Search Results (735)

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

Accurate rainfall monitoring is essential for mitigating hydrometeorological disasters and understanding hydrological changes under climate change. This study presents a deep learning-based rainfall estimation framework using multispectral GEO-KOMPSAT-2A (GK2A) satellite imagery. The analysis primarily focuses on daytime observations to take advantage of visible channel information, which provides richer representations of cloud characteristics during daylight conditions. The core model, Model-HSP, is built on the Pix2PixCC architecture and trained with Hybrid Surface Precipitation (HSP) data from weather radar. To further enhance accuracy, an ensemble model (Model-ENS) integrates the outputs of Model-HSP and a radar based Model-CMX, leveraging their complementary strengths for improved generalization, robustness, and stability across rainfall regimes. Performance was evaluated over two periods—a one year period from May 2023 to April 2024 and the August 2023 monsoon season—at 2 km and 4 km spatial resolutions, using RMSE and CC as quantitative metrics. Case analyses confirmed the superior capability of Model-ENS in capturing rainfall distribution, intensity, and temporal evolution across diverse weather conditions. These findings show that deep learning greatly enhances GEO satellite rainfall estimation, enabling real-time, high-resolution monitoring even in radar sparse or limited coverage regions, and offering strong potential for global and regional hydrometeorological and climate research applications. Full article

Electrochemical deposition of gold from a sulfite-thiosulfate electrolyte was studied on GaAs–TiAu substrates using polarization curve measurements, gold layer morphology analysis (AFM), and current efficiency determination in the temperature range of 20–65 °C. It was found that increasing the temperature to 50–65 °C makes it possible to raise the gold deposition current density from 2 to 7 mA/cm² while maintaining a current efficiency close to 100% and obtaining compact coatings with a surface root mean square roughness Sq of 6–8 nm. The activation energy of the process is 20–25 kJ/mol. It was shown that electrochemical conditioning of the electrolyte prevents sulfur precipitation, whereas the introduction of excess sulfite ions dissolves the sediment but leads to poorer coating quality. Thus, the feasibility of electrolyte regeneration has been demonstrated, and optimal gold deposition regimes have been determined: 7 mA/cm² at 50 °C and 10 mA/cm² at 65 °C. Full article

Quantitative precipitation estimation (QPE) from radar reflectivity is fundamental for weather nowcasting and water resource management. Conventional Z-R relationship formulas, derived from Rayleigh scattering theory, rely heavily on empirical parameter fitting, which limits the estimation accuracy and generalization across different precipitation regimes. Recent deep learning (DL)-based QPE methods can capture the complex nonlinear relationships between radar reflectivity and rainfall. However, most of them overlook fundamental physical constraints, resulting in reduced robustness and interpretability. To address these issues, this paper proposes FusionQPE, a novel Physics-Constrained DL framework that integrates an adaptive Z-R formula. Specifically, FusionQPE employs a Dense convolutional neural network (DenseNet) backbone to extract multi-scale spatial features from radar echoes, while a modified squeeze-and-excitation (SE) network adaptively learns the parameters of the Z-R relationship. The final rainfall estimate is obtained through a linear combination of outputs from both the DenseNet backbone and the adaptive Z-R branch, where the trained linear weight and Z-R parameters provide interpretable insights into the model’s physical reasoning. Moreover, a physical-based constraint derived from the Z-R branch output is incorporated into the loss function to further strengthen physical consistency. Comprehensive experiments on real radar and rain gauge observations from Guangzhou, China, demonstrate that FusionQPE consistently outperforms both traditional and state-of-the-art DL-based QPE models across multiple evaluation metrics. The ablation and interpretability analysis further confirms that the adaptive Z-R branch improves both the physical consistency and credibility of the model’s precipitation estimation. Full article

Dryland grasslands face elevated risks of rapid threshold crossing under a regime of warming, precipitation redistribution, and intensified interannual hydrothermal variability. Using the Ebinur Lake Basin (ELB) as a case, we developed an integrated structure × function assessment—linking land-use/cover change (LUCC) transitions with functional indicators of net primary productivity (NPP), net ecosystem production (NEP), soil conservation (SC), and grass supply (GS)—and coupled it with Bayesian-optimized XGBoost, SHAP, and partial dependence plots (PDPs) at a 30 m pixel scale to identify dominant drivers and ecological thresholds, subsequently translating them into governance zones. From 2003 to 2023, overall grassland status was dominated by degradation (20,160.62 km²; 69.42%), with restoration at 8878.85 km² (30.57%) and stability at 2.79 km² (0.01%). NPP/NEP followed a rise–decline–recovery trajectory, while SC exhibited marked bipolarity. Precipitation and temperature emerged as primary drivers (interaction X3 × X4 = 0.0621), whose effects, together with topography and accessibility, shaped a spatial paradigm of piedmont sensitive–oasis sluggish–lakeshore vulnerable. Key thresholds included an annual precipitation recovery threshold of ~200 mm and an optimal window of 272–429 mm; a road-density divide near ~0.06 km km⁻²; and sustainable grazing windows of ~2.2–4.2 and ~4.65–5.61 livestock units (LU) km⁻². These thresholds underpinned four management units—Priority Control (52.53%), Monitoring and Alert (21.53%), Natural Recovery (20.40%), and Optimized Maintenance (5.55%)—organized within a “two belts–four zones–one axis” spatial framework, closing the loop from threshold detection to adaptive governance. The approach provides a replicable paradigm for climate-adaptive management and ecological risk mitigation of dryland grasslands under warming. Full article

