Simulation of Daily Flow Pathways,Tile‐Drain Nitrate Concentrations,and Soil‐Nitrogen Dynamics Using SWAT

Tile drainage significantly alters flow and nutrient pathways and reliable simulation at this scale is needed for effective planning of nutrient reduction strategies. The Soil and Water Assessment Tool (SWAT) has been widely utilized for prediction of flow and nutrient loads, but few applications have evaluated the model's ability to simulate pathway‐specific flow components or nitrate‐nitrogen (NO₃‐N) concentrations in tile‐drained watersheds at the daily time step. The objectives of this study were to develop and calibrate SWAT models for small, tile‐drained watersheds, evaluate model performance for simulation of flow components and NO₃‐N concentration at daily intervals, and evaluate simulated soil‐nitrogen dynamics. Model evaluation revealed that it is possible to meet accepted performance criteria for simulation of monthly total flow, subsurface flow (SSF), and NO₃‐N loads while obtaining daily surface runoff (SURQ), SSF, and NO₃‐N concentrations that are not satisfactory. This limits model utility for simulating best management practices (BMPs) and compliance with water quality standards. Although SWAT simulates the soil N‐cycle and most predicted fluxes were within ranges reported in agronomic studies, improvements to algorithms for soil‐N processes are needed. Variability in N fluxes is extreme and better parameterization and constraint, through use of more detailed agronomic data, would also improve NO₃‐N simulation in SWAT. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Testing of the Modified Streambank Erosion and Instream Phosphorus Routines for the SWAT Model

In some watersheds, streambanks are a source of two major pollutants, phosphorus (P) and sediment. P originating from both uplands and streambanks can be transported and stored indefinitely on floodplains, streambanks, and in closed depressions near the stream. The objectives of this study were to (1) test the modified streambank erosion and instream P routines for the Soil and Water Assessment Tool (SWAT) model in the Barren Fork Creek watershed in northeast Oklahoma, (2) predict P in the watershed with and without streambank‐derived P, and (3) determine the significance of streambank erosion P relative to overland P sources. Measured streambank and channel parameters were incorporated into a flow‐calibrated SWAT model and used to estimate streambank erosion and P for the Barren Fork Creek using modified streambank erosion and instream P routines. The predicted reach‐weighted streambank erosion was 40 kg/m vs. the measured 42 kg/m. Streambank erosion contributed 47% of the total P to the Barren Fork Creek and improved P predictions compared to observed data, especially during the high‐flow events. Of the total P entering the stream system, approximately 65% was removed via the watershed outlet and 35% was stored in the floodplain and stream system. This study successfully applied the SWAT model's modified streambank erosion and instream P routines and demonstrated that streambank‐derived P can improve P modeling at the watershed scale. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

SWATmodel: A Multi‐Operating System,Multi‐Platform SWAT Model Package in R

The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is a popular watershed management tool. Currently, the SWAT model, actively supported by the U.S. Department of Agriculture and Texas A&M, operates only on Microsoft^® Windows, which hinders modelers that use other operating systems (OS). This technical note introduces the Comprehensive R Archive Network (CRAN) distributed “SWATmodel” package which allows SWAT 2005 and 2012 to be widely distributed and run as a linear model‐like function on multiple OS and processor platforms. This allows researchers anywhere in the world using virtually any OS to run SWAT. In addition to simplifying the use of SWAT across computational platforms, the SWATmodel package allows SWAT modelers to utilize the analytical capabilities, statistical libraries, modeling tools, and programming flexibility inherent to R. The software allows watershed modelers to develop a simple hydrological watershed model conceptualization of the SWAT model and to obtain a first approximation of the minimum expected results a more complicated model should deliver. As a proof of concept, we test the SWAT model by initializing and calibrating 314 U.S. Geological Survey stream gages in the Chesapeake Bay watershed and present the results.     

