accumulated rainfall depth based on the TMPA grids covering the river basin,
and N is the number of storm events.
3.3. Results
3.3.1 Storm Event Comparison
Scatter plots in Figure 3.4 display the accumulated storm event rainfall depths
observed by TMPA versus rain gage for storm events in the 1998 to 2007 record. Table
3.3 lists the summary comparison statistics. The slope of the linear trend is included in
the plots to indicate the relative relationship of TMPA to rain gage. A trendline slope
greater than one indicates TMPA is in general underestimating and a slope less than one
indicates TMPA is overestimating storm event rainfall. Distinct patterns were noted in
the different climatic and urbanized case studies. First, semiarid and humid regions
display different TMPA accuracy. The TMPA data for the Las Vegas case study had the
smallest bias (slightly negative meaning TMPA is slightly underestimating), but showed
the smallest difference between urban and nonurban river basins. The humid locations
(Houston, Atlanta, and Cheongju) all illustrated significant positive bias, suggesting
consistent overestimation, except for the Atlanta urbanized watershed. In general, the
results suggest TMPA underestimates rainfall in semi-arid regions and overestimates in
humid regions. Considering the differences of TMPA accuracy in urban and
nonurbanized watersheds, one notes TMPA to be less overestimated (more accurate) in
the urbanized watersheds in the humid case study locations of Houston and Atlanta. Very
small differences were noted between the urban and nonurban watersheds in Las Vegas
37
Figure 3.4 Scatter plots of TMPA versus rain gage estimates of rainfall accumulation for
all storm events for the four case study areas (in each plot, the upper left equation
represents the linear trendline for the urban watershed and the lower right equation
represents the nonurban watershed).
38
Table 3.3 Comparison statistics of TMPA and rain gage rainfall estimates for all storm
events
# Storms Bias
MAE
Eff
Urban
249
-1.8
0.56
0.66
Las Vegas
Nonurban
379
-1.1
0.45
0.82
Urban
508
3.1
0.53
0.74
Houston
Nonurban
546
11.4
0.74
0.46
Urban
552
-2.1
0.42
0.72
Atlanta
Nonurban
572
4.1
0.52
0.61
Urban
468
5.0
0.53
0.72
Cheongju
Nonurban
441
8.9
0.66
0.68
and Cheongju, which will be further analyzed below in the context of urban effects on
precipitation processes.
3.3.2 Higher Intensity Storm Event Comparison
Figure 3.5 displays the scatter plots of the TMPA versus rain gage storm event
rainfall accumulation estimates for the 20% most intense events (based on mm/day
intensity). Table 3.4 lists the comparison statistics. Interestingly, the linear trendline fit to
the scatter plots indicates the TMPA is less overestimated or more underestimated,
suggesting a decreasing trend in TMPA rainfall estimates relative to rain gage estimates.
The MAE statistic indicates overall the TMPA estimates are more accurate for higher
intensity events than for all storm events (Figure 3.3).
3.3.3 Hurricanes and Tropical Storm Event Comparison
In order to estimate TMPA accuracy for extreme intensity and wind speed events,
four tropical storms and two hurricanes were selected from the 1998-2007 TMPA data
record for the Houston case study location (Table 3.5). The accumulated rainfall depth
for the entire event duration was estimated by spatial average of rain gages and TMPA
39
Figure 3.5 Scatter plots of TMPA versus rain gage estimates of rainfall accumulation for
the 20% highest intensity (mm/day) events for the four case study areas (in each plot, the
upper left equation represents the linear trendline for the urban watershed and the lower
right equation represents the nonurban watershed).
40
Table 3.4 Comparison statistics of TMPA and rain gage rainfall estimates for higher
intensity storm events
# Storms Bias
MAE
Eff
Urban
50
-8.6
0.46
0.35
Las Vegas
Nonurban
76
-5.8
0.86
0.88
Urban
102
0.8
0.38
0.67
Houston
Nonurban
110
22.9
0.48
0.38
Urban
111
-8.5
0.34
0.54
Atlanta
Nonurban
115
2.6
0.37
0.44
Urban
94
2.0
0.36
0.65
Cheongju
Nonurban
89
9.2
0.42
0.69
Table 3.5 Characteristics and rainfall accumulations for selected tropical storm and
hurricane events in the Houston case study region (1998 to 2007).
Storm
Storm
Rain Gage
TMPA
Begin
Duration
Depth
Depth
Name
Type
Date
(days)
(mm)
(mm)
Charley
Tropical Storm
8-21-98
6
56
72
Frances
Tropical Storm
9-7-98
6
320
233
Allison
Tropical Storm
6-5-01
6
280
188
Claudette Hurricane
7-14-03
4
30
41
Grace
Tropical Storm
8-30-03
7
102
101
Rita
Hurricane
9-23-05
3
8
22
41
grid cells covering the case study basins. The accumulated rainfall depths of Tropical
Storm Charley and Hurricanes Claudette and Rita are relatively small because those
storms had either dissipated by the time they reached the case study watersheds or they
only partially covered the watersheds. Overall, the TMPA accuracy is within +/- 50%
difference in total storm depth, consistent with observations by Curtis et al. (2007) for
1999 Hurricane Floyd. It is important to note the TMPA accuracy trends with storm event
magnitude are similar to the observed relative accuracy for all storm events and higher
intensity storm events. As the rainfall event magnitude increases, TMPA tends to
underestimate the event magnitude (Figure 3.6).
3.3.4 Storm Event Monthly Comparison
Monthly distributions of MAE are shown in Figure 3.7. Monthly Eff plots were
also produced but provide similar insight and are not shown. There is a clear distribution
in the MAE, showing lower errors during the warm season months from June to
September. The distribution is similar in the urbanized and nonurbanized basins. The
average warm season months MAE for all the cities is 0.49, which is significantly less
than the average for the other months (0.75). The improved accuracy in the warm season
is especially evident in Houston and Cheongju with moderate improvement in Atlanta
and Las Vegas. One possible explanation for the better performance in the warm season
is that the rainfall is more convective with higher rainfall intensities. Heavier rainfall
rates in warm seasons have been found to be accurately estimated in satellite precipitation
data products (TRMM 3B42 and Precipitation Estimation from Remotely Sensed
Information using Artificial Neural Networks (PERSIANN)) (Zhou et al., 2008).
42
Gage-TMPA (mm)
120
80
40
0
-40
0
100
200
300
400
Rain Gage (mm)
Figure 3.6 Storm-based scatter plot graphs (Tropical Storms and Hurricanes)
Figure 3.7 Monthly MAE distributions for four case study locations.
44
TMPA accuracy for Cheongju and Houston were significantly improved, though
Atlanta is less sensitive during warm seasons. One possible explanation is the distance
from the coastal area. Cheongju (Korea) and Houston are close to coastal areas (less than
160 km), but Atlanta is more than 500 km from the coast. According to Ebert et al.
(2007), the accuracy of satellite precipitation over the ocean is higher than over land
because the passive microwave algorithms, which are used in TMPA, can take advantage
of the microwave emission channels. The close proximity of Houston and Cheongju to
coastal areas may increase accuracy, especially during the warm season with higher T
and RH.
3.3.5 Accuracy Related to Temperature and Relative Humidity
In the previous sections, it was revealed that TMPA accuracy is related to storm
event magnitude and season. In this section, the effect of temperature (T) and relative
humidity (RH) on TMPA accuracy is investigated. Of special interest is identifying a
possible explanation of the urban – nonurban TMPA accuracy differences that may be
related to urban-modified T and RH and hypothesized impact on enhanced convection.
Figure 3.8 displays the monthly T and RH observations in the urbanized and nonurbanized river basins for the four case study locations. Generally, T is higher and RH is
lower in the urban watersheds compared to the nonurban watersheds, which is consistent
with numerous previous studies of urban effects on microclimate (Souch and Grimmond,
2006; Hidalgo et al., 2008; Seto and Shepherd, 2009). The RH increase in semi-arid Las
Vegas is different than the trend in the humid cities of Atlanta and Cheongju and may be
attributed to water importation and application for landscape irrigation (Grimmond et al.,
Figure 3.8 Average monthly temperature and relative humidity in the urbanized and nonurbanized river basins for the four case study
locations.
46
1986; Shepherd, 2006). The higher RH in the urbanized watershed in the Houston region
can be attributed to proximity to coastal areas and the nonurbanized watershed being
further inland.
Building on the seasonal analysis and to specifically investigate T and RH effect
on TMPA accuracy, multiple linear correlation coefficients between monthly averaged
climatic factors (T and RH) and monthly storm-by-storm accuracy metrics (MAE and
Eff) were calculated. The results are summarized in Table 3.6. Although there is not a
distinguishable relationship between urbanized and nonurbanized watersheds, the
correlation of the accuracy metrics with T suggest a strong relationship. In previous
research, Jiang et al. (2008) noted the influence of climatological moisture on tropical
cyclones (TCs) using multiple linear correlation coefficients. The highest multiple
correlation coefficient value was 0.7 in volumetric rain versus maximum wind, total
precipitable water (TPW), horizontal moisture convergence (HMC), and ocean surface
flux (OSF) in land and ocean areas. In the research presented here, the multiple
correlation coefficients of T versus MAE and Eff in Houston and Cheongju regions are
higher than 0.7, which suggests a strong relationship. This is consistent with the
relationship noted in the seasonal analysis, with higher temperatures expected in the
summer months. The combined seasonal, monthly, and T regression analysis results
suggest the potential explanation for the observed higher accuracy of TMPA in Houston
and Cheongju urban watersheds is an urban effect on T and resulting heightened
convective activity.
47
Table 3.6 Multiple linear correlation coefficients of climatological parameters (T and
RH) versus storm-by-storm metric (MAE and Eff) for case study areas
Multiple linear
correlation coefficient
T vs. MAE and Eff
RH vs. MAE and Eff
Las Vegas
Urban Non
0.42
0.52
0.44
0.50
Houston
Urban Non
0.81
0.74
0.14
0.33
Atlanta
Urban Non
0.53
0.38
0.12
0.19
Cheongju
Urban
Non
0.80
0.94
0.81
0.71
AVG
0.64
0.40
3.4. Conclusion
In this section, the TMPA accuracy was evaluated for different geographical
conditions (semi-arid and humid regions and urbanized and nonurbanized watersheds),
storm types (all storms, higher intensity storms, and tropical storms and hurricanes), and
climatological conditions (T and RH). TMPA estimates were, in general, found to be
highly accurate in semi-arid and humid regions and in urbanized and nonurbanized
watersheds. TMPA was found to be slightly underestimated in the semi-arid region and
slightly overestimated in the humid regions. The rainfall accumulation estimated by
TMPA was found to decrease relative to the amounts estimated by rain gages in
urbanized watersheds and for higher intensity storm events. In general, the TMPA
estimates were also found to be overestimated for smaller rainfall events and
underestimated for the larger events. This trend was noted for all storm events, higher
intensity storm events, and for tropical storms and hurricanes. The seasonal analysis
indicated TMPA has higher accuracy during the warm seasons in all four case study
locations, especially in the coastal areas of Houston and Cheongju. The analysis of the
relationship of TMPA accuracy to temperature and relative humidity suggests a strong
relationship of increased temperature and increased TMPA accuracy, corroborating the
seasonal observations. The observations of improved TMPA accuracy for higher intensity
and warm season events and the relationship of improved accuracy and increased
48
temperature in general support the conclusion of improved TMPA accuracy for
convective rainfall events compared to frontal events. This conclusion also provides a
hypothesized explanation for the observed improved accuracy of TMPA in the urbanized
case study watersheds in the humid regions. It is suggested the increased TMPA accuracy
in the urban areas may be explained by urban-enhanced temperatures affecting
convection and precipitation processes.
