Input data resolution‐induced uncertainty in watershed modelling

Patil, Abhijit; Deng, Zhiqiang; Malone, Ronald F.

doi:10.1002/hyp.8018

Cited by 9 publications

(3 citation statements)

References 15 publications

(15 reference statements)

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“…As the number of extreme rainfall events grows globally, the spatial-temporal heterogeneity of rainfall has become the focus of study. Many in-depth researches dealt with topics as the spatial-temporal characteristics of rainfall erosion force (da Silva et al, 2020;Wang et al, 2013), the relationship between the spatial-temporal characteristics of rainfall and flood characteristics (Villarini et al, 2011), the inversion of the spatial-temporal characteristics of rainfall (Fuentes et al, 2008;Scher & Peßenteiner, 2021;Zhou et al, 2018), the trend of spatial-temporal characteristics of rainfall in a certain area (Jung et al, 2017;Mamunur Rashid et al, 2015;Varouchakis et al, 2018), and the impact of spatialtemporal resolution of rainfall on the accuracy of numerical simulation (Patil et al, 2011;Zhou et al, 2018), and so on.…”

Section: Introductionmentioning

confidence: 99%

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

Chen

Hou

et al. 2022

Hydrological Processes

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The spatial-temporal characteristics of rainfall is one of the important elements affecting urban inundation. In this work, the effect of spatial-temporal characteristics of rainfall on urban inundation processes was investigated. To solve the problem that it is difficult to acquire the spatial-temporal characteristics of rainfall, the weather generator model was used to generate rainfall data in accordance with the climatic conditions of the study area. Then, precise rainfall data with 1 km 2 spatial resolution and 5 min temporal resolution were obtained through the downscaling method.

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Section: Introductionmentioning

confidence: 99%

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

Chen

Hou

et al. 2022

Hydrological Processes

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“…Finally, input uncertainties exist due to missing and/or uncertain input data (Breinl 2016;McMillan et al 2012) as well as due to simplifications of the processes that finally represent the model input, e.g. the groundwater recharge (Kavetski et al 2006;Patil et al 2011;Vrugt et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

2021

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Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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“…While appropriate at the large watershed-scale, use of aggregated county level data for smaller watersheds, particularly those requiring site-specific TMDLs, may not be appropriate because such data may poorly represent local site-specific conditions. Studies [1,2,3] have addressed the impacts of geographical information system (GIS) spatial data resolution of site-specific data on model output uncertainty. However, there is considerably less information on uncertainty due to the disaggregation of county-level data to local watersheds requiring watershed management efforts such as TMDLs.…”

Section: Introductionmentioning

confidence: 99%

Effects of Watershed Land Use Data on HSPF Water Quality in the Upper Opequon Watershed in northern Virginia, USA

Alukwe¹,

Dillaha²

2014

AJWR

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Land use data source can contribute to errors in watershed modeling. This paper evaluated the effects of using site-specific versus county-level aggregated land use data on Hydrologic Simulation Program-Fortran (HSPF) simulated contaminant losses. Site-specific land use was derived from the local watershed land use inventory while aggregated land use was derived from county-level data (percentage of county land in various land use categories and sub-categories). County level data are useful when modeling large watersheds such as the Chesapeake Bay Watershed when collection and use of site-specific data may be cost prohibitive. The study site was the 14,941 ha predominately rural Upper Opequon Watershed in northern Virginia, USA. Percentage relative errors in model output were calculated and compared using the two land use data sources. Results showed that use of aggregated land use data resulted in 13, 3 and 4 percent higher simulated sediment, and total nitrogen and phosphorus losses, respectively due to overestimated cropland area. The higher contaminant losses would suggest the need for more management measures to meet water quality goals. This study suggests that while the use of county-level aggregated land use data may be appropriate for developing basin scale pollutant reduction goals such as those in total maximum daily load (TMDL) plans, it should be used with extreme caution for watershed planning and implementation activities on smaller watersheds that may mandate site-specific changes in land management and costs for landowners. For smaller watersheds, TMDLs and their watershed implementation plans should utilize local site-specific spatial data that accurately reflects watershed conditions. This will help target resources where they are most needed and maintain credibility with local stakeholders while improving the accuracy of the developed pollution reduction plans.

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Input data resolution‐induced uncertainty in watershed modelling

Cited by 9 publications

References 15 publications

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Effects of Watershed Land Use Data on HSPF Water Quality in the Upper Opequon Watershed in northern Virginia, USA

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