Search

The changes to the hydrological cycle as a consequence of climate change significantly affect the water security of countries worldwide. The Brahmaputra Basin (BB), a biodiversity hotspot, is among the world's wettest regions and is vulnerable to climate change. Using ground and satellite‐based datasets from multiple sources and a percentile‐based analysis from 1998 to 2018, it is found that an asymmetric trend exists in extreme rainfall over the hills and the adjacent plains of the basin. The hills experience a decline in rainfall, whereas, over the plains, an increase is observed on similar rainfall days. During 1998–2018, mean accumulated rainfall over the whole basin declined by as much as ∼60% during the October to December (OND) period. Similarly, a decline of ∼20% is observed for June to September (JJAS). The most significant reduction in mean rainfall was mainly over the wettest regions of the basin, such as Cherrapunji/Mawsynram and is concentrated in the highest percentile (extreme events) of rainfall distribution. Such an overall decline in total rainfall coupled with altitudinal asymmetry in percentile distributions of rainfall may contribute towards flood hazards with changing lead time for warning, posing severe socio‐economic challenges. [ABSTRACT FROM AUTHOR]

In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM-3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model's performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling.

In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM-3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model’s performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling. [ABSTRACT FROM AUTHOR]

In some regions of the world, like southern Ecuador, meteorological data are scarce or do not exist. However, the data from products estimated by satellite are an alternative in these places and, in remote areas, can be the sole source of information available. This study evaluated the precipitation products CMORPH, PERSIANN-CDR and TRMM-3B42, and ERAInterim temperature on the basin of the Catamayo River, located in southern Ecuador. CMORPH overestimates in average 50 % of the precipitation observed, while PERSIANN-CDR and TRMM-3B42 underestimate in average 20 and 10 %. Thus, the bias of the precipitation products was corrected with rain gauges. The CEQUEAU model was calibrated with the observed and estimated data (original and corrected) at 0.25°/~27 km of spatial resolution with the objective of modelling the daily discharges of the Catamayo River at Vicin station. The efficiency of the modelling was quantified with the following mathematical criteria: Nash-Sutcliffe efficiency coefficient (NSE), BIAS percentage (PBIAS) and coefficient of determination (r2). The results according to the NSE criterion, in the periods of calibration and validation, were 0.80 and 0.75 with the observed data, and with the satellite data (original): TRMM-3B42, 0.84 and 0.79, PERSIANN-CDR, 0.80 and 0.65, and CMORPH, 0.59 and 0.39. Correction of the bias of the estimated rainfall was positive when modelling with the CMORPH product and quite low with PERSIANN-CDR and TRMM-3B42. This study reveals the advantage of using satellite products to simulate the discharges in ungauged basins, in addition to using these data in their original spatial scale on the distributed hydrological models. [ABSTRACT FROM AUTHOR]

This study presents the first comprehensive evaluation of the performance of three globally high‐resolution remotely sensed products in replicating the main characteristics of rainfall over the Middle East, with special emphasis on extreme wet events. Specifically, we employed daily observational data from a network of rain gauges (N = 217), spanning the retrospective period 1998–2013 and covering six countries in the Middle East (i.e., Egypt, Iraq, Jordan, Libya, Saudi Arabia, and Syria), against data derived from three global satellite‐based precipitation products: the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi‐satellite Precipitation Analysis 3B42 product (TRMM‐3B42), the Climate Prediction Center MORPHing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Alongside a range of conventional statistical error measures (e.g., bias, normalized root‐mean‐square error [nRMSE] and Spearman's rho correlation coefficient), this study also gives priority to evaluate the skill of these products in reproducing characteristics of extreme wet events (e.g., frequency, intensity, duration, onset, anomaly). Results demonstrate that TRMM‐3B42 generally performs well in estimating rainfall totals during the rainy season (ONDJFMA), with a mean bias of 0.05 mm, nRMSE of 0.15 mm, and Spearman's rho of 0.74 for the whole Middle East. In contrast, PERSIANN‐CDR and CMORPH‐BLD underestimate the observed rainfall. Importantly, TRMM‐3B42 outperforms other products in reproducing the frequency and intensity of the most extreme wet events, while PERSIANN‐CDR and CMORPH‐BLD fail to reproduce these key characteristics. Notably, all products perform poorly in reproducing the climatology of the anomalous wet events in the region, indicating that careful scrutiny must be warranted before using these products, particularly for hydrological modelling. Considering the daily resolution of these remotely sensed precipitation products and their reasonable spatial resolution (0.25 × 0.25°) in comparison to available in situ data over the Middle East, results of this work provide a solid scientific reference for national stakeholders and policy makers to decide on the most useful product for their specific applications (e.g., hydrological modelling, streamflow forecasts, water resources management, and hydrometeorological hazard assessment and mitigation). Our study demonstrates a low skill of all products in reproducing the climatology and intensity of anomalous wet events, for both regions with synoptic (e.g., Egypt, Syria) as well as orographic rainfall (e.g., Northeastern Iraq, western Saudi Arabia). This deficit is even more evident for local convective‐type events, which are typical for Saudi Arabia. [ABSTRACT FROM AUTHOR]