CHAPTER 4
FACTORS INFLUENCING RAINFALL-RUNOFF
IN GLOBAL RUNOFF MODELS
4.1 Introduction
Recently, global scale hydrologic models have emerged to assess impacts of
global change, to forecast hazards at the global scale, and to address water conflicts in
international river basins (Vorosmarty et al., 1989; Arnell, 1996; Yates, 1997; Klepper
and Van Drecht, 1998; Arnell, 1999a,b; Alcamo et al., 2003; Doll et al., 2003). The
models have been advanced by the development of improved satellite data acquisition,
data management, and computational power. To continue to advance the models, input
data improvements and model improvements are needed. The previous chapter addressed
the need for improved input data by assessing the accuracy of satellite precipitation
inputs to global hydrologic models. This chapter seeks to build on the study of the global
precipitation data accuracy by investigating the factors that may be affecting the global
rainfall-runoff process, but are not accounted for in the global hydrologic models.
The approach employed in this chapter involves the analysis of rainfall-runoff
data and global runoff model results for the same 10-yr period used in the previous
chapter to identify factors that significantly influence the rainfall–runoff relationship. An
important aspect of this study is the use of a finer spatial resolution in the analysis (1 km)
50
compared to most existing global hydrologic models (0.5 º), permitting a much higher
fidelity assessment.
Common factors influencing the hydrologic response at the catchment and river
basin scale have been determined in past research to be land slope (Arnold and Williams,
1995; Williams, 1995; Nietsch et al., 2002; Garg et al., 2003; VerWeire et al., 2005), land
use/cover (Simanton et al., 1977; Hawkins and Ward, 1998; Price, 1998; Rietz, 1999;
Rietz and Hawkins, 2000), drainage size (Osborn et al., 1980; Osborn, 1983; Simanton et
al., 1996), and storm characteristics (Van Mullem, 1997; Hawkins and VerWeire, 2005).
Although there have been studies of factors influencing the hydrologic response at the
catchment and river-basin scales, there was no study completed at the global scale or of
global hydrologic models. Although likely important, the effect of storm event
characteristics on global rainfall-runoff was not included in this study because of the
challenges isolating storm event statistics for a large enough sample of watersheds.
Rather, the land surface slope, land use/cover, and the drainage area size were selected as
factors to include in the study.
The main objective of the research presented in this chapter is to assess the
influence of selected factors on the rainfall-runoff process in global runoff models. The
study process flow is presented in Figure 4.1. The investigation to quantify the effect of
the factors was achieved by analyzing observed and simulated runoff. The observed
runoff dataset was created from observed streamflow data from the USGS, WAMIS, and
GRDC. The simulated runoff dataset was calculated using the global runoff model
(GRM) based on the inputs of global precipitation, land use/cover, and soil type. The
study incorporated two analysis components: (1) case study watersheds and (2) WMO
Rainfall
Land Use
Soil
Observed
Streamflow
SRTM DEM
Land Slope Map
Global Runoff Model
Land Slope
Simulated Runoff
Land Use/Cover
Drainage Size
Observed Runoff
Potential Hydrologic Effective Factors
Case Study
Watersheds
WMO River
Basins
Storm Events
Monthly
Comparison
Snow Cover Map
Analyses of Global Hydrologic Effective Factors
-
CN Difference (WMO River-basins)
Trend-line (Case Study Watersheds)
Statistics (Case Study Watersheds)
Identify Hydrologic Effective
Factors on Global
Rainfall-Runoff Process
Figure 4.1 Flowchart outlining the investigation of hydrologic effective factors on the global rainfall-runoff process.
52
river basins. Storm event analysis is performed in the case study watersheds, but a
monthly analysis is necessary for the WMO watersheds because of their large size. To
assess the influence of the selected factors on the rainfall-runoff response in the case
study watersheds, trends and comparison statistics are considered. For the WMO river
basins, the difference in CN values based on observed and simulated runoff is the basis
for the assessment.
4.2 Methods
4.2.1 Global Runoff Model
A Global Runoff Model (GRM) based on the model from Hong et al. (2007a) was
used in this study to investigate the hydrologic effective factors. The GRM implements
the Curve Number (CN) approach to compute runoff. The CN method was developed in
the 1950s by the Soil Conservation Service (SCS) (Now the Natural Resources
Conservation Service (NRCS)) and it has been widely applied to compute direct runoff
given a rainfall input using land use/cover, soil, rainfall depth, and soil moisture
condition (Hawkins et al., 2009). The approach has recently been extended for
application in global runoff models (e.g., Hong and Adler, 2008).
The GRM is a grid-based model and the CN values are determined for each grid
cell using three global datasets: daily satellite rainfall data, land use/cover, and soil data.
In order to determine the CN value by each pixel, a global CN lookup table presented by
Hong and Adler (2008) is used for average antecedent soil moisture condition. This
global CN lookup table was extended to dry and wet antecedent soil moisture conditions
using an adjustment of CN (McCuen, 1998). The extended global CN lookup table is
53
included in Appendix D. To obtain the CN values, the main input data for GRM were
exactly the same as Hong’s CN lookup approach: rainfall (TMPA 3B42), land use/cover
(MODIS), and soil type (TERRASTAT). An overview of the GRM runoff estimation
process flow is shown in Figure 4.2.
The antecedent soil moisture condition is determined using the previous five days
of rainfall. The CN value is then determined for each 1-km2 model pixel using the
antecedent moisture condition, the land use/cover type, and the soil type. After
determining the CN values, the GRM computes the runoff depth for each pixel following
the standard equations used in the CN hydrologic approach. The runoff depth/volume is
summed to determine the runoff volume for selected hydrologic units (e.g., river basins).
The model is executed at a daily time step.
4.2.1.1 Global Satellite Rainfall Dataset
One of the three input datasets to the GRM is global precipitation. For this study,
the TMPA 3B42 data product (research version) from TRMM was used. More details
about the TRMM data products used in this research are included in Appendix A. TMPA
3B42 data have 3-hour temporal resolution and 0.25º spatial resolution. TMPA 3B42 data
products cover from 180º West to 180º East longitudes and 50º North and 50º South
latitude.
TMPA
data
is
obtained
from
the
NASA
TRMM
web-site
(http://trmm.gsfc.nasa.gov/) in zip file format. After unzipping the data, TMPA data is in
HDF (Hierarchical Data Format). To add this data to a Geographic Information System
(GIS) such as ArcGIS, HDF files were converted to ASCII text files using the “Orbit
viewer” program (http://tsdis.gsfc.nasa.gov), which is an HDF file viewer and convertor.
54
Land Use/Cover
Data
Soil Data
5 days Antecedent
Rainfall Data
Calculate Soil Moisture
Condition
Determine CN using
CN lookup table
Calculate
Runoff
Global Runoff Map
(Daily 1km resolution)
Figure 4.2 Approach taken by GRM to estimate runoff
Daily Rainfall
Data
55
The 3-hour TMPA data was aggregated to daily values for use in the GRM.
4.2.1.2 Land Use/Cover Dataset
The
Moderate
Resolution
Imaging
Spectroradiometer
(MODIS)
land
classification map was used for this study to define land use/cover classes. MODIS has
four data product categories: 1) MODIS level 1 data, geo-location, cloud mask, and
atmosphere products, 2) MODIS land products, 3) MODIS cryosphere products, and 4)
MODIS ocean color and sea surface. The category 2) MODIS land products, which have
1-km spatial resolution, were selected for this study following the approach of Hong et al.
(2007 a). The MODIS land use data products have five types of land cover classifications
distinguished by a supervised decision tree classification method. Land Cover Type 1 is
the
IGBP
(International
Geosphere-Biosphere
Programme)
global
vegetation
classification scheme. The other classification schemes include the University of
Maryland modification of the IGBP scheme, UMD, (Land Cover Type 2), the MODIS
LAI/FPAR (Land Cover Type 3) scheme, the MODIS Net Primary Production (NPP)
(Land Cover Type 4) scheme, and the Plant Functional Types (PFT) (Land Cover Type
5). The IGBP Type 1 dataset is used here. The IGBP Type 1 has 18 land use/cover
categories at 1-km spatial resolution. The IGBP (Type 1) land use/cover classification is
shown in Table 4.1. Among 18 land use/cover classes, classes (12, 13, and 14) are human
impacted land use/cover. Additional details of the land use/cover categories are included
in Appendix B. Land use/cover data has the finest spatial resolution (1 km) among the
three main input datasets.
56
Table 4.1 IGBP (Type 1) land use/cover classification
Class IGBP (Type 1)
0
Water
1
Evergreen needleleaf forest
2
Evergreen broadleaf forest
3
Deciduous needleleaf forest
4
Deciduous broadleaf forest
5
Mixed forests
6
Closed shrublands
7
Open shrublands
8
Woody savannas
9
Savannas
10
Grasslands
11
Permanent wetlands
12
Croplands
13
Urban and built-up
14
Cropland/natural vegetation mosaic
15
Permanent snow and ice
16
Barren or sparsely vegetated
254 Unclassified
4.2.1.3 Soil Type Dataset
The soil type dataset used in the GRM is obtained from the TERRASTAT
database (2003) (http://www.fao.org/AG/agl/agll/dsmw.htm). The spatial resolution of
the soil type dataset is 10 km and it covers the entire Earth. The TERRASTAT soil type
dataset is reclassified to 4 Hydrologic Soil Groups (HSG) for applying the CN lookup
table. In order to reclassify, the minimum infiltration rate (mm/hr) and characteristics of
HSG are used (see Appendix C). In the Hong et al. (2007) model, following the USDA
(1986) handbook, four HSGs are derived from these soil properties (Table 4.2). The same
classifications are used here.
57
Table 4.2 Hydrological soil group (HSG) derived from soil properties (Source: Hong and
Adler, 2008)
HSG USDA soil
Earth’s
texture class Soil Content
Surface, Property
%
A
1, 2, 3
Sand, loamy and or
4.69
Low runoff potential and high
sandy loam types of
infiltration rates even when
soils
thoroughly wetted; consist chiefly of
deep, well to excessively drained
sands or gravels
B
4, 5, 6
Silt loam, loam, or
8.41
Moderate infiltration rate and consist
Silt
of soils chiefly with moderately fine
to moderately coarse textures
C
7
Sandy clay loam
3.98
Low infiltration rates when
thoroughly wetted and consist
chiefly of soils with moderately fine
to fine structure
D
8, 9, 10,
Clay loam, silty clay 5.78
Highest runoff potential, very low
11, 12
loam, sandy clay,
infiltration rates when thoroughly
silty clay or clay
wetted and consist chiefly of clay
soils
0
0
Water bodies
65.55
N/A
-1
13
Permanent ice/snow 11.59
N/A
Modified from USDA (1986) and NEH-4 (1997) lookup tables.
4.2.1.4 Antecedent Moisture Condition (AMC)
The Antecedent Moisture Condition (AMC) has an important influence on the
rainfall-runoff process at a range of scales (McCuen, 1998). It is well known to be an
important element in estimating the CN; therefore, it is accounted for in the modeling
component of this research. Hong’s global runoff model (Hong et al., 2007 a) applied
AMC to estimate time-variant CN values. In this dissertation research, the AMC is
calculated using the same method. AMC is categorized by three different soil moisture
conditions that specify different CN values (Hawkins, 1993). The descriptions of the
three soil moisture conditions are:
58
•
Condition I (Dry): Soils are dry but not to wilting point; satisfactory
cultivation has taken place
•
Condition II (Average): Average moisture levels
•
Condition III (Wet): Heavy rainfall, or light rainfall and low temperatures
have occurred within the last five days; saturated soil
The change of AMC is highly correlated with antecedent precipitation (NRCS,
1997). Antecedent precipitation can be calculated by the Antecedent Precipitation Index
(API) (Kohler and Linsley, 1951). A common API equation is shown as Equation 4-1:
API =
−T
∑Pk
−t
t
t = −1
(4-1)
where T is the number of antecedent days, k is the decay constant (k is generally between
0.80 and 0.98 (Viessman and Lewis, 1996), k=0.85 in this research), P is the precipitation
during day t, and P is average daily precipitation.
Hong et al. (2007a, b) applied API for estimating the AMC. Generally, the
previous five days of precipitation data are used to estimate the API (NRCS, 1997). They
used a Normalized API (NAPI) for obtaining a time-variant CN. The NAPI equation is:
−T
∑Pk
−t
t
NAPI = t =−1−T
P ∑ k −t
(4-2)
t =−1
Dry, wet and average AMC are defined by the NAPI values (Dry: NAPI < 0.33, Wet:
NAPI >3, and Average: 0.33 < NAPI < 3).
59
4.2.1.5 CN Lookup Table
Given the land use/cover type, HSG, and AMC for an area, the CN value can be
determined. CN lookup tables have been developed for global runoff model applications
using the USDA (1986) and NEH-4 (1997) standard lookup tables (Hong and Adler,
2008) (see Table 4.3). Using the method of Hong et al. (2007 a) and adjustment of CN for
dry and wet AMC (McCuen, 1998), CN values for wet and dry conditions were added to
Hong’s global CN lookup table for use in this research. The resulting table is included in
Appendix D.
Table 4.3 CN derived from MODIS land cover classification and hydrological soil groups
(HSGs) under fair hydrological conditions (Source: Hong and Adler, 2008)
Hydrological
condition
CN for different HSG
(ABCD)
(poor/fair/
MODIS land cover classification
ID
Content
A
B
C
D
good)
0
Water bodies
N/A
N/A
N/A
N/A
N/A
1
Evergreen needles
34
60
73
79
Fair
2
Evergreen broadleaf
30
58
71
77
Fair
3
Deciduous needle leaf
40
64
77
83
Fair
4
Deciduous broadleaf
42
66
79
85
Fair
5
Mixed forests
38
62
75
81
Fair
6
Closed shrublands
45
65
75
80
Fair
7
Open shrublands
49
69
79
84
Fair
8
Woody savannas
61
71
81
89
Fair
9
Savannas
72
80
87
93
Fair
10
Grasslands
49
69
79
84
Fair
11
Permanent wetlands
30
58
71
78
Fair
12
Croplands
67
78
85
89
Fair
13
Urban and built-up
80
85
90
95
Fair
14
Cropland/natural vegetation
52
69
79
84
Fair
mosaic
15
Permanent snow and ice
N/A
N/A
N/A
N/A
N/A
16
Barren or sparsely vegetated
72
82
83
87
Fair
17
Missing data
N/A
N/A
N/A
N/A
N/A
Modified from USDA (1986) and NEH-4 (1997) lookup tables.
60
Using the determined CN values and daily rainfall depth, the GRM calculates the
runoff depth for each 1-km2 pixel. Equations used in the CN runoff calculation are:
Q=
( P − IA) 2
( P − IA + PR)
PR =
25400
− 254
CN
(4-3)
(4-4)
where P is rainfall accumulation (mm), IA is initial abstraction (IA is approximated by
0.2PR), Q is runoff generated by P (mm), PR is potential retention (mm), and CN is the
runoff curve number.
4.2.1.6 Land Surface Slope Data
The effect of land surface slope on the rainfall-runoff process and CN values for
watersheds have been determined in the past (Arnold and Williams, 1995; Williams,
1995; Nietsch et al., 2002; Garg et al., 2003; VerWeire et al., 2005). The SWAT, EPIC,
and SWRRB models have applied the land slope effect in the CN approach. In this study,
to analyze the effect of land slope on the global rainfall-runoff process, land surface
slopes were computed from a global Digital Elevation Map (DEM). For the DEM data,
Shuttle Radar Topography Mission (SRTM) DEM data were selected. SRTM was
acquired using a specially modified radar system that flew on board the Space Shuttle
Endeavour during an 11-day mission in February 2000 (http://www.nasa.gov). Although
there are many DEM datasets that have been made to cover administrative areas at the
regional scale, SRTM DEM data are one of the few that are available for the entire globe,
making it suitable for use in the GRM. SRTM obtained elevation data on a near-global
61
scale at 90 m spatial resolution. The spatial resolution of GRM is 1 km. Therefore, SRTM
DEM data is resampled from 90 m spatial resolution to 1 km for use in GRM.
To evaluate land surface slope effect on the global rainfall-runoff process, SRTM
DEM data were converted to a slope dataset using the slope calculation function in
ArcGIS. Slope degree and percentage as illustrated in Figure 4.3 is calculated using those
equations:
Degree of slope = θ
Percent of slope =
Rise
⋅ 100
Run
Rise
= tan θ
Run
Figure 4.3 Slope calculation
(4-5)
(4-6)
(4-7)
62
In Figure 4.3, “Run” is same with spatial resolution of SRTM DEM, 1 km and
“Rise” is the elevation difference. The rate of change (delta) of the surface in the
horizontal (dz/dx) and vertical (dz/dy) directions from the center cell determines the slope
(Burrough and McDonell, 1998). First, slope is measured in degrees, which uses:
Slope( Degree) = ATAN ( [dz / dx] + [dz / dy] ) ∗ 57.29578
2
2
(4-8)
For example, the rate of change of the surface in the horizontal (dz/dx) and vertical
(dz/dy) directions for center cell “e” in Figure 4.4 can be calculated by:
[dz / dx ] = (c + 2 f
+ i ) − (a + 2d + g )
8 ∗ ( X _ cell _ size)
(4-9)
[dz / dy ] = (g + 2h + i ) − (a + 2b + c )
(4-10)
8 ∗ (Y _ cell _ size )
In this chapter, the calculated land surface slope data are used to investigate the
effect of slope on runoff from the case study watersheds and the WMO river basins.
Figure 4.4 Approach to calculate the rate of change of surface elevation in the horizontal
and vertical direction (a to i: elevation)
63
4.2.2 Case Study Areas
This study incorporated two components of analyses: case study watersheds and
WMO river basins. The selected case study watersheds are the ones previously described
in Chapter 3: Las Vegas, Houston, Atlanta, and Cheongju. The case study watersheds
have relatively small drainage sizes, making it possible to conduct this component of the
analysis on a storm event basis. Therefore, in the case study watersheds, storm event
trends and storm event statistics were compared to investigate the three selected
hydrologic effective factors during the 10-year study period (1998 to 2007). Similar to
the study described in Chapter 3, scatter plots and summary statistics (bias, MAE, Eff)
were used to assess the hydrologic effective factors. In addition, storm-based analysis is
also used for validating the approach to estimate CN values calculated from the annual
average rainfall and runoff volumes. For the smaller case study watersheds, local
streamflow data from the USGS for the U.S. watersheds and from the Water Resources
Management Information System (WAMIS) (http://www.wimis.go.kr) for the Korean
watershed were used. As stated in Chapter 3, each location included an urbanized and
nonurbanized watershed, which will again be used to assess the urban influence on the
factors affecting global scale rainfall-runoff. Case study areas are shown in Figure 4.5
and the characteristics are summarized on Table 4.4.
Although the storm-based analyses over the 10-yr study period provides excellent
insight into the processes for those four watersheds, the number of watersheds does not
provide insight for a range of watershed types across the globe. To extend the analysis, a
larger sample of larger watersheds was selected from the WMO river basins. For
observed runoff data in the WMO river basins, GRDC streamflow data were used. The
Las Vegas
Ch eongjn
l egend
•
Stream Gage
-Stream
_
UrbanArea
Urban Watershed
Non urban Watersh ed
o
o
,N
o
Figure 4.5 Case study areas (Grid is TRMM grid cell)
25 50
100 Kill _ \
65
Table 4.4 Characteristics of case study areas
Stream Gage
ID or
Name**
City
Las Vegas
Urban
13.1
09419700
Non Urban
0.3
09419000
Houston
Urban
55.6
08075000
08073700
08074500
08076000
Non Urban
1400
17.2
0.30
08068740
08068500
Atlanta
Urban
2700
60.8
1.19
02337170
02334430
Non Urban
1800
2.2
3.26
02387000
Cheongju
Urban
1425
17.3
0.63
SukHwa
Non Urban
620
1.6
0.96
SongChen
*Estimated from Moderated Resolution Imaging Spectroradiometer (MODIS) land
classification map (Urban and Built-up lands index)
**Stream Gage ID from USGS or name from WAMIS
Area*
(km2)
4000
17550
1450
Urban* (%)
Average Slope
(%)
6.38
6.02
0.46
GRDC collect, store, and disseminate streamflow data and associated metadata from
7300 stations in 156 countries (http://www.gewex.org/grdc.html). In the GRDC dataset,
the world is divided into 486 WMO river basins. In this research, 67 WMO river basins
are selected using two criteria - the location of the river basin and data limitation. First, in
order to compare the observed and simulated runoff correctly, the entire river basin
should be located within the modeling boundary of the GRM (50° South and North
latitude). Second, selected WMO river basins should contain daily GRDC streamflow
stations and the drainage area to the GRDC stations should be close to the size of the
WMO river basin size. In this research, one year (2000) of WMO river basins data were
used for investigating and adjusting the hydrologic effective factors because of the
availability of daily streamflow data at the selected stations in 2000.
66
Daily streamflow data from WMO river basins are obtained from GRDC daily
streamflow gage stations. From each WMO river basin, the GRDC gage station located
on the most downstream point of the river is selected to represent the daily streamflow
data of the WMO river basin. If the WMO river basin consists of several rivers that flow
to another WMO river basin or the ocean, the most downstream GRDC gage stations
from each river are selected and the sum of daily streamflow data from the GRDC gage
stations is considered the daily streamflow of the larger WMO river basin. GRDC and
GRDC streamflow stations provide river basin information such as drainage size,
location, river distribution, and so on. Then, that information was used to verify the
drainage area to the selected GRDC stations matched closely the WMO river basin areas.
Figure 4.6 displays the GRDC stations and the WMO river basins, and identifies
the WMO river basins selected for this study. Also, WMO regions, number of subriver
basins, and the characteristics of selected river basins are listed in Tables 4.5 and 4.6.
The CN method is not meant for application to ice and snowmelt; therefore,
months when the watersheds are covered with snow must be determined and screened
from the analysis. The Terra/MODIS snow cover map obtained from the NASA web-site
was used to classify a watershed as snow covered for a given time period (see Appendix
E). The Terra/MODIS dataset was selected because the snow cover map provides
relatively fine temporal resolution (monthly) and is available at near global coverage.
