Search Results (345)

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

The mean annual water balance is essential for evaluating water availability in a catchment and planning water resources. Climate change alters this balance by affecting precipitation, evapotranspiration, and overall water availability. This study analyses the impact of climate change on the mean annual water balance in the Catamayo catchment, a key water source for irrigation and hydropower in southern Ecuador and northern Peru. A Budyko-based approach was employed due to its conceptual simplicity and proven robustness for estimating long-term water balances under changing climatic conditions. Using outputs from 23 Global Circulation Models (GCMs) under CMIP6’s SSP2-4.5 and SSP8.5 scenarios, the results indicate increasing aridity, particularly in the lower and middle parts of the catchment, which correspond to arid and semi-arid zones. Water availability may decrease by 26.3 ± 12.3% to 33.3 ± 17% until 2080 due to negligible changes (statistically speaking) in average precipitation but rising evapotranspiration. However, historical precipitation analysis (1961–2020) reveals an increasing trend over this historical period which can be attributed to natural climatic variability associated to the El Nino-Southern Oscillation (ENSO), possibly enhanced by anthropogenic climate change. A novel hybrid method combining the statistics of historical precipitation deficits with GCM mean projections provides estimates of future precipitation deficits. These findings suggest potential reductions in crop yields and hydropower capacity, which (although not quantitatively assessed in this study) are inferred based on the projected decline in water availability. Such impacts could lead to higher energy costs, increased reliance on fossil fuels, and intensified competition for water. Mitigation measures, including water-saving strategies, energy diversification, and integrated water resource management, are recommended to address these challenges. Full article

In the context of increasingly severe climate change, studying the relationship between climate factors and vegetation dynamics is crucial for ecological conservation and sustainable development. This study focuses on the Oujiang River Basin from 1981 to 2022, aiming to quantitatively model the interactions among temperature, precipitation, the NDVI, and the LAI. Addressing the lack of approaches for forecasting high-resolution LAI data and existing LAI data that are usually interpreted from NDVI data, we proposed a two-step inversion framework: first, modeling the response of the NDVI to climate variables; second, predicting the LAI using the NDVI as a mediating variable. By integrating long-term remote sensing datasets (GIMMS and MODIS NDVI) with meteorological data and applying trend analysis, spatial correlation analysis, and clustering techniques (K-Means and Possibilistic C-Means), we identified spatial heterogeneity in vegetation response patterns. The study results showed that (1) climate factors have a distinctly spatially heterogeneous impact on the NDVI and LAI; (2) temperature is identified as the dominant factor in most regions; and (3) the LAI prediction model based on the climate factors NDVI and NDVI–LAI relationships shows good accuracy in the medium-to-high range of the LAI, with an R² value ranging from 0.516 to 0.824. This study provides a scalable approach to improve LAI estimation and monitor vegetation dynamics in complex terrain under changing climate conditions. Full article

The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products. Full article

Weather radars are essential in the Quantitative Precipitation Estimates (QPE) but are susceptible to calibration errors. Previous work demonstrated that observations from the Ku-band Dual Polarization Radar (DPR) radar on board the Global Precipitation Measurement Mission Dual-Precipitation Radar (GPM) are suitable for ground radar calibration. Several studies volume-matched ground radar and GPM DPR Ku-band reflectivities for the absolute calibration of ground radars, by applying different constraints and filters in the volume-matching procedure. This study compares and evaluates volume-matching thresholds and data filtering schemes for the Rizoelia, Larnaca (LCA) and Nata, Pafos (PFO) radars of the Cyprus weather radar network from October 2017 till May 2023. Excluding reflectivities below and within the melting layer with a 250 m buffer yielded consistent results for both ground radars. The selected calibration schemes were combined, and the resulting offsets were compared to stable radar parameters to identify stable calibration periods. The consistency of the wet hydrological year October 2019 to September 2020 suggests that radar calibration results are prone to differences in meteorological conditions, as scarce rainfall can result in insufficient data for reliable calibration. Future work will incorporate disdrometer measurements and extend the analysis to quantitative precipitation estimation. Full article