During the past two decades, numerous datasets have been developed for global/regional hydrological assessment and modeling, but these datasets often show differences in their spatial and temporal distributions of precipitation, which is one of the most critical input variables in global/regional hydrological modeling. This paper is aimed to explore the precipitation characteristics of the Water and Global Change (WATCH) forcing data (WFD) and compare these with the corresponding characteristics derived from satellite-gauge data (TRMM 3B42 and GPCP 1DD) and rain gauge data. It compared the consistency and difference between the WFD and satellite-gauge data in India and examined whether the pattern of seasonal (winter, pre-monsoon, monsoon and post-monsoon) precipitation over six regions [e.g. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI)] of India agrees well for the gridded data to be useful in precipitation variability analyses. The multi-time scale of precipitation in India was analysed by wavelet transformation method using gauged and WFD precipitation data. In general, precipitation from WFD is larger than that from satellite-gauge data in NMI and Western Ghats region whereas it is smaller in the dry region of NWI. Both WFD and satellite-gauge datasets underestimate precipitation compared to the measured data but the precipitation from WFD is better estimated than that from satellite-gauge data. It was found that the wavelet power spectrum of precipitation based on WFD is reasonably close to that of measured precipitation in NWI and NCI, while slightly different in NMI. It is felt that the WFD data can be used as a potential dataset for hydrological study in India. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the authors inter-compared the performance of three satellite-rainfall products in representing the diurnal cycle of rain occurrence and rain rate over the Nile basin in eastern Africa. These products are the real time (RT) and post-real-time (PRT) (bias adjusted) versions of Tropical Rainfall Measuring Mission (TRMM) and other sources product known as TRMM-3B42 and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) product which is based on the CPC morphing technique (CMORPH). The rainfall diurnal cycles are re-produced using these products with specific focus on assessing effects of geographic location and topographic features. The performance of the satellite products in representing rainfall diurnal cycle shows large variation over the Nile basin. The products overestimate rain occurrence over the lakes, islands, and shores and underestimate occurrence over mountain tops. Overall, CMORPH performs better than TRMM-3B42 RT and TRMM-3B42 PRT in capturing the diurnal cycle of rain rate in Lake Tana basin. However, the difference between the two products is very small for Lake Victoria basin, where both products perform more favorably. Over most of the Nile basin areas, the use of fine versus coarse temporal and spatial resolution of the CMORPH product showed large differences for diurnal cycle of rain occurrence than that of rain rate. Results also show that the bias adjustment of TRMM-3B42 product does not necessarily bring improvements probably since the adjustments are not performed based on local rain gauge data. [Copyright &y& Elsevier]

Understanding the performance of remotely-sensed precipitation with rain gauge as reference is essential before their application in estimating multiple processes. In current study, the Global Precipitation Measurements from Integrated Multi-satellite Retrievals (GPM-IMERG) and Tropical Rainfall Measuring Mission (TRMM-3B42) precipitation products were evaluated, taking the rain gauge data as a reference for the period of 2004 to 2018 over Pakistan. The data at daily, monthly, annual and seasonal timescales were evaluated using multiple statistical (three continuous and four categorical) error metrics. The Satellite Precipitation Products (SPPs) showed consistent precipitation climatology with some regional differences. All SPPs overall performed well in the central and southern regions compared to the northern region of study area. Evaluation revealed that the performance of SPPs was influenced by topography, while the error characteristics of SPPs were dependent on the precipitation intensity and climatic condition of the region. The SPPs were able to capture the extreme precipitation events (95th percentile) across different climatic regions. The highest values for probability of detection (POD), critical success index (CSI), and accuracy (ACC) at the rate of 0.7, 0.4, and 0.9 were observed respectively, while the lowest value for false alarm ratio (FAR) is 0.4. The IMERG final run harmonizes rain gauges very closely in plain and medium elevation regions, while root mean square difference greater than 20 mm/month were observed for high altitude. The spatial distribution of correlation coefficients with the values greater than 0.7 suggest that selected SPPs well captured the seasonality and mutual comparison shows that IMERG final run performed well, followed by TRMM-3B42, whereas in winter and pre-monsoon, the IMERG early run and IMERG late run captured higher precipitation values in the northeast of the study area. The results can help the researchers to select reliable SPPs as an alternate source of rain gauge in the country and other adjoining regions and the data evaluation information can help algorithm developers to rectify the errors and improve the selected SPPs. • This study evaluates the performance of GPM-IMERG and TRMM-3B42 products over Pakistan. • The selected SPPs captured consistent precipitation distribution across different terrains in Pakistan. • The FAR, POD, CSI, and ACC showed better results in the areas of higher annual average rainfall. • The IMERG-F harmonizes rain gauges very closely in plain and medium elevation regions. [ABSTRACT FROM AUTHOR]