Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (JuneSeptember) as compared to the dry season (NovemberJanuary). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA

Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product

Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1-12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991-1994 for 38 months and the driest one in 2015-2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition

Satellite-based precipitation datasets evaluation using gauge observation and hydrological modeling in a typical arid land watershed of Central Asia

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Comparing the performance of high‐resolution global precipitation products across topographic and climatic gradients of Central Asia

Accurate and reliable precipitation data with high spatial and temporal resolution are essential in studying climate variability, water resources management, and hydrological forecasting. A range of global precipitation data are available to this end, but how well these capture actual precipitation remains unknown, particularly for mountain regions where ground stations are sparse. We examined the performance of three global high‐resolution precipitation products for capturing precipitation over Central Asia, a hotspot of climate change, where reliable precipitation data are particularly scarce. Specifically, we evaluated MSWEP, CHIRPS, and GSMAP against independent gauging stations for the period 1985–2015. Our results show that MSWEP and CHIRPS outperformed GSMAP for wetter periods (i.e., winter and spring) and wetter locations (150–600 mm·year−1), lowlands, and mid‐altitudes (0–3,000 m), and regions dominated by winter and spring precipitation. MSWEP performed best in representing temporal precipitation dynamics and CHIRPS excelled in capturing the volume and distribution of precipitation. All precipitation products poorly estimated precipitation at higher elevations (>3,000 m), in drier areas (<150 mm), and in regions characterized by summer precipitation. All products accurately detected dry spells, but their performance decreased for wet spells with increasing precipitation intensity. In sum, we find that CHIRPS and MSWEP provide the most reliable high‐resolution precipitation estimates for Central Asia. However, the high spatial and temporal heterogeneity of the performance call for a careful selection of a suitable product for local applications considering the prevailing precipitation dynamics, climatic, and topographic conditions.We present the first quantitative evaluation of global high‐resolution (below 12 km) precipitation products against independent ground observations over Central Asia. Our results show that MSWEP was best at representing temporal precipitation dynamics, and CHIRPS was most prominent in representing the volume and distribution of precipitation. This is especially the case of wet seasons, altitudes below 3,000 m, and regions dominated by spring and winter precipitation. Our analysis provides key insights on the precipitation products' suitability for local hydrological applications. imageLeibniz‐Institut für Agrarentwicklung in TransformationsökonomienVolkswagen Foundation http://dx.doi.org/10.13039/501100001663Peer Reviewe

A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future

Delimitation of water areas using remote sensing in Brazil’s semiarid region

Remote sensing techniques are of fundamental importance to investigate the changes occurred in the terrestrial mosaic over the years and contribute to the decision-making by increasing efficient environmental and water management. This article aimed to detect, demarcate and quantify the hydric area of Poço da Cruz reservoir, located in Ibimirim, Pernambuco, semiarid region of Brazil, with modeling based on Landsat 8/OLI satellite multispectral images from 2015 to 2020, and to relate it with data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellites average rainfall. For this purpose, the Modified Normalized Difference Water Index (MNDWI) was modeled, being produced georeferenced theme maps and extracted only the pixels represented by positive spectral values, which represent water targets. The open-access software Quantum Geographic Information System (QGIS, version 2.18.16) was used for all stages of digital image processing and connection with complementary databases on the theme maps elaboration. In the results, changes in the spatial distribution of Poço da Cruz were evidenced and analyzed using precipitation data from the CHIRPS product, allowing a better understanding of the rainfall behavior in the region and its influence. The MNDWI was lined with the CHIRPS product, in which the spatial correlation between the rainy event and the water area’s delimitation is documented, especially in October 2017 (minimum values) and October 2020 (maximum values).As técnicas de sensoriamento remoto são de fundamental importância para investigar as alterações ocorridas no mosaico terrestre ao longo dos anos e contribuir para tomadas de decisão cada vez mais eficientes em gestão ambiental e hídrica. Os objetivos deste artigo foram detectar, delimitar e quantificar a área hídrica do reservatório Poço da Cruz, localizado em Ibimirim, Pernambuco, Semiárido do Brasil, com modelagem baseada em imagens multiespectrais do satélite Landsat 8/OLI datadas de 2015 a 2020, bem como relacioná-la com dados de precipitação pluvial média do produto CHIRPS. Para tanto, foi modelado o Índice de Água por Diferença Normalizada Modificado (MNDWI), com o qual se geraram os mapas temáticos georreferenciados e extraíram-se apenas os pixels representados por valores espectrais positivos, que representam alvos hídricos. Utilizou-se o software de livre acesso QGIS 2.18.16 para todas as etapas de processamento digital de imagens e conexão com bancos de dados complementares para a elaboração dos mapas temáticos. Nos resultados foram evidenciadas as mudanças na distribuição espacial do Poço da Cruz, analisadas com a utilização de dados de precipitação com base no produto CHIRPS, permitindo melhor compreensão do comportamento da pluviometria na região e sua influência. O MNDWI foi condizente com o produto de precipitação do CHIRPS, e ficou evidente a variação área hídrica do reservatório com relação à ocorrência de eventos chuvosos, especialmente em outubro/2017 (mínimos valores) e outubro/2020 (máximos valores)