Seasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Region

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1 spatial resolution and for a 0.25 grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product

Benefits of the successive GPM based satellite precipitation estimates IMERG–V03, –V04, –V05 and GSMaP–V06, –V07 over diverse geomorphic and meteorological regions of Pakistan

Launched in 2014, the Global Precipitation Measurement (GPM) mission aimed at ensuring the continuity with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 that has provided unprecedented accuracy in Satellite Precipitation Estimates (SPEs) on the near-global scale. Since then, various SPE versions have been successively made available from the GPM mission. The present study assesses the potential benefits of the successive GPM based SPEs product versions that include the Integrated Multi–Satellite Retrievals for GPM (IMERG) version 3 to 5 (–v03, –v04, –v05) and the Global Satellite Mapping of Precipitation (GSMaP) version 6 to 7 (–v06, –v07). Additionally, the most effective TRMM based SPEs products are also considered to provide a first insight into the GPM effectiveness in ensuring TRMM continuity. The analysis is conducted over different geomorphic and meteorological regions of Pakistan while using 88 precipitations gauges as the reference. Results show a clear enhancement in precipitation estimates that were derived from the very last IMERG–v05 in comparison to its two previous versions IMERG–v03 and –v04. Interestingly, based on the considered statistical metrics, IMERG–v03 provides more consistent precipitation estimate than IMERG–v04, which should be considered as a transition IMERG version. As expected, GSMaP–v07 precipitation estimates are more accurate than the previous GSMaP–v06. However, the enhancement from the old to the new version is very low. More generally, the transition from TRMM to GPM is successful with an overall better performance of GPM based SPEs than TRMM ones. Finally, all of the considered SPEs have presented a strong spatial variability in terms of accuracy with none of them outperforming the others, for all of the gauges locations over the considered regions

Water resources of the Andean Altiplano : remote sensing contribution

Tese (doutorado)—Universidade de Brasília, Instituto de Geociências, Pós-Graduação em Geociências Aplicadas, 2017.Localizado a uma elevação média de 4.000 m, o sistema endorreico do Altiplano (190.000 km2), é delimitada pelas serras andinas com picos de mais de 6.000 m de altitude. A bacia inclui ecossistemas icônicos, como o lago Titicaca, o lago Poopó e os desertos de sal de Coipasa e de Uyuni na parte sul da bacia. Os recursos hídricos do Altiplano, estão sob pressão climática com um aumento de temperatura de 0,15 a 0,25 °C por década que contribuiu a diminuição de 43% da superfície dos glaciares entre 1981 e 2014. Além dos fatores climáticos, fatores antrópicos, tais como, as atividades agrícolas e industriais são conhecidas por contribuir na diminuição do recurso hídrico, mas não foram quantificadas na região. Nesse contexto, o monitoramento hidrometeorológico deve ajudar a prevenir e antecipar os diferentes impactos ocasionados pelas mudanças climáticas e pelas práticas agrícolas. No entanto, devido ao contexto geopolítico, dificuldades econômicas e de acesso, a bacia sofre de escassez de infraestrutura meteorológica, e assim, poucos dados estão disponíveis. Assim, os dados de sensoriamento remoto fornecem uma excelente alternativa para observar o comportamento hidrometeorológica regional. A primeira etapa do doutorado foi dedicada à avaliação de grupos de dados de sensoriamento remoto primordiais na hidrologia como (i) modelo digital de elevação (MDE) (descrições topográficas e caracterização do escoamento), (ii) estimativas de precipitações (entrada de água), (iii) estimativas da evapotranspiração (saída de água) e (iiii) imagens do visível (variação espaço-temporal da superfície dos lagos. Finalmente, foi realizado no lago Poopó o uso integrado dos dados previamente avaliados e validados para entender a seca completa do lago de dezembro 2015 em base (i) a variabilidade climática e (ii) o desenvolvimento agrícola na região. O estudo permitiu observar que o lago já secou duas vezes em 1994 e 1995. Por entanto esses eventos foram associados a fortes anomalias negativas de precipitações em quanto a seca de 2015 foi associada a fortes anomalias positivas de precipitações. O estudo revelou também um aumento significativo da evapotranspiração real (ETr) de aproximadamente 13%, independentemente da variabilidade climática. Esse aumento da ETr foi registrado nas zonas agrícolas sugerindo assim o papel significativo da agricultara no processo de desertificação da região. De fato devido ao aumento do preço da Chenopodium Quinoa (quinoa) no mercado externo, a superfície ocupada pelas plantações passou de 10.000 para 50.000 ha entre 1980 e 2011. O uso de métodos de irrigação aumentou a disponibilidade de água para a evaporação e diminui a quantidade das águas superficial e subterrânea. Este trabalho pioneiro no Altiplano, permite demonstrar a grande potencialidade da integração de dados de sensoriamento remoto em regiões áridas remotas para seguir e entender as problemáticas socioambiental relacionadas a pressões antrópicas e climáticas.Located at an average elevation of 4000 m, the Altiplano (190.000 km2) is an endoreic system delimited by the Andean mountains with peaks higher than 6000 m. The basin includes iconic ecosystems such as Lake Titicaca, Lake Poopó and the salt deserts of Coipasa and Uyuni in the southern part of the basin. The water resource of the Altiplano is under climatic pressure with a temperature increase of 0.15°C and 0.25°C per decade, which decreased the glacier surface by 43% between 1981-2014. In addition to climatic variability, anthropic factors, such as agriculture and industrial activities, is known to contribute to the water resource decrease but remains unquantified in the region. In this context, hydro-meteorological monitoring should help to prevent and anticipate the different impacts of climate change and agricultural practices. However, given the geopolitical context, economic and access problems few stations are available. Therefore, remote sensing data provide an alternative to observe regional hydro-meteorological behavior. The first stage of the PhD was dedicated to the assessment of remote sensing data useful in hydrology such as (i) digital elevation model (topographic description, flows characterization), (ii) precipitation (water input), (iii) evapotranspiration (water output) and (iiii) visible images (water superficial dynamic) which is the first study of such remote sensing data potential over the region. Finally, an integrated use of the evaluated/validated remote sensing data was carried out to understand the lake Poopó drought of December 2015 considering (i) climate variability and (ii) agriculture increase. The study highlighted that the lake already drought in 1994 and 1995. However, these droughts events were associated to strong rainfall anomalies while the 2015 one was associated to positive rainfall anomalies. The study also highlighted an increase of real evapotranspiration (ETr) of approximately 13% independently to climate variability in the region. The ETr increase was observed over cropped areas suggesting a significate influence of agriculture to the regional desertification process. Indeed, between 1980 and 2011, quinoa's cultivated area increased from 10.000 to 50.000 ha, related to the increase of quinoa's price. The use of irrigation method increased the availability of water for evapotranspiration and, therefore, decreased the amount of water in the surface and underground reservoir. This work, a pioneer in the Altiplano, demonstrates the great potential of the integration of remote sensing data in remote arid regions subject to anthropic and climate pressures

