UC Irvine

Precipitation is a crucial input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations “SPEs” appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this dissertation, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated, and the method consists of three major parts.

The first part investigates the calculation of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimate over Saudi Arabia. The first six years (2010–2015) are used for calibration, while one year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the annual root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.

The second part is to provide a merged product by using the interpolated gauge estimates and the bias-corrected PERSIANN-CCS (0.04°×0.04°) estimates. Interpolated gauge estimates are calculated by the inverse weighted distance to the existing gauge points. The merged product is calculated based on the uncertainty of SPE and gauge estimation. Results show both the annual mean bias and root mean square error are reduced by 98% and 83% respectively. Statistical analysis of the merged product over different temporal scales and climate regions are discussed.

The third part of this dissertation is to examine the effectiveness of the bias-corrected approach that is based on quantile mapping and climatic zones in hydrological modeling. Three watersheds, which are Bisha, Byash, and Hail watersheds are chosen over Saudi Arabia. The evaluation is carried between 2011 and 2016 where the monthly and yearly runoff volumes are available. To model, the monthly and annual runoff volumes, Soil Conservation Service (SCS) model is implemented. Three evaluation statistics include correlation coefficient, mean bias, and root mean square error. The results show that the proposed method is effective for removing the bias from the SEPs, and for improving hydrological modeling.