a comparative study of the effects of input resolution on the ......a comparative study of the...

A comparative study of the effects of input resolution on the SWAT model

J. Earls & B. Dixon Department of Environmental Science, Policy and Geography, University of South Florida St. Petersburg, U.S.A.

Abstract

Resolution is a sensitive issue in environmental modeling and geo-spatial analysis. This study utilizes the SWAT (Soil and Water Assessment Tool) model integrated with ArcView. The overall goal of this research was to determine how sensitive the SWAT model was to the resolution of input data. For example, input data layers that originated at 250m, 125m, 30m and 3m were resampled to 30m, 90m, 120m and 240m. Data was collected for the Alafia River watershed in the Tampa Bay Estuary in West Central Florida. Initial input layers to SWAT were: Digital Elevation Models (DEMs), soils and landuse; all analyzed for sensitivity at four different resolutions; viz. 30m, 90m, 120m and 240m. Landuse data photo-interpreted from 1:12,000 color-infrared digital ortho-quarter quadrangles (DOQQs) were obtained from the Southwest Florida Water Management District (SWFWMD). GIRAS landuse data was obtained from the EPA BASINS website at 125m resolution. Soil data at 30m resolution was obtained from Soil Survey Geographic Database (SSURGO) and 250m resolution was obtained from State Soil Survey Database (STATSGO). DEMs were obtained from the EPA BASINS website at 90m resolution and from United States Geological Survey (USGS) at 30m resolution. This study makes no assumption that original resolution was in any way improved by resampling to higher resolution. Output variables tested were: model flow out, sediment out, total phosphorous (TP) out and NO3

-N out. From analysis of these outputs, it is evident that model outputs were, in fact, sensitive to the variation in input resolutions, some outputs more than others. Results indicate coarser landuse, soils and DEMs all tend to under-predict the higher resolution soils, DEMs and landuse at annual temporal resolution. Keywords: remote sensing, SWAT, GIS, soil, streamflow, sensitivity, resolution, landuse.

© 2005 WIT Press WIT Transactions on Ecology and the Environment, Vol 83, www.witpress.com, ISSN 1743-3541 (on-line)

River Basin Management III 213

1 Introduction

Mathematical models have become indispensable part of planning and management of watersheds. Ability to predict the effect of management decisions on water, sediment, nutrient and pesticide yields with reasonable accuracy on large watersheds is a critical environmental management tool. Advent of Geographic Information System (GIS) facilitates spatially explicit implementation of such mathematical models. However, resolution is a sensitive issue in environmental modelling and geo-spatial analysis since accuracy of model predictions depends on availability of data at a high spatio-temporal resolution. This study utilizes the SWAT (Soil and Water Assessment Tool) model integrated with a GIS (ArcView). The overall goal of this research was to determine how sensitive the SWAT model was to the resolution of input data.

2 Methods

2.1 Location

The study area for this research was the Alafia River drainage basin, located on the western coast of central Florida (Figure 1). This drainage basin is approximately 1135 km2 in size (Figure 2) and located within Hillsborough and Polk Counties and drains to the Upper Tampa Bay. This drainage basin has been largely impacted by phosphate mining and agriculture as well as some urbanization. The SWAT model sub-divided this basin into 27 to 33 sub-basins, depending on the resolution of the input digital elevation model (DEM).

Figure 1: Location of study.

2.2 SWAT model

The SWAT model is a distributed model that uses GIS data layers to create an integrated depiction at river basin or catchment scale (Neitsch et al [1]). The model operates off multiple physical parameters and expert knowledge to determine impacts of landuse (LU) and land management at the catchment scale and provides results of water yield, runoff, loadings of sediments, organics and much more. This model runs within the ESRI ArcView 3.2 GIS interface.


214 River Basin Management III

Initial input data layers are available by 8-digit hydrologic unit code (HUC) via the EPA Better Assessment Science Integrating Point & Non-point Sources (BASINS) website (http://www.epa.gov/waterscience/basins/b3webdwn.htm#web), though better resolution data may be available locally. In this study, we included both original data from the website as well as finer resolution local input layers.