The evaluation of raindrop size distribution (DSD) is a crucial subject in radar meteorology, as it determines the relationship between radar reflectivity (Z) and rainfall rate (R). The coefficients (a and b) of the Z-R relationship vary significantly due to several factors (e.g., climate and rainfall intensity), rendering the characterization of local DSD essential for improving radar quantitative precipitation estimation. This study used a unique network of 21 disdrometers with high spatio-temporal resolution in Mexico City to investigate changes in the local drop size distribution (DSD) resulting from seasonal fluctuations, rain rates, and topographical regions (flat urban and mountainous). The results indicate that the DSD modeling utilizing the normalized gamma distribution provides an adequate fit in Mexico City, regardless of geographical location and season. Regional variation in DSD’s slope, shape, and parameters was detected in flat urban and mountainous areas, indicating that distinct precipitation mechanisms govern rainfall in each season. Severe rain intensities (R > 20 mm/h) exhibited a more uniform and flatter DSD shape, accompanied by increased dispersion of DSD parameter values among disdrometer locations, particularly for intensities exceeding R > 60 mm/h. The coefficients a and b of the Z-R relationship exhibit significant geographic variability, dependent on the city’s topographic gradient, underscoring the necessity for regionalization of both coefficients within the metropolis. Full article

A new quantitative precipitation estimation (QPE) method for Himawari-9 (H9) and Fengyun-4B (FY4B) satellites has been developed based on cloud top brightness temperature (TBB). The 24-hour, 6-hour, and hourly rainfall estimates of H9 and FY4B have been compared with rain gauge datasets and precipitation estimation data from the GPM IMERG V07 (IMERG) and Global Precipitation Satellite (GSMaP) products, especially based on the case study of landfalling super typhoon “Doksuri” in 2023. The results indicate that the bias-corrected QPE algorithm substantially improves precipitation estimation accuracy across multiple temporal scales and intensity categories. For extreme precipitation events (≥100 mm/day), the FY4B-based estimates exhibit markedly better performance. Furthermore, in light-to-moderate rainfall (0.1–24.9 mm/day) and heavy rain to rainstorm ranges (25.0–99.9 mm/day), its retrievals are largely comparable to those from IMERG and GSMaP, demonstrating robust consistency across varying precipitation intensities. Therefore, the new QPE retrieval algorithm in this study could largely improve the accuracy and reliability of satellite precipitation estimation for extreme weather events such as typhoons. Full article

Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months. Full article

Although phenotypic plasticity facilitates the understanding of trait variation, its study remains limited. To study phenotypic plasticity, in this study, 203 inbred maize lines were phenotyped for flowering time and plant height in Zhengzhou, Ningjin, Tieling, and Binxian and genotyped with 121,871 high-quality SNPs. The regression slopes and intercepts of flowering time and plant height on four meteorological factors in their corresponding, most significant correlation windows were used to estimate the phenotypic plasticity of the above traits and to further indirectly detect the interactions between quantitative trait nucleotides and meteorological factors. Of the two known and seven candidate genes identified in this study, ZmCCT, GRMZM2G035417, GRMZM2G069651, and GRMZM2G359322 can be used to explain why spring maize has a longer flowering time than summer maize, as these genes delay pollen development and flowering regulators under long day and low temperature; while ZmPIN1b, GRMZM2G062045, GRMZM2G370777, GRMZM2G077752, and GRMZM2G126397 can be used to explain why Tieling has higher plant height than other regions, as these genes enhance auxin transport and suppress dwarfing genes under increased precipitation and low temperature. This study explains the phenotypic plasticity of these traits. Full article

The aim of this study is to quantitatively analyze the long-term climate variability in Poland during the period 1901–2010, using Shannon entropy as a measure of uncertainty and complexity within the atmospheric system. The analysis is based on the premise that variations in temperature and precipitation reflect the dynamic nature of the climate, understood as a nonlinear system sensitive to fluctuations. This study focuses on monthly distributions of temperature and precipitation, modeled using the bivariate Clayton copula function. A normal marginal distribution was adopted for temperature and a gamma distribution for precipitation, both validated using the Anderson–Darling test. To improve estimation accuracy, a bootstrap resampling technique and numerical integration were applied to calculate Shannon entropy at each of the 396 grid points, with a spatial resolution of 0.25° × 0.25°. The results indicate a significant increase in Shannon entropy during the summer months, particularly in July (+0.203 bits) and January (+0.221 bits), compared to the baseline period (1901–1971), suggesting a growing unpredictability of the climate. The most pronounced trend changes were identified in the years 1985–1996 (as indicated by the Pettitt test), while seasonal trends were confirmed using the Mann–Kendall test. A spatial analysis of entropy at the levels of administrative regions and catchments revealed notable regional disparities—entropy peaked in January in the West Pomeranian Voivodeship (4.919 bits) and reached its minimum in April in Greater Poland (3.753 bits). Additionally, this study examined the relationship between Shannon entropy and global climatic indicators, including the Land–Ocean Temperature Index (NASA GISTEMP) and the ENSO index (NINO3.4). Statistically significant positive correlations were observed between entropy and global temperature anomalies during both winter ($\rho$ = 0.826) and summer ($\rho$ = 0.650), indicating potential linkages between local climate variability and global warming trends. To explore the direction of this relationship, a Granger causality test was conducted, which did not reveal statistically significant causality between NINO3.4 and Shannon entropy ($p$ > 0.05 for all lags tested), suggesting that the observed relationships are likely co-varying rather than causal in the Granger sense. Further phase–space analysis (with a delay of $\tau$ = 3 months) allowed for the identification of attractors characteristic of chaotic systems. The entropy trajectories revealed transitions from equilibrium states (average entropy: 4.124–4.138 bits) to highly unstable states (up to 4.768 bits), confirming an increase in the complexity of the climate system. Shannon entropy thus proves to be a valuable tool for monitoring local climatic instability and may contribute to improved risk modeling of droughts and floods in the context of climate change in Poland. Full article

