Temporal and spatial assessment of four satellite rainfall estimates over French Guiana and North Brazil

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance

SPATIOTEMPORAL VARIATION IN THE PRECIPITATION OF THE AMAZON COASTAL ZONE: USE OF REMOTE SENSING AND MULTIVARIATE ANALYSIS

Reliable data on the spatiotemporal variability in precipitation patterns are vital to the development of effective public policies for environmental management. The analysis of the variation in rainfall rates is currently limited severely by the dependence on data from rain gauges, in particular in regions with a relatively sparsely-distributed network of meteorological stations, as in the Amazon region. The present study investigated the variability in the precipitation and the principal rainfall patterns at different time scales in the coastal zone of the Amazon region, and associated these patterns with the precipitant meteorological systems present in the region. The study was based on the application of remote sensing (CMORPH) data taken at half-hourly intervals on a 0.088 latitude/longitude scale. The spatiotemporal variability in the region’s precipitation was analyzed at different time scales (monthly, seasonal, and annual), with distribution patterns being assessed using a Principal Components Analysis (PCA). The estimates obtained from the CMORPH data provided a satisfactory overview of the precipitation climatology of the study region at the distinct time scales. The PCA identified a precipitation gradient in the two principal pluviometric modes, which together explained 88% of the total variance in the data. The first mode explained 83% of the variance, with two distinct periods, a rainy season and a dry (or less rainy) period, which are influenced by large-scale precipitant systems, the Intertropical Convergence Zone (ITCZ) and High Level Cyclonic Vortices (HLCVs). The second mode, which explains 5% of the variance in the rainfall data, is associated with mesoscale systems that affect primarily the transition periods between the seasons, and depend on the southern extreme of the annual shift in the ITCZ. The understanding of the variation of precipitation patterns using high-resolution CMORPH data, with a comprehensive coverage in both time and space, provides an effective tool for the establishment of public policies at a municipal level, in particular the development of models, and the mediation of the vulnerability of local populations to climatic extremes

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coeffcient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

International audienceSatellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance

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

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.</p

De la modélisation du rayonnement solaire à la production d'énergie : recherches sur l'optimisation de la production photovoltaïque en contexte amazonien

L’objectif des recherches décrites dans ce mémoire est de répondre aux problématiques de la filière photovoltaïque en contexte amazonien afin d’assurer le développement de cette dernière. Les actions de recherche initiées ont pour but de contribuer à l’amélioration du fonctionnement des installations photovoltaïques et à la diminution des risques associés aux projets et technologies photovoltaïques implantés dans cette région. Nous abordons dans un premier temps la question de la formalisation de modèles capables d'assurer le suivi de l'éclairement solaire en vue de réaliser le contrôle et la gestion de l'énergie produite, et pour cela nous utilisons le formalisme de la modélisation par espace d’état. Le filtre Bayésien récursif est utilisé comme méthode de résolution d'un problème à espace d'état dont les observations sont issues d'images satellites. Nous veillons au caractère générique de la méthode proposée afin qu’elle soit transposable à d’autres ressources naturelles. En vue d'améliorer le caractère prédictif du filtre bayésien nous nous intéresserons à la définition des propriétés statistiques des termes d’erreur intervenant dans des modèles mathématiques simulant l'évolution de l'état de l'éclairement solaire. Nous définissons une nouvelle méthode de sélection des densités de probabilité des termes d’erreur qui interviennent dans de nombreux modèles d'évolution, puis nous appliquons cette méthode de sélection à deux modèles d'évolution de l'éclairement solaire, ce qui nous amène à remettre en cause des aspects, pourtant réputés solides, de principes communément admis. Nous proposons la définition d'une nouvelle méthode de construction de fonctions/modèles d'observation afin d’extraire des images satellites une information pertinente sur l'état de l'éclairement solaire, en particulier nous analysons la possibilité d’utiliser une loi de probabilité jointe non-paramétrique pour formaliser un concept générique qui serait applicable à d’autres variables représentatives d’une ressource renouvelable. L’évaluation de l’efficacité et de la précision du filtre bayésien conçu avec les modèles d’évolution et d’observation que nous avons développés est assurée avec des critères statistiques tels que le Biais, le RMSE et le coefficient de corrélation.Dans un second chapitre, nous abordons la question de l’estimation du potentiel de l’éclairement solaire au sol sur la zone du continent Sud-américain qui entoure la Guyane. Compte tenu de l’étendue de la zone étudiée et de la parcimonie des stations de mesures présentes dans cette zone, nous choisissons d’utiliser un algorithme permettant d'estimer l’éclairement solaire en utilisant des images satellites. Ces travaux aboutissent à la réalisation de plusieurs cartographies du potentiel de l’éclairement solaire sur un plan horizontal (Global Horizontal Irradiance - GHI) mais aussi du potentiel d’éclairement direct (Direct Normal Irradiance - DNI) ainsi que des cartographies d’indicateurs liés à l’exploitabilité du potentiel solaire, comme la variabilité journalière ou le pourcentage d’énergie reçue au sol avant et après le midi solaire par rapport à la quantité d’énergie journalière.Dans le troisième chapitre, nous abordons le thème de la production d’énergie photovoltaïque en sites isolés en contexte amazonien, nous présentons les études menées et les résultats obtenus qui visent à l'amélioration de la productivité des systèmes photovoltaïques dans un tel contexte. Le but de ces travaux n’est pas simplement de décrire les analyses faites sur la productivité des systèmes solaires mais d’établir des recommandations à partir de cas concrets, et proposer quelques pistes claires et opérationnelles pour exploiter au mieux la ressource solaire afin de satisfaire les besoins énergétiques des populations non raccordées au réseau électrique. Enfin, en guise de conclusion, nous proposerons un état synthétique des principales avancées issues de ce parcours et nous présenterons les perspectives scientifiques et techniques qu’elles ont permis d’ouvrir