Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

Abstract

Satellite-based Precipitation Estimates (SPEs) have gained importance due to enhanced spatial and temporal resolution, particularly in Indus basin, where raingauge network has fewer observation stations and drainage area is laying in many countries. Formulation of SPEs is based on indirect mechanism, therefore, assessment and correction of associated uncertainties is required. In the present study, disintegration of uncertainties associated with four prominent real time SPEs, IMERG, TMPA, CMORPH and PERSIANN has been conducted at grid level, regional scale, and summarized in terms of regions as well as whole study area basis. The bias has been disintegrated into hit, missed, false biases, and Root Mean Square Error (RMSE) into systematic and random errors. A comparison among gauge- and satellite-based precipitation estimates at annual scale, showed promising result, encouraging use of real time SPEs in the study area. On grid basis, at daily scale, from box plots, the median values of total bias (−0.5 to 0.5 mm) of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias (−0.15 to 0.05 mm/day). Relatively higher values of missed (0.3 to 0.5 mm/day) and false (0.5 to 0.7 mm/day) biases were observed. The detected average daily RMSE, systematic errors, and random errors were also comparatively higher. Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas, depicting the better performance of satellite-based products in these areas. However, in areas of high altitude (>4000 m), due to complex topography and climatic conditions (orographic precipitation and glaciated peaks) higher values of biases and errors were observed. Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas, where precipitation is more on ridges and less in valleys where gauge stations are usually located. Precipitation system’s size and intensity can also be a reason of higher biases, because Microwave Imager underestimate precipitation in small systems (<200 km²) and overestimate in large systems (>2000 km²). At present, use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area. Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors, missed and false biases.