4.2.3 Base Flow Separation
The USGS, WAMIS, and GRDC dataset contains streamflow records, not the
runoff observations needed to analyze the rainfall-runoff relationship. Therefore, the
67
Legend
GRDC station
WMO
Selected WMO
Figure 4.6 WMO river basins and GRDC streamflow gage stations
68
Table 4.5 WMO regions and the number of WMO river basins
WMO ID
Region
Number of River
Basins
1
Africa
97
2
Asia
90
3
South America
67
4
North, Central America &
79
Caribbean
5
South West Pacific
62
6
Europe
91
Number of Selected
River Basins
9
6
0
31
13
8
69
Table 4.6 The characteristics of selected WMO river basins
Continent
WMO ID
Africa
(1)
Asia
(2)
North, Central
America and
Caribbean
(4)
ID
Name
101
102
104
108
109
110
111
113
114
207
210
211
212
213
214
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
427
428
429
430
431
432
433
Mediterranean Sea Coast
(Western Pa)
Tafna
Volta
Oubangui
Lake Tanganyika
Cunene
Okavango(NM)/Cubango(AN)
Orange (Oranje)
Cape Coast
Yangtze/Chang(CI)
Hokkaido
Honshu (Pacific Coast)
Honshu (East Sea Coast)
Kyushu
Shikoku
USA (Internal Basins)
Upper Mississippi
Upper Missouri
Middle Missouri
Lower Missouri
Ohio
Arkansas
Red
Lower Mississippi
St.John, St.Croix
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
St.Lawrence
USA (Pacific Ocean/North)
USA (Pacific Ocean/South)
USA (Atlantic Ocean/North)
USA (Atlantic Ocean/South)
USA (Gulf of Mexico/East)
USA (Gulf of Mexico/West)
Grande(US)/Bravo(MX)
Colorado
Mexico (Pacific Ocean/North)
Mexico (Internal Basins)
Mexico (Gulf of Mexico/North)
Mexico (Gulf of Mexico/South)
Candelaria
Usumacinta-Grijalva
Panama (Pacific Ocean)
Puerto Rico
Drainage
size (km2)
210617
9454
414004
659589
243462
110548
705056
972388
410877
1884515
78008
136796
91476
36573
18603
592377
446166
418489
407351
529602
514838
418092
231958
240221
59790
176266
207754
112555
76330
274135
94665
234966
359469
376804
358163
503487
553391
644352
366424
349675
187284
218078
15930
127675
73612
9011
Slope
(%)
3.72
4.00
0.92
1.09
2.43
1.90
0.42
1.34
4.35
7.40
5.16
6.22
7.36
6.38
9.45
4.96
0.61
3.17
0.96
1.16
2.03
1.28
0.76
0.83
2.03
0.43
0.69
0.41
1.03
1.89
8.81
6.09
2.76
0.88
0.70
0.61
2.94
4.47
5.18
3.04
4.85
2.86
0.68
6.02
4.98
4.69
Urban*
(%)
2.01
1.18
0.10
0.03
0.08
0.09
0.01
0.40
0.58
1.07
1.60
23.98
9.51
16.89
9.80
0.83
1.28
0.04
0.12
0.74
1.70
0.65
0.55
1.33
0.10
2.02
0.43
5.65
3.56
0.66
1.03
4.48
5.74
3.62
2.40
1.90
0.49
0.68
0.27
0.20
1.14
0.17
0.00
0.06
0.17
4.66
Human
Impacted
Area**
(%)
17.26
4.71
5.57
0.09
5.60
0.35
0.24
1.43
7.79
37.39
26.99
28.26
16.79
18.94
11.29
3.61
87.14
13.29
62.33
48.56
45.41
18.87
16.81
51.67
8.77
40.25
23.56
69.10
45.92
15.22
1.40
10.92
25.36
34.09
27.61
27.08
1.58
2.19
3.54
0.38
25.27
21.64
12.22
19.95
30.29
35.52
70
Table 4.6 continued
Continent
WMO ID
South-West
Pacific
(5)
Europe
(6)
-
ID
Name
Drainage
size (km2)
Slope
(%)
Urban*
(%)
502
503
504
505
506
507
508
509
510
511
513
515
516
601
602
Australia (South-East Coast)
Tasmania
Murray
Australia (South-West Coast)
Australia (Indian Ocean Coast)
Australia (Timor Sea Coast)
Australia (Gulf of Carpentaria)
Lake Eyre
Golok
Perak
Pahang
Langat, Klang, Selangor
Kerian
Rhone
Danube/Donau(DL)/Duna(HU)/
Dunav (YG/BU)/D
Inn
Tisza(HU)/Tisa(RS/YU)
Sava
Drava(YG/HU)/Drau(OS)
Po
Venetian Coast
265251
64930
1059508
266926
817851
549216
642898
1188841
1917
29733
28437
9363
10762
97480
448912
3.56
5.30
1.00
0.94
0.65
1.32
0.67
0.44
2.15
5.44
5.19
3.27
2.27
8.38
3.27
1.27
0.23
0.09
0.35
0.01
0.03
0.02
0.01
0.22
0.87
0.03
6.31
1.25
5.01
2.29
Human
Impacted
Area**
(%)
17.65
2.54
20.35
40.01
2.40
0.04
0.02
0.02
21.54
20.28
9.89
25.93
40.48
34.37
61.88
26204
150095
95660
39125
73050
38876
13.39
3.33
5.45
8.57
10.11
12.06
1.36
1.68
1.27
1.46
10.46
7.16
14.97
65.37
39.00
26.58
49.29
38.60
603
604
605
606
607
608
Urban* (%): The percentage of category 13, Urban and built-up from MODIS
land use/cover data
Human active land** (%): The percentage of category 12 (cropland), 13, 14
(cropland/natural vegetation mosaic) from MODIS land use/cover data
71
runoff observations must be determined by subtracting the base flow from streamflow
data. Three base-flow separation methods (constant-discharge, constant-slope, and
concave) (McCuen, 1998) were considered for this research. The constant-discharge
method was selected because the daily temporal resolution and the number of storm
events to be analyzed made the use of the other methods difficult. The constant discharge
used in the technique was selected to be the minimum observed streamflow in a given
month.
4.2.3.1 Storm Runoff Event Definition
Although the GRM can calculate runoff at daily temporal resolution, storm-based
temporal resolution was used in the case study watersheds because of the time of
measurement (rain gages) and integration (TMPA) differences explained in Chapter 3
and the limitation of the CN approach (absence of lag time calculation). Storm events
were defined based on the precipitation and streamflow observations and base flow
estimation. Storm events were defined by periods of continuous runoff based on the base
flow separation. Maximum runoff duration for a storm event is required to define the
storm event. Maximum runoff duration of five days is used to separate the 10- year
historical records into storm events for the case study areas. The beginning of a storm
event was identified when the observed streamflow increases and precipitation is
detected. The storm event end is marked by the end of runoff (streamflow reaches the
defined base flow discharge or maximum runoff duration is exceeded without
precipitation occurring again). The observed runoff volume used in the study is then
72
computed as the difference between the accumulated streamflow volume and the
baseflow volume for the storm event duration.
4.2.3.2 Monthly Runoff Calculation for WMO River Basins
Runoff volume for the WMO river basins was calculated at the monthly interval
because the large size of the river basins prevents the designation of a storm event and
screens the snow covered month. The average river-basin size is more than 300,000 km2,
with the largest greater than 1,880,000 km2. It will be difficult to align the precipitation
input with the runoff response at the outlet. Using a monthly interval provides a
convenient, yet reasonable, division of events for analysis purposes. The monthly runoff
volume is determined by subtracting the computed monthly base flow volume from the
monthly streamflow volume.
4.2.4 Analysis Overview
4.2.4.1 Studying the Rainfall-Runoff Process Using the CN
This study employs the CN based on observations as the measure of the rainfallrunoff process rather than the rainfall and runoff volumes themselves. This choice was
made because of the diversity of WMO river basins characteristics and the approach to
approximate the rainfall-runoff process and other hydrologic effective factors. The scatter
graphs of runoff volume and depth for observed and simulated runoff is shown in Figure
4.7. As seen in Figure 4.7, WMO river basins that have large runoff volume or depth can
largely affect the relationship between model and simulated runoff volume or depth.
73
(a)
(b)
Figure 4.7 The scatter graphs of (a) runoff volume and (b) runoff depth
When compared with runoff volume, the investigation of hydrologic effective factors can
be significantly affected by large river basins. Also, runoff depth comparison would be
affected by river basins that have high annual average runoff depth.
Furthermore, it is required to estimate hydrologic effective factors using rainfall
and runoff processing. In previous research, rainfall and runoff depth are usually
converted to CN and analyzed to investigate the hydrologic influencing factors
(Williams, 1995; Huang et al., 2006). Therefore, in this research, calculated CN values
from rainfall and runoff are used to investigate hydrologic effective factors in WMO river
basins. CN value can be calculated by a pair of rainfall and runoff depths using Equation
4.11 and 12 (Hawkins, 1973)
CN =
25400
254 + PR
[
(4-11)
]
PR = 5 P + 2Q − 4Q2 + 5PQ
(4-12)
74
where P is storm rainfall, Q is storm runoff, and PR is the maximum potential retention
(unit: mm).
However, there is a potential problem to the use of CN calculations to represent
the rainfall-runoff relationship of the WMO river basins because CN values have a
negative relationship with storm intensity and duration (Van Mullem, 1997; Hawkins and
VerWeire, 2005). As rainfall and runoff depth increases, the CN value generally
decreases. A pair of rainfall and runoff depths from WMO river basins is relatively large,
because it is accumulated rainfall and runoff depth from no snow-covered months.
Therefore, in this research, the change of CN values by increase of rainfall and runoff
depth is tested using a hypothetical watershed.
The hypothetical watershed is given a CN of 75 and observed CN is 80. The
model CN is the determined CN value using the CN lookup table from land use/cover,
soil, and soil moisture condition for the hypothetical watershed. The observed CN is the
average calculated CN value from rainfall and runoff from previous storm events. When
the average rainfall depth for each storm is 50 mm, the runoff depth can be calculated by
the model and observed CN and rainfall depth. Using rainfall and runoff depth, runoff
ratio can be calculated for model and observation. Runoff ratio is the ratio of runoff depth
divided by rainfall depth. Calculated runoff depth and runoff ratio for the hypothetical
watershed are summarized in Table 4.7.
Table 4.7 Calculated runoff and runoff ratio
CN
P (mm) Runoff (mm)
Model
75
50
9.3
Observation
80
50
13.8
Runoff ratio (%)
18.6
27.6
75
When the rainfall depth changes from 1 to 2000 mm, the model (observed) runoff
depth can be calculated by model (observed) runoff ratio. Using the changed rainfall and
calculated runoff, CN values are calculated. The calculated CN values are shown in
Figure 4.8. The descriptions of each line are shown below:
•
CN (Model): Calculated CN values from a pair of rainfall and calculated runoff
by model runoff ratio in Table 4.6.
•
CN (Observation): Calculated CN values form a pair of rainfall and calculated
runoff by observed runoff ratio in Table 4.6.
•
CN Difference (O-M): CN difference between calculated CN values (Observation
–Model).
•
CN Difference (5): CN difference between Observation (80) and Model (75) in
Table 4.6.
Figure 4.8 Sensitivity analysis for CN calculation
76
Calculated CN from both model and observation are significantly changed by
increased rainfall depth. When rainfall depth increases, CN value is considerably
decreased. However, the “CN Difference (O-M)” is relatively constant and is close to the
values for “CN Difference (5)” regardless of the rainfall depth. However, the largest
difference between “CN Difference (O-M)” and “CN Difference (5)” is shown for
smaller or larger rainfall depths.
In WMO river basins, rainfall and runoff depth is accumulated rainfall and runoff
depth during no snow-covered month. Therefore, rainfall and runoff depths are likely
larger than a single storm event. As seen in Figure 4.8, the CN difference can be bigger
for larger rainfall events. Therefore, it is required to calculate the average rainfall runoff
depth for one storm event. One suggested method in this research is to calculate the
rainfall and runoff depth of representative storm events from the annual accumulated
rainfall and runoff depth and the number of runoff days that occurred. The rainfall and
runoff depth of representative storm events can be calculated by Equation 4.13 and 4.14.
PAP =
PAA
NR
(4-13)
R AP =
R AA
NR
(4-14)
where, PAP (QAP) is annual representative rainfall (runoff) depth (mm); PAA (QAA) is
annual accumulated rainfall (runoff ) depth during no snow-covered month (mm); NR is
the number of runoff event days for each river basin.
77
The number of runoff days can be counted by considering the Initial Abstraction
(IA) quantity. If the daily rainfall depth is larger than IA, that day is considered as runoff
day. The IA formulation is shown in Equations 4.15 and 16.
IA = 0.2 ⋅ PR
PR =
25400
− 254
CN *
(4-15)
(4-16)
where PR is Potential Retention; CN* is SCS Curve Number. CN* values in the PR
calculation can be determined by land use/cover, soil type, and three different soil
moisture conditions (Wet, Average and dry soil condition) for each river basin.
The hypothesis for using the calculated CN differences in this study is that the
calculated CN differences using a pair of rainfall and runoff depths from “derived” storm
events and actual storm events are close to each other. In order to validate this approach,
the CN difference from derived rainfall runoff depths and actual storm events were tested
using storm events in the case study watersheds. For validation, the storm events from the
year 2000 were used because rainfall and runoff data from 2000 are used for the WMO
river basin analysis. In the semi-arid case study location (Las Vegas), not enough storm
events occurred during 2000 to meaningfully include that location in the validation.
