Search Results (66)

The southeastern Tibetan Plateau (SETP) lies at the intersection of extreme topography and the Indian summer monsoon, producing intense orographically driven precipitation. Resolving these extremes requires convection-permitting simulations (CPMs) due to the combined influence of complex terrain and vigorous convection. However, kilometer-scale simulations over SETP remain limited to short periods because of computational cost, preventing robust estimation of precipitation extremes. We analyze a decade of 1 km CPMs and apply the Simplified Metastatistical Extreme Value (SMEV) framework, which uses all wet-day precipitation rather than annual maxima, increasing the effective sample size by an order of magnitude. We apply the SMEV framework to estimate daily precipitation return levels up to 100 years. While SMEV increases the effective sample size, uncertainty remains non-negligible for long return periods when derived from a decadal record. Results show that CPMs’ estimates align with observations within 90% bootstrap confidence intervals (CIs). For instance, at a representative station (Obs: 51.6 mm/d), the 50-year return level is estimated at 57.1 mm/d (CI: 47.1–68.9 mm/d). In contrast, coarse-resolution products systematically overestimate these extremes by 50–100%, with their estimates often falling far outside the observed range beyond 20-year return periods. CPMs also reveal a model-derived, non-monotonic elevation dependence absent in coarse datasets. Instead of monotonic decline, three phrases emerge: a weak increase below 2700 m, a sharp decrease across mid-elevations, and a reversal above ~5300 m where orographic uplift enhances extremes, yielding a 2.3-fold increase. These results show that CPMs alter not only magnitude but also the spatial structure and elevation scaling of precipitation extremes, providing a physically constrained framework for extreme-value estimation in data-sparse mountains. Full article

The mountainous southwest of Saudi Arabia exhibits complex, highly seasonal precipitation driven by Indian Ocean monsoon inflows and orographic lifting. To characterize storm hyetographs, cluster analysis was applied to 8972 rainfall events recorded at 151 gauges. Two primary clusters emerged: one with early, intense peaks and another with later peak intensities, broadly reflecting windward versus leeward storm behavior. A locally derived hyetograph profile (AI) was constructed from the cluster centroids and benchmarked against standard design-storm distributions (Uniform, SCS Type II, Huff quartiles). Across fit metrics—cumulative RMSE, Kolmogorov–Smirnov distance, and cosine-intensity similarity—the AI distribution provided the best match for ~46% of storms, markedly outperforming canonical profiles (Uniform and SCS Type II each best-fit only ~11–12%). These results indicate that region-specific rainfall distributions more accurately represent precipitation patterns than conventional profiles, and that tailored hyetographs can improve hydrologic modeling and water-resources assessments in this climatically heterogeneous region. Full article

Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three architecture families on precipitation prediction across the Colombian Andes: ConvLSTM (convolutional recurrent), FNO-ConvLSTM (spectral–temporal), and GNN-TAT (graph attention LSTM). Using CHIRPS v2.0 and SRTM topography for Boyacá department (61 × 65 grid, 3965 nodes), we evaluate 39 configurations across feature bundles (BASIC, KCE elevation clusters, and PAFC autocorrelation lags) and horizons from 1 to 12 months. GNN-TAT matches ConvLSTM accuracy ($R^2$: 0.628 vs. 0.642; RMSE: 82.29 vs. 79.40 mm) with 95% fewer parameters (∼98K vs. 2.1M). Across configurations, GNN-TAT produces a lower mean RMSE (92.12 vs. 112.02 mm; $p=0.015$) and a 74.7% lower variance. The explicit graph structure, with edges weighted by elevation similarity, appears to reduce sensitivity to hyperparameter choices. Pure FNO struggles with precipitation’s spatial discontinuities ($R^2=0.206$), though adding a ConvLSTM decoder recovers much of the lost skill ($R^2=0.582$). Elevation clustering improves GNN-TAT significantly ($p=0.036$) but not ConvLSTM, suggesting that feature design should match the spatial encoding paradigm. ConvLSTM achieves peak accuracy on local patterns; GNN-TAT provides robust predictions with interpretable spatial reasoning. These complementary strengths motivate stacking ensembles that combine grid-based and graph-based representations. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day⁻¹ in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article

