Search Results (335)

Various stakeholders seek effective methods to communicate the potential impacts of tropical cyclone (TC) rainfall and subsequent flood hazards. While current methods, such as Intensity–Duration–Frequency curves, offer insights, they do not fully capture TC rainfall complexity and variability. This research introduces an innovative workflow utilizing GPM-IMERG satellite precipitation estimates to cluster TC rainfall spatial–temporal patterns, thereby illustrating their potential for flood hazard assessment by simulating associated flood responses. The methodology is tested using rainfall time series from a single TC as it traversed a 500 km diameter buffer zone around Dominica. Spatial partitional clustering with K-means identified the spatial clusters of rainfall time series with similar temporal patterns. The optimal value of K = 4 was most suitable for grouping the rainfall time series of the tested TC. Representative precipitation signals (RPSs) from the quantile analysis generalized the cluster temporal patterns. RPSs served as the rainfall input for the openLISEM, an event-based hydrological model simulating related flood characteristics. The tested TC exhibited three spatially distinct levels of rainfall magnitude, i.e., extreme, intermediate, and least intense, each resulting in different flood responses. Therefore, TC rainfall varies in space and time, affecting local flood hazards; flood assessments should incorporate variability to improve response and recovery. Full article

This study evaluates the impact of sub-daily bias correction of gridded rainfall products (RPs) on the estimation rainfall erosivity in Burkina Faso (West African Sahel). Selected RPs, offering half-hourly to hourly rainfall, are assessed against 10 synoptic stations over the period 2001–2020 to appraise their accuracy. The optimal product (the integrated multi-satellite retrievals for GPM, IMERG) is further used as a reference for bias correction, to adjust the rainfall distribution in the remaining RPs. RPs-derived rainfall erosivity is compared to the global rainfall erosivity database (GloREDa) estimates. The findings indicate that bias correction improves the rainfall accuracy estimation for all RPs, in terms of quantitative, categorial metrics and spatial patterns. It also improved the distributions of rainfall event intensities and duration across all products, which further significantly improved the annual rainfall erosivity estimates at various timescales along with spatial patterns across the country, as compared to raw RPs. The study also highlights that bias correction is effective at aligning annual trends in rainfall with those in rainfall erosivity derived from RPs. The study therefore underscores the added value of bias correction as a practice for improving the rainfall representation in high-resolution RPs before long-term rainfall erosivity assessment, particularly in data-scarce regions vulnerable to land degradation. Full article

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region. Full article

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG. Full article

Several configurations of the Canadian Precipitation Analysis system (CaPA) currently produce precipitation analyses at Environment and Climate Change Canada (ECCC). To improve CaPA’s performance during the winter season, the impact of assimilating the IMERG V06 product (IMERG: Integrated Multi-satellitE Retrievals for GPM—Global Precipitation Measurement mission) into CaPA is examined in this study. Tests are conducted with CaPA’s 10 km deterministic version, evaluated over Canada and the northern part of the United States (USA). Maps from a case study show that IMERG plays a contradictory role in the production of CaPA’s precipitation analyses for a synoptic-scale winter storm over North America’s eastern coast. While its contribution appears to be physically correct over southern portions of the meteorological system, and early in its intensification phase, IMERG displays unrealistic spatial structures over land later in the system’s life cycle when it is located over northern (colder) areas. Objective evaluation of CaPA’s analyses when IMERG is assimilated without any restrictions shows an overall decrease in precipitation, which has a mixed effect (positive and negative) on the bias indicators. But IMERG’s influence on the Equitable Threat Score (ETS), a measure of CaPA’s analyses accuracy, is clearly negative. Using IMERG’s quality index (QI) to filter out areas where it is less accurate improves CaPA’s objective evaluation, leading to better ETS versus the control experiment in which no IMERG data are assimilated. Several diagnostics provide insight into the nature of IMERG’s contribution to CaPA. For the most successful configuration, with a QI threshold of 0.3, IMERG’s impact is mostly found in the warmer parts of the domain, i.e., in northern US states and in British Columbia. Spatial means of the temporal sums of absolute differences between CaPA’s analyses with and without IMERG indicate that this product also contributes meaningfully over land areas covered by snow, and areas where air temperature is below −2 °C (where precipitation is assumed to be in solid phase). Full article

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas. Full article

Accurate observation of precipitation data is crucial for hydrometeorological applications, requiring temporal and spatial precision. Satellite precipitation products offer a promising solution for obtaining precipitation estimates, facilitating long-term observations from global to local scales. However, assessing their accuracy compared to rain gauge observations is essential. This study aims to assess the accuracy and applicability of precipitation data from CMORPH, IMERG, and PERSIANN CCS in the Rio Grande–San Juan Basin in northeast Mexico. The evaluation of estimated precipitation was assessed using the Pearson and Spearman correlations, RMSE, MAE, and BIAS for both monthly and yearly averages. CMORPH showed minimal errors and low underestimation, while IMERG exhibited high correlations with consistent underestimation. PERSIANN CCS had lower correlations, significant overestimation, and higher errors. The Mann–Kendall (MK) test was used to determinate the precipitation trends of observed and estimated data. The observed data showed a significant positive trend in monthly averages, which is not reflected in the annual trend. Furthermore, negative annual trends were found in at least 10 stations across the basin. The application of satellite precipitation data yielded mixed outcomes, with CMORPH showing the highest level of agreement with the trend analysis results from rain gauge data. This demonstrates its reliability for weather and climate studies and suggests the potential for CMORPH to be used as an input in hydrological modeling. Full article

This study evaluated the accuracy of two new generation satellite rainfall estimates (SREs): Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Integrated Multi-satellite Retrieval for GPM (IMERG) over Bolivia’s complex terrain. These SREs were compared against rainfall data from rain gauge measurements on a point-to-pixel basis for the period 2002–2020. The evaluation was performed across three regions with distinct topographical settings: Altiplano (Highland), Valles (Midland), and Llanos (Lowland). IMERG exhibited better accuracy in rainfall detection than CHIRPS, with the highest rainfall detection skills observed in the Highland region. However, IMERG’s higher rainfall detection skill was countered by its higher false alarm ratio. CHIRPS provided a more accurate estimation of rainfall amounts across the three regions, exhibiting low random errors and relative biases below 10%. IMERG tended to overestimate rainfall amounts, with marked overestimation by up to 75% in the Highland region. Bias decomposition revealed that IMERG’s high false rainfall bias contributed to its marked overestimation of rainfall. We showcase the utility of long-term CHIRPS data to investigate spatio-temporal rainfall patterns and meteorological drought occurrence in Bolivia. The findings of this study offer valuable insights for choosing appropriate SREs for informed decision-making, particularly in regions of complex topography lacking reliable gauge data. Full article

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG’s ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information. Full article

Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with severe typhoon Khanun (2017) and super-typhoon Mangkhut (2018). The satellite products include the CPC MORPHing technique (CMORPH) data, Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement Mission (GPM IMERG). Eight precision evaluation indexes and statistical methods were used to analyze and evaluate the monitoring capabilities of CMORPH, TRMM 3B42, and GPM IMERG satellite precipitation products. The results indicated that the monitoring capability of TRMM satellite precipitation products was superior in capturing the spatial distribution, and GPM products captured the temporal distributions and different category precipitation observed from gauge stations. In contrast, the CMORPH products performed moderately during two heavy rainfall events, often underestimating or overestimating precipitation amounts and inaccurately detecting precipitation peaks. Overall, the three satellite precipitation products showed low POD, high FAR, low TS, and high FBIAS for heavy rainfall events, and the differences in monitoring torrential TRP may be related to satellite retrieval algorithms. Full article

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m³/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m³/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness. Full article

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin. Full article

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R² values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m³ s⁻¹, and the average discharge was 20611 m³ s⁻¹. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. Full article

Drawing upon a comprehensive global database of landslides and utilizing high-resolution IMERG satellite precipitation data, this study investigates the spatial and temporal variations of landslide occurrences across the member states of the Association of Southeast Asian Nations (ASEAN). This study constructs a region-specific, graded warning system by formulating an average effective intensity–duration (I–D) rainfall threshold curve for each ASEAN member. Examination of 1747 landslide events spanning from 2006 to 2018 illustrates a significant association between the frequency of landslides in ASEAN regions and the latitudinal movement of local precipitation bands. Incidences of landslides hit their lowest in March and April, while a surge is observed from October to January, correlating with the highest mortality rates. Geographical hotspots for landslide activity, characterized by substantial annual rainfall and constrained landmasses, include the Philippine archipelago, Indonesia’s Java Island, and the Malay Peninsula, each experiencing an average of over 2.5 landslides annually. Fatalities accompany approximately 41.4% of ASEAN landslide events, with the Philippines and Indonesia registering the most substantial numbers. Myanmar stands out for the proportion of large-scale landslide incidents, with an average casualty rate of 10.89 deaths per landslide, significantly surpassing other countries in the region. The I–D rainfall threshold curves indicate that the Philippines experienced the highest precipitation levels before landslide initiation, whereas Myanmar has the threshold set at a considerably lower level. Full article

Global concern over optimizing transboundary water resources for residents is hindered by the lack of observational data, particularly in ungauged basins, mainly due to inaccessibility or security issues. Remote sensing and GIS technology provide a practical solution for monitoring and managing water resources in such basins. This research evaluates surface water resources in the Qaretikan ungauged transboundary basin using satellite products for precipitation, temperature, and evapotranspiration from 2005 to 2014. The accuracy of these datasets was assessed using statistical measures. The water balance components, i.e., precipitation and evaporation, were utilized to calculate runoff over the basin using the Justin method. Downstream environmental flow was estimated using the Lyon method, and available water was determined. This study identified a potential annual storage water of 11.8 MCM in the Qaretikan basin. The Analytic Hierarchy Process (AHP) integrated expert opinions to prioritize water usage decisions based on proposed decision options. The results revealed greenhouse cultivation water allocation as the top priority among the identified options, highlighting its importance in sustainable water resource management within the basin. Full article