Figure 2: Alafia River basin with SWAT delineations.

The data used for the SWAT model was as follows: DEMs, LU and soils. The meteorology was model simulated from the closest national weather stations. For each model run, all data was resampled to a uniform resolution: 30m, 90m, 120m or 240m (it should be noted that we hold no belief that this would in any way increase the accuracy of the original data). For the entirety of this report, the original resolution will be noted and the resampled resolution a subscript with the letter “R” next to it, as indicated in the example below (Eq. (1)). All units will be meters when referring to resolution.

RDEM 9030 (1)

Landuse data photo-interpreted from 1:12,000 (3m) color-infrared digital ortho-quarter quadrangles (DOQQs) were obtained from the Southwest Florida Water Management District (SWFWMD). GIRAS LU data was obtained from the EPA BASINS website at 125m resolution. Soil data from 30m resolution was obtained from Soil Survey Geographic Database (SSURGO) and 250m resolution was obtained from State Soil Survey Database (STATSGO). DEMs were obtained from the EPA BASINS website at 90m resolution and from United States Geological Survey (USGS) at 30m resolution. In this study, all default selections were used for model parameters with the exception of calculation of ET method and temporal resolution (annual, monthly,



daily). Though the SWAT model can use various equations or real measurements for potential evapotranspiration ET calculation, for this study we used the Penman-Monteith calculation (Monteith [2]). Lack of spatial concentration and temporal completeness of climate station data in the Alafia river basin during the periods of study led to a decision to use the simulated weather data within the model. Further, an annual time step was decided for use in this study as well. When examining the rainfall patterns within the Alafia River drainage basin (obtained from the SWFWMD rain database), the simulation period selected included both very wet (2004) and very dry (2000) years (Figures 3a) b)). Interestingly, some of the seasons are reversed in these years, e.g., in 1999 and again in 2003, there were very wet “dry” seasons (Figure 3a). This does appear to have some impact on the model results, while in 1999 the model performs well, in 2003 when there is a wet “wet” season as well, the model under-predicts reality. All flow out data was tested against the USGS streamflow gage located at Alafia River at Lithia. Further, all available data for TP (Total Phosphate, in this case Organic Phosphate + Mineral Phosphate), NO3

--N and suspended sediments were examined, but the sparseness of the data made it hard to draw useful conclusions. For example, there was a fairly long record of suspended sediments sampling, but it ended in 1994, not overlapping with this study’s time period. The nutrient and solids data available were all discrete non-uniformly spaced samples, not continuous recorded data like the streamflow gage. a) b)

Alafia Annual Rain (1997-2004)

0

10

20

30

40

50

60

70

1997 1998 1999 2000 2001 2002 2003 2004

Calendar Year

Rai

n (in

)

Figure 3: a) and b): Alafia Rain, 1997 – 2004.

2.3 Calibration and validation

There are two basic ways to calibrate data in the SWAT model, 1) to use the new autocalibration tool, which runs using a daily time step only and 2) to manually calibrate the data in the annual and monthly and then down to daily time step (or stop wherever your level of accuracy requires). For this study, since we were

Alafia Wet & Dry Season Rain - Water Years (1997-2004)

0

5

10

15

20

25

30

35

40

45

50

1997 1998 1999 2000 2001 2002 2003 2004Water Year

Rai

n (in

)



using annual data, the autocalibration tool was not used; rather a more manual approach was attempted for the first four years of the time period and is shown in Figure 4 below. This calibration was attempted only at the 30m DEM resolution with both SSURGO and STATSGO with SWFWMD (hereafter called “WMD”) LU, not resampling from 90m DEM. The parameters that were changed are summarized in Table 1. The calibration of the extreme wet (1998) and dry (2000) years is not easy as it makes the years with more average rainfall disagree.

Table 1: Parameters altered in manual calibration.

