Next Article in Journal
Spatial and Temporal Characteristics of Precipitation and Potential Influencing Factors in the Loess Plateau before and after the Implementation of the Grain for Green Project
Next Article in Special Issue
Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size
Previous Article in Journal
Improving Thermal Distribution in Water-Cooled PV Modules and Its Effect on RO Permeate Recovery
Previous Article in Special Issue
Use of Ensemble-Based Gridded Precipitation Products for Assessing Input Data Uncertainty Prior to Hydrologic Modeling
Open AccessFeature PaperArticle

Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment

Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland
Water 2021, 13(2), 230; https://doi.org/10.3390/w13020230
Received: 23 December 2020 / Revised: 12 January 2021 / Accepted: 15 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Modelling Precipitation in Space and Time)
Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of interpolation is particularly challenging over mountainous areas due to complex orography and a usually sparse network of rain gauges. This paper investigates two deterministic interpolation methods (inverse distance weighting (IDW), and first-degree polynomial) and their impact on the outputs of semi-distributed rainfall-runoff modeling in a mountainous catchment. The performed analysis considers the aspect of interpolation grid size, which is often neglected in other than fully-distributed modeling. The impact of the inverse distance power (IDP) value in the IDW interpolation was also analyzed. It has been found that the best simulation results were obtained using a grid size smaller or equal to 750 m and the first-degree polynomial as an interpolation method. The results indicate that the IDP value in the IDW method has more impact on the simulation results than the grid size. Evaluation of the results was done using the Kling-Gupta efficiency (KGE), which is considered to be an alternative to the Nash-Sutcliffe efficiency (NSE). It was found that KGE generally tends to provide higher and less varied values than NSE which makes it less useful for the evaluation of the results. View Full-Text
Keywords: grid resolution; HEC-HMS; inverse distance weighting; polynomial interpolation; precipitation interpolation grid resolution; HEC-HMS; inverse distance weighting; polynomial interpolation; precipitation interpolation
Show Figures

Figure 1

MDPI and ACS Style

Gilewski, P. Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment. Water 2021, 13, 230. https://doi.org/10.3390/w13020230

AMA Style

Gilewski P. Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment. Water. 2021; 13(2):230. https://doi.org/10.3390/w13020230

Chicago/Turabian Style

Gilewski, Paweł. 2021. "Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment" Water 13, no. 2: 230. https://doi.org/10.3390/w13020230

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop