Search Results (4,797)

How modeled ecosystem carbon balance varies across the contrasting hydroclimatic zones of the Yellow River Basin, and whether its relationships with climate extremes differ among these zones, remain insufficiently understood. To address this knowledge gap, this study examined the spatiotemporal variability of modeled net ecosystem productivity (NEP) and its associations with temperature- and precipitation-extreme indices across three major ecological zones of the basin. Using satellite-derived NDVI, reanalysis climate data, and extreme climate indices for 2000–2022, we estimated NEP from CASA-simulated net primary productivity and modeled heterotrophic respiration, and assessed its temporal trends and climatic associations across three ecological zones. Three principal findings were obtained: (1) Over the 23-year study period, basin-wide NEP showed a statistically significant increasing tendency (β = 2.699 g C m⁻² a⁻¹), with a well-defined spatial gradient characterized by relatively lower productivity in the northwestern areas and elevated productivity toward the southeastern regions. (2) Modeled NEP increased across all four seasons. Summer showed the largest positive trend, whereas autumn and winter remained net carbon-release seasons but became progressively less negative over the study period. (3) The statistical associations between modeled NEP and extreme climate indices varied markedly across ecological zones. Extreme-precipitation indices were generally positively associated with modeled NEP in the arid northwestern zone, whereas several extreme-temperature indices showed predominantly negative associations in the northeastern monsoon-influenced region. Collectively, these findings provide model-based evidence of spatially differentiated associations between ecosystem carbon balance and climate extremes across the YRB, supporting regional ecosystem assessment and climate-adaptation planning. Full article

The combined effects of global climate change and human activities are profoundly reshaping the geographic distribution patterns of rare and endangered plants in arid and semi-arid regions, accelerating habitat fragmentation and population decline, thereby posing a core challenge to biodiversity conservation. As a rare semi shrub endemic to the Mongolian Plateau, P. mongolica faces continuous population shrinkage due to habitat fragmentation and climate change caused by extreme weather conditions, overgrazing, and mineral extraction, leaving its survival status critically vulnerable. Based on data from 153 distribution sites, this study employed the MaxEnt model to predict the species’ optimal habitat range, analyzed key environmental factors influencing its distribution, and projected that by the 2050s and 2070s, its total suitable habitat will potentially expand northwestward with increased expansion rates as carbon emissions rise. Its distribution is mainly driven by the minimum temperature of the coldest month, the average temperature in the coldest season and precipitation in the warmest season, which conforms to the law of species movement to the northwest caused by climate warming. Full article

Wildlife-vehicle collisions (WVCs) represent an important ecological and road-safety problem, yet the influence of short-term meteorological variability on their occurrence remains insufficiently understood. This study investigated the associations between temperature extremes, abrupt temperature changes, and broader hydro-meteorological conditions and reported WVC frequency in Serbia over a 10-year period (2016–2025). A municipality-day dataset comprising 6281 police-reported WVCs was analyzed using an event-based methodology. Exploratory analyses were complemented by Poisson regression models to evaluate prolonged temperature episodes, abrupt temperature shocks, and broader hydro-meteorological conditions, while additional regional analyses examined the consistency of the observed associations across Serbia. The results demonstrated pronounced seasonal variation, with the highest reported WVC frequencies occurring during spring and late autumn. At the national level, prolonged cold episodes were associated with significantly lower reported WVC frequencies during their onset and middle phases, whereas heat episodes showed no significant associations. Abrupt cool-down shocks were associated with a short-term increase in reported WVC frequency on the day of the temperature decrease, while warm-up shocks showed no significant effects. Moderate and heavy precipitation, snow-day conditions, and prolonged dry spells were associated with reduced reported WVC frequency, whereas daily mean temperature and atmospheric pressure were not independently associated with reported WVC frequency. Regional analyses generally supported the national findings, although no regional associations remained statistically significant after false discovery rate correction. These findings demonstrate that short-term meteorological variability is associated with reported WVC frequency in a temporally dependent manner and highlight the importance of considering both environmental events and regional variability when investigating wildlife-vehicle collisions. Full article

