Author(s)

George Andiego¹, Muhammad Waseem², Muhammad Usman³, Nithish Mani⁴

¹Water Resources and Environmental Services Department, Nairobi, Kenya.
²Faculty of Agriculture and Environmental Sciences, Universität Rostock, Rostock, Germany.
³Institute of Water Resources and Water Supply, TUHH Hamburg, Am Schwarzenberg-Campus 3 (E), Hamburg, Germany.
⁴Sahasrara Earth Services & Resources Ltd., Hanover, Germany.

Rain gauge data suffers from spatial errors because of precipitation variability within short distances and due to sparse or irregular network. Use of interpolation is often unreliable to evaluate due to the aforementioned irregular sparse networks. This study is carried out in the Nette River catchment of Lower Saxony to alleviate the problem of using gauge data to measure the performance of interpolation. Radar precipitation data was extracted in the positions of 53 rain gauge stations, which are distributed throughout the range of the weather surveillance radar (WSR). Since radar data traditionally suffers from temporal errors, it was corrected using the Mean Field Bias (MFB) method by utilizing the rain gauge data and then further used as the reference precipitation in the study. The performances of Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) interpolation methods by means of cross validation were assessed. Evaluation of the effect of the gauge densities on HBV-IWW hydrological model was achieved by comparing the simulated discharges for the two interpolation methods and corresponding densities against the simulated discharge of the reference precipitation data. Interpolation performance in winter was much better than summer for both interpolation methods. Furthermore, Ordinary Kriging performed marginally better than Inverse Distance Weighting in both seasons. In case of areal precipitation, progressive improvement in performance with increase in gauge density for both interpolation methods was observed, but Inverse Distance Weighting was found more consistent up to higher densities. Comparison showed that Ordinary Kriging outperformed Inverse Distance Weighting only up to 70% density, beyond which the performance is equal. The hydrological modelling results are similar to that of areal precipitation except that for both methods, there was no improvement in performance beyond the 50% gauge density.

Gauge Density, Inverse Distance Weighting, Nette River, Ordinary Kriging

Andiego, G. , Waseem, M. , Usman, M. and Mani, N. (2018) The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model. Computational Water, Energy, and Environmental Engineering, 7, 27-50. doi: 10.4236/cweee.2018.71002.