Therefore, only humid region case study areas were used. The computed CN values from
the storm events derived from the annual rainfall and runoff and number of runoff days
and the actual storm events are summarized in Table 4.7. The Derived Storm Event CNs
are calculated from the annual rainfall and runoff and runoff days using Equations 4.13
and 4.14. The CN values for the actual storm events are the average CN values for 2000.
78
Also, it is important to note the observed CN is calculated from TMPA rainfall and
observed runoff. The Model CN is calculated from TMPA rainfall depth and GRM
simulated runoff. The CN difference then is the computed difference between the
observed CN and the simulated CN. Three soil moisture conditions are used to count the
number of runoff days. As seen in Table 4.8, the CN difference is small, validating the
approach taken to conduct the study.
4.2.4.2 Comparative Statistics
The study of the hydrologic effective factors uses the same statistical measures as
used in the study described in Chapter 3. The bias, MAE, and Eff were used for the storm
event comparisons:
Bias =
1
N
∑ ( M − O)
1
MAE = N
Eff
∑ ( M − O)
O
∑ ( M − O)
= 1−
∑ (O − O)
(4-17)
(4-18)
2
2
(4-19)
where O is the runoff volume from observation data, O is average runoff volume from
observation data, M is the runoff volume from GRM, and N is the number of storm
events. Positive (negative) Bias values mean that the model is overestimating
(underestimating). MAE and Eff indicate the integrated accuracy of the model for several
events. When the MAE value is close to 0, the model has higher accuracy. However, for
Eff, when the value is close to 1, the model has higher accuracy.
79
Table 4.8 Comparison of CN values calculated using the approach based on annual data
and the average of storm event CN values for 2000.
Soil
Condition
CN*
IA (mm)
Annual
Hypothetical
Storm
Events
Runoff Days
CN** (Model)
CN**
(Observation)
CN Difference
(O-M)
Real Storm
Events
Model CN
Obs. CN
CN Diff (O-M)
Wet
Avg
Dry
Wet
Avg
Dry
Wet
Avg
Dry
Wet
Avg
Dry
Wet
Avg
Dry
Wet
Avg
Dry
-
Houston
Urban
Non
Urban
98
92
92
79
82
63
1.0
4.4
4.4
13.5
11.2
29.8
89
63
69
36
40
18
94.5
84.3
93.0
75.4
88.5
60.6
91.9
77.5
89.7
66.3
83.5
49.6
-2.6
-6.8
-3.3
-9.1
-5.0
-10.9
84.6
68.4
80.5
61.9
-4.1
-6.5
Atlanta
Urban
Non
Urban
93
85
79
67
62
48
3.8
9.0
13.5
25.0
31.1
55.0
81
50
36
17
20
11
91.8
78.8
83.2
55.8
73.4
45.2
93.5
83.1
86.4
62.5
77.9
52.1
1.7
4.3
3.2
6.7
4.5
7.0
79.1
64.0
85.0
72.4
5.9
8.4
Cheongju
Urban
Non
Urban
96
84
86
66
73
47
2.1
9.7
8.3
26.2
18.8
57.3
60
43
43
21
23
9
93.8
80.9
91.6
67.4
85.3
47.0
95.3
89.1
93.6
80.0
88.7
63.1
1.5
8.2
2.0
12.6
3.4
16.2
82.5
54.1
84.4
68.5
1.9
14.4
CN*: Calculated from land use/cover, soil and soil moisture condition
CN**: Calculated from annual hypothetical rainfall and runoff
4.2.4.3 Hydrologic Effective Factors
In this research, the hydrologic effective factors on the rainfall-runoff process at
the global scale were investigated. First, in the land slope effect estimation, statistics are
used for the case study watersheds and the relationship between the CN differences
(Observation – Model) and average land slope distribution is used for the WMO river
basins. For land use/cover effect estimation, the trend-line of the storm scattered graphs
in urbanized and nonurbanized watersheds for the case study watersheds was
investigated. Also, more investigation of land use/cover effects was completed using
WMO river basins. The WMO river basins were classified into two categories based on
whether the land use category represented a significantly human impacted land area. The
80
MODIS IGBP land use/cover classes that were identified as human impacted for this
study were cropland (12), urban and built-up (13) and cropland/natural vegetation mosaic
(14). In the case study watersheds, urbanized and nonurbanized watersheds were
categorized by the percentage of urban and built-up (13) category within the watershed
boundary. However, the percentages of the urban and built-up category in the WMO river
basins is very small (average percentage is less than 2%), because the WMO river basins
are very large. Only three watersheds have greater than 10% urban and built-up (13) and
most watersheds have less than 1%. Therefore, the IGBP land classes 12-14 were used to
compute the percentage of human impacted area within the WMO river basins.
The WMO river basins were classified into Human Impacted River-basins (HIR)
and No Impacted River-basins (NIR). A range of human impact thresholds defined for
this study were 10%, 20%, 30%, and 40%, with river basins having human impacted
percentages less than these being categorized as NIR. Percentage of human impacted land
for each river basin is shown in Figure 4.9. River basins that have high human impacted
percentages are concentrated in Eastern North American and Europe (High latitude of
Northern hemisphere).
Drainage size is one of the potential hydrologic effective parameters on global
scale runoff modeling. Although there is no strict limitation of drainage size for the CN
method, NEH 4 suggested that drainage size should be no greater than 20 mi2 (52 km2 ),
because watersheds for the first CN table construction were between 0.001 to 186 km2
with smaller than 52 km2 for most watersheds.
81
Figure 4.9 Percentage of human impacted land area in the selected WMO river basins.
4.3 Results
4.3.1 Land Slope Effect
4.3.1.1 Case Study Watersheds
Scatter plots of observed runoff volumes and simulated runoff volumes for the
comparative statistics are summarized in Table 4.9 and the four case study watersheds are
shown in Figure 4.10.
In the scatter plot graphs, the semi-arid region of Las Vegas (especially the nonurbanized watershed) shows a poor correlation between the observed and simulated
runoff volumes. The reasons for this poor correlation are suspected to be significant water
storage features in the human impacted regions and substantial water management works.
In addition, significant infiltration and evaporation may increase the losses of the
observed runoff, but these processes are not represented in the GRM. In the humid
regions, while the model runoff is overestimated in Houston (which has the lowest land
surface slopes), the model runoff is underestimated in Atlanta (which has the steepest
land surface slopes in the study). The Cheongju case study watershed (which has the
most average slopes of 0.63 and 0.96) was found to have the highest model accuracy. The
82
Table 4.9 Calculated statistics of the comparison of observed and simulated storm event
runoff volume
# Storms Bias
MAE
Eff
Las Vegas Urban
43
-595.2
0.733
0.408
Non Urban 186
1397.4
5.839
-21.752
Urban
251
5986.4
0.728
0.009
Houston
Non Urban 256
4472.9
0.879
0.221
Atlanta
Urban
242
-12758.3 0.575
0.567
Non Urban 228
-13998.5 0.764
0.289
Cheongju
Urban
248
83.0
0.441
0.806
Non Urban 162
-6030.2
0.659
0.533
83
(a)
(b)
(c)
(d)
Figure 4.10 Scatter plots of observed and simulated runoff volume for storm events in the
study period for the selected case study watersheds in the (a) Las Vegas, (b) Houston, (c)
Atlanta, and (d) Cheongju regions (Urban: Upper-left equation and R-squared value,
Nonurban: Lower-right equation and R-squared value)
84
relationship between the land surface slope and the runoff estimation bias was
investigated by creating the plot shown in Figure. 4.11. In Figure 4.11., humid and semiarid case study watersheds were categorized to different groups. In Figure 4.11, positive
(negative) bias values mean that the model is overestimating (underestimating). In the
humid watersheds, the simulated runoff is overestimated for flat watersheds, but
simulated runoff is underestimated for steep watersheds.
4.3.1.2 WMO River Basins
For the WMO river basins, the land surface slope effect was analyzed by plotting
the difference in the CN determined from observed runoff and the CN based on simulated
runoff as a function of the average land surface slope (Figure 4.12). The climatic
classification of the WMO river basins is not possible as it was for the case study
watersheds because the WMO river basins are so large they cover several climatic zones.
Figure 4.12 shows a similar trend to what was noted for the case study watersheds. A
significant model underprediction occurs in WMO river basins with low average land
surface slopes, while a significant overprediction occurs in WMO rivers basins with high
average land surface slopes. The trend is fairly consistent.
In this research, the land slope effect for 1 km spatial resolution GRM from case
study watersheds and WMO river basins shows a positive relationship similar to
observations from previous studies. However, the land slope effect shows a stronger
influence in the case study watersheds than the WMO river basins. One suspected reason
for this is several other uncertain effects in WMO river basins. The characteristics of
WMO river basins are diverse, such as drainage size, climate, human impact, etc., and
85
Figure 4.11 Plot of the relationship between runoff estimation bias and average land
surface slope.
86
Figure 4.12 Relationship between watershed average slope and CN difference.
those diverse WMO river basins characteristics may cause a weakening of the land slope
effect.
4.3.2 Land Use/Cover Effect
4.3.2.1 Case Study Watersheds
Figure 4.10 displayed the scatter plots of the observed versus simulated runoff
volumes for urbanized and nonurbanized watersheds. In the semi-arid Las Vegas area
watersheds, the nonurbanized watershed shows a massive model overprediction of runoff
volume. In general, in the humid regions, the model overestimates runoff volume in the
urbanized watersheds. Synthesizing the urbanized effect with the land surface slope
effect, it is noted the average land surface slopes of the nonurbanized watersheds in
Atlanta and Cheongju are higher than the land surface slopes of the urbanized
87
watersheds. Although in Houston the average land slope of the urbanized watershed is
higher than the nonurbanized watershed, model runoff is more overestimated in the
urbanized watershed than in the nonurbanized watershed. This result in Houston is
opposite from previous research about urbanization effects on rainfall runoff. According
to previous research, urbanization increases runoff volume (Brun and Band, 2000; Shaw,
1994). However, in this section, land use/cover effect analysis is limited, because of the
small number of case study watersheds. In the next section, the land use/cover effect is
analyzed using WMO river basins.
4.3.2.2 WMO River Basins
The WMO river basins were classified as four levels of HIR and NIR based on
the percentage of the river basin covered by the designated human impacted land
categories (10, 20, 30, and 40%). Synthesizing the human impacted designations with the
land surface slope, Figure 4.13 was created. It shows the plots of the differences between
the CN estimated with the observed runoff and the CN estimated with the modeled runoff
as a function of the average land surface slope. Each plot corresponds to a different
threshold HIR level (10, 20, 30, and 40 %.). The trend lines for the HIR and NIR
designations are also shown. The trend lines are based on fixing the Y-intercept (HIR: 3.4 and NIR: -3.2) to be the average of the Y-intercepts from each group, because of
comparison of trend line slope and R-squared values with the same conditions. As the
percentage of human impacted land area in the WMO river basins increases, the model is
more sensitive to the land slope effect (the model overestimation of runoff increases).
When the HIR is categorized by more than 30% of human impacted land, the strongest
(a)
(b)
(c)
(d)
Figure 4.13 The relationship between slopes and two human activated groups (HIR is categorized by higher than the percentage of
human activation land 10% (a), 20% (b), 30% (c), and 40% (d).(HIR: Upper-left equation and R-squared value, NIR: Lower-right
equation and R-squared value)
89
correlation with slope is shown. However, NIR does not have a strong correlation with
slope.
In the Houston urbanized watershed, the simulated runoff volume was more
overestimated than in the nonurbanized watershed. One suspected reason based on the
HIR grouping result of the WMO river basins is that HIR has strong correlation with land
slope and Houston is a flat area. Therefore, in the urbanized watershed in the flat Houston
area, the simulated runoff is more overestimated than in the nonurbanized watershed.
