Search Results (198)

Spaceborne precipitation products such as NASA IMERG V07 provide sub-hourly data required for hydrological modelling, but systematic biases in wet-event frequency and extreme-intensity representation limit their reliability for applications sensitive to precipitation extremes. This study develops a three-stage calibration pipeline combining probability-of-precipitation frequency adaptation, empirical quantile mapping of the distribution body, and Generalised Pareto Distribution tail modelling with constrained blending. The approach is calibrated against 202 Roshydromet stations using 3-hourly observations and evaluated on 15 spatially independent stations over a 9-year validation period. At the station-optimal blending weight, the proposed pipeline reduces median absolute percentage bias at the P99 quantile from 43.9% to 10.2%, while maintaining comparable volume balance (|PBIAS| 6.5%). To suppress a disaggregation artefact arising from amplification of multi-hour accumulations, the operational gridded R-factor product instead adopts a more conservative blend (|PBIAS@P99| = 24.9%) together with an empirically constrained accumulation cap, although the absence of sub-hourly calibration data remains the principal limitation. The calibrated dataset is applied to derive a 24-year (2001–2024) rainfall erosivity climatology for the Volga region, yielding a domain-mean R-factor of 254 ± 55 MJ mm ha⁻¹ h⁻¹ yr⁻¹ with no detectable monotonic trend. The proposed framework improves the representation of precipitation extremes and provides a transferable preprocessing approach for hydrological modelling applications. Full article

Rainfall contributes to slope instability when infiltrating water reduces matric suction and elevates pore water pressure beyond critical thresholds. Empirical rainfall intensity–duration (I-D) thresholds define the minimum rainfall conditions necessary to initiate landslides and are widely adopted in regional early warning systems. This study derives I-D thresholds for shallow landslide initiation in South-East Queensland (SEQ), Australia, using quantile regression applied to 104 rainfall-induced shallow landslide events recorded between 1974 and 2018. Thresholds at the 2nd, 10th, 50th, and 90th percentiles were derived over a duration range of 0.3 to 383 h and intensity range of 0.15 to 13.7 mm h⁻¹. The 2nd percentile, adopted as the conservative regional early warning threshold, is expressed as I = 0.719 × D^−0.220, where I is rainfall intensity (mm h⁻¹) and D is event duration (h). To facilitate inter-regional comparability, normalised thresholds expressed in terms of mean annual precipitation (MAP) were also derived, yielding a 2nd percentile threshold of I_MAP = 6.070 × 10⁻⁴ × D^−0.207. Both I-D and I_MAP -D thresholds fall substantially below existing global benchmarks, reflecting the pronounced susceptibility of SEQ’s deeply weathered residual soils to infiltration-driven failure. Independent validation against real-time tilt sensor and volumetric water content monitoring data from five kinematic failure events recorded at Maleny, Queensland (2016–2020), confirmed that all events plotted above the 2nd percentile threshold, with zero false negatives. The results provide a quantitative, operationally validated framework for regional shallow landslide early warning in subtropical Australia. Full article

Soil organic carbon (SOC) of cropland is a key indicator of soil fertility and contributes to climate regulation and carbon storage. The understanding of SOCchanges in cropland in Northeast China still lacks high-precision long-term empirical evidence. This study is of great significance for ensuring national food security and regional sustainable development. Taking Changchun, a representative black soil region, as the study area, this study integrated 953 field samples with 19 predictors to estimate cropland soil organic carbon density (SOCD) from 2000 to 2022. The performance of quantile regression neural network (QRNN), random forest (RF), and extreme gradient boosting (XGBoost) models was compared. QRNN showed the best overall performance (R² = 0.74, RMSE = 0.57 kg/m², MAE = 0.40 kg/m², and RPIQ = 2.46) and also exhibited greater stability in temporal-stage validation. Results indicated that SOCD exhibited an overall declining trend with intermittent recoveries, decreasing from 3.72 kg/m² in 2000 to 3.36 kg/m² in 2005, then increasing to 3.55 kg/m² in 2010, slightly declining to 3.46 kg/m² in 2015, and recovering to 3.63 kg/m² in 2022. Spatially, SOCD remained low in the southwest, fluctuated markedly in the north, and was relatively stable in the central region. The analysis of the optimal parameter geographic detector (OPGD) showed that Y-latitude, elevation, and mean annual temperature (MAT) were stable dominant factors, while precipitation (PRE) and remote sensing variables showed stage-dependent effects. Interactions among multiple factors further enhanced the explanation of SOCD variations. These findings provide theoretical support for enhancing soil carbon retention and promoting long-term cropland sustainability in black soil areas. Full article