High Mountain Asia (25–40° N, 70–100° E) plays a critical role in sustaining water resources for nearly two billion people; however, the accurate estimation of precipitation remains challenging. Numerous gridded products have been developed, yet their performance across the region remains uncertain and is often analyzed only over small areas or short periods. This study provides a comprehensive evaluation of five major gridded precipitation datasets (ERA5, HARv2, GPCC, APHRODITE, and PERSIANN-CDR) over 1983–2007 throughout the entire domain through spatial intercomparison, validation against ground stations, and assessment against observed river discharge. Results show that reanalysis products (ERA5, HARv2) better capture spatial precipitation patterns, particularly along the Himalayas and Kunlun range, with HARv2 more accurately representing elevation-dependent gradients. Gauge-based (GPCC, APHRODITE) and satellite-derived (PERSIANN-CDR) datasets exhibit smoother fields and weaker orographic responses. In catchment-scale evaluations, reanalysis shows a superior performance, with ERA5 achieving the lowest bias, highest Kling–Gupta Efficiency, and best water-balance consistency. GPCC and PERSIANN-CDR underestimate discharge, and APHRODITE performs worst overall. No single dataset is optimal for all applications. Gauge-based datasets and PERSIANN-CDR are suitable for localized climatology in well-instrumented areas, while reanalysis products offer the best compromise between spatial realism and hydrological consistency for large-scale modelling in high-altitude regions where observations are limited. Full article

Global glacier models (GGMs) are effective tools for assessing changes in water resources in mountainous regions and studying glacier degradation. Moreover, with the rapid development and increasing complexity of Earth System Models (ESMs), the incorporation of mountain glaciation parametrizations into ESMs is only a matter of time. GGMs, being computationally efficient and physically well-founded, provide a solid basis for such parametrizations. In this study, we present a new global glacier model, IGRICE. Its dynamic core is based on the Oerlemans minimal model, and surface mass balance (SMB) is explicitly simulated, accounting for orographic precipitation, radiation redistribution on the glacier surface, turbulent heat fluxes, and snow cover evolution on ice. The model is tested on glaciers situated in climatically and topographically contrasting regions—the Caucasus and Svalbard—using observational data for validation. The model is forced with ERA5 reanalysis data and employs morphometric glacial and topographic parameters. The simulated components of the surface energy and mass balance, as well as glacier dynamics over the period of 1984–2021, are presented. The model results demonstrate good agreement with observations, with correlation coefficients for accumulation, ablation, and total SMB ranging from 0.6 to 0.9. The primary driver of glacier retreat in the Caucasus is identified as an increase in net shortwave radiation balance caused by reduced cloudiness and albedo. In contrast, rapid glacier degradation in Svalbard is linked to an increased fraction of liquid precipitation and an extended snow-free period, leading to a sharp decrease in albedo. Full article

This study models the spatiotemporal heterogeneity of extreme rainfall over the Korean Peninsula using the generalised additive extreme value models (EVGAM) to address the limitations of traditional stationary approaches under climate change. Analyzing 30 years of daily precipitation data (1995–2024), we conducted a comparative analysis between typhoon-inclusive and non-typhoon scenarios to isolate the meteorological drivers of extremes. The results revealed distinct covariate dependencies: while spatial location (latitude and longitude) governs rainfall variability in non-typhoon conditions, elevation emerged as the critical determinant for the scale parameter during typhoon events, highlighting the role of orographic effects. Furthermore, the shape parameter exhibited multi-decadal oscillations corresponding to climate variability indices. To ensure local accuracy, a dual fitting strategy was implemented, supplementing EVGAM with standalone generalized extreme value (GEV) estimation for stations exhibiting poor goodness-of-fit. The resulting 50-year and 100-year return level maps quantify regional risks, identifying the southern coast as a high-vulnerability zone driven by typhoons, while inland basins benefited from orographic shielding. This comprehensive framework provides a robust scientific basis for adaptive water resource management and infrastructure design. Full article