Evaluation of Dynamically Dimensioned Search Algorithm for Optimizing SWAT by Altering Sampling Distributions and Searching Range

The primary advantage of Dynamically Dimensioned Search (DDS) algorithm is that it outperforms other optimization techniques in both convergence speed and searching ability for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation factor) in the optimization process. Conventionally, a default value of 0.2 is used as the perturbation factor, where a normal distribution is applied with mean sampling distribution of zero and variance of one. However, the perturbation factor sensitivity to the performance of DDS for watershed modeling is still unknown. The fixed‐form sampling distribution may result in finding parameters at the local scale rather than global in the sampling space. In this study, the efficiency of DDS was evaluated by altering the perturbation factor (from 0.05 to 1.00) and the selection of sampling distribution (normal and uniform) on hydrologic and water quality predictions in a lowland agricultural watershed in Texas, United States. Results show that the use of altered perturbation factor may cause variations in convergence speed or the ability to find better solutions. In addition, DDS results were found to be very sensitive to sampling distribution selections, where DDS‐N (normal distribution) outperformed DDS‐U (uniform distribution) in all case scenarios. The choice of sampling distributions could be the potential major factor to be attributed for the performance of auto‐calibration techniques for watershed simulation models.     

Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case‐dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Modeling Water Quantity and Sulfate Concentrations in the Devils Lake Watershed Using Coupled SWAT and CE‐QUAL‐W2

Devils Lake is an endorheic lake in the Red River of the North basin in northeastern North Dakota. During the last two decades, the lake water level has risen by nearly 10 m, causing floods that have cost more than 1 billion USD in mitigation measures. Another increase of approximately 1.5 m in the lake water level would cause spillage into the Sheyenne River. To alleviate this potentially catastrophic spillage, two artificial outlets were constructed. However, the artificial drainage of water into the Sheyenne River raises water quality concerns because the Devils Lake water contains significantly higher concentrations of dissolved solids, particularly sulfate. In this study, the Soil and Water Assessment Tool (SWAT) was coupled with the CE‐QUAL‐W2 model to simulate both water balance and sulfate concentrations in the lake. The SWAT model performed well in simulating daily flow in tributaries with E_NS > 0.5 and |PBIAS| < 25%, and reproduced the lake water level with a root mean square error of 0.35 m for the study period from 1995 to 2014. The water temperature and sulfate concentrations simulated by CE‐QUAL‐W2 for the lake are in general agreement with the field observations. The model results show that the operation of the two outlets since August 2005 has lowered the lake level by 0.70 m. Furthermore, the models show pumping water from the two outlets raises sulfate concentrations in the Sheyenne River from ~100 to >500 mg/L. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Use of SWAT to Estimate Spatial Scaling of Phosphorus Export Coefficients and Load Reductions Due to Agricultural BMPS

Phosphorus export coefficients (kg/ha/yr) from selected land covers, also called phosphorus yields, tend to get smaller as contributing areas get larger because some of the phosphorus mobilized on local fields gets trapped during transport to regional watershed outlets. Phosphorus traps include floodplains, wetlands, and lakes, which can then become impaired by eutrophication. The Sunrise River watershed in east central Minnesota, United States, has numerous lakes impaired by excess phosphorus. The Sunrise is tributary to the St. Croix River, whose much larger watershed is terminated by Lake St. Croix, also impaired by excess phosphorus. To support management of these impairments at both local and regional scales, a Soil and Water Assessment Tool (SWAT) model of the Sunrise watershed was constructed to estimate load reductions due to selected best management practices (BMPs) and to determine how phosphorus export coefficients scaled with contributing area. In this study, agricultural BMPs, including vegetated filter strips, grassed waterways, and reduction of soil‐phosphorus concentrations reduced phosphorus loads by 4‐20%, with similar percentage reductions at field and watershed spatial scales. Phosphorus export coefficients from cropland in rotation with corn, soybeans, and alfalfa decreased as a negative power function of contributing area, from an average of 2.12 kg/ha/yr at the upland field scale (~0.6 km²) to 0.63 kg/ha/yr at the major river basin scale (20,000 km²). Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Application of SWAT with and without Variable Source Area Hydrology to a Large Watershed

