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

Self Cite

Abstract:The Soil and Water Assessment Tool 2012 (SWAT2012) offers four sediment routing methods as optional alternatives to the default simplified Bagnold method. Previous studies compared only one of these alternative sediment routing methods with the default method. The proposed study evaluated the impacts of all four alternative sediment transport methods on sediment predictions: the modified Bagnold equation, the Kodoatie equation, the Molinas and Wu equation, and the Yang equation. The Arroyo Colorado Watershed, Texas, USA, was first calibrated for daily flow. The sediment parameters were then calibrated to monthly sediment loads, using each of the four sediment routing equations. An automatic calibration tool-Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT)-was used to fit model parameters. The four sediment routing equations yielded substantially different sediment sources and sinks. The Yang equation performed best, followed by Kodoatie, Bagnold, and Molinas and Wu equations, according to greater model goodness-of-fit (represented by higher Nash-Sutcliffe Efficiency coefficient and percent bias closer to 0) as well as lower model uncertainty (represented by inclusion of observed data within 95% confidence interval). Since the default method (Bagnold) does not guarantee the best results, modelers should carefully evaluate the selection of alternative methods before conducting relevant studies or engineering projects.

Section: Model Calibration and Validationmentioning

confidence: 99%

“…The scenario name and equation tested in each scenario were [49]. IPEAT is a framework for both auto-calibration and uncertainty analysis which adopts Dynamically Dimensioned Search (DDS) as the major parameter estimation technique [50] (however, DDS can be replaced by other methods under the IPEAT framework).…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Assessment of Optional Sediment Transport Functions via the Complex Watershed Simulation Model SWAT

Yen

Feng

et al. 2017

Self Cite

“…In addition, GLUE accounts partly for uncertainty due to the possible non-uniqueness (or equifinality) of parameter sets during calibration and could therefore underestimate total model uncertainty [68]. For instance, Sellami et al [69] showed that the GLUE predictive uncertainty band was larger and surrounded more observation data when uncertainty in the discharge data was explicitly considered.…”

Section: Model Performancementioning

confidence: 99%

Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

Begou

Jomaa

Benabdallah

et al. 2016

Abstract:The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT) model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. Potential Evapotranspiration (PET) and biomass were considered in the verification of model outputs accuracy. Global Sensitivity Analysis (GSA) was used for identifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.

“…Such situations are common in multi-disciplinary modeling applications that link multiple, complex components [78]. Therefore, a research frontier in designing useful DSS for water-resource managers involves the rigorous assessment of uncertainty associated with each dataset and model component, coupled with state-of-the-art computational techniques for propagating uncertainty through complex hierarchical systems (e.g., [79,80]). …”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

“…In practice, uncertainty quantification is challenging-and in some cases impossible-due to the presence of multiple interacting (and often unmeasured) forms of uncertainty including observational limitations, parameterization, feedbacks, and stochastic effects [78,79]. For example in our case study, sources of uncertainty included: (a) input data, e.g., stream-gauge data, fish community data, and water-use data; (b) statistical models, e.g., regional regression equations from [61] to estimate streamflow characteristics at ungauged sites; and (c) process-based models, e.g., hydrologic accounting methods that assumed temporally static water-use rates; none of which were accompanied by estimates of uncertainty.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Putting Flow–Ecology Relationships into Practice: A Decision-Support System to Assess Fish Community Response to Water-Management Scenarios

Cartwright

Caldwell

Nebiker

et al. 2017

This paper presents a conceptual framework to operationalize flow-ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow-ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow-ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.