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

Paweł Gilewski

doi:10.3390/w13020230

Abstract

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.

Highlights

Precipitation is one of the major driving forces in the hydrological cycle that affects hydrological processes [1,2]
Mountainous areas often face the problem of sparse rain gauge networks, which limits the accessibility of the data and affects the interpolation accuracy [6]
Taking into account that the grid size aspects are mostly neglected when interpolating precipitation, the main objective of this paper is to investigate both the impact of grid resolution and deterministic interpolation technique and evaluate them via rainfallrunoff simulations using the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) semi-distributed hydrological model over a sparsely gauged mountainous catchment

Summary

Introduction

Precipitation is one of the major driving forces in the hydrological cycle that affects hydrological processes [1,2]. Precipitation data are mainly acquired from rain gauges, weather radars, and satellites, while the first two are considered as the best data sources for catchment modeling [3]. Even though there are measurement alternatives to rain gauges, the data acquired at in-situ measurements are still frequently used in many hydrological applications as they provide reliable and measured (not estimated) point information on the precipitation. Before further use, the rain gauge data must be spatially interpolated, which might significantly affect the accuracy of the spatial precipitation field [4]. The process of obtaining a reliable interpolated precipitation field is challenging in mountainous environments. Mountainous areas often face the problem of sparse rain gauge networks, which limits the accessibility of the data and affects the interpolation accuracy [6]

Objectives

Methods

Results

Conclusion