Karst water supplies freshwater to approximately a quarter of the global population and plays a crucial role in supporting the socioeconomic development of karst regions. As a key indicator for assessing and managing karst water resources, karstic water storage variation is influenced not only by the complex structure of karst aquifer media but also by the variability in natural precipitation infiltration. Based on the hydrogeological conditions of a typical karst aquifer system in northern China, this study developed a three-dimensional physical experimental setup and established a corresponding groundwater flow numerical model coupled with equivalent porous media and conduits. The factors affecting spring flow recession were investigated from a source–sink perspective. Precipitation events were categorized into two types: those with the same duration but different intensities and those with the same total volume but different intensities. The influence of varying precipitation events on the estimation of karstic water storage variation was quantitatively evaluated using the exponential fitting method, based on the analysis of spring flow recession curves. These findings could provide scientific guidance for the development, utilization, and protection of karst water resources. Full article

Feature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in Crossville, Tennessee, during warm-season convection. Analysis of polarimetric radar variables revealed that strong updrafts, mixed-phase precipitation, and large hailstones in the radar resolution volume during the event were driven by the existence of supercell thunderstorms. The results of feature detection highlight that the regions with convective–stratiform cores and strong–faint features in the reflectivity field are similar to those in the rainfall field, demonstrating how the algorithm more effectively detects features in both fields. The results of the estimates, accounting for uncertainty during feature detection, indicate that an offset of +2 dB overestimated convective features in the northeast in both the reflectivity and rainfall fields, while an offset of −2 dB underestimated convective features in the northwest part of both fields. The results highlight that convective cores cover a small area with high rainfall exceeding 50 mmh⁻¹, while stratiform cores cover a larger area with greater horizontal homogeneity and lower rainfall intensity. These findings are significant for nowcasting weather, numerical models, hydrological applications, and enhancing climatological computations. Full article

Quantitative precipitation estimation using polarimetric radar in attenuation-prone frequency (X-band) in tropical regions characterized by convective rain systems with high intensities is a major challenge due to strong attenuations that can lead to total signal extinction over short distances. However, some authors have addressed this issue in Benin since 2006 in the framework of the African Monsoon Multidisciplinary Analysis program. Thus, with an experimental setup consisting of an X-band polarimetric weather radar (Xport) and a network of rain gauges, investigations have started on the subject with the aim of improving rainfall estimates. Based on simulated polarimetric variables and using a Multilayer Perceptron artificial neural network, several bi-variable and tri-variable algorithms were assessed in this study. The data used in this study are of two categories: (i) simulated polarimetric variables (Rayleigh reflectivity $Z$, horizontal attenuation $A_h$, horizontal reflectivity $Z_h$, differential reflectivity $Z_{dr}$, and specific differential phase $K_{dp}$) and rainfall intensity (R) obtained from Rain Drop Size Distribution (DSD) measurements used for algorithm evaluation (training and testing); (ii) polarimetric variables measured by the Xport radar and rainfall intensity measured by rain gauges used for algorithm validation. The simulations are performed using the T-matrix code, which leverages the scattering properties of spheroidal particles. The DSD measurements taken in northwest Benin were used as input for this code. For each spectrum, the T-matrix code simulates multiple variables. The simulated data (first category) were divided into two parts: one for training and one for testing. Subsequently, the best algorithms were validated with the second category of data. The performance of the algorithms during training, testing, and validation was evaluated using metrics. The best selected algorithms are $A1:R(Z,K_{dp})$ and $A12:R(Z_{dr},K_{dp})$ (among the bi-variable); $B2:R(Z_h,Z_{dr},K_{dp})$ and $B3:R(A_h,Z_{dr},K_{dp})$ (among the tri-variable). Tri-variable algorithms outperform bi-variable algorithms. Validation with observation data (Xport measurements and rain gauge network) showed that the algorithm $B3:R(A_h,Z_{dr},K_{dp})$ performs better than $B2:R(Z_h,Z_{dr},K_{dp})$. Full article