HIRs are very important places for global hydrology modeling because of the
concentration of population and assets. According to the results of this section, the higher
HIR areas had strong correlation with land slope surface effects. In the next chapter, the
land slope effect is adjusted to improve the global runoff model accuracy.
4.3.3 Drainage Size Effect
4.3.3.1 Case Study Watersheds
The relationship between drainage size and calculated statistics from storm-based
analysis in seven case study areas, except the nonurbanized watershed in Las Vegas
because of the large error, is shown in Figure 4.14. No distinguishable pattern is noted.
4.3.3.2 WMO River Basins
The relationship between CN difference and drainage size effect for WMO riverbasins are graphed in Figure 4.15. In this graph, larger CN differences are found in the
smaller river basins, while the larger river basins had better agreement between the
observed and simulated runoff volumes.
90
(a)
(b)
Figure 4.14 Relationship between drainage size and storm-by-storm metrics (a) MAE and
Eff, (b) Bias
91
Figure 4.15 CN difference by drainage size distribution
4.4 Conclusion
In this chapter, three selected hydrologic effective factors were investigated for
their impact on the global rainfall-runoff process and their impact on a global runoff
model. The three factors studied were land slope, land use/cover, and drainage size.
First, land surface slope was found in the case study watersheds and WMO river
basins to be an important factor. In general, the global runoff model underestimates
runoff in steeper watersheds and overestimates in watersheds with milder slopes. Land
slope was shown to have different trends in different climatic regions (semi-arid and
humid regions) for the selected case study watersheds. In the humid regions, a strong
correlation between land slope and bias was found. The land surface slope effect was
found to have a similar influence in the WMO river basins, although weaker.
92
The effect of human modification of the land surface is another known factor
influencing rainfall-runoff transformation. In general, the case study watersheds suggest
the model overestimates the runoff from urbanized watersheds and underestimates runoff
from nonurbanized watersheds. However, the case study watersheds analyzed herein are
relatively flat watersheds. The analyses of the WMO river basins indicated that when
more than 30% of river basin is covered by human impacted land uses/covers, the CN
difference is most influence by the land surface slope, demonstrating the strong potential
for human modification of the land surface to be more important in steep areas.
The last factor investigated in this research is drainage size. In the storm-based
analysis of the selected case study watersheds, there was no noticeable pattern or trend.
However, better agreements between model and observation were found in the larger
WMO river basins, but more CN difference was found in the small WMO river basins.
Hong et al. (2007) mentioned a similar result and they concluded that watersheds that are
larger than 10,000 km2 have significantly better runoff estimation when using the CN
approach. According to previous research (Osborn et al., 1980; Osborn, 1983; Simanton
et al., 1996), generally larger river basins have bigger error, because of runoff loss during
long channel transmission. However, this is different than the conclusions of the present
study. One suspected reason is that the grid-based nature of the global runoff model used
in this study does not consider the channel routing or losses.
CHAPTER 5
INTRODUCING A LAND SURFACE SLOPE
CORRECTION FACTOR INTO
A GLOBAL RUNOFF MODEL
5.1 Introduction
In the previous chapter, the hydrologic effective factors were investigated
through an analysis of observed and simulated runoff in a range of watersheds and river
basins. The three factors of land surface slope, land use/cover, and drainage size were
considered. Among those factors, the land surface slope was noted to be the most
important of the three, because land surface slope effect was clearly shown on case study
watersheds and WMO river basins. HIR also has strong correlation with land surface
slope effect. Past work has introduced slope correction factors into watershed models
(e.g., SWAT, EPIC, and SWRRB). However, no effort has been made to make similar
adjustments to global runoff models. This chapter introduces and tests a land surface
slope correction for the global runoff model described in the previous chapter.
94
5.2 Methods
The organization and elements of the research described in this chapter are shown
in flowchart form in Figure 5.1. The land surface slope correction factors are calculated
based on a derived relationship between CN difference (based on observed and simulated
results) and the average land surface slope in WMO river basins. Then, the derived
equation for land surface slope correction factor is applied to adjust CN values in each
pixel of the global runoff model. The approach applies a relationship developed at the
watershed and river basin scale, but is applied at the much smaller pixel scale. This was
necessary because the runoff data used to derive the CN land slope correction is not
available at the pixel scale. The guiding hypothesis of this chapter is that the relationship
between average land slope and CN difference can be applied to adjust the CN value in a
global runoff model with pixel-based hydrologic computation units much smaller than
the river basin. In order to validate this hypothesis, 67 WMO river basins are divided into
two groups: (1) basins used to develop the land slope correction factor and (2) basins
used to test the derived correction relationship. The study is further extended by assessing
the land use and drainage size effects because of the importance of these factors
identified in Chapter 4 and also studying the global trends of runoff adjustment from the
implementation of the CN slope correction.
5.3 Results
5.3.1 Land Surface Slope Correction
To derive the equation to adjust the CN for land slope effects on runoff, 67 WMO
river basins were used. The river basins are divided into two groups. Fifty of the river
Developing River-basins Group
Model Runoff
Land Slope Map
Observed Runoff
Testing River-basins Group
Model Runoff
Observed Runoff
CN Difference
CN Difference
CN – Land Surface Slope
Correction Factor
Analyze CN – Land Surface
Slope
Adjustment Results
Analyze Land Use and
Drainage Size Effect
CN Adjustment Based
on Land Surface Slope
Analyze Model Result, Land
Use and Slope by Globally
SRTM DEM
1. Introduce Land Slope Adjustment Method
2. Characterize the Hydrologic Effective Factors
after Land Slope Adjustment
Figure 5.1 Flowchart for adjusting the land slope and analyzing method
96
basins are used for developing the CN – land surface slope correction and 17 of the river
basins are used to test the effectiveness of the correction in the global runoff model. To
randomly select the development and testing groups without creating a bias in the
average land surface slopes of the river basins, the river basins are first sorted by average
slope. Then, the sorted list is divided into 17 groups and one river basin from each group
is randomly selected to be in the testing group. The unselected river basins are grouped
into the set of river basins used to develop the equation for the CN - land surface slope
correction. The resulting two groups of river basins are shown in Figure 5.2. The selected
river basins are fairly evenly distributed in the WMO regions. The characteristics of the
river basins are shown in Tables 5.1 and 5.2. As seen in Table 5.2, the two groups have
similar characteristics with the exception of the average drainage size. The average
drainage size in the testing group is larger than the development group, because the
testing group has the largest river basin, Yangtze/Chang(CI). However, the median values
of drainage size for the two groups are similar.
Figure 5.2 Selected rivers basins used for developing and testing the CN – land surface
slope correction
97
Table 5.1 The characteristics of the development and testing groups of river basins (Dev.:
relationship development group, Test: testing group)
Slope (%)
Human Impacted
TMPA (mm)
Drainage size
Land (%)
(km2)
Dev.
Test
Dev.
Test
Dev.
Test
Dev.
Test
AVG
3.54
3.76
24.09
19.95
1013
1055
280892 413018
Max
12.06
13.39
87.14
39.00
3006
3416
972388 1884515
Min
0.41
0.44
0.02
0.02
201
287
9454
1917
Median
2.90
3.17
20.12
18.87
802
885
237594 210617
98
Table 5.2 The characteristics of the river basins in the development and testing groups
Development
Group
ID
Area (Km2)
Slope (%)
CN (GRDC)
CN (GRM)
102
108
109
110
111
113
114
211
212
213
214
402
403
405
406
407
410
412
413
414
415
416
417
418
419
420
421
422
423
424
425
427
428
429
430
431
432
502
503
505
507
508
511
513
516
602
604
606
607
608
9454
659589
243462
110548
705056
972388
410877
136796
91476
36573
18603
592377
446166
407351
529602
514838
240221
176266
207754
112555
76330
274135
94665
234966
359469
376804
358163
503487
553391
644352
366424
349675
187284
218078
15930
127675
73612
265251
64930
266926
549216
642898
29733
28437
10762
448912
150095
39125
73050
38876
4.00
1.09
2.43
1.90
0.42
1.34
4.35
6.22
7.36
6.38
9.45
4.96
0.61
0.96
1.16
2.03
0.83
0.43
0.69
0.41
1.03
1.89
8.81
6.09
2.76
0.88
0.70
0.61
2.94
4.47
5.18
3.04
4.85
2.86
0.68
6.02
4.98
3.56
5.30
0.94
1.32
0.67
5.44
5.19
2.27
3.27
3.33
8.57
10.11
12.06
48
92
87
32
85
90
92
91
86
83
84
32
88
86
85
93
89
71
56
88
80
34
38
71
83
85
86
90
92
35
90
85
92
94
60
93
88
75
28
63
92
91
76
63
59
84
78
19
90
67
63
94
91
30
89
93
96
94
89
87
82
34
91
90
90
90
92
73
55
88
79
30
29
64
83
88
87
95
93
38
91
86
95
94
67
89
91
80
13
64
93
94
76
58
56
80
78
16
82
40
CN Difference
(GRDC–GRM)
-15
-2
-4
2
-4
-2
-4
-3
-3
-4
2
-3
-3
-5
-4
3
-3
-2
1
0
1
5
10
7
0
-3
-2
-5
-1
-3
-1
-1
-2
0
-7
4
-3
-5
15
-2
-1
-3
0
5
3
4
0
3
8
28
99
Table 5.2 Continued
Testing
Group
ID
Area (Km2)
Slope (%)
CN (GRDC)
CN (GRM)
101
104
207
210
404
408
409
411
433
504
506
509
510
515
601
603
605
210617
414004
1884515
78008
418489
418092
231958
59790
9011
1059508
817851
1188841
1917
9363
97480
26204
95660
3.72
0.92
7.40
5.16
3.17
1.28
0.76
2.03
4.69
1.00
0.65
0.44
2.15
3.27
8.38
13.39
5.45
93
93
98
91
37
91
86
21
78
79
91
89
60
77
88
19
77
95
94
97
89
38
90
89
11
80
87
91
93
50
76
71
9
60
CN Difference
(GRDC–GRM)
-2
-2
1
2
-1
1
-3
10
-2
-8
-1
-5
10
1
17
10
17
100
To derive the equation for land slope effect, several functional fits such as linear,
logarithmic-correlation, and 2nd and 3rd order nonlinear correlation of the relationship
between average land slope and CN difference were calculated. R-squared value (0.30) of
liner-correlation is higher than R-squared value (0.21) of logarithmic-correlation. Rsquared values (0.33) of 2nd and 3rd order nonlinear correlation were very similar with the
R-squared of linear-correlation. Those little differences in R-squared values are easily
changed by selection of case study river-basins. Furthermore, the strong linear correlation
(R-squared values: 0.6) was found in the case study watersheds for land slope effect.
Therefore, in this research, the linear functional fit is selected to represent the CN
difference – land surface slope relationship. The linear-correlation trend line for both the
development and testing groups is shown in Figure 5.3.
The CN correction can be calculated by using the equation of the trend line fit to
the CN difference as a function of the land surface slope. For each pixel in the global
runoff model, the CN adjustment based on land surface slope in the pixel can be
computed using Equations 5.1 and 5.2:
CN NEW = CN OLD + α
(5-1)
α = a⋅X +b
(5-2)
where CNnew is adjusted CN value and CNold is the CN value from the CN lookup table. α
is the CN – land surface slope correction factor, a and b is line-slope and Y-intercept
from the CN difference – land surface slope relationship. The adjusted CN value must be
within the range of 0 to 100; therefore, adjusted values outside this range are corrected to
either 0 or 100.
