Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments

Mateusz Szcześniak,Mikołaj Piniewski

doi:10.3390/w7020747

Abstract

Ground-based precipitation data are still the dominant input type for hydrological models. Spatial variability in precipitation can be represented by spatially interpolating gauge data using various techniques. In this study, the effect of daily precipitation interpolation methods on discharge simulations using the semi-distributed SWAT (Soil and Water Assessment Tool) model over a 30-year period is examined. The study was carried out in 11 meso-scale (119–3935 km2) sub-catchments lying in the Sulejów reservoir catchment in central Poland. Four methods were tested: the default SWAT method (Def) based on the Nearest Neighbour technique, Thiessen Polygons (TP), Inverse Distance Weighted (IDW) and Ordinary Kriging (OK). =The evaluation of methods was performed using a semi-automated calibration program SUFI-2 (Sequential Uncertainty Fitting Procedure Version 2) with two objective functions: Nash-Sutcliffe Efficiency (NSE) and the adjusted R2 coefficient (bR2). The results show that: (1) the most complex OK method outperformed other methods in terms of NSE; and (2) OK, IDW, and TP outperformed Def in terms of bR2. The median difference in daily/monthly NSE between OK and Def/TP/IDW calculated across all catchments ranged between 0.05 and 0.15, while the median difference between TP/IDW/OK and Def ranged between 0.05 and 0.07. The differences between pairs of interpolation methods were, however, spatially variable and a part of this variability was attributed to catchment properties: catchments characterised by low station density and low coefficient of variation of daily flows experienced more pronounced improvement resulting from using interpolation methods. Methods providing higher precipitation estimates often resulted in a better model performance. The implication from this study is that appropriate consideration of spatial precipitation variability (often neglected by model users) that can be achieved using relatively simple interpolation methods can significantly improve the reliability of model simulations.

Highlights

The growing needs in the field of hydrological modelling necessitate the continual improvement of existing hydrological models
Model efficiency of daily and monthly discharge simulation over a 30-year period was examined in 11 meso-scale catchments in central Poland
The results showed that the most complex Ordinary Kriging (OK) method outperformed other methods in terms of Nash-Sutcliffe Efficiency (NSE), whereas OK, Inverse Distance Weighted (IDW) and Thiessen Polygons (TP) outperformed default Soil and Water Assessment Tool (SWAT) method (Def) in terms of bR2, regardless of temporal aggregation

Summary

Introduction

The growing needs in the field of hydrological modelling necessitate the continual improvement of existing hydrological models. Gauge data are traditionally the most widely used data type for hydrological modelling They pose multiple problems such as gauge undercatch [9] and the high costs of supporting dense networks of gauges which are crucial for reliable areal precipitation estimates [10], in particular during intensive and spatially variable rainfall events causing flash floods [11]. Reanalysis data products such as the WATCH Forcing Data (Water and Global Change, “WFD”; [12]) are promising in that they usually cover long time periods (100 years in the case of the WFD), but their spatial resolution is not sufficient for modelling of small and medium-sized catchments. Their accessibility in many countries is low, and is, in particular, not sufficient for simulation periods covering a few decades, e.g., a climate normal 30-year period

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