In this study, two different versions of the Soil and Water Assessment Tool (SWAT) model were used to simulate the hydrology and biogeochemical response of the Cannonsville Reservoir watershed, in New York. The first version distributes overland flow in ways that are consistent with variable source area (VSA) hydrology driven by saturation excess runoff, whereas the second version is the standard version of SWAT. These two models were each calibrated for streamflow (Flow), particulate phosphorus (PP), total dissolved phosphorus (TDP), and sediment (Sed) against measured data from the 1,200 km² Cannonsville watershed. The standard version of the model yielded an r² between the measured and simulated data of 0.85, 0.73, 0.70, and 0.72 for Flow, Sed, TDP, and PP, respectively. The VSA version yielded an r² of 0.84, 0.69, 0.72, and 0.53 for Flow, Sed, TDP, and PP, respectively. The two models were then used to determine the maximum upper bound on the reduction in phosphorus loading by removing all of the corn in the watershed. The average reductions between the two models were 65 and 37% for PP and TDP, respectively. The VSA version was also used to estimate the effect of moving corn land in the watershed from the wettest, most runoff prone areas to the driest, least runoff prone areas, which cannot be done directly with the standard SWAT model.     

Assessment of Bioenergy Cropping Scenarios for the Boone River Watershed in North Central Iowa,United States

Several biofuel cropping scenarios were evaluated with an improved version of Soil and Water Assessment Tool (SWAT) as part of the CenUSA Bioenergy consortium for the Boone River Watershed (BRW), which drains about 2,370 km² in north central Iowa. The adoption of corn stover removal, switchgrass, and/or Miscanthus biofuel cropping systems was simulated to assess the impact of cellulosic biofuel production on pollutant losses. The stover removal results indicate removal of 20 or 50% of corn stover in the BRW would have negligible effects on streamflow and relatively minor or negligible effects on sediment and nutrient losses, even on higher sloped cropland. Complete cropland conversion into switchgrass or Miscanthus, resulted in reductions of streamflow, sediment, nitrate, and other pollutants ranging between 23‐99%. The predicted nitrate reductions due to Miscanthus adoption were over two times greater compared to switchgrass, with the largest impacts occurring for tile‐drained cropland. Targeting of switchgrass or Miscanthus on cropland ≥2% slope or ≥7% slope revealed a disproportionate amount of sediment and sediment‐bound nutrient reductions could be obtained by protecting these relatively small areas of higher sloped cropland. Overall, the results indicate that all biofuel cropping systems could be effectively implemented in the BRW, with the most robust approach being corn stover removal adopted on tile‐drained cropland in combination with a perennial biofuel crop on higher sloped landscapes. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System

This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Characterizing the Extent of Spatially Integrated Floodplain and Wetland Systems in the White River,Indiana, USA

Floodplain delineation may inform protection of wetland systems under local, state, or federal laws. Nationally available Federal Emergency Management Agency Flood Insurance Rate Maps (FIRMs, “100‐year floodplain” maps) focus on urban areas and higher‐order river systems, limiting utility at large scales. Few other national‐scale floodplain data are available. We acquired FIRMs for a large watershed and compared FIRMs to floodplain and integrated wetland area mapping methods based on (1) geospatial distance, (2) geomorphic setting, and (3) soil characteristics. We used observed flooding events (OFEs) with recurrence intervals of 25‐50 to >100 years to assess floodplain estimate accuracy. FIRMs accurately reflected floodplain areas based on OFEs and covered 32% of river length, whereas soil‐based mapping was not as accurate as FIRMs but characterized floodplain areas over approximately 65% of stream length. Geomorphic approaches included more areas than indicated by OFE, whereas geospatial approaches tended to cover less area. Overall, soil‐based methods have the highest utility in determining floodplains and their integrated wetland areas at large scales due to the use of nationally available data and flexibility for regional application. These findings will improve floodplain and integrated wetland system extent assessment for better management at local, state, and national scales.     