In this study, we compare two contrasting years within the 2020–2025 period—one characterized by extreme heat and drought, and another by unusually high precipitation. We used five years of climatic data provided by the Hungarian Meteorological Service (OMSZ), along with vegetation activity indices (NDVI—Normalized Difference Vegetation Index; NDWI—Normalized Difference Water Index) derived from Sentinel-2A satellite imagery. In parallel, during three years of the study period (2020, 2022, and 2025), we collected five phytosociological relevés in each of the five vegetation types subjected to different management regimes. For data analysis, we applied Principal Component Analysis (PCA), Detrended Correspondence Analysis (DCA), and the Additive Main Effects and Multiplicative Interaction (AMMI) model. Vegetation index patterns were compared with the relative water requirements of the constituent plant species. In the ungrazed dry sandy site, climatic fluctuations did not significantly affect vegetation composition and the habitat remained a stable open sandy grassland. Among the four grazed sites, grazing intensity remained unchanged during the study in three cases (N1, N2, and SZ). Thus, vegetation changes observed in these areas can be attributed to climatic factors. Vegetation composition shifted in N1 and N2, whereas no significant change was detected in the drier SZ site. This indicates higher resistance to grazing in SZ, which can therefore be sustainably used as pasture, while the N1–N2 sites responded sensitively to precipitation variability under identical grazing pressure and are better suited for use as meadows. The most pronounced changes occurred at the P site, which had previously functioned as an animal resting area and began regenerating after abandonment in 2022. Vegetation composition shifted markedly within two years, demonstrating that land-use practices exert a stronger influence on sandy grassland vegetation than climatic fluctuations. Overall, the drier habitats were more resilient to both grazing pressure and climatic variability and are suitable for grazing, whereas the moister vegetation types were more sensitive and should preferably be managed as hay meadows. Full article

Compound drought and heatwave (CDHW) events pose a rising threat to global water security and ecosystem stability. While their increased frequency under global warming is recognized, their spatiotemporal evolution and subsequent cascading impacts on hydrological processes in monsoonal lake basins remain poorly quantified. This study investigates the characteristics and hydrological impacts of CDHW in the Poyang Lake Basin, China’s largest freshwater lake, from 1981 to 2016. Using a daily rolling-window approach with the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI), we identified CDHW events and characterized them with metrics of frequency, severity, and intensity. Event coincidence analysis (ECA) was employed to quantify the trigger relationship between CDHW and subsequent hydrological droughts (streamflow and lake water level). Our results reveal a paradigmatic shift in the CDHW regime post-2000, marked by statistically significant increases in all three metrics and a fundamental alteration in their statistical distributions. ECA demonstrated that intensified CDHW events significantly enhance hydrological drought risk, primarily through a robust and increasing lagged influence at seasonal timescales (peaking at 40–90 days). Decomposition of compound events attributes this protracted impact predominantly to the heatwave component, which imposes prolonged hydrological stress, in contrast to the more immediate but rapidly decaying influence of drought alone. This study highlights the necessity of integrating compound extremes and their non-stationary, lagged impacts into water resource management and climate adaptation strategies for monsoonal basins. Full article

The water sustainability in Chihuahua City is challenged by rapid urbanization, population growth, industrial expansion, and climate variability. This study examines how these factors impact water demand by analyzing six decades of local precipitation, extreme temperature, demographic, and water consumption data. Statistical methods (time series and gamma distribution with R-package) and spatial analysis using Landsat and Spot satellite imagery were employed. Chihuahua’s urban area grew at an average annual rate of 7.4% from 1992 to 2020. Minimum and maximum temperatures have increased by 0.07 °C and 0.05 °C per year, respectively, leading to more frequent heatwaves over the past 30 years. Since the 1990s, there has been a noticeable trend towards more frequent extreme precipitation events coinciding with a sustained rise in extreme temperatures. Urban expansion and rising temperatures have increased water consumption by approximately 40% per °C over the past 30 years, accelerating the depletion of groundwater reserves in the city’s three main aquifers. These trends highlight the urgent need for integrated urban planning and climate-adaptation measures to reduce vulnerability and ensure long-term water security for Chihuahua. Full article