101
(a)
(b)
Figure 5.3 Scatter plots of (a) developing river-basins group and (b) testing river-basins
group
102
5.3.2 Testing the CN - Land Surface Slope Correction
The CN values were adjusted to account for the land surface slope effect using the
relationship derived in the previous section. The results from the global runoff model
before and after the CN adjustments are summarized in Table 5.3 for the 17 river basins
selected for testing. After the CN adjustment for slope effect, the CN difference of 7 river
basins among the 17 in the test group were improved, while 9 were unchanged and only 1
was less accurate. It is important to note the 7 river basins showing improvement have
either flat or steep land slopes – precisely the types of river basins identified in Chapter 4
as having the greatest need for adjustment. The average land slope of testing group is
3.76. The slopes of three of the river basins that showed improvement are smaller than 1
and the slopes of other four river basins are larger than 5.
Table 5.3 Comparison of Observed, Table, and Adjusted CN values.
ID
101
104
207
210
404
408
409
411
433
504
506
509
510
515
601
603
605
Slope
(%)
3.72
0.92
7.40
5.16
3.17
1.28
0.76
2.03
4.69
1.00
0.65
0.44
2.15
3.27
8.38
13.39
5.45
Area
(km2)
210617
414004
1884515
78008
418489
418092
231958
59790
9011
1059508
817851
1188841
1917
9363
97480
26204
95660
Observation
CN
93
93
98
91
37
91
86
21
78
79
91
89
60
77
88
19
77
Model CN
Before
After
95
95
94
94
97
98
89
89
38
38
90
90
89
88
11
11
80
82
87
86
91
91
93
92
50
48
76
76
71
75
9
14
60
63
CN difference
Before
After
-2
-2
-1
-1
1
0
2
2
-1
-1
1
1
-3
-2
10
10
-2
2
-8
-7
0
0
-4
-3
10
12
1
1
17
13
10
5
17
14
Improve*: Improve (I), Deterioration (D) and Same (S)
Observation CN: Calculated CN from hypothetical rainfall and observed runoff
Model CN: Calculated CN from hypothetical rainfall and model runoff
Improve*
S
S
I
S
S
S
I
S
S
I
S
I
D
S
I
I
I
103
To estimate the overall improvement rate for 67 river basins, statistics such as
Bias, MAE, and Eff were calculated before and after the model adjustment (Table 5.4).
After the adjustment, Bias values were increased from 3.18 to 4.36. Positive Bias value
means that model CN is higher than observation CN by a positive number for each riverbasin. One reason for this is the highly improved CN difference in the steep river-basins.
In the steep regions, CN difference (observation CN – model CN) was very high before
the adjustment. After the adjustment, the CN difference was decreased by an increase in
model CN. It caused the increase of runoff Bias values. However, MAE was decreased
from 0.72 to 0.52 and Eff was increased from 0.85 to 0.95. When MAE value is closer to
0 and Eff value is closer to 1, the accuracy is increased. Therefore, overall model
accuracy was largely improved after CN adjustments accounting for the land surface
slope effect.
5.3.3 Land Use/Cover Effect
In the previous chapter, land use/cover effect was investigated by using
regrouping for HIR and NIR. When HIR was categorized by more than 30% of human
impacted land, the strongest correlation with land slope effect was shown. As seen by this
result, several factors can affect rainfall runoff. Therefore, in this chapter, human impact
effect on the rainfall runoff process is further investigated after implementing the CN
Table 5.4 Statistics of model performance for the WMO river basins for the year 2000
based on the before and after CN adjustment comparison.
Statistics
Before adjustment
After adjustment
Bias
3.18
4.36
MAE
0.72
0.52
Eff
0.85
0.95
104
adjustment to account for land slope effect. In this chapter, the same sensitivity analyses
are completed using adjusted model runoff (Figure 5.4). Similar to Chapter 4, HIR (30%)
had the strongest correlation with land slope. The slope and R-squared values of each
group before and after the adjustment is summarized on Table 5.5. After the CN
adjustment, the average Y-intercept from the trend-lines is close to 0 and the slope and Rsquared values are decreased. Although, after the adjustment, the relationship between
human impacted land uses and land slope became weaker, HIR (30%) still had relatively
strong correlation with land-slope effect.
For HIR (30%), the changes of maximum, minimum and Absolute Error before
and after the CN adjustment are summarized on Table 5.6. Absolute Error (AE) was
calculated for estimating accuracy for each group by Equation 5.3.
N
AE =
∑ CN
i =1
i
O
− CN Mi
N
(5-3)
where CN Oi is the calculated CN value from observed runoff in the ith river basin, CN Mi
is the calculated CN value from the simulated runoff in the ith watershed, and N is the
number of river basins in the group.
HIR is highly improved after the adjustment, but NIR does not change much
before and after the adjustment. Although HIR is highly improved, AE of HIR is still
higher than NIR. Therefore, HIR is still sensitive to the land slope with strong
correlation.
(a)
(b)
(c)
(d)
Figure 5.4 The relationship between slopes and two human impacted groups after slope-effect adjustment (HIR is categorized by
higher than the percentage of human impacted land 10% (a), 20% (b), 30% (c), and 40% (d).(HIR: Upper-left equation and R-squared
value, NIR: Lower-right equation and R-squared value)
106
Table 5.5 Slope and R-squared values of trend-line from changed HIR and NIR groups
before and after adjustment (a) HIR (b) NIR
(a)
Before CN adjustment
After CN adjustment
HIR
(Y-Intercept: -3.4)
(Y-Intercept: -1.25)
Slope
R-squared
Slope
R-squared
10 %
1.19
0.35
0.56
0.13
20 %
1.48
0.44
0.83
0.23
30 %
1.81
0.61
1.18
0.51
40 %
1.36
0.56
0.8
0.32
(b)
NIR
10 %
20 %
30 %
40 %
Before slope adjustment
(Y-Intercept: -3.2)
Slope
R-squared
0.96
0.13
0.81
0.18
0.79
0.16
1.10
0.28
After slope adjustment
(Y-Intercept: -1.83)
Slope
R-squared
0.20
0.01
0.23
0.02
0.21
0.01
0.53
0.09
Table 5.6 Statistic on HIR (30%) and NIR for before and after adjustment
CN difference (Observation – Model)
Before adjustment
After adjustment
HIR
NIR
HIR
NIR
Max
27.5
14.8
17.0
14.5
Min
-4.5
-15.2
-3.6
-15.8
AE
5.48
3.94
4.60
3.79
107
5.3.4 Drainage Size Effect
The CN difference by drainage size distribution is shown in Figure 5.5. Compared
to the before CN - slope effect adjustment (Figure 4.13), the range of CN difference was
slightly decreased in the small river basins. To quantify the decrease rate for large and
small river basins, they were categorized into two groups. In the NEH 4, an upper limit
for the CN approach is identified as 52 km2. This limit is much smaller than the smallest
WMO river basin (1917 km2). Furthermore, in previous research, there is no standard to
categorize small and large river basins as there is with the relatively large WMO river
basin. In this research, the small and large river basins are categorized by the largest river
basin that has a CN difference of larger than 10. The largest river basin that has a CN
different larger than 10 is ID 601 river basin (97,480 km2). Therefore, river basins
smaller than 97,480 km2 are categorized as “small” and the others are categorized as
“large.” The ranges of CN difference and AE before and after the CN - slope effect
adjustment are summarized in Table 5.7. After the CN adjustment, the range of data was
narrowed and AE was decreased for the small river basins group. However, for the large
river basin group, little difference was noted.
5.3.5 Global Model Runoff Analysis
This study seeks to improve a global runoff model; therefore, an analysis of the
global runoff adjustments from the CN – slope adjustment was conducted. A comparison
of simulated global runoff to observed global runoff was not attempted. Rather, the
global runoff pattern before and after the CN – slope adjustment were compared to the
global distribution of land use, land slope, and population density, as displayed in Figure
108
Figure 5.5 CN difference by drainage size distribution (after implementing the CN –slope
adjustment)
Table 5.7 Statistics for large and small river basin groups before and after implementing
the CN – slope adjustment
CN difference (Observation – Model)
Before adjustment
After adjustment
Small River
Large River
Small River
Large River
Basins
Basins
Basins
Basins
Max
28
7
17
6
Min
-15
-8
-15
-8
AE
7.68
2.69
6.59
2.69
109
5.6. As seen Figure 5.6 (a), there were significant changes to the simulated runoff pattern
from the CN – slope adjustment, especially in three latitude ranges: increased runoff
noted in North 25 to 35° and South 40 to 50° and decreased runoff noted in North 10 to
20°. Simulated runoff is affected by the land surface slope and this was confirmed in the
global pattern in Figure 5.6 as the two areas of increased runoff corresponded to areas of
higher land surface slopes. The reason for flat areas not displaying significant decreases
in simulated runoff can be attributed to other factors including land use, soil type, and
rainfall patterns.
The percentages of urbanized land (MODIS: 13 category) are relatively low
throughout the entire latitude regions. However, the percentages of human impacted land
(MODIS: 12 to 14 categories) are relatively high in high Northern and Southern latitude
regions. The percentage of land slope is relatively high at low and high latitude regions.
Population density (Figure 5.6 (d)) as expected had a similar pattern with the human
impacted profile. Note that HIR is sensitive to the land slope even after the land-slope
effect adjustment. Therefore, the locations between the North 30 to 45° latitudes
represent areas of significant hydrologic impacts from land surface factors that might not
be captured effectively in lookup table CN values.
5.4 Conclusion
In this section, a CN adjustment relationship was derived to account for land
surface slope effect in a global runoff model. Using data from 50 WMO river basins, the
relationship was developed and tested with 17 separate WMO river basins covering a
range of slope magnitudes. After the CN adjustment was implemented, more than 40% of
110
(a)
(b)
Figure 5.6 Comparison by latitude (a) model runoff (before and after adjustment), (b) the
percentage of land (Human activation land: 12 to 14 categories of MODIS, Urban: 13
category of MODIS), (c) the percentage of land slope, and (d) the world population
density (persons/km2(Source: www.ciesin.org)).
111
(c)
Figure 5.6 continued
(d)
112
river basins tested showed improved CN values and simulated runoff results.
After the CN - slope effect adjustment was implemented in the global runoff
model, the land use/cover effect was investigated. Although minor improvement was
found in river basins with higher percentages of the land surface categorized as human
impacted, the runoff from human impacted river basins was still found to be sensitive to
the land slope effect. A general relationship was not found for drainage basin size effects,
but the accuracy of estimated runoff in small river basins was improved. Globally, the
areas with significant adjustments to simulated runoff patterns after the CN – slope
adjustment was implemented were found to correspond to areas of steeper land surface
slope and human impacted areas.
CHAPTER 6
CONCLUSION
Satellite rainfall data, TMPA, was assessed in different geographical areas and for
different climate types in Chapter 3. In Chapter 4, potential hydrologic effective factors
for global scale runoff modeling were investigated. In Chapter 5, one of the most globally
effective factors, slope, was used to adjust the CN value in the global runoff model in an
attempt to account for the slope effect in global runoff simulations. The key conclusions
of this study are as follows:
•
Although TMPA was generally highly accurate in semi-arid and humid regions
and in urbanized and nonurbanized watersheds, TMPA was slightly
underestimated in the semi-arid regions and slightly overestimated in humid
regions.
•
In general, the TMPA was overestimated for small rainfall events and
underestimated for the larger events.
•
TMPA has higher accuracy during the warm season. The analysis of the
relationship of TMPA accuracy to temperature and relative humidity suggests a
strong relationship of increased temperature and increased TMPA accuracy
corroborating the seasonal observations. Based on previous results, TMPA
accuracy was improved for convective rainfall events compared to frontal events.
114
•
Land slope, land use/cover, and drainage size affect the rainfall runoff process at
the global scale. Generally, the global runoff model overestimates runoff for flat
river basins and underestimates for steep river basins.
•
The runoff from river basins with fractions of area covered by human impacted
land surfaces greater than 30% of the basin area was found to be highly sensitive
to the effect of land surface slope.
•
The smaller river basins were found to have greater simulated to observed runoff
differences than larger river basins.
•
After the CN adjustment to account for the land surface slope effect, 40% of the
17 river basins used to test the adjustment were found to have improved CN
estimates and runoff estimates.