Evaluation of CFSR climate data for hydrologic prediction in data‐scarce watersheds: an application in the Blue Nile River Basin

Data scarcity has been a huge problem in modeling the water resources of the Upper Blue Nile basin, Ethiopia. Satellite data and different statistical methods have been used to improve the quality of conventional meteorological data. This study assesses the applicability of the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) climate data in modeling the hydrology of the region. The Soil and Water Assessment Tool was set up to compare the performance of CFSR weather with that of conventional weather in simulating observed streamflow at four river gauging stations in the Lake Tana basin — the upper part of the Upper Blue Nile basin. The conventional weather simulation performed satisfactorily (e.g., NSE ≥ 0.5) for three gauging stations, while the CFSR weather simulation performed satisfactorily for two. The simulations with CFSR and conventional weather yielded minor differences in the water balance components in all but one watershed, where the CFSR weather simulation gave much higher average annual rainfall, resulting in higher water balance components. Both weather simulations gave similar annual crop yields in the four administrative zones. Overall the simulation with the conventional weather performed better than the CFSR weather. However, in data‐scarce regions such as remote parts of the Upper Blue Nile basin, CFSR weather could be a valuable option for hydrological predictions where conventional gauges are not available.     

Regional Blue and Green Water Balances and Use by Selected Crops in the U.S.

The availability of freshwater is a prerequisite for municipal development and agricultural production, especially in the arid and semiarid portions of the western United States (U.S.). Agriculture is the leading user of water in the U.S. Agricultural water use can be partitioned into green (derived from rainfall) and blue water (irrigation). Blue water can be further subdivided by source. In this research, we develop a hydrologic balance by 8‐Digit Hydrologic Unit Code using a combination of Soil and Water Assessment Tool simulations and available human water use estimates. These data are used to partition agricultural groundwater usage by sustainability and surface water usage by local source or importation. These predictions coupled with reported agricultural yield data are used to predict the virtual water contained in each ton of corn, wheat, sorghum, and soybeans produced and its source. We estimate that these four crops consume 480 km³ of green water annually and 23 km³ of blue water, 12 km³ of which is from groundwater withdrawal. Regional trends in blue water use from groundwater depletion highlight heavy usage in the High Plains, and small pockets throughout the western U.S. This information is presented to inform water resources debate by estimating the cost of agricultural production in terms of water regionally. This research illustrates the variable water content of the crops we consume and export, and the source of that water.     

Groundwater Nitrate Concentration Reductions in a Riparian Buffer Enrolled in the NC Conservation Reserve Enhancement Program

Riparian buffers have been used for many years as a best management practice to decrease the effects of nonpoint pollution from watersheds. The NC Conservation Reserve Enhancement Program (NC CREP) has established buffers to treat groundwater nitrate‐nitrogen (NO₃^?‐N) from agricultural sources in multiple river basins. A maturing 46 m wide riparian buffer enrolled in NC CREP was studied to determine its effectiveness in reducing groundwater NO₃^?‐N concentrations from a cattle pasture fertilized with poultry litter. Three monitoring blocks that included groundwater quality wells, water table wells, and soil redox probes, were established in the buffer. NO₃^?‐N concentrations decreased significantly across the buffer in all of the monitoring blocks with mean reductions of 76‐92%. Many biological processes, including denitrification and plant uptake, may have been responsible for the observed NO₃^?‐N reductions but could not be differentiated in this study. However, mean reductions in Cl^? concentrations ranged from 48‐65% through the blocks, which indicated that dilution was an important factor in observed NO₃^?‐N reductions. These findings should be carefully considered for future buffer enrollments when assigning nitrogen removal credits.     

Hydrological Variability and Uncertainty of Lower Missouri River Basin Under Changing Climate

The lower Missouri River Basin has experienced increasing streamflow and flooding events, with higher risk of extreme hydrologic impacts under changing climate. The newly available North American Regional Climate Change Assessment Program (NARCCAP) climate projections were used as atmospheric forcing for Soil and Water Assessment Tool (SWAT) model which runs with varying potential evapotranspiration (PET) methods to assess the hydrological change and uncertainty of 2040‐2069 over 1968‐1997. The NARCCAP temperature and precipitation predictions were refined using a bias correction method. The results show that, following the seasonal variability of precipitation, various water fluxes would increase in most seasons except the summer. Expected precipitation tends to increase in intensity with little change in frequency, triggering faster surface water concentration to form floods. The greatest streamflow increase would occur from November to February, increasing by around 10% on average. An increase of 3% occurs in the other months except for July and August in which river discharge decreases by around 2%. The climate predictions contribute more uncertainty annually, but PET algorithms gain more influence in winter or when other weather factors such as wind play a relatively more important role on evapotranspiration flux. This study predicts an even wetter environment compared to the historically very wet period, with the possibility of more flooding.     