Parameters changed SSURGO Cal 1 CN2 -8, SOL_AWC value +0.05, GWQMN +40% SSURGO Cal 2 CN2 -6, SOL_AWC +0.05, GWQMN +60%, GWREVAP +40%, SLOPE +10% SSURGO Cal 3 CN2 -8, SOL_AWC +0.05, GWQMN +60%, GWREVAP +50% SSURGO Cal 4 ESCO +50% SSURGO Cal 5 CN2 -8, SOL_AWC -0.05 STATSGO Cal 1 CN2 -8, SOL_AWC value +0.05, GWQMN +40% STATSGO Cal 2 CN2 -6, SOL_AWC +0.05, GWQMN +60%, GWREVAP +40%, SLOPE +10% STATSGO Cal 3 CN2 -8, SOL_AWC +0.05, GWQMN +60%, GWREVAP +50% STATSGO Cal 4 ESCO +50%

Figure 4: Alafia annual manual calibration attempts.

3 Results and discussion

The results of the model runs were compared against each other to determine the sensitivity of the model to resolution of input variables. The results of the runoff (flow out) from the model runs are shown in Table 2 below. The USGS measured station appears to the left of each model run. In the driest year (2000), the best agreement is between some of the worst of the resolutions for all input data (e.g. STATSGO soil layer, giras LU layer and the 90m DEM resampled to



120m for watershed delineation). In the wetter years (1998, 2003, 2004), the model greatly under-predicts the USGS measured flow, but the best resolution input layers (e.g. SSURGO soil layer, WMD LU layer and 30m DEM) produce the highest output, therefore closest to the measured data.

Table 2: Runoff SWAT model (m3).

DEM 30m DEM 90m30R Year USGS ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 8.56 3.15 2.67 5.39 5.01 2.93 2.54 4.72 4.34 1998 16.74 7.83 7.72 10.06 9.67 7.42 7.28 9.08 8.69 1999 5.49 3.10 2.78 4.79 4.53 2.83 2.55 4.20 3.95 2000 2.26 4.33 4.15 5.88 5.57 4.04 3.82 5.27 4.98 2001 6.52 4.99 4.86 7.16 6.87 4.65 4.49 6.39 6.07 2002 7.58 8.81 8.82 10.60 10.38 8.15 8.11 9.59 9.37 2003 15.39 5.40 4.94 7.12 6.72 4.99 4.58 6.36 5.98 2004 15.76 7.29 6.98 9.23 8.77 6.81 6.48 8.31 7.87

DEM 30m90R DEM 90m Year USGS ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 8.56 3.40 2.83 5.36 4.98 2.93 2.54 4.87 4.50 1998 16.74 8.19 8.02 10.11 9.73 7.29 7.19 9.18 8.81 1999 5.49 3.27 2.90 4.77 4.50 2.83 2.55 4.31 4.05 2000 2.26 4.52 4.29 5.90 5.59 3.99 3.80 5.35 5.04 2001 6.52 5.28 5.05 7.17 6.86 4.64 4.48 6.51 6.19 2002 7.58 9.13 9.10 10.67 10.44 8.07 8.07 9.68 9.43 2003 15.39 5.63 5.12 7.13 6.73 4.95 4.54 6.45 6.07 2004 15.76 7.60 7.21 9.26 8.79 6.74 6.42 8.41 7.94

DEM 30m120R DEM 90m120R Year USGS ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 8.56 3.12 2.68 5.24 4.86 2.89 2.49 4.85 4.49 1998 16.74 7.87 7.77 9.89 9.51 7.31 7.21 9.19 8.83 1999 5.49 3.06 2.75 4.66 4.38 2.80 2.51 4.30 4.05 2000 2.26 4.32 4.12 5.76 5.44 3.98 3.78 5.343 5.06 2001 6.52 4.99 4.85 7.01 6.70 4.61 4.45 6.52 6.20 2002 7.58 8.78 8.78 10.43 10.20 8.091 8.08 9.69 9.47 2003 15.39 5.38 4.92 6.97 6.57 4.94 4.52 6.46 6.08 2004 15.76 7.28 6.95 9.05 8.58 6.74 6.41 8.41 7.96