To address the limitation where the traditional Optimal Threshold Selection (OTS) scheme achieves a high Threat Score (TS) at the expense of an increased False Alarm Rate (FAR), this study develops an Objective Improvement of Optimal Threshold Selection (OIOTS) scheme based on the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble precipitation forecasts. The correction performance is verified using high-resolution observations during the post-flood season in Guangdong Province. The results indicate that (1) optimal quantiles exhibit significant spatial heterogeneity and decrease sharply with increasing precipitation intensity, confirming the necessity of grid-specific correction over uniform provincial thresholds. (2) The Optimal Precipitation (OP) threshold remains stable across different lead times but shows distinct regional characteristics influenced by topography, whereas the corresponding Probability Threshold (PT) demonstrates a downward trend as the lead time extends. (3) Verification reveals that, compared with the OTS scheme, the OIOTS scheme effectively rectifies the high FAR inherent in the optimal quantile method while maintaining a comparable TS. By minimizing the absolute difference between TS and FAR, the OIOTS scheme achieves a superior balance between detection accuracy and error suppression, with its FAR showing a significant downward trend as precipitation magnitude and lead time increase. Given its high computational efficiency and robust performance, the proposed scheme offers a reliable solution for operational meteorological forecasting. Full article

Reliable climate data are essential for sustainable water management systems, especially under the challenges posed by climate change. In data-scarce regions, reanalysis products such as ERA5 can support flood and drought risk assessment and water security analysis. However, raw reanalysis precipitation is systematically biased relative to local observations and can distort hydrological indicators; bias correction is therefore needed. This study tests five bias correction techniques (Linear Scaling, Empirical Quantile Mapping, Quantile Mapping Spline Bias Correction, Mean Bias Subtraction, and Simple Linear Regression) on ERA5 precipitation data for Georgia, using classical and sliding window approaches at daily and monthly scales. Results show the importance of selecting the most appropriate method according to data availability and study objectives. The sliding window approach improved performance, especially at the daily scale, and distribution-based methods proved most effective in data-scarce regions. Full article

Quantifying the mixture effects on humans exposed remains challenging because mixture components are correlated and may act bidirectionally by exhibiting nonlinear dose-response relationships, which may contribute to subclinical organ dysfunction. The liver is a vital organ in the body with broad functions, making it vulnerable to injury as it is the first organ exposed to circulating toxicants, which can precipitate hepatic damage. Our study’s objective was to evaluate the combined and component-specific associations of a multi-chemical exposure mixture of heavy metals, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (dioxins), and polychlorinated dibenzofurans (furans), with liver biomarkers, and to compare concentration-based results with the toxic equivalent (TEQ) potency of the weighted results for dioxin-like compounds. In an unweighted analytic sample of U.S. adults from NHANES 2003–2004 with 947 complete cases, we examined heavy metals (cadmium, lead, and mercury), PCBs (12 congeners), dioxins (7 congeners), and furans (10 congeners) in relation to eight liver biomarkers (albumin, ALP, ALT, AST, GGT, LDH, total bilirubin, and total protein). We applied multi-exposure linear regression, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR), with parallel TEQ-based models using WHO 2005 TEFs for dioxin-like PCBs, dioxins, and furans. Across mixture methods, the mixture structure was chemically sparse, with a limited set of recurring contributors. Total bilirubin showed the most consistent positive mixture association across qgcomp and BKMR and persisted under TEQ weighting, with prominent PCB- and dioxin-like contributions (notably PCB81/PCB TEQs and dioxin-related components). Albumin demonstrated inverse mixture patterns in BKMR and TEQ-BKMR, with dioxin-like components (notably Dioxin3 and Dioxin3_TEQ) repeatedly emerging as key drivers. For ALT, ALP, AST, GGT, LDH, and total protein, overall mixture effects were frequently attenuated or null in qgcomp despite structured component weights, indicating bidirectional sub-mixtures and internal counterbalancing. BKMR PIPs similarly concentrated on a small number of dominant predictors (e.g., lead for ALP, mercury for ALT, PCB28 for AST, and cadmium and PCB189 for LDH), while interaction summaries provided limited evidence of stable non-additivity. Using multiple complementary mixture methods, we identified outcome-specific mixture patterns suggesting hepatobiliary vulnerability. TEQ concordance supports toxicological relevance of the dioxin-like axis, while metals and non–dioxin-like mechanisms likely contribute additional pathways. Full article

