Haw Yen

A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling

Failure to consider major sources of uncertainty may bias model predictions in simulating watershed behavior. A framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), was developed utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Applications of the framework at the Eagle Creek Watershed in Indiana shows that watershed behavior was more realistically represented when the four uncertainty sources were considered jointly without having to embed watershed behavior constraints in auto-calibration. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the models predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

C-SWAT

The temptation to include model parameters and high resolution input data together with the availability of powerful optimization and uncertainty analysis algorithms has significantly enhanced the complexity of hydrologic and water quality modeling. However, the ability to take advantage of sophisticated models is hindered in those models that need a large number of input files, such as the Soil and Water Assessment Tool (SWAT). The process of reading large amount of input files containing spatial and computational units used in SWAT is cumbersome and time-consuming. In this study, the Consolidated SWAT (C-SWAT) was developed to consolidate 13 groups of SWAT input files from subbasin and Hydrologic Response Unit (HRU) levels into a single file for each category. The utility of the consolidated inputs of model is exhibited for auto-calibration of the Little Washita River Basin (611km2). The results of this study show that the runtime of the SWAT model could be reduced considerably with consolidating input files. The advantage of the proposed method was further promoted with application of the optimization method using a parallel computing technique. The concept is transferrable to other models that store input data in hundreds or thousands of files. The majority of SWAT input files are consolidated into 13 integrated files in C-SWAT.Parallelized Shuffled Complex Evolution algorithm is incorporated with C-SWAT.Significant improvement in computational speed is achieved using C-SWAT.C-SWAT is beneficial to large scale studies with potentially great number of data.

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

The availability of fresh water is a prerequisite for municipal development and agricultural production especially in the arid and semi-arid portions of the western 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 (HUC8) using a combination of Soil and Water Assessment Tool (SWAT) simulations and available human water use estimates. These data are used partition agricultural groundwater usage blue water 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 depletion. 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.

Accounting for Conceptual Soil Erosion and Sediment Yield Modeling Uncertainty in the APEX Model Using Bayesian Model Averaging

AbstractThe effects of soil erosion and sedimentation are important for natural resources conservation planning. However, although tremendous resources have been invested in developing more erosion models, the prevailing modeling studies are relying on a single model due to various reasons. The Agricultural Policy Environmental Extender (APEX) provides multiple water erosion equations. This study tests and evaluates the Bayesian model averaging (BMA) scheme on sediment predictions based on four water erosion methods in the APEX model using data from two watersheds. The APEX hydrology and soil erosion and sedimentation components were calibrated simultaneously using the APEX autocalibration tool APEX-CUTE. The BMA scheme is employed to obtain consensus predictions by weighing individual predictions based on their probabilistic likelihood measures. Simulated monthly flow was satisfactory for both the calibration and validation periods, with BMA resulting in Nash–Sutcliffe efficiency (NSE) values from 0.56 t...