ORCID Profile
0000-0002-5509-8792
Current Organisation
Bayer CropScience AG
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Publisher: Wiley
Date: 04-2017
DOI: 10.1002/FEE.1472
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.SCITOTENV.2016.06.202
Abstract: 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 model's 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.
Publisher: MDPI AG
Date: 14-08-2019
DOI: 10.3390/W11081681
Abstract: For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users (ii) compiled and integrated directly with SWAT+ source code as a single executable (iii) supported by the SWAT developer group and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.
Publisher: Copernicus GmbH
Date: 28-06-2022
Publisher: Copernicus GmbH
Date: 28-06-2022
Abstract: Abstract. To improve the capacity of watershed modeling, remotely sensed products are frequently used to reduce the uncertainty resulting from data limitations. Although remotely sensed evapotranspiration (RS-ET) products are widely used, vegetation parameters governing spatial and temporal variations in evapotranspiration (ET) are often not constrained by benchmark data. Recently, remotely sensed leaf area index (RS-LAI) products are becoming increasingly available, providing an opportunity to assess and improve simulated vegetation dynamics. The objective of this study is to assess the role of the two remotely sensed products (i.e., RS-ET and RS-LAI) in improving the accuracy of watershed model predictions. Specifically, we investigated the role of RS-ET and RS-LAI products in 1) reducing parameter uncertainty and 2) improving model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level. The watershed-level assessment of the degree of equifinality (denoted as the number of parameter sets that produce equally acceptable model simulations) shows that less than half of the acceptable parameter sets for two constraints (streamflow and RS-ET 14 parameter sets) are acceptable for three constraints (streamflow, RS-ET, and RS-LAI six parameter sets). Among those six parameter sets, only three can satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level. Our results suggest that the use of multiple remotely sensed datasets holds great potential to reduce parameter uncertainty and increase the credibility of watershed modeling, particularly for characterizing spatial variability of hydrologic fluxes that are relevant to agricultural management.
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 03-2020
Publisher: Copernicus GmbH
Date: 12-01-2021
Abstract: Abstract. Remotely sensed evapotranspiration (RS-ET) products have been widely adopted as additional constraints on hydrologic modeling to enhance the model predictability while reducing predictive uncertainty. However, vegetation parameters, responsible for key time/space variation in evapotranspiration (ET), are often calibrated without the use of suitable constraints. Remotely sensed leaf area index (RS-LAI) products are increasingly available and provide an opportunity to assess vegetation dynamics and improve calibration of associated parameters. The goal of this study is to assess the Soil and Water Assessment Tool (SWAT) predictive uncertainty in estimates of ET using streamflow and two remotely sensed products (i.e., RS-ET and RS-LAI). We explore how the application of RS-ET and RS-LAI products contributes to 1) reducing the parameter uncertainty 2) improving the model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level and 3) assessing the model predictions of ET and LAI at the basic modeling unit (i.e., the hydrologic response unit [HRU]) under the assumption that ET and LAI are related in croplands. Our results suggest that most of the parameter sets with acceptable performances for two constraints (i.e., streamflow and RS-ET 12 parameter sets) are also acceptable for three constraints (i.e., streamflow, RS-ET, and RS-LAI 11 parameter sets) at the watershed level. This finding is likely because both the ET simulation algorithm and the RS-ET products consider LAI as an input variable. Relative to the watershed-level assessment, the number of parameter sets that satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level are reduced from 11 to 6. Among the 11 parameter sets acceptable for three constraints (i.e., streamflow, RS-ET and RS-LAI) at the sub-watershed level, two parameter sets appear to provide high spatial and temporal consistency between ET and LAI at the HRU level. These results suggested that use of multiple remotely sensed products as model constraints enables model evaluations at finer scales – thereby constraining acceptable parameter sets and accurately representing the spatial characteristics of hydrologic variables. As such, this study highlights the potential of remotely sensed data to increase the predictability and utility of hydrologic models.
Publisher: Copernicus GmbH
Date: 12-01-2021
Publisher: Wiley
Date: 13-05-2020
DOI: 10.1111/FWB.13515
No related grants have been discovered for Haw Yen.