The impact of climate change on rainfall extremes has become increasingly obvious in many climatic regions including arid regions where extreme precipitation events are thought to have augmented or at least intensified. Driven by global factors such as greenhouse gas emissions, deforestation, and industrialization, climate change has augmented hydrological variability, thus making traditional stationary models inadequate for the estimation of extreme rainfall at various return periods. Extreme value analyses, which were traditionally derived under the assumption of stationarity (i.e., constant statistical properties over time) and typically do not account for temporal variability or external climatic drivers (e.g., temperature or large-scale climate indices), may lead to inaccurate estimation of rainfall quantiles under changing climate conditions. This paper presents a structured review of applied methodologies for quantifying the influence of climate change on extreme rainfall events, with special attention to how non-stationarity is addressed in arid regions applications, which was not a major focus in previous review papers. Relevant statistical techniques, extreme value theory, machine learning models, and high-resolution climate simulations are reviewed. From an initial pool of over 340 studies, 91 were selected based on their relevance to quantify rainfall extremes induced by climate change in arid regions. Based on the reviewed studies, the analysis revealed a strong reliance on trend analysis of downscaled Global Climate Models (GCMs) and Regional Climate Models (RCMs) within a stationary framework, with limited integration of covariates, other than time, in non-stationary frequency analysis to estimate the climate change-related value. This review identifies the research gaps in the scientific literature related to climate change impact assessment on extreme rainfall in arid regions. It emphasizes the necessity for adopting more robust hybrid approaches, adopting statistical distributions more suitable to arid conditions, careful treatment of outliers, conducting regional analyses to better understand the overall climate behavior of the region, addressing the impact on short-duration rainfall, integrating key climatic drivers through the incorporation of additional climate covariates and the impact of climate change on sub-daily rainfall patterns. Full article

Spaceborne microwave imagers are vital for monitoring global precipitation due to their wide swath and high sensitivity. This study proposes a deep learning approach that integrates a U-Net with a multi-task learning (MTL) framework. The model was separately trained over land and ocean using GPM Microwave Imager (GMI) brightness temperatures, with collocated precipitation rates and types from the Dual-frequency Precipitation Radar (DPR) as labels. This combines the accuracy of radars with the coverage of imagers to produce high-precision, wide-swath precipitation estimates. In the MTL setup, near-surface precipitation rate retrieval is the main task, and precipitation type classification is the auxiliary task. A composite loss (weighted MSE and quantile regression) is used for the main task, and weighted cross-entropy for the auxiliary task. Residual blocks and an attention mechanism are incorporated to improve physical representation and generalization, thereby significantly enhancing the model’s capability to retrieve heavy precipitation. The model was trained on 2015–2024 GPM data and evaluated on an independent six-month 2025 GMI dataset. Compared to a standard U-Net, the MTL model achieved significant gains: Pearson Correlation Coefficient (PCC) increased by 9.7% (ocean) and 13.7% (land), and Critical Success Index (CSI) by 10.7% (ocean) and 10.8% (land). The method was also applied to the FY-3G Microwave Radiation Imager (MWRI-RM). In case studies, it outperformed the official product, achieving average increases of 20.1% in PCC and 15.7% in CSI, respectively. Validation against FY-3G Precipitation Measurement Radar (June–August 2024) yielded over ocean PCC = 0.757, RMSE = 1.588 mm h⁻¹, MAE = 0.355 mm h⁻¹; over land PCC = 0.691, RMSE = 2.007 mm h⁻¹, MAE = 0.692 mm h⁻¹. The study demonstrates that the MTL-enhanced U-Net significantly improves the accuracy of spaceborne microwave imager rainfall retrieval and shows robust practical applicability. Full article

Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide spatially continuous estimates but are affected by systematic biases, and improvements in statistical rainfall accuracy do not necessarily translate into hydrologically consistent model forcing. This study interpreted satellite rainfall bias correction through a rainfall–runoff framework in the Phetchaburi River Basin, Thailand, using the DWCM-AgWU hydrological model. Simulations were driven by gauge observations and multiple satellite-based rainfall products (GSMaP, CMORPH, CHIRPS, and PERSIANN-CCS), with bias correction applied using Linear Scaling and Quantile Mapping under rainfall-specific calibration. Results showed that bias correction significantly modified rainfall characteristics in distinct ways. Linear Scaling primarily preserved temporal and spatial structure while adjusting rainfall magnitude, whereas Quantile Mapping improved the distributional representation of rainfall intensities. These differences propagated through hydrological processes, leading to systematic variations in runoff responses across multiple metrics, including water balance consistency, peak magnitude, and timing errors. This suggests that each method performs differently depending on the aspect of system response. Rather than identifying a universally optimal method, the findings highlight trade-offs in how rainfall correction strategies influence hydrological system response. Runoff behavior is interpreted as a process-level indicator of rainfall representation, emphasizing that hydrological consistency depends not only on rainfall accuracy but also on its interaction with model structure. These results suggest a process-oriented perspective for interpreting the role of satellite rainfall products in regulated and monsoon-affected basins. Full article

This study examines the historical and anticipated effects of climate change on essential hydroclimatic variables (temperature, precipitation, evapotranspiration, and soil moisture) in the Southeastern Anatolia Project (GAP) region of Türkiye, a semi-arid and agriculturally significant basin experiencing heightened water stress. The analysis employs a collection of CMIP6 Global Climate Models (GCM) and integrates three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5), utilizing statistical bias correction methods such as Delta Change, Quantile Mapping (QM), and Empirical Quantile Mapping (EQM) to improve the regional accuracy of the projections. The ACCESS-CM2 model, validated with data from Türkiye’s Meteorological General Directorate (MGM), was chosen for comprehensive spatial mapping, utilizing Inverse Distance Weighting (IDW) interpolation across seven temporal intervals encompassing past, present, and future periods. The findings indicate a steady increase in temperature and evapotranspiration, especially under high-emission scenarios, with temperature rises above +4 °C and considerable water losses anticipated by century’s end. Soil moisture exhibits a declining tendency, particularly in the southern and eastern regions, signifying increasing drought susceptibility. Precipitation patterns demonstrate significant spatial variability and rising uncertainty, with relative error (RE%) values increasing under SSP5-8.5. Historical data from 1963 to 2022 corroborate these conclusions, indicating a progressive shift towards a warmer and drier regional climate. These observations highlight the importance of climate adaptation strategies and water management in the GAP region. The research provides decision-makers a high-resolution, bias-corrected hydroclimatic dataset. Full article

Due to ongoing global warming, the frequency and intensity of extreme weather events have increased substantially. Compared to individual extremes, compound drought and high temperature (CDHT) events represent a major climate risk in China. However, their spatiotemporal characteristics remain insufficiently understood, particularly at fine temporal scales. To address this gap, this study systematically investigated CDHT events across China from 1982 to 2023. Methodologically, CDHT events were identified at the raster level by combining an improved daily Standardized Precipitation Evapotranspiration Index (SPEI) with daily maximum temperature using a quantile relative dynamic threshold. The results show strong spatial heterogeneity: the longest event durations are primarily observed in Xizang, while higher event severity is concentrated in regions south of 30° N. Trend analysis reveals a widespread increase in the duration, frequency, and severity of CDHT events across most of China, with the most pronounced intensification detected in Xinjiang, Inner Mongolia, and Yunnan. Overall, these findings highlight a clear climate-driven intensification of CDHT events, offering new insights into their spatiotemporal dynamics. The results offer a robust scientific basis for improving risk assessment and developing targeted adaptation strategies to mitigate the impacts of compound climate extremes in China. Full article

