Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region

Frédéric Satgé,Ramiro Pillco,Marie-Paule Bonnet,Jorge Molina,Denis Ruelland

doi:10.5194/hess-23-595-2019

Abstract

Abstract. This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.

Highlights

Introduction1.1 On the need for and difficulty involved in estimating precipitation fields
1.1 On the need for and difficulty involved in estimating precipitation fieldsWater resources are facing unprecedented pressure due to the combined effects of population growth and climate change
CHIRPS v.2, centre MORPHing (CMORPH)–BLD v.1, PERSIANN–CDR and TRMM Multisatellite Precipitation Analysis (TMPA)–Adj v.7 were closer to the reference dot than their respective nonadjusted versions, Climate Hazards Group InfraRed Precipitation (CHIRP) v.2, CMORPH–RAW v.1, PERSIANN and TMPA–RT v

Summary

Introduction

1.1 On the need for and difficulty involved in estimating precipitation fields. Water resources are facing unprecedented pressure due to the combined effects of population growth and climate change. F. Satgé et al.: Consistency of satellite-based precipitation products in space and over time of precipitation, which has favoured the occurrence of both drought and extreme flood events (Trenberth, 2011). As a key component of the hydrologic cycle, it is crucial to have accurate precipitation estimates in many research fields, including hydrological and snow modelling Espinoza Villar et al, 2009), extreme flooding Satgé et al, 2017a), and monitoring to understand past and ongoing changes and to optimize water resources management As a key component of the hydrologic cycle, it is crucial to have accurate precipitation estimates in many research fields, including hydrological and snow modelling (e.g. Hublart et al, 2016), climate studies (e.g. Espinoza Villar et al, 2009), extreme flooding (e.g. Ovando et al, 2016), drought (e.g. Satgé et al, 2017a), and monitoring to understand past and ongoing changes and to optimize water resources management (e.g. Fabre et al, 2015, 2016)

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