•
After the CN adjustment was implemented in the global runoff model, the
accuracy of the runoff from river basins with high fractions of the area covered by
human impacted land surfaces was found to be highly sensitive to land surface
slope.
•
The global sector between North 30 to 45° latitudes was identified as an area with
steeper slopes and higher percentages of human impacted land surfaces – areas
that are in need of improvements to runoff simulation because of the sensitive
nature of runoff to these factors identified in this study.
APPENDIX A
SATELLITE PRECIPITATION DATA
116
A.1. Tropical Rainfall Measuring Mission (TRMM)
The satellite precipitation dataset used in this study was acquired from TRMM.
TRMM is a satellite instrument designed to measure tropical precipitation launched on
November 28, 1997. The elevation of the satellite orbit is 250 miles and it can measure
precipitation between 50° latitude and longitude North and South area (National Research
Council (U.S.) Committee on the future of rainfall measuring missions, 2007). TRMM
has five main sensors: Precipitation Radar (PR), TRMM Microwave Imager (TMI),
Visible and Infrared Scanner (VIRs), Lightning Imaging Sensor (LIS), and Clouds and
Earth’s Radiant Energy System (CERES) (http://trmm.gsfc.nasa.gov/). Among the five
sensors, PR, TMI, and VIRs sensors are the primary ones used for measuring
precipitation. PR measures the three-dimensional rainfall distribution over land and
ocean. This sensor produces much information such as the intensity and distribution of
the rain, the rain type, the storm depth, and the height at which the snow melts into rain.
The TMI sensor is a passive microwave sensor and designed to provide information on
the integrated column precipitation content, its real distribution, its intensity, and rainfall
type. The VIRs sensor yields very high resolution information for cloud coverage type
and cloud top temperature. Another function of VIRs is to serve as a transfer standard to
other measurements that are made regularly using the meteorological Polar Orbiting
Environmental Satellites (POES) and the Geostationary Operational Environmental
Satellites (GOES). The LIS sensor is used for detecting and locating lightning over the
tropical region of the globe and the CERES instrument collects the observations of the
energy exchanged among the Sun, the Earth’s atmosphere, surface, clouds and space.
117
Based on data acquired from TRMM satellite, several data products are produced.
TRMM products are continuously upgraded with version 6 being the product available at
the start of this research. The TRMM V6 data processing overview is shown in Figure
A.1. TRMM standard products have three levels: 1) single TRMM instrument (PR, TMI,
and VIRS), 2) combined TRMM products (PR and TMI), and 3) TRMM and other
satellites (combined, TMI, SSMI, AMSRE, and AMSU) (Stocker, 2007). Among those
data products, 3B42 product is appropriate for global hydrologic modeling, because it is
the level 3 product that is combined TRMM and other satellites, so it can overcome
limitations of satellite data, temporal gap, etc. 3B42 data product estimates precipitation
on a gridded format with a 3-hour temporal resolution and a 0.25 degree by 0.25 degree
spatial resolution. The spatial extent of the 3B42 data product is from 50 degrees South to
50 degrees North latitude and 180 degrees East to 180 degrees West longitude.
The 3B42 uses an optimal combination of 2B31, 2A12, Special Sensor
Microwave/Imager (SSM/I), Advanced Microwave Scanning Radiometer (AMSR), and
Advanced Microwave Sounding (AMSU) precipitation estimates to adjust IR estimates
from geostationary IR observation. The 3B42 estimates are produced in four stages: (1)
the microwave estimates precipitation are calibrated and combined, (2) infrared
precipitation estimates are created using the calibrated microwave precipitation, (3) the
microwave and IR estimates are combined, and (4) rescaling to monthly data is applied
(http://trmm.gsfc.nasa.gov/).
Ie
C
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Figure A.1 TRMM V6 data processing overview (source from: http://disc.sci.gsfc.nasa.gov/precipitation/TRMM_v6.shtml)
119
A.2. Global Precipitation Measurement (GPM)
In the near future, GPM satellites will be launched and the system implemented.
GPM is the next generation of TRMM and will be able to measure precipitation between
65° North and South latitudes. For precipitation measurement, GPM will carry a dualfrequency precipitation radar and a passive microwave sensor. Ka band (35.55 GHz) of a
dual-frequency precipitation radar that has two frequencies, Ku band (13.6 GHz) and Ka
band (35.55 GHz), is very sensitive to low precipitation rates (light rain and drizzle) and
snow; therefore, it will be able to catch lower rain rates than TRMM (National Research
Council (U.S.), 2007). Also, GPM will improve the geographical limitation of TRMM
from 50° to 65° North and South latitude. GPM will improve the quality of precipitation
data obtained from TRMM and thus improve the capability of global runoff models such
as the one described in this research.
APPENDIX B
LAND USE/COVER DATA (MODIS)
121
Table B.1 Land Cover Types of MODIS land use/cover data
Class
0
1
5
IGBP (Type 1)
Water
Evergreen
needleleaf forest
Evergreen broadleaf
forest
Deciduous
needleleaf forest
Deciduous broadleaf
forest
Mixed forests
UMD (Type 2)
Water
Evergreen needleleaf
forest
Evergreen broadleaf
forest
Deciduous needleleaf
forest
Deciduous broadleaf
forest
Mixed forest
6
Closed shrublands
Closed shrublands
Needleleaf forest
7
8
9
10
11
12
13
14
Open shrublands
Woody savannas
Savannas
Grasslands
Permanent wetlands
Croplands
Urban and built-up
Cropland/natural
vegetation
mosaic
Permanent snow and
ice
Barren or sparsely
vegetated
Unclassified
Open shrublands
Woody savannas
Savannas
Grasslands
Unvegetated
Urban
NPP (Type 4)
Water
Evergreen needleleaf
vegetation
Evergreen broadleaf
vegetation
Deciduous needleleaf
vegetation
Deciduous broadleaf
vegetation
Annual broadleaf
vegetation
Annual grass
vegetation
Non-vegetated land
Urban
Unclassified
Unclassified
2
3
4
15
16
254
LAI/FPAR (Type 3)
Water
Grasses/cereal crops
Shrubs
Broadleaf crops
Savanna
Broadleaf forest
Croplands
Urban and built-up
Barren or sparsely
vegetated
Unclassified
122
Land use/cover
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
254
Figure B.1 MODIS land use/cover map
APPENDIX C
SOIL DATA (TERRASTAT)
124
Table C.1 Infiltration rate for Hydrologic Soil Group (Source: McCuen, 1998)
Group
Minimum Infiltration
Rate (mm/hr)
A
7.62 – 11.43
B
3.81 – 7.62
C
1.27 – 3.81
D
0 – 1.27
Table C.2 Characteristics of Soils Assigned to Soil Groups (Source: McCuen, 1998)
Group
Characteristics of Soils
A
Deep sand, deep loess, aggregated silts
B
Shallow loess, sandy loam
C
Clay loams, shallow sandy loam, soils low in organic content, soils usually
high in clay
D
Soils that swell significantly when wet, heavy plastic clays, certain saline
soils
Table C.3 Reclassified soil groups from TERRASTAT database to Hydrological soil
group (HSG)
ID
Soil Description
HSG
10
Coarse textured soils (loamy)
C
12
Coarse textured soils (sandy clay)
C
13
Medium textured soils (loamy)
C
14
Fine textured soils (clay)
D
20
Coarse texture soils (sand)
A
21
Organic soils
C
23
Medium textured soils (loamy)
A
24
Fine textured soils (clay)
D
30
Silt loam
B
31
Organic soils
C
32
Coarse textured soils (sandy)
A
34
Fine textured soils (clay)
D
40
Fine texture soil (silt)
D
41
Organic soils
D
42
Coarse textured soils (sandy)
D
43
Medium textured soils (loamy)
D
99 Glaciers, Rocks, Shifting Sand, No Data
97
Water
125
a) Soil Data from TERRASTAT database
TERRASTAT
10
12
13
14
20
21
23
24
30
31
32
34
40
41
42
43
97
99
(b) Reclassified soil data
Legend
Nodata
A
B
C
D
Figure C.1 Soil Data ((a) Soil Data from TERRASTAT database, (b) Reclassified soil
data)
APPENDIX D
GLOBAL SCS CN LOOKUP TABLE
127
Table D.1 Global SCS CN lookup table
MODIS land cover classification
ID
Content
0
Water bodies
1
Evergreen needles
2
Evergreen broad leaf
3
Deciduous needle leaf
4
Deciduous broadleaf
5
Mixed forests
6
Closed shrublands
7
Open shrublands
8
Woody savannas
9
Savannas
10
Grasslands
11
Permanent wetlands
12
Croplands
13
Urban and built-up
14
Cropland/natural vegetation
mosaic
15
Permanent snow and ice
16
Barren or sparsely vegetated
17
Missing data
CN for different HSG
Soil
moisture
condition
A
B
C
D
N/A
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
Wet
Average
Dry
N/A
Wet
Average
Dry
N/A
N/A
54
34
18
50
30
15
60
40
23
62
42
25
58
38
21
65
45
27
69
49
30
80
61
41
88
72
53
69
49
30
50
30
15
85
67
47
94
80
63
72
52
33
N/A
88
72
53
N/A
N/A
79
60
40
77
58
38
82
64
44
84
66
46
81
62
42
83
65
45
86
69
50
88
71
52
94
80
63
86
69
50
77
58
38
93
78
60
97
85
70
86
69
50
N/A
95
82
65
N/A
N/A
89
73
54
88
71
52
92
77
59
93
79
62
91
75
57
91
75
57
93
79
62
95
81
64
97
87
73
93
79
62
88
71
52
97
85
70
98
90
78
93
79
62
N/A
95
83
67
N/A
N/A
93
79
62
92
77
59
95
83
67
97
85
70
95
81
64
94
80
63
96
84
69
98
89
77
98
93
83
96
84
69
93
78
60
98
89
77
99
95
87
96
84
69
N/A
97
87
73
N/A
Hydrological
condition
(poor/fair/
good)
N/A
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
Fair
N/A
Fair
N/A
APPENDIX E
SNOW-COVERED MONTH
129
Table E.1 Snow-covered months
WMS riverbasins ID
101
102
104
108
109
110
111
113
114
207
210
211
212
213
214
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
427
428
429
430
431
432
433
502
503
504
505
506
507
508
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
N
N
S
S
S
S
S
S
S
S
S
S
S
S
S
S
N
S
S
S
S
S
N
N
S
S
S
S
S
S
S
S
S
N
N
N
S
S
N
N
S
S
N
S
S
N
S
S
S
S
S
S
S
N
N
S
S
S
S
S
S
S
S
S
S
S
S
S
S
N
S
S
S
S
S
N
N
S
S
S
S
S
S
S
S
S
N
N
N
S
S
N
N
S
S
N
S
S
N
S
S
S
S
S
S
S
N
N
N
N
N
N
N
N
N
N
S
S
S
N
N
N
N
S
N
N
N
N
N
N
S
N
S
N
N
S
N
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
S
N
N
N
N
S
N
N
N
N
N
N
S
N
S
N
N
S
N
N
N
N
N
N
N
S
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
N
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
S
N
N
S
N
N
S
N
N
N
N
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
S
N
S
S
S
S
S
S
N
N
N
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
S
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
S
S
S
S
N
N
N
N
S
N
N
N
N
S
N
N
N
N
N
N
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
N
S
N
N
S
S
S
S
S
S
S
N
N
S
S
S
S
S
S
N
N
S
N
N
N
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
130
Table E.1 continued
WMS riverbasins ID
509
510
511
513
515
516
601
602
603
604
605
606
607
608
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
N
N
N
N
N
N
S
S
S
S
S
S
N
S
N
N
N
N
N
N
S
N
S
N
N
N
N
S
N
N
N
N
N
N
N
N
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
S
S
S
S
S
N
N
N
N
N
N
N
N
N
S
N
S
S
S
N
S
N
S
S
S
S
N
N
N
N
S
S
S
S
S
N
S
S
S
S
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