Spatial and Temporal Evaluation of Hydrological Response to Climate and Land Use Change in Three South Dakota Watersheds

This study analyzed changes in hydrology between two recent decades (1980s and 2010s) with the Soil and Water Assessment Tool (SWAT) in three representative watersheds in South Dakota: Bad River, Skunk Creek, and Upper Big Sioux River watersheds. Two SWAT models were created over two discrete time periods (1981‐1990 and 2005‐2014) for each watershed. National Land Cover Datasets 1992 and 2011 were, respectively, ingested into 1981‐1990 and 2005‐2014 models, along with corresponding weather data, to enable comparison of annual and seasonal runoff, soil water content, evapotranspiration (ET), water yield, and percolation between these two decades. Simulation results based on the calibrated models showed that surface runoff, soil water content, water yield, and percolation increased in all three watersheds. Elevated ET was also apparent, except in Skunk Creek watershed. Differences in annual water balance components appeared to follow changes in land use more closely than variation in precipitation amounts, although seasonal variation in precipitation was reflected in seasonal surface runoff. Subbasin‐scale spatial analyses revealed noticeable increases in water balance components mostly in downstream parts of Bad River and Skunk Creek watersheds, and the western part of Upper Big Sioux River watershed. Results presented in this study provide some insight into recent changes in hydrological processes in South Dakota watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Estimation of Nonpoint Source Nitrate Concentrations in Indiana Rivers Based on Agricultural Drainage in the Watershed

Subsurface tile‐drained agricultural fields are known to be important contributors to nitrate in surface water in the Midwest, but the effect of these fields on nitrate at the watershed scale is difficult to quantify. Data for 25 watersheds monitored by the Indiana Department of Environmental Management and located near a U.S. Geological Survey stream gage were used to investigate the relationship between flow‐weighted mean concentration (FWMC) of nitrate‐N and the subsurface tile‐drained area (DA) of the watershed. The tile DA was estimated from soil drainage class, land use, and slope. Nitrate loads from point sources were estimated based on reported flows of major permitted facilities with mean nitrate‐N concentrations from published sources. Linear regression models exhibited a statistically significant relationship between annual/monthly nonpoint source (NPS) nitrate‐N and DA percentage. The annual model explained 71% of the variation in FWMC of nitrate‐N. The annual and monthly models were tested in 10 additional watersheds, most with absolute errors within 1 mg/l in the predicted FWMC. These models can be used to estimate NPS nitrate for unmonitored watersheds in similar areas, especially for drained agricultural areas where model performance was strongest, and to predict the nitrate reduction when various tile drainage management techniques are employed.     

Investigation of the Curve Number Method For Surface Runoff Estimation In Tropical Regions

This study tests the applicability of the curve number (CN) method within the Soil and Water Assessment Tool (SWAT) to estimate surface runoff at the watershed scale in tropical regions. To do this, surface runoff simulated using the CN method was compared with observed runoff in numerous rainfall‐runoff events in three small tropical watersheds located in the Upper Blue Nile basin, Ethiopia. The CN method generally performed well in simulating surface runoff in the studied watersheds (Nash‐Sutcliff efficiency [NSE] > 0.7; percent bias [PBIAS] < 32%). Moreover, there was no difference in the performance of the CN method in simulating surface runoff under low and high antecedent rainfall (PBIAS for both antecedent conditions: ~30%; modified NSE: ~0.4). It was also found that the method accurately estimated surface runoff at high rainfall intensity (e.g., PBIAS < 15%); however, at low rainfall intensity, the CN method repeatedly underestimated surface runoff (e.g., PBIAS > 60%). This was possibly due to low infiltrability and valley bottom saturated areas typical of many tropical soils, indicating that there is scope for further improvements in the parameterization/representation of tropical soils in the CN method for runoff estimation, to capture low rainfall‐intensity events. In this study the retention parameter was linked to the soil moisture content, which seems to be an appropriate approach to account for antecedent wetness conditions in the tropics.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.