Abstract:Climate is one of the single most important factors affecting watershed ecosystems and water resources. The effect of climate variability and change has been studied extensively in some places; in many places, however, assessments are hampered by limited availability of long-term continuous climate data. Weather generators provide a means of synthesizing long-term climate data that can then be used in natural resource assessments. Given their potential, there is the need to evaluate the performance of the generators; in this study, three commonly used weather generators-CLImate GENerator (CLIGEN), Long Ashton Research Station Weather Generator (LARS-WG), and Weather Generators (WeaGETS) were compared with regard to their ability to capture the essential statistical characteristics of observed data (distribution, occurrence of wet and dry spells, number of snow days, growing season temperatures, and growing degree days). The study was based on observed 1966-2015 weather station data from the Western Lake Erie Basin (WLEB), from which 50 different realizations were generated, each spanning 50 years. Both CLIGEN and LARS-WG performed fairly well with respect to representing the statistical characteristics of observed precipitation and minimum and maximum temperatures, although CLIGEN tended to overestimate values at the extremes. This generator also overestimated dry sequences by 18%-30% and snow-day counts by 12%-19% when considered over the entire WLEB. It (CLIGEN) was, however, well able to simulate parameters specific to crop growth such as growing degree days and had an added advantage over the other generators in that it simulates a larger number of weather variables. LARS-WG overestimated wet sequence counts across the basin by 15%-38%. In addition, the optimal growth period simulated by LARS-WG also exceeded that obtained from observed data by 16%-29% basin-wide. Preliminary results with WeaGETS indicated that additional evaluation is needed to better define its parameters. Results provided insights into the suitability of both CLIGEN and LARS-WG for use with water resource applications.

Calibration and validation of process based hydrological models are two major processes while simulating the water balance components of watershed systems. However, these processes need a better understanding of the parameters which influence hydrologic processes within the system. In this study, we used SWAT model to simulate the stream flow for Skunk Creek (SK) watershed in South Dakota for the period from 1980-2000. Model calibration and validation were performed for both daily and monthly time periods using SUFI-2 within SWAT-CUP using 24 parameters selected from past available literature. Our calibration outputs for the period from 1987-1994 showed a good correlation between observed and model simulated values with NSE=0.56 and R 2 =0.70 for daily simulation. However, the model showed a better performance for monthly simulation with NSE and R 2 values of 0.84 and 0.84 respectively. During validation period (1995)(1996)(1997)(1998)(1999)(2000), the NSE and r 2 values were 0.55 and 0.44, respectively for daily simulation and these statistical values were 0.76 and 0.77, respectively for monthly time step. Following calibration, the overall effect of each parameter used was ranked using global sensitivity function within SWAT-CUP. From the analysis, SOL_AWC was found to be the most sensitive parameter with absolute t-value of 17.50 and p-value of 0.00 to simulate the stream flow of the SK watershed. The CH_K2 was observed as the least sensitive parameter with t-statistic and p-value of 0.02 and 0.97, respectively. It was concluded from the study that coupling of the SWAT and SWAT-CUP made the calibration process quicker and reliable to simulate local hydrology within the watershed.

Abstract:Weather extremes and climate variability directly impact the hydrological cycle influencing agricultural productivity. The issues related to climate change are of prime concern for every nation as its implications are posing negative impacts on society. In this study, we used three climate change scenarios to simulate the impact on local hydrology of a small agricultural watershed. The three emission scenarios from the Special Report on Emission Scenarios, of the Intergovernmental Panel on Climate Change (IPCC) 2007 analyzed in this study were A2 (high emission), A1B (medium emission), and B1 (low emission). A process based hydrologic model SWAT (Soil and Water Assessment Tool) was calibrated and validated for the Skunk Creek Watershed located in eastern South Dakota. The model performance coefficients revealed a strong correlation between simulated and observed stream flow at both monthly and daily time step. The Nash Sutcliffe Efficiency for monthly model performace was 0.87 for the calibration period and 0.76 for validation period. The future climate scenarios were built for the mid-21st century time period ranging from 2046 to 2065. The future climate data analysis showed an increase in temperatures between 2.2 • C to 3.3 • C and a decrease in precipitation from 1.8% to 4.5% expected under three different climate change scenarios. A sharp decline in stream flow (95.92%-96.32%), run-off (83.46%-87.00%), total water yield (90.67%-91.60%), soil water storage (89.99%-92.47%), and seasonal snow melt (37.64%-43.06%) are predicted to occur by the mid-21st century. In addition, an increase in evapotranspirative losses (2%-3%) is expected to occur within the watershed when compared with the baseline period. Overall, these results indicate that the watershed is highly susceptible to hydrological and agricultural drought due to limited water availability. These results are limited to the available climate projections, and future refinement in projected climatic change data, at a finer regional scale would provide greater clarity. Nevertheless, models like SWAT are excellent means to test best management practices to mitigate the projected dry conditions in small agricultural waterhseds.

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