DEM 30m240R DEM 90m240R Year USGS ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 8.56 3.16 2.69 5.29 4.91 3.176 2.732 5.188 4.785 1998 16.74 7.93 7.80 9.98 9.59 7.813 7.692 9.723 9.34 1999 5.49 3.06 2.74 4.70 4.43 3.028 2.701 4.585 4.306 2000 2.26 4.34 4.13 5.81 5.50 4.294 4.056 5.659 5.332 2001 6.52 5.03 4.86 7.13 6.79 4.952 4.753 6.916 6.574 2002 7.58 8.82 8.79 10.53 10.27 8.65 8.613 10.24 9.984 2003 15.39 5.37 4.91 7.00 6.60 5.275 4.803 6.837 6.429 2004 15.76 7.32 6.96 9.14 8.66 7.206 6.841 8.888 8.421

For the rest of the data, suspended solids, NO3-N and TP (Tables 3-5), the available measured data was not annual and therefore was impossible to



accurately compare. However, when comparing the model performance alone, for TP, the results appear to be strongly impacted by each layer/resolution in the wet years and not strongly impacted by any one layer over another during the drier years.

Table 3: TP SWAT model (kg).

DEM 30m DEM 90m30R Year ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 40,580 27,623 52,410 38,540 33,930 22,146 42,480 38,540 1998 61,520 57,320 54,930 47,820 54,060 49,860 48,130 47,820 1999 24,035 22,521 24,192 22,548 21,429 19,520 22,155 22,548 2000 24,152 22,690 25,539 23,557 21,237 19,365 22,713 23,557 2001 28,200 26,155 31,910 30,380 25,617 23,084 29,320 30,380 2002 41,200 39,920 42,250 40,660 36,360 34,270 37,710 40,660 2003 25,230 20,956 27,622 24,858 24,063 19,466 26,309 24,858 2004 43,070 78,450 48,380 126,470 38,560 69,750 42,110 126,470

DEM 30m90R DEM 90m Year ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 37,240 24,420 47,460 34,230 30,830 20,439 40,820 28,674 1998 55,710 51,290 51,340 43,110 49,290 45,320 46,120 37,570 1999 21,642 19,975 22,742 20,321 21,532 18,048 23,308 18,535 2000 21,372 19,846 23,790 21,139 19,489 18,040 21,972 19,329 2001 25,106 22,847 29,890 27,070 23,704 21,606 28,650 25,471 2002 36,330 34,960 39,480 36,470 33,520 31,730 36,410 32,770 2003 22,484 18,255 26,699 22,374 22,066 18,187 25,577 21,541 2004 37,790 62,600 45,760 110,370 35,430 67,930 44,610 125,870

DEM 30m120R DEM 90m120R Year ssurgo,

giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 37,870 24,808 48,410 34,290 31,070 20,641 40,840 29,185 1998 59,240 51,230 54,900 43,160 50,630 46,310 46,240 38,150 1999 23,591 20,438 24,435 20,930 22,235 18,379 23,753 18,871 2000 23,081 20,739 24,568 21,787 20,513 18,467 22,787 19,609 2001 27,501 24,407 31,230 28,200 24,942 22,078 29,650 26,690 2002 39,510 36,480 40,740 37,290 35,270 32,410 37,880 33,870 2003 24,957 20,314 27,931 23,810 23,574 18,566 26,466 22,156 2004 41,350 73,410 47,250 128,620 37,450 63,520 43,860 128,920


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 34,430 22,172 43,000 30,016 32,090 20,091 38,890 27,311 1998 55,410 46,470 47,820 38,720 52,940 44,510 43,380 36,040 1999 21,840 18,596 22,450 18,686 22,627 17,727 20,811 17,837 2000 20,930 18,747 23,431 19,800 19,386 17,343 21,034 18,520 2001 25,126 22,157 30,300 25,893 23,725 20,620 27,770 24,779 2002 35,860 33,070 39,150 33,950 33,220 30,530 35,240 31,780 2003 23,365 18,569 27,436 21,758 22,308 17,567 25,138 20,923 2004 37,420 64,960 45,940 121,650 35,510 70,150 44,640 129,120

For the sediment out, the model tends to be more impacted by LU input layers, particularly in that the coarser LU (giras) tends to be significantly higher



when using the higher resolution DEMs. This is most noticeable in the wetter years and the difference in impact during the dry years is negligible.

