Search Results (888)

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

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

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. 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

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems. Full article

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability. Full article

The CYGMEN (Cyprus GNSS Meteorology Enhancement) infrastructure project aims to establish a meteorological cluster (CyMETEO) in Cyprus of a lightning detection network, a dense GNSS (Global Navigation Satellite System) network for atmospheric water vapor estimation, a Radar Wind Profiler, and a microwave radiometer. Additionally, observational data generated by CyMETEO infrastructure will be assimilated into the Weather Research and Forecasting (WRF) model with the aim of improving short-term weather forecasting. The preliminary results of precipitable water vapor (PWV) estimation by employing (a) a GNSS network, (b) a microwave radiometer, (c) radiosonde, and (d) ERA5 reanalysis datasets over the Athalassas super-site in Nicosia, during May 2025, are intercompared in this study. Full article

Monitoring the hydrological processes of lakes can provide reliable data for regional water resources assessment. This paper analyzed changes in the lake area and water level of Hala Lake from 2011 to 2023, subsequently estimating its lake water storage change (LWSC). We used image data from Landsat series satellites and multi-source satellite altimetry data, and then quantitatively assessed the influence of various driving factors on the LWSC in combination with hydrological and meteorological models. The results show three stages of parallel changes in the area, water level and LWSC of Hala Lake in the past 13 years. The first stage is from 2011 to 2014, when the lake expanded slightly, the second stage is from 2015 to 2019, when the lake expanded rapidly, and the last stage is from 2020 to 2023, with relatively stable conditions. Over the entire study period, the LWSC increased with a trend of 0.192 ± 0.009 km³/a. Lake surface precipitation, precipitation-caused runoff, and glacier meltwater contributed to the total recharge input by 51%, 40.96%, and 8.04%, respectively, while the lake surface evaporation accounted for 59.37% of the total recharge input as water loss. Thus, the left 40.63% of the input caused the LWSC increase. Although lake surface precipitation provided the primary contribution to the Hala Lake LWSC, precipitation-caused runoff was the key factor forming the three stages in the LWSC. The results of this study provide valuable information for the rational development and utilization of water resources by government departments and are also beneficial to the study of global change. Full article

Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. Full article

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m⁻²) and lowest in deciduous needleleaf forests (148.31 gC m⁻²). NEP in China’s forests increased by a rate of 1.67 gC m⁻² annually, with most regions exhibiting a 275.32 gC m⁻² annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies. Full article

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to 2023, using both categorical metrics (to assess daily rainfall detection skill) and continuous validation (to evaluate rainfall amount), complemented by bias decomposition and spatiotemporal analysis. Our results show that IMERG demonstrated higher skill in detecting daily rainfall, while CHIRPS delivered a more stable performance during dry conditions, with fewer false alarms. Both products capture the main seasonal precipitation patterns but differ in bias behavior: CHIRPS tends to under-estimate daily rainfall less, whereas IMERG provides more reliable volumetric estimates overall. These findings suggest that IMERG may be best suited for flood risk and hydrological modelling, while CHIRPS could be preferred for drought monitoring and climatological studies in Andean catchments. Full article

Monitoring rainfall is essential to understanding hydrological processes, managing water resources, and mitigating drought and flood risks. Many regions, particularly in developing countries, have sparse rain gauge networks, which limit spatial coverage and result in inaccurate rainfall estimates. By combining remote sensing data with rain gauge measurements, rainfall estimates can be improved, and spatial coverage can be enhanced. Remote sensing techniques provide a valuable resource for supplementing and enhancing rainfall monitoring in such areas. This study leverages Global Precipitation Measurement (GPM) satellite data to enhance rainfall estimation in White Nile State, Sudan, where only two rain gauge stations are operational and the state’s total area is 39.600 km². GPM data, well-known for its high temporal and spatial resolution, offers a promising alternative to mitigate the limitations of sparse ground-based networks. The study integrates GPM satellite data with ground-based measurements through statistical and geostatistical techniques, as well as validation, to improve rainfall accuracy. The results show that, on average, GPM data and rain gauge measurements exhibit a strong correlation of 0.87, with an annual RMSE of 10.23 mm and an AME of 8.25 mm. These findings demonstrate that GPM data effectively complements traditional rain gauge observations by accurately capturing spatial rainfall distributions and extreme precipitation events. The findings underscore the potential of remote sensing to provide reliable rainfall information in data-scarce regions, contributing to better water resource management and disaster risk reduction strategies. Full article

Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R² values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions. Full article