Browsing by Autor "Denis Ruelland"

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Consistency of satellite precipitation estimates in space and over timecompared with gauge observations and snow-hydrological modellingin the Lake Titicaca region
(2018) Frédéric Satgé; Denis Ruelland; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá
Abstract. This paper proposes a protocol to assess the space-time consistency of satellite precipitation estimates (SPEs) according to various indicators including: (i) direct comparison of SPEs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPEs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPEs 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 SPEs and to a large dataset composed of 12 SPEs (CMORPH-RAW, CMORPH-CRT, CMORPH-BLD, CHIRP, CHIRPS, GSMaP, MSWEP, PERSIANN, PERSIANN-CDR, TMPA-RT, TMPA-Adj and SM2Rain), an unprecedented comparison. The aim of using different space-time scales and indicators was to evaluate whether the efficiency of SPEs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPEs compared to precipitation gauge observations. Some SPEs (CMORPH‒RAW, CMORPH‒CRT, GSMaP, PERSIANN, TMPA‒RT and SM2Rain) are unable to estimate regional precipitation whereas the others (CHIRP, CHIRPS, CMORPH‒BLD, MSWEP, PERSIANN‒CDR and TMPA‒Adj) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In nine out of ten of the cases studied, streamflow was more realistically simulated by the hydrological model tested when SPEs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks. Interestingly, the potential of SPEs to reproduce the observed streamflow varied significantly depending on the basin and period considered and did not systematically corroborate SPE potential compared with gauge precipitation observations. SPE’s ability to reproduce the duration of MODIS-based snow cover also showed variable consistency over time with poorer simulations in comparison to those simulated from available precipitation gauges. Using snow cover simulations as indicator led to a different efficiency ranking of the SPEs that the ones obtained when using observed gauge precipitation and streamflow. SPEs thus present space-time errors that may not be detected when short time windows and/or scarce gauge networks and/or single indicators are used, underlining how important it is to carefully consider their space-time consistency before using them for hydro-climatic studies. Moreover SPE efficiency ranked differently depending on the assessment indicators used, suggesting that SPE efficiency should be assessed using indicators related to their final use. Among all the SPEs assessed, MSWEP showed the highest space-time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration. After some adjustment over Lake Titicaca, MSWEP should thus be preferred for the regional hydro-meteorological survey.
Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region
(Copernicus Publications, 2019) Frédéric Satgé; Denis Ruelland; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá
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.