Global climate change has amplified extreme weather events, threatening ecological security and sustainable development. The impact of extreme weather events on sustainable land use (SLU) has attracted increasing attention. While previous studies have focused on the average effect, the question of whether its impacts vary across SLU levels remains unexplored. This knowledge gap is particularly pronounced in mountainous Chongqing, where complex terrain and urban heat islands interact to compound heterogeneous climate risks. To bridge this gap, we construct an indicator system based on the United Nations Sustainable Development Goals (SDGs) with which to assess the spatio-temporal evolution of SLU across all 38 districts of Chongqing from 2013 to 2023. The results show that Chongqing’s SLU level fluctuated upwards between 2013 and 2023, with high-value areas concentrated in the central urban core areas and regional gaps narrowing. Extreme climate indices exhibited significant spatial heterogeneity, with heat islands in urban areas, cold events in mountainous regions, floods in the southeast and droughts in the central western area. By overcoming average-effect limitations through quantile regression, this study further examines the heterogeneous effects of extreme weather on SLU across different development levels. It finds that simple daily intensity index (SDII) exerted stable negative effects—and the number of heavy precipitation days (R10), very wet days (R95p), monthly maximum value of daily maximum temperature (TXx) and diurnal temperature range (DTR) showed positive effects. While most climate variables exhibited stable effects, a critical finding was the divergent effect of variables such as tropical nights (TR20), which negatively impacted low SLU areas but positively influenced high SLU areas. This mechanistically confirms that regions at different levels of development experience fundamentally distinct impacts from the same climatic stressors. By focusing on uneven regional impacts and identifying region-specific extreme weather types, this study provides empirical support for targeted climate adaptation and balanced land management in mountainous cities. Full article

Climate change increasingly threatens the sustainability of regional water resources; therefore, robust station-scale precipitation projections are essential for basin-level planning. This study aims to develop and evaluate a hybrid, machine-learning-based statistical downscaling framework to generate monthly precipitation projections for the 21st century in the Büyük Menderes Basin, western Türkiye, using the HadGEM3-GC31-LL global climate model from the CMIP6. Monthly observations from 23 rainfall observation stations and ERA5 reanalysis predictors were employed to train station-specific Random Forest (RF) models, with optimal predictor sets identified through a multistage selection procedure (MPSP). Coarse-resolution general circulation model (GCM) fields were harmonized with ERA5 data using a three-stage inverse distance weighting (IDW), Delta, and Variance rescaling approach. The downscaled projections were bias-corrected using Quantile Delta Mapping (QDM) to maintain the climate-change signal. The RF models exhibited strong predictive skill across most stations, with test Nash–Sutcliffe Efficiency (NSE) values ranging from 0.45 to 0.81, RSR values from 0.43 to 0.74, and PBIAS values from −21.99% to +5.29%. Future projections indicate a basin-wide drying trend under both scenarios. Relative to the baseline, mean annual precipitation is projected to decrease by approximately 12.2, 19.6, and 33.7 mm in the near (2025–2050), mid (2051–2075), and late (2076–2099) periods under SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5, a moderate greenhouse gas scenario). Under the high-emission SSP5-8.5 scenario, projected decreases are 25.2, 53.2, and 86.9 mm, respectively. Late-century reductions reach approximately 15–22% in several sub-basins. These findings indicate a substantial decline in future water availability and underscore the value of RF-based hybrid downscaling and trend-preserving bias correction for water resources planning in semi-arid Mediterranean basins. Full article

Traditional villages serve as crucial carriers of natural and cultural heritage worldwide. Current research on traditional villages, however, exhibits several shortcomings. On one hand, existing studies tend to focus solely on spatial patterns while neglecting issues of distributional equity from a sustainability perspective. On the other hand, few studies have explored the underlying spatial and non-spatial characteristics influencing the distribution of traditional villages through multidimensional factors. To address these gaps, this study selects 8171 Chinese traditional villages as research subjects. Utilizing spatial analysis of GIS, spatial econometrics, and statistical methods, we first analyze the spatial pattern of traditional villages, then assess distributional equity of traditional villages from a sustainability perspective. Finally, we investigate the influence of six multidimensional factors on their distribution and the potential characteristics of these influences. The findings are as follows: (1) Traditional villages in China form three high-density cores, with distribution density significantly higher in the eastern and central regions compared to the western and northeastern regions. The western and northeastern regions exhibit notable low–low clustering. (2) Equity analysis reveals a Gini coefficient of 0.525 for accessibility, indicating notable spatial deprivation. There is also evidence of social inequity, reflected in the deprivation of aging populations by non-aging groups. (3) Except for population density, factors such as elevation and annual precipitation significantly influence the distribution of traditional villages, with effects varying regionally. Quantile regression further confirms that the six factors exert heterogeneous impacts depending on village density levels. For example, as village density increases, road density exerts a stronger positive effect. This study provides a theoretical reference for future sustainability assessments of traditional villages. Full article

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning. Full article