Contribution of automatically generated radar altimetry water levels from unsupervised classification to study hydrological connectivity within Amazon floodplains

Study region: The Curuaí floodplain in the low Amazon river in the Pará state of Brazil and Juruá basin, a major Solimões tributary. Study focus: Characterizing the hydrological dynamics of Amazon floodplains is essential to better understand and preserve these environments providing important resources to local populations. Radar altimetry is an effective remote sensing tool for monitoring water levels of continental hydrosystems, including floodplains. An unsupervised classification approach on radar echoes to determine hydrological regimes has recently been tested and showed a strong potential on the Congo River basin. This method is adapted to Envisat and Saral satellite radar altimetry data on two study areas in the Amazon Basin. The aim is to improve inland water detection along altimeter tracks to automatically generate water level time series (WLTS) over rivers, lakes, and poorly monitored floodplains and wetlands. New hydrological insights: Results show a good agreement with land cover maps obtained with optical imagery over selected Amazonian wetlands (70–80% accuracies with Envisat data and 50–60% with Saral data). Automatically generated WLTS are strongly correlated to the manually generated WLTS (R² ≈ 0.9; RMSE < 1 m). Compared to the manual method, the automatic method is faster, more efficient and replicable. Densifying the WL network in the floodplains bring crucial information on the connectivity dynamic between lakes and rivers

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study region: Data-scarce basins located in West Africa and northern Central Africa. Study focus: Multiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the region: The results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization

From TRMM to GPM: How Reliable Are Satellite-Based Precipitation Data across Nigeria?

International audienceIn this study, 16 satellite-based precipitation products (SPPs) comprising satellite, gauge and reanalysis datasets were assessed on a monthly time step using precipitation data from 11 gauge stations across Nigeria within the 2000–2012 period as reference. Despite the ability of some of the SPPs to reproduce the salient north–south pattern of the annual rainfall field, the Kling–Gupta efficiency (KGE) results revealed substantial discrepancies among the SPP estimates. Generally, the SPP reliability varies spatially and temporally, with all SPPs performing better over part of central Nigeria during the dry season. When we compared the real-time and adjusted satellite-based products, the results showed that the adjusted products had a better KGE score. The assessment also showed that the reliability of integrated multi-satellite retrievals for Global Precipitation Mission (IMERG) products was consistent with that of their predecessor Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). Finally, the best overall scores were obtained from multi-source weighted-ensemble precipitation (MSWEP) v.2.2 and IMERG-F v.6. Both products are therefore suggested for further hydrological studies

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

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Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar

International audienceStudy region this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones.Study focus the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity