Search Results (1,320)

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved $T_C$ estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

The quantitative assessment of acoustic rain enhancement technology is highly significant for improving the ecological environment. A scientific and accurate evaluation of its operational effects provides an important basis for continued government and public support and investment in artificial weather modification activities. To effectively analyze the effects of acoustic rain enhancement on the vegetation of grassland ecosystems in arid and semi-arid areas and to clarify its mechanism, this study constructed eight vegetation indices based on Sentinel-2 satellite data. A comprehensive assessment of the changes in vegetation within the grassland ecosystem of the experimental zone was conducted by analyzing spatiotemporal distribution patterns, double-ratio analysis, and difference value comparisons. The results showed that (1) following the acoustic rain enhancement experiment, vegetation growth improved significantly. The mean values of all eight vegetation indices increased more substantially than before the experiment, with kNDVI showing the most notable gain. The proportion of the zone with kNDVI values greater than 0.417 increased from 52.62% to 71.59%, representing a relative increase of 36.05%. (2) The rain enhancement experiment significantly raised the values of eight vegetation indices: kNDVI increased by 0.042 (18.68%), ARVI by 0.043 (18.67%), and the remaining indices also increased to varying degrees (9.51–12.34%). (3) Vegetation improvement was more pronounced in areas closer to the acoustic rain enhancement site. Under consistent climate conditions, vegetation growth in the experimental zone showed significant enhancement. This study demonstrates that acoustic rain enhancement technology can mitigate drought and low rainfall, improve grassland ecosystem services, and provide a valuable foundation for ecological restoration and aerial water resource utilization in arid and semi-arid regions. Full article

This study presents a water vapor gradient (WVG) retrieval method based on Global Navigation Satellite System (GNSS) tropospheric parameter estimation. A case study examined the method’s applicability to the extreme rainstorm event in North China in July 2023. Precipitable water vapor (PWV) and WVG data from 332 GNSS sites in this area were retrieved. Radar and precipitation data were combined to perform a spatiotemporal comparison study. The results show that GNSS PWV and WVG of this weather process were highly consistent with radar reflectivity and precipitation. When a high PWV (>60 mm) was accompanied by WVG convergence, radar reflectivity was significantly strong and precipitation occurred at the leading edge of large gradients and the convergence region. Based on the edge of big WVGs, observed by multiple GNSS stations, the location and movement of rainfall could be identified. In case of large amounts of PWV accompanied by plummeting WVG (down to 0.1–0.4 mm/km), high or persistent precipitation occurs. During the event, compared to the northern plateau, the plain region demonstrated higher PWV, lesser WVG variation, and more intense precipitation, likely caused by the topographic dynamic effect. GNSS PWV and WVG can be key indicators for short-range weather forecasting of extreme rainstorm events. Full article

This study investigates how landcover change between 1935 and 2020 have shaped hydrological responses in the semi-arid highlands of Tigray, Ethiopia. Focusing on the Tsili catchment (27.5 km²), it examines links between landcover change, drainage network evolution, and river channel width under conditions of population growth and climate variability. Landcover and drainage maps were derived from historical aerial photographs and satellite imagery for four time steps, and surface runoff was simulated using the SWAT model with uniform meteorological forcing to isolate landcover effects. Results show a 37.6% increase in cropland and substantial declines in shrubland (−29.3%) and forest (−10.1%). River channel width at the outlet widened from 7.5 to 10.5 m, while drainage density increased 1.5-fold. These physical changes aligned with modelled increases in surface runoff. Strong correlations were found between runoff, channel width, drainage density, and landcover types. The findings highlight that cropland expansion—at the expense of natural vegetated land—has intensified runoff and erosion risks. As climate change is expected to bring more intense rainfall to East Africa, this underscores the need for land management strategies that reduce hydrological connectivity and support sustainable agriculture in data-scarce regions. Full article

This study evaluates GPM-IMERG (Global Precipitation Measurement-Integrated Multi-satellite Retrievals) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) satellite precipitation data in Türkiye’s Akçay Sub-Basin by comparing them with rain gauge observations from the Finike and Elmali meteorological stations. Statistical metrics including Pearson’s correlation coefficient, Nash-Sutcliffe Efficiency (NSE), and Root Mean Square Error (RMSE) were used to assess performance. The study also examines distributional fit via the Kolmogorov–Smirnov (K-S) test and evaluates extreme rainfall detection accuracy using metrics like Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). Results indicate that GPM-IMERG agrees well with rain gauge observations at the monthly scale (Pearson = 0.943; RMSE = 50.81 mm), but shows reduced accuracy at the daily scale (Pearson = 0.592; RMSE = 12.45 mm). The K-S test showed that the Beta distribution best fits monthly rainfall (threshold = 253.39 mm), while the Weibull distribution suits daily rainfall (threshold = 5.34 mm). GPM-IMERG achieved a POD of 0.778 and FAR of 0.222 for monthly extremes, while daily performance was lower (POD = 0.478; FAR = 0.388). These findings highlight the value of comparing satellite and ground-based data to improve flood risk assessment and enhance climate resilience in data-scarce basins. Full article

Arid mountainous regions are vulnerable to extreme hydrological events such as floods and droughts. Providing accurate and continuous rainfall records with no gaps is crucial for effective flood mitigation and water resource management in these and downstream areas. Satellite data and geospatial interpolation can be employed for this purpose and to provide continuous data series. However, it is essential to thoroughly assess these methods to avoid an increase in errors and uncertainties in the design of flood protection and water resource management systems. The current study focuses on the mountainous region in northern Oman, which covers approximately 50,000 square kilometers, accounting for 16% of Oman’s total area. The study utilizes data from 279 rain gauges spanning from 1975 to 2009, with varying annual data gaps. Due to the limited accuracy of satellite data in arid and mountainous regions, 51 geospatial interpolations were used to fill data gaps to yield maximum annual and total yearly precipitation data records. The root mean square error (RMSE) and correlation coefficient (R) were used to assess the most suitable geospatial interpolation technique. The selected geospatial interpolation technique was utilized to generate the spatial distribution of annual maxima and total yearly precipitation over the study area for the period from 1975 to 2009. Furthermore, gamma, normal, and extreme value families of probability density functions (PDFs) were evaluated to fit the rain gauge gap-filled datasets. Finally, maximum annual precipitation values for return periods of 2, 5, 10, 25, 50, and 100 years were generated for each rain gauge. The results show that the geostatistical interpolation techniques outperformed the deterministic interpolation techniques in generating the spatial distribution of maximum and total yearly records over the study area. Full article

Accurate precipitation estimates are essential for hydrological modeling and flood forecasting, particularly in regions like Ireland where rainfall patterns are highly variable and extreme events are becoming more frequent. This study evaluates the performance of two widely used gridded precipitation datasets, ERA5 reanalysis and GPM IMERG (Early, Late, and Final run) precipitation products, against ground-based observations from 25 synoptic stations operated by Met Éireann, Ireland’s national meteorological service, over the period of 2014–2021. A grid-to-point matching method was applied to ensure spatial alignment between gridded and point-based data. The datasets were assessed using seven statistical and categorical metrics across hourly and daily timescales, meteorological seasons, and rainfall intensity classes. Results show that ERA5 consistently outperforms IMERG across most evaluation metrics, particularly for low-to-moderate intensity rainfall associated with winter frontal systems, and demonstrates strong temporal agreement and low bias in coastal regions. However, it tends to underestimate short-duration, high-intensity events and displays higher false alarm rates at the hourly scale. In contrast, IMERG-Final exhibits improved detection of extreme rainfall events, especially during summer, and performs more reliably at daily resolution. Its spatial performance is stronger than the Early and Late runs but still limited in Ireland’s western regions due to complex climatological settings. IMERG-Early and Late generally follow similar trends but tend to overestimate rainfall in mountainous regions. This study provides the first systematic intercomparison of ERA5 and IMERG datasets over Ireland and supports the recommendation of adopting a hybrid approach of combining ERA5’s seasonal consistency with IMERG-Final’s event responsiveness for enhanced rainfall monitoring and hydrological applications. Full article

Accurate sub-seasonal-to-seasonal (S2S) forecasts are critical for mitigating extreme weather impacts and supporting development in West Africa. This study evaluates real-time ECMWF S2S rainfall forecasts during the 2020–2021 West African monsoon (March–October) and uses corresponding hindcasts for comparison. We verify forecasts at 1–4 dekads lead against two satellite-based rainfall datasets (TAMSAT and GPM-IMERG) to cover observational uncertainty. The analysis focuses on spatio-temporal monsoon patterns over the Gulf of Guinea (GoG) and Sahel (SAH). The results show that ECMWF-S2S captures key monsoon features. The forecast skill is generally higher over the Sahel than the GoG, and peaks during the main monsoon period (July–August). Notably, forecasts achieve approximately 80% synchronization with observed rainfall-anomaly timing, indicating that roughly 4 out of 5 dekads have correctly predicted wet/dry phases. Probabilistic evaluation shows strong reliability. The debiased ranked probability skill score (RPSS) is high across thresholds, whereas the average ROC AUC (~0.68) indicates moderate discrimination. However, forecasts tend to under-predict very low rains in the GoG and very high rains in the Sahel. Using multiple datasets and robust metrics helps mitigate observational uncertainty. These results, for the first real-time S2S pilot over West Africa, demonstrate that ECMWF rainfall forecasts are skillful and actionable (especially up to 2–3 dekads ahead), providing confidence for early-warning and planning systems in the region. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

The Kingdom of Saudi Arabia launched a Regional Cloud Seeding Program in 2022 to enhance rainfall in central and southwestern regions. This study highlights a cloud seeding case on 1 November 2023, using convective development products derived from the Nowcasting Satellite Application Facility (NWCSAF), part of the SAF Network coordinated by the European Organization for the Exploitation of Meteorological Satellites. NWCSAF provided real-time satellite data for assessing cloud dynamics and precipitation. Analysis focused on Convection Initiation (CI) products issued 30–90 min before cloud seeding activities. Results showed the CI+30, +60, and +90 min outputs had high predictive accuracy, aligning with observed convection and demonstrating the value of satellite-based nowcasting in potential adaptation during cloud seeding operations. Full article

This study investigates two extreme convective rainfall events that struck Poland in August 2024, affecting Warsaw (Okęcie) on 19 August and Zamość on 21 August. The aim is to evaluate the meteorological background, intensity, and spatial characteristics of these short-duration storms. We used high-resolution meteorological observations, radar imagery, and satellite data provided by the Institute of Meteorology and Water Management (IMGW-PIB). The storms were analyzed using temporal rainfall profiles, Chomicz α index classification, and comparison with World Meteorological Organization (WMO) thresholds for extreme precipitation. Both events exceeded national and international criteria for torrential rainfall. In Zamość, over 88.3 mm of rain fell within one hour, and 131.3 mm within three hours—ranking this episode among the most intense short-duration rainfall events in the region. Convective organization patterns, including multicellular clustering and convective training, were identified as key factors enhancing rainfall intensity. The results demonstrate the diagnostic value of combining national indices with global benchmarks in rainfall assessment. These findings support further integration of convection-permitting models and real-time nowcasting into urban hydrometeorological warning systems. Full article

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following $\sigma$-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article