Low-altitude palaeoglaciation in Atlantic mountain regions provides important insights into past climatic conditions and moisture dynamics during the Last Glacial Cycle. This study presents the first quantitative reconstruction of palaeoglaciers in Serra da Cabreira (northwest Portugal), a mid-altitude granite massif located along the Atlantic fringe of the Iberian Peninsula. Detailed geomorphological mapping (1:14,000) and field surveys identified 48 glacial and periglacial landforms, enabling reconstruction of two small valley glaciers in the Gaviões and Azevedas valleys using GlaRe numerical modelling. The spatial distribution of palaeoglacial landforms shows a pronounced west–east asymmetry: periglacial features prevail on wind-exposed west-facing slopes, whereas glacial erosion and depositional landforms characterise the more protected east-facing valleys. The reconstructed glaciers covered 0.24–0.98 km², with maximum ice thicknesses of 72–89 m. Equilibrium-line altitudes were estimated using AABR, AAR, and MELM methods, yielding consistent palaeo-ELA values of ~1020–1080 m. These results indicate temperature depressions of ~6–10 °C and enhanced winter precipitation associated with humid, Atlantic-dominated conditions. Comparison with regional ELA datasets situates Cabreira within a clear Atlantic–continentality gradient across northwest Iberia, aligning with other low-altitude maritime palaeoglaciers in the northwest Iberian mountains. The findings highlight the strong influence of the orographic barrier position, moisture availability, valley hypsometry, and structural controls in sustaining small, climatically sensitive glaciers at low elevations. Serra da Cabreira thus provides a key reference for understanding Last Glacial Cycle palaeoclimatic variability along the Western Iberian margin. Full article

Dynamical downscaling is an essential approach for bridging the gap between coarse-resolution global climate models and regional details required for climate impact assessment and sustainable development planning. Thailand, a climate-sensitive country in Southeast Asia, requires robust climate information to support its adaptation and resilience strategies. This study evaluated the Weather Research and Forecasting (WRF) model in dynamically downscaling selected Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations over Thailand during the baseline period of 1976–2005. A two-way nested WRF configuration was employed, with domains covering Southeast Asia (36 km) and Thailand (12 km) in the model. Model outputs were compared with gridded observations from the Climatic Research Unit (CRU TS), and spatial variations were analyzed across six administrative regions in Thailand. The WRF successfully reproduces broad climatological patterns, including the precipitation contrast between mountainous and lowland areas and the north–south gradient of temperature. Seasonal cycles of rainfall and temperature are generally well represented, although systematic biases remain, specifically the overestimation of orographic rainfall and a cold bias in high-elevation regions. The 12 km WRF simulations demonstrated improved special and temporal agreement with the CRU TS dataset, showing a national-scale wet bias (MBE = +17.14 mm/month), especially during the summer monsoon (+65.22 mm/month). Temperature simulations exhibited seasonal derivations, with a warm bias in the pre-monsoon season and a cold bias during the cool season, resulting in annual cold biases in both maximum (−1.25 C) and minimum (−0.80 C) temperatures. Despite systematic biases, WRF-CMIP5 downscaled framework provides enhanced regional climate information and valuable insights to support national-to-local climate change adaptation, resilience planning, and sustainable development strategies in Thailand and the broader Southeast Asian region. Full article

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

Air quality in the Po Valley (Northern Italy), one of Europe’s most polluted regions, remains a major concern due to its unfavorable orographic setting and intense anthropogenic emissions. Climate change may further hinder progress by modifying meteorological conditions that regulate pollutant dispersion and chemistry. This study applies a modeling framework combining regional climate simulations and chemical transport models to assess the climate penalty, i.e., the adverse impact of climate-driven meteorology on air quality independent of emissions. Three scenarios were analyzed: Baseline Reference Scenario (SRB) (2011–2015), Near-Future Medium Scenario (NF) (2028–2032), and Mid-Future Medium Scenario (2048–2052), with emissions held constant. A mitigation scenario (SC_MF_2050) under the Current Legislation was also tested to accomplish the new EU Ambient Air Quality Directive. Results show that PM₁₀ and NO₂ increase under future climates, mainly due to reduced wind speed and precipitation, enhancing pollutant accumulation. Multivariate analyses confirm the strong association between stagnant conditions and higher concentrations. Even with projected emission reductions, compliance with stricter EU targets may not be achieved everywhere. Climate penalty zones, especially in lowland and transport corridors, underscore the need to integrate climate resilience into air quality planning and adopt adaptive strategies for long-term effectiveness. Full article

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin. Full article

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article