Table 4: Suspended solids SWAT model (tons).


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 65,520 37,060 60,350 28,390 23,660 44,160 20,770 45,830 1998 132,900 118,800 99,300 67,040 93,550 75,700 52,150 99,660 1999 41,170 38,910 29,420 23,800 30,290 22,820 18,830 30,140 2000 24,650 24,440 20,270 18,060 17,030 15,110 14,090 16,960 2001 28,550 29,030 24,560 21,910 20,900 18,720 17,760 20,080 2002 46,080 48,370 36,730 36,320 34,910 27,760 29,430 32,060 2003 21,870 19,930 18,360 15,410 14,440 14,070 12,500 15,300 2004 49,470 51,690 40,760 38,300 36,620 30,100 30,760 33,710


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 60,700 32,320 55,430 26,740 43,020 22,500 40,300 18,400 1998 123,300 102,100 90,670 58,470 88,610 77,210 68,480 43,670 1999 37,600 33,590 26,780 20,930 26,790 25,840 20,500 16,040 2000 21,880 20,760 18,030 15,860 14,920 14,800 13,070 11,560 2001 26,370 24,530 22,090 18,920 17,870 17,920 16,380 14,200 2002 40,770 41,000 32,810 31,180 28,310 29,710 23,940 23,290 2003 19,720 16,870 16,400 13,420 13,600 12,440 12,290 10,180 2004 44,330 43,490 36,480 32,940 29,580 31,040 25,970 24,210


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 54,540 29,400 51,030 23,360 42,100 22,150 39,650 18,790 1998 112,200 92,790 86,380 53,830 90,100 77,560 66,600 42,780 1999 34,800 30,800 25,540 19,330 27,240 25,880 20,060 15,770 2000 19,900 18,900 16,370 14,270 14,620 14,670 13,270 11,800 2001 23,430 22,530 20,140 17,220 17,550 17,830 16,630 14,470 2002 37,240 37,440 29,650 28,310 27,830 29,620 24,290 23,680 2003 17,950 15,560 15,120 12,310 13,270 12,340 12,480 10,400 2004 39,760 39,520 32,710 29,580 29,060 31,000 26,300 24,650


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 48,970 25,580 41,820 20,330 42,510 21,360 36,470 16,610 1998 100,500 81,510 68,830 45,570 90,390 74,020 61,210 40,500 1999 30,810 27,280 21,280 16,680 27,780 24,820 18,890 14,930 2000 17,040 16,110 15,020 12,730 14,880 13,550 12,760 10,940 2001 20,330 19,540 19,030 15,430 18,280 16,440 16,220 13,520 2002 32,160 32,350 27,790 25,240 28,130 27,260 23,610 22,160 2003 15,420 13,500 14,120 11,030 13,670 11,380 12,150 9,707 2004 34,120 33,870 30,090 26,170 30,080 28,550 25,610 22,930

Looking at NO3-N, there is more impact from the LU resolution (e.g. giras tends to under-predict WMD) every year but 2004, which the 30m DEM, STATSGO soils and WMD LU slightly over-predicted the 30m DEM, SSURGO soils and WMD LU in that year only (but for each resolution).



Table 5: NO3-N SWAT model (kg).


