Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

Abstract

In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from downscaled reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km<sup>2</sup>). ERA-40 reanalysis data are downscaled with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF downscaled ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from downscaled reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period

The value of satellite‐derived snow cover images for calibrating a hydrological model in snow‐dominated catchments in Central Asia

“…The hydrological model WASA (Model of Water Availability in Semi‐Arid Environments) [ Güntner , ; Güntner and Bronstert , ] was originally developed for large semiarid basins, and later extended for erosion and sediment transport [ Mueller et al ., ] and for mountainous regions influenced by snow and glacier melt [ Duethmann et al ., ].…”

Deep‐seated gravitational slope deformation (DSGSD) and slow‐moving landslides in the southern Tien Shan Mountains: new insights from InSAR, tectonic and geomorphic analysis

“…The study area is snow covered during winter, with a thickness of 20–100 cm, depending on elevation and exposition. The average temperature is about −10°C in winter (December to February) and +24°C in summer (June to August) (Duethmann et al ., ).…”

Evaluating the present annual water budget of a Himalayan headwater river basin using a high‐resolution atmosphere‐hydrology model

“…Additionally, a global reanalysis product, the 0.25° ERA‐Interim reanalysis data set [ Simmons et al ., ; Dee et al ., ], and the APHRODITE V1101R2 daily precipitation (0.25°) [ Yasutomi et al ., ] are also employed. All data sets have been applied previously to hydrological analyses [ Duethmann et al ., ; Li et al ., , ; L. Li et al ., ; Xu et al ., ]. Seven rain gauge stations and two weather stations (1996–2005) obtained from Bhakra Beas Management Board (BBMB) in India were used to assess performance of the WRF model precipitation.…”