The Lacul fără nume landslide dam in the Vrancea Mountains (Romania) represents a unique example of a dynamic landslide dam system characterized by recurrent damming–breaching cycles. Through the combined use of remote sensing, field investigations, and historical reconstruction, eight such cycles were documented over a period of 49 years. To the best of current knowledge, this is one of the few documented landslide dams reported in the scientific literature that exhibits frequent damming–breaching episodes involving repeated dam failure, renewed slope instability, and subsequent re-damming with renewed lake impoundment over comparatively short timescales. The observed persistence and spatial extent of the associated lake are highly variable, ranging from 12 days to almost 10 years and from 23,920 m² to 82,610 m², respectively. Antecedent precipitation was frequently elevated prior to lake state transitions, but a seasonally constrained Monte Carlo analysis showed no significant departure from the climatic background, while numerous intense rainfall periods occurred without documented transitions. Similarly, no systematic temporal association was identified between recurrent lake state transitions and regional seismicity, although the initial dam formation coincided with the 1977 Mw 7.4 Vrancea earthquake. These findings suggest that precipitation conditions and seismicity alone cannot explain the recurrent damming and drainage, which likely result from interactions between hydrometeorological forcing, geomorphic processes, and human influences. Taken together, the results and the proposed conceptual model demonstrate that debris-flow-generated landslide dams can evolve into persistent and dynamic geomorphic systems capable of posing recurring hazards over multiple decades. Full article

This paper investigates the use of a hybrid renewable heat-supply system based on photovoltaic–thermal collectors, an industrial heat pump, thermal energy storage, and electrical energy storage for a small- to medium-scale district-heating network. A dynamic lumped-parameter model was developed and applied to hourly data from an existing network. The photovoltaic–thermal collector model accounts for low-temperature operation, wind effects, precipitation, and condensation-related heat gains, while the heat pump is represented using compressor performance characteristics under variable source and sink temperatures. The analysis focuses on whether the proposed configuration can meet summer heat demand and reduce reliance on a conventional peak or backup source during shoulder periods. The results show that, during an extended non-heating season, the system can supply approximately 90–100% of the district-heating demand while maintaining a daily mean coefficient of performance in the range of approximately 2.0–3.1. The photovoltaic–thermal field and electrical energy storage do not provide full electrical self-sufficiency, but they reduce grid electricity import; in July and August, the electricity autarky coefficient is approximately 48–49%. The results indicate that the proposed system can serve as a seasonal renewable heat source for district heating. Further refinement of the configuration, operating setpoints, and control strategy could improve its shoulder-season performance. Full article

Reliable flood simulation in large cold-region basins requires understanding how meteorological forcing differences propagate through runoff generation and river routing. We compared CMFD, GSWP3v1, and CRUNCEPv7 using a controlled, uncalibrated offline CLM5–CaMa-Flood framework for the Songhua River Basin during 1996–2014, with all non-forcing settings fixed. Evaluation included daily and monthly discharge, seasonal hydrographs, annual maximum daily discharge (AMAX), observed Q95/Q99 thresholds, selected 1998 and 2013 warm-season high-flow cases, runoff-process diagnostics, event-window sensitivity tests, 5000 paired year-wise bootstrap resamples, and auxiliary water-level anomalies. CMFD generally produced the highest r, KGE, and daily NSE, but also the largest positive long-term Bias. CRUNCEPv7 systematically underestimated discharge, whereas GSWP3v1 more often yielded the smallest absolute Bias. For both selected events, CMFD reduced peak and volume underestimation, although peaks remained smoothed and delayed. Event-window precipitation differences did not translate proportionally into CLM5 runoff, and the larger CMFD response involved increases in both surface runoff and subsurface drainage. The event-magnitude ordering remained stable across ±30-, ±45-, and ±60-day windows. Bootstrap results showed a robust CMFD advantage over GSWP3v1 for temporal agreement and efficiency, while several CMFD–CRUNCEPv7 comparisons remained sample-dependent. Forcing-product performance was therefore scale-, metric-, and target-dependent and conditional on the fixed model configuration. Full article