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 88,700 73,790 126,600 109,200 53,250 103,100 86,500 67,190 1998 116,900 96,360 159,600 139,800 76,460 129,300 114,500 92,370 1999 59,930 50,980 92,490 86,360 42,170 76,960 71,080 48,850 2000 64,880 52,770 94,320 85,380 43,640 78,980 71,060 53,540 2001 77,360 64,700 121,600 108,900 52,490 101,400 89,700 63,200 2002 131,200 112,200 175,900 152,500 92,670 148,000 128,300 109,500 2003 76,230 63,690 116,400 102,900 53,600 98,090 86,240 64,170 2004 93,620 103,000 148,000 155,700 73,340 121,200 117,200 74,990


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 81,500 64,540 114,300 98,000 64,340 51,900 101,300 87,080 1998 103,400 83,570 143,000 125,800 87,130 72,690 129,300 114,100 1999 55,940 46,300 84,550 78,440 46,750 40,290 76,360 70,790 2000 62,440 48,760 87,450 78,090 52,180 41,760 78,840 70,770 2001 73,680 59,040 110,900 98,710 61,100 50,310 101,100 89,640 2002 124,300 102,200 161,200 139,500 104,600 88,540 147,100 126,500 2003 72,810 59,930 107,500 94,410 61,100 51,290 97,180 85,900 2004 85,540 75,640 133,300 129,200 71,390 64,440 121,200 115,500


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 79,880 60,260 116,700 96,510 66,490 52,150 104,400 88,870 1998 107,000 80,630 152,400 125,700 93,530 74,750 134,400 119,200 1999 55,300 45,130 86,370 78,400 47,650 41,010 79,120 73,000 2000 59,520 47,440 87,610 78,700 53,250 42,820 81,230 72,330 2001 70,860 57,560 113,500 99,090 63,030 51,810 104,900 93,170 2002 120,300 100,800 163,700 141,500 108,200 90,710 151,900 131,300 2003 71,310 58,900 109,200 95,230 62,670 52,930 100,400 88,880 2004 84,880 78,040 134,800 140,000 74,340 67,940 126,200 119,900


giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

ssurgo, giras LU

statsgo, giras LU

ssurgo, wmd LU

statsgo, wmd LU

1997 75,640 55,830 112,600 94,200 65,470 50,520 100,400 83,970 1998 102,800 80,320 144,800 125,600 90,690 74,780 130,800 112,800 1999 53,140 43,580 85,130 77,340 49,680 40,530 78,930 70,540 2000 59,190 45,820 89,060 77,690 55,630 43,490 81,380 72,010 2001 69,670 55,270 113,400 98,620 65,960 52,410 104,200 90,390 2002 118,500 98,120 164,100 140,700 112,100 91,600 150,100 126,900 2003 69,300 56,510 109,200 93,780 65,520 53,570 99,830 86,100 2004 82,060 73,490 135,300 136,500 76,430 65,870 123,400 109,300

4 Conclusions

The research showed that the model outputs were sensitive to annual rainfall in general, and results depended on the variable of interest and sources of data. The model output also shows sensitivity to the dominant variable(s) influencing the



parameter of interest (e.g., the model output of flow is dominated by the model input of the DEM). For the driest year (2000) all simulations for sediment yield show comparable results. Wettest year (1998), SSURGO data showed highest sediment yield whereas STASGO showed minimum yield irrespective of the resampling resolution. Relatively drier years (1999 and 2000) showed comparable TP irrespective of resolution of the data and source of input layers. As the wetness increased TP loading showed increased sensitivity to resolution and sources of data. The highest resolution (30 m of DEM, SSURGO and WMD LU) of all input data consistently predicted higher loading of NO3-N except for extremely wet year (2004). During the wet year STATSGO data (resampled to 30 m) along with 30m DEMs and WMD LU showed slightly increased NO3-N loading. Discharge or stream flow predicted by SWAT consistently showed lower discharge rate when compared to the measured data by USGS except for the year 2000, 2001 and 2002. This trend is observed across all resolutions of source data. This could be attributed to the precedence of the driest years (2000). Resolution of the DEMs appears to play most critical role in producing discharge data comparable to the USGS data.

References

[1] Neitsch, M.A., Arnold, J.G., Kiniry, J.R., Williams, J.R. Soil and Water Assessment Tool User’s Manual. Blackland Research Center. Temple TX, 2000.

[2] Monteith, J.L. Evaporation and the environment. In The State and Movement of Water in Living Organisms, 19th Symposia of the Society for Experimental Biology. Cambridge University Press: London; 205-234, 1965.



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