Fit-for-purpose analysis of uncertainty using split-sampling evaluations

Griensven, Ann van; Meixner, Thomas; Srinivasan, Raghavan; Grunwald, Sabine

doi:10.1623/hysj.53.5.1090

Cited by 42 publications

(16 citation statements)

References 41 publications

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“…Because of uncertainties associated with model structure, input, parameter and output, the model prediction is not a certain value and should be represented with a confidence range (Gupta et al ., ; Beven, ; Van Griensven et al ., ; Yang et al ., ; Zhang et al ., ; Shen et al ., ). In this study, the SWAT model was used to simulate runoff and then, to analyze the impacts of land use on annual and monthly runoff in wet, average and dry years through scenario simulation.…”

Section: Resultsmentioning

confidence: 99%

Simulated runoff responses to land use in the middle and upstream reaches of Taoerhe River basin, Northeast China, in wet, average and dry years

Jiang

Hou

et al. 2012

Hydrological Processes

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Abstract:Study on runoff variations and responses can lay a foundation for flood control, water allocation and integrated river basin management. This study applied the Soil and Water Assessment Tool model to simulate the effects of land use on annual and monthly runoff in the Middle and Upstream Reaches of Taoerhe River basin, Northeast China, under the wet, average and dry climate conditions through scenario analysis. The results showed that from the early 1970s to 2000, land use change with an increase in farmland (17.0%) and decreases in forest (10.6%), grassland (4.6%) and water body (3.1%) caused increases in annual and monthly runoff. This effect was more distinct in the wet season or in the wet year, suggesting that land use change from the early 1970s to 2000 may increase the flood potential in the wet season. Increases in precipitation and air temperature from the average to wet year led to annual and monthly (March and from June to December) runoff increases, while a decrease in precipitation and an increase in air temperature from the average to dry year induced decreases in annual and monthly (all months except March) runoff, and moreover, these effects were more remarkable in the wet season than those in the dry season. Due to the integrated effects of changing land use and climate conditions, the annual runoff increased (decreased) by 70.1 mm (25.2 mm) or 197.4% (71.0%) from the average to wet (dry) year. In conclusion, climate conditions, especially precipitation, played an important role in runoff variations while land use change was secondary over the study area, and furthermore, the effects of changes in land use and/or climate conditions on monthly runoff were larger in the wet season.

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Section: Resultsmentioning

confidence: 99%

Simulated runoff responses to land use in the middle and upstream reaches of Taoerhe River basin, Northeast China, in wet, average and dry years

Jiang

Hou

et al. 2012

Hydrological Processes

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“…The existence of parameter uncertainty will lead to uncertainties in model output (e.g., carbon fluxes). Therefore, a proper sensitivity and uncertainty analysis is preferable because it produces model simulations with a confidence range (band) instead of specific output [ Beven , ; Van Griensven et al ., ; Ng et al ., ]. In this sense, it is critical to investigate the parameter uncertainty and quantify its effects on the model output especially for large complex ecological models [ Keenan et al ., ; Wang et al ., ; Richardson et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Huang

Yan

2014

J Adv Model Earth Syst

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Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package-Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

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“…and has been used to predict hydrologic responses at ungauged watersheds. SWAT has an advantage when applied to large-scale ungauged watersheds by adapting auto-calibration tools, such as Parameter Solution (ParaSol) [36], Sources of Uncertainty Global Assessment using Split Samples (SUNGLASSES) [37,38], Shuffled Complex Evolution-University of Arizona (SCE-UA) [36] and SWAT-calibration and uncertainty procedures (CUP) [39]. For example, Park et al [40] and Lee et al [41] tested the applicability of SWAT to small watersheds and performed the estimation of runoff curve coefficients, respectively.…”

Section: Introductionmentioning

confidence: 99%

Development of Web-Based RECESS Model for Estimating Baseflow Using SWAT

2014

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Abstract:Groundwater has received increasing attention as an important strategic water resource for adaptation to climate change. In this regard, the separation of baseflow from streamflow and the analysis of recession curves make a significant contribution to integrated river basin management. The United States Geological Survey (USGS) RECESS model adopting the master-recession curve (MRC) method can enhance the accuracy with which baseflow may be separated from streamflow, compared to other baseflow-separation schemes that are more limited in their ability to reflect various watershed/aquifer characteristics. The RECESS model has been widely used for the analysis of hydrographs, but the applications using RECESS were only available through Microsoft-Disk Operating System (MS-DOS). Thus, this study aims to develop a web-based RECESS model for easy separation of baseflow from streamflow, with easy applications for ungauged regions. RECESS on the web derived the alpha factor, which is a baseflow recession constant in the Soil Water Assessment Tool (SWAT), and this variable was provided to SWAT as the input. The results showed that the alpha factor estimated from the web-based RECESS model improved the predictions of streamflow and recession. Furthermore, these findings showed that the baseflow characteristics of the ungauged watersheds were influenced by the land use and slope angle of watersheds, as well as by precipitation and streamflow. OPEN ACCESSSustainability 2014, 6 2358

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Fit-for-purpose analysis of uncertainty using split-sampling evaluations

Cited by 42 publications

References 41 publications

Simulated runoff responses to land use in the middle and upstream reaches of Taoerhe River basin, Northeast China, in wet, average and dry years

Simulated runoff responses to land use in the middle and upstream reaches of Taoerhe River basin, Northeast China, in wet, average and dry years

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Development of Web-Based RECESS Model for Estimating Baseflow Using SWAT

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