Arduino-based creepmeters utilizing a Linear Variable Differential Transformer (LVDT) and ultrasonic sensors were fabricated to monitor displacement changes along the creeping segment of the West Valley Fault (WVF) in southeastern Metro Manila, Philippines. Along with a custom-assembled, Arduino-based rain gauge, these instruments were initially intended to prevent data gaps during the COVID-19 pandemic when commercial data recorders experienced operational downtime. However, they have since proven to be cost-effective alternatives for determining short-term slip rates and monitoring displacement variations driven by episodic and seasonal precipitation changes. The LVDT creepmeter provides higher accuracy for displacement and slip rate determination. Conversely, the ultrasonic creepmeter is better suited for tracking abrupt displacement changes and, to some extent, longer-term displacement trends as it is more sensitive to environmental conditions. Deploying low-cost monitoring instruments in active fault regions bridges critical data gaps and improves the understanding of creep triggers and mechanisms. Although vertical creep occurs along pre-existing tectonic features of the WVF creeping segment, our creepmeter monitoring reveals sustained, accelerated creep within its southern portion. This localized movement is driven primarily by nontectonic forces—chiefly groundwater extraction, with episodic and seasonal precipitation influences. Consequently, this implies a continued ground rupture hazard and the potential for induced seismicity. Full article

Whale shark aggregations in tropical coastal systems are linked to environmental variability and prey dynamics, yet the drivers of surface sightings remain poorly understood. In Nosy Be, Madagascar, a seasonal aggregation of immature whale sharks occurs within a productive coastal ecosystem. This study investigated the relationship between daily environmental conditions and whale shark surface sighting probability while accounting for heterogeneous sampling effort. Boat-based survey data collected from 2019 to 2025 were aggregated by sampling day, and daily whale shark surface sighting probability was analysed using a bias-reduced grouped binomial Generalized Linear Model. Environmental covariates included sea surface temperature, sea surface chlorophyll-a concentration, cloud cover, wind speed, and precipitation, while El Niño–Southern Oscillation variability was assessed as an interannual climatic descriptor. Using data from 103 recorded whale shark surface sightings, chlorophyll-a emerged as the strongest predictor, showing a positive association with daily sighting probability, whereas other environmental variables exhibited weaker and inconsistent effects. These findings suggest that whale shark surface sightings in Nosy Be are primarily associated with prey aggregation processes driven by local productivity rather than with direct responses to local physical environmental conditions. By integrating environmental variability and sampling effort into ecological models, this study provides insights into whale shark habitat use and supports ecosystem-based management of sustainable whale shark tourism in tropical coastal ecosystems. Full article

The Loess Plateau is a typical dryland agricultural region where water scarcity constrains crop production. Plastic film mulching is widely used to mitigate this limitation, but its long-term application has caused soil degradation and environmental pollution, highlighting the need for sustainable alternatives. This study evaluated the effects of post-wheat sequential green manure cropping—common vetch (CV) and winter rape (CR)—on soil water balance, potato yield, water use efficiency (WUE), and economic returns within a winter wheat-potato rotation system. A field experiment (2019–2023) was conducted on the Loess Plateau, comparing CV, CR, plastic film mulching (PM), and no mulching (NM). Soil water content (0–200 cm), evapotranspiration (ET), tuber yield, WUE, and economic performance were measured. Although CV and CR depleted soil water in the 0–100 cm layer during their growth, subsequent fallow precipitation (86 mm) and low soil water loss replenished the deficit, resulting in comparable soil water storage at potato sowing among CV, CR, and NM (518.8, 508.8, and 518.9 mm, respectively). Over the full rotation cycle, a positive soil water balance (63.1–147.5 mm) was maintained across all treatments, indicating no detectable net depletion within the measured 0–200 cm profile over the four potato seasons. Potato tuber yield increased by 61.7% (CV), 52.4% (CR), and 74.8% (PM) relative to NM. The WUE of CV (73.5 kg ha⁻¹ mm⁻¹) and CR (71.5 kg ha⁻¹ mm⁻¹) was comparable to that of PM (74.2 kg ha⁻¹ mm⁻¹) and significantly higher than that of NM (46.1 kg ha⁻¹ mm⁻¹). Net incomes under CV and CR did not differ significantly from PM, while their input costs were lower. Collectively, the integrated system combining post-wheat sequential green manure cropping—particularly with common vetch—and straw mulching represents a technically feasible, economically viable, and environmentally beneficial alternative to plastic film mulching, contributing to sustainable dryland agriculture on the Loess Plateau and analogous semi-arid regions. Full article

Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist due to the scarcity of high-resolution, multi-layer in situ observations in these remote areas. Using hourly data from three sites in the Tanggula Mountains (2020–2024), this study employs an event-based analytical framework combining logistic regression and linear regression to quantify multi-layer (10–100 cm) SM responses to rain, snow, and mixed-phase precipitation across seasons. Core findings indicate the following: (1) Precipitation thresholds with 80% probability of triggering SM responses rise sharply with depth during the cold period (10 cm: 1–11 mm; 50–100 cm: often >15 mm or unreachable) but increase gradually in the warm period (10 cm: 0.4–5 mm; 50 cm: <15 mm). Mixed-phase precipitation refers to the lowest amount of precipitation (0.4–2.5 mm at 10 cm), followed by rain (1–11 mm) and snow (2–5 mm). (2) Warm-period regression slopes are consistently steeper than cold-period slopes (at 10 cm, 0.0024 vs. 0.0010 for rainfall). Mixed-phase precipitation yields the steepest slopes, approximately 50% higher than rainfall at 10 cm in the warm period (0.0037 vs. 0.0024), due to its longer duration and dual-supply mode. For lag time, cold-period values are more widely dispersed due to multiple interacting factors, while warm-period values are concentrated; only warm-period rainfall exhibits a clear monotonic increase in lag time with depth, consistent with unsaturated flow theory. (3) The quantified regression slopes, threshold values, and phase-specific efficiencies provide transferable metrics for calibrating infiltration models and evaluating frozen-ground hydrology schemes. The finding that mixed-phase events are the primary driver of deep-layer recharge, despite accounting for a smaller fraction of the total event count, has direct implications for water resource assessment in high-altitude catchments where precipitation phase composition is often oversimplified. Overall, this study moves beyond qualitative descriptions by providing quantifiable, transferable metrics that advance the mechanistic understanding of precipitation–SM coupling in alpine permafrost regions. Full article

Radar-based Quantitative Precipitation Estimation (QPE) in semi-arid Mediterranean climates is critically challenged by systematic underestimation of shallow precipitation, yet gauge–radar merging frameworks tailored to such environments remain poorly evaluated. This study develops and assesses a merging pipeline for Cyprus, combining X-band polarimetric observations from the Paphos and Larnaca operational radar network with accumulations from a 50-station rain gauge network across 11 rainfall events spanning the 2024 wet season (January and November–December 2024). Four approaches were evaluated: raw radar mosaic, global mean field bias (MFB) correction, spatially varying local inverse distance weighting (IDW) bias correction assessed through leave-one-out cross-validation (LOOCV), and a Random Forest (RF) machine-learning retrieval trained on polarimetric, geometric, and orographic predictors and evaluated through leave-one-event-out cross-validation (LOEO-CV). Raw radar exhibited severe and highly variable underestimation, with station-level bias factors ranging from 1.4 to 200×. Global MFB correction removed systematic offset but, as a single spatially uniform scalar, could not improve spatial correspondence; it was beneficial only where the bias field was spatially coherent. Local IDW correction provided cross-validated reduction in RMSE for most events (commonly 40–53%), but this improvement reflected removal of mean bias rather than recovery of spatial pattern: only 17 January 2024 combined RMSE reduction (24.07 mm to 11.31 mm) with genuine spatial skill (leave-one-out r = 0.850, bias-field coherence r = 0.742), while several events improved in RMSE yet retained near-zero spatial correlation, and 30 and 31 January degraded outright. These results characterise the limits of distance-weighted (IDW) interpolation specifically; whether geostatistical estimators incorporating topographic external drift can restore spatial skill where the present gauge network constrains the bias field remains to be tested. When re-evaluated on the same rainy matched-pair set (N = 2378), the Random Forest reduced 10 min RMSE by only 2.6% relative to the best classical Z-R estimator (from 10.38 mm to 10.10 mm) and reduced the systematic bias from −5.06 mm to −4.25 mm, but did not improve point-to-point spatial correspondence (r ≈ 0), indicating that this mean-regression Random Forest provides effective bias-correction skill without spatial-correspondence skill, leaving the fundamental representativeness gap between CAPPI sampling and gauge point measurements unresolved. Three pre-conditions for local bias correction skill are identified as empirical diagnostics under the sample conditions of this study: a minimum of approximately 40 contributing gauges, a spatially coherent bias field, and a moderate bias range. A formal bootstrap or resampling-based uncertainty estimate for these indicators was not attempted, because eleven events constitute too small a sample for stable resampling statistics; the per-event relationships between the number of contributing gauges, the bias-factor range, the bias-field spatial autocorrelation, and the LOOCV error are therefore presented as the empirical basis for these diagnostic indicators, which should be refined and tested for statistical robustness as longer event records become available. These findings demonstrate that the suitability of spatial merging can be diagnosed from network and bias field properties prior to correction, and that machine-learning retrieval offers complementary value through systematic bias removal where spatial interpolation fails. Probabilistic merging frameworks, denser gauge networks, and ML approaches that explicitly target spatial correspondence are identified as priority developments for eastern Mediterranean QPE. Full article

