|Youssef, M - North Carolina State University|
|Yen, H - Texas Agrilife Research|
|Sheshukov, A - Kansas State University|
|Amatya, D - Us Forest Service (FS)|
|Skaggs, Richard - North Carolina State University|
|Haney, Elizabeth - Texas Agrilife Research|
|Jeong, Jaehak - Texas Agrilife Research|
|Arabi, Mazdak - Colorado State University|
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/2014
Publication Date: 12/28/2015
Publication URL: http://handle.nal.usda.gov/10113/61901
Citation: Arnold, J.G., Youssef, M.A., Yen, H., White, M.J., Sheshukov, A.Y., Sadeghi, A.M., Moriasi, D.N., Steiner, J.L., Amatya, D.M., Skaggs, R.W., Haney, E.B., Jeong, J., Arabi, M., Gowda, P. 2015. Hydrological processes and model representation: Impact of soft data on calibration. Transactions of the ASABE. 58(6):1637-1660.
Interpretive Summary: Hydrology and water quality models are routinely used to determine environmental impacts of land management and climate variability. When applying models, users calibrate input variables by adjusting model inputs related to climate, soil, management and topographic properties to accurately match measured stream gage flows. Currently, there are no universally accepted procedures for calibrating models. In an effort to develop guidelines, calibration procedures for 25 water quality models were reported in a previous special collection of papers. This paper is one of a series of follow-up papers that deals with outstanding model calibration issues, specifically, the impact of processes and model representation on calibration. In this paper, case studies are used to illustrate situations where a model can show excellent agreement with measured stream gage data, while misrepresented processes (water balance, nutrient balance, sediment source/sinks) within the watershed can cause errors when running management scenarios. Typical management scenarios include running the model with different land use and varying tillage and fertilizer management. Recommendations are given for assembling data to better understand and represent the balances within a watershed, integrate that information into the calibration procedure, and ultimately provide realistic scenario analysis for water policy makers.
Technical Abstract: Hydrologic and water quality models are increasingly used to determine the environmental impacts of climate variability and land management. Due to differing model objectives and differences in monitored data, there are currently no universally accepted procedures for calibration and validation in the literature. In an effort to develop accepted model calibration and validation procedures or guidelines, a special collection of 22 research articles that present and discuss calibration strategies for 25 hydrologic and water quality models was previously assembled. The models vary in scale temporally as well as spatially from point source to the watershed level. One suggestion for future work was to synthesize relevant information from this special collection and to identify significant calibration and validation topics. The objective of this paper is to discuss the importance of proper model processes representation and its impact on calibration and scenario analysis using the information from these 22 research articles and other relevant literature. In general, if the processes are not accurately represented (i.e. if surface water runoff and/or evapotranspiration is over or underestimated), the nutrient and/or sediment balance will be misjudged. These errors may be amplified at the watershed scale where additional sources and transport processes are simulated. The calibration and validation procedure should consider the accuracy/adequacy of the representation of environmental processes at the appropriate scale of the model given the modelling objectives. To account for processes in calibration, a diagnostic approach is recommended using both hard and soft data. The diagnostic approach looks at signature patterns of behavior of model outputs to determine which processes, and thus parameters, need further adjustment during calibration. This overcomes the weaknesses of traditional regression based calibration by discriminating between multiple processes within a budget. Hard data is defined as long term, measured time series, typically at a point within a watershed. Soft data is defined as information on individual processes within a balance that may not be directly measured within the study area, and may be just an average annual estimate, and may entail considerable uncertainty. The advantage of developing soft data for the calibration is that it requires a basic understanding of processes (water, sediment, nutrient, and carbon budgets) within the spatial area being modeled and constrains the calibration.