Lakes on the Tibetan Plateau are sensitive indicators of regional climate change and hydrological variability. Comprehensive analyses of the area, level, and volume of typical lakes across multiple time scales remain limited. In this study, based on the Google Earth Engine platform, we integrated Landsat TM/ETM+/OLI and Sentinel-2 imagery, nine satellite altimetry datasets (Jason-1, Jason-2, Jason-3, Cryosat-2, Sentinel-3A, Sentinel-3B, ICESat, ICESat-2, and GEDI), and four machine learning methods (Lasso, SVM, Random Forest, and XGBoost) to investigate multi-timescale (annual, seasonal, and monthly) variations in lake area, level, and volume of Qinghai Lake, Nam Co, and Bangong Co, from 2000 to 2022. All three lakes showed overall increases in lake area, level, and volume, but with different magnitudes and temporal patterns. Qinghai Lake showed the most pronounced expansion, Nam Co experienced moderate growth, characterized by temporal fluctuations, whereas Bangong Co showed a relatively small but persistent increase. Seasonal analysis showed that lake area expansion was generally strongest in autumn, whereas monthly lake volume peaked in October for Qinghai Lake and Nam Co and in September for Bangong Co. Climate analyses indicated that Qinghai Lake may be mainly influenced by increased precipitation and runoff as well as reduced evaporation, Nam Co may be mainly influenced by precipitation, runoff, and delayed hydrological responses to glacier meltwater, and Bangong Co was likely associated with runoff and cryospheric water supply, while basin characteristics may have also contributed to its long-term hydrological response. These results highlight regional differences in lake responses to climate variability across the Tibetan Plateau. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

Agricultural systems face heightened risks from extreme weather events and water insecurity. Producers commonly use irrigation ponds to secure or improve crop yields. The hydrology and storage efficiency of irrigation ponds in the Willamette Valley, Oregon, USA, are not well understood. This study evaluated the hydrological interactions of a tile-drain-fed irrigation pond. A water balance approach was applied over the irrigation season using weather data, evaporation estimates, metered irrigation withdrawals, and bathymetry analysis for pond stage–volume estimates to quantify water budget components. Irrigation withdrawals were the largest output, with 75% of effective pond storage utilized, followed by evaporation (24.5%). Evaporation far exceeded precipitation over the same period. The unaccounted-for proportion of the water balance was negligible, indicating that net drain tile inflows and groundwater exchange had a minimal impact on seasonal irrigation water availability and seepage losses. This study provides an example for measuring water balance components and assessing water input–output relationships of an irrigation pond within a headwaters stream and catchment (<5 ha) of an important agricultural corridor in the Pacific Northwest region in the USA. The study methodology can be replicated in other similar agricultural areas with irrigation ponds worldwide. Findings from this study can be used by farmers, irrigation districts, and other stakeholders to better inform irrigation planning and water management decisions for similar site conditions. Full article

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season. Full article