Climate change is altering hydrological regimes across the North Aegean and Marmara regions of Türkiye, with increasing relevance for both drought occurrence and flood generation. This study examines long-term variability in temperature, precipitation, and evaporation using meteorological observations over a long time series and relates these changes to urban water management issues. Daily records from 12 meteorological stations, with data availability varying by station and extending back to 1926, were analysed using the non-parametric Mann–Kendall trend test and Sen’s slope estimator. The results indicate statistically significant warming trends across all stations, with several locations recording daily maximum temperatures exceeding 44 °C. Precipitation trends exhibit pronounced spatial heterogeneity: while most stations show decreasing long-term tendencies, others display unchanging or non-significant trends. Nevertheless, extreme daily rainfall events exceeding 200 mm are observed at multiple coastal and island stations, indicating a tendency toward high-intensity precipitation. Evaporation trends also vary across the region, with increasing rates at stations such as Tekirdağ and Çanakkale and decreasing trends at Bandırma and Yalova, reflecting the influence of local atmospheric conditions. Taken together, these findings point to a coupled risk of intensified flooding during short-duration rainfall events and increasing water stress during warm and dry periods. Such conditions challenge the effectiveness of conventional grey infrastructure. The results are therefore interpreted within the framework of the Sponge City approach, which emphasizes permeable surfaces, decentralized storage, infiltration, and the integration of green and blue infrastructure. By linking long-term hydroclimatic trends with urban design considerations, this study provides a quantitative basis for informing adaptive urban water management and planning strategies in Mediterranean-type climate regions. Full article

Rainfall and streamflow analyses have long been central to hydrological research, yet traditional approaches often overlook the complexity introduced by changing climate signals, land-use dynamics, and human infrastructure. This study applies an integrated, data-driven framework to explore emerging hydroclimatic shifts in the Navasota River Watershed of east-central Texas. By combining autocorrelation analysis, Mann–Kendall and modified Mann–Kendall trend tests, and Pettitt’s change-point detection, we examine more than a century of precipitation and streamflow records alongside post-1978 reservoir operations. Results reveal an accelerating wetting tendency, particularly evident in decadal rolling averages and early-summer precipitation, accompanied by a statistically significant increase in 10-year moving averages of annual peak streamflow. While abrupt regime shifts were not detected, subtle but persistent changes point to evolving watershed memory and heightened flood risk in the post-dam era. This study reframes rainfall and streamflow trend analysis as a dynamic tool for anticipating hydrologic regime shifts, highlighting the urgent need for adaptive water infrastructure and flood management strategies in rapidly urbanizing and climate-sensitive watersheds. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

Climate change and river flow alterations in the Mekong River have significantly exacerbated drought conditions in the Vietnamese Mekong Delta (VMD). Understanding the temporal dynamics and propagation mechanisms of drought, coupled with the compounded impacts of human activities, is crucial. This study analyzed meteorological (1992–2021) and hydrological (2000–2021) drought trends in the Lower Mekong River Basin (LMB) using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), respectively, complemented by Mann–Kendall (MK) trend analysis. The results show an increasing trend of meteorological drought in Cambodia and Lao PDR, with mid-Mekong stations exhibiting a strong positive correlation with downstream discharge, particularly Tan Chau (Pearson r ranging from 0.60 to 0.70). A key finding highlights the complexity of flow regulation by the Tonle Sap system, evidenced by a very strong correlation (r = 0.71) between Phnom Penh and the 12-month SDI lagged by one year. Crucially, the comparison revealed a shift in drought severity since 2010: hydrological drought has exhibited greater severity (reaching severe levels in 2020–2021) compared to meteorological drought, which remained moderate. This escalation is substantiated by a statistically significant discharge reduction (95% confidence level) at the Chau Doc station during the wet season, indicating a decline in peak flow due to upstream dam operations. These findings provide a robust database on the altered hydrological regime, underlining the increasing vulnerability of the VMD and motivating the urgent need for comprehensive, adaptive water resource management strategies. Full article

The watersheds of the Senegal River, particularly the Gorgol River, are increasingly affected by hydrological extremes such as floods and soil erosion, pressures that are intensified by ongoing climate change and human activities. This study investigates the hydrological functioning and erosion susceptibility of the Gorgol tributaries to support sustainable watershed management. A multidisciplinary approach was applied, combining spatial analysis of watershed characteristics with hydrological modeling and erosion risk mapping. Key datasets included satellite-derived climate variables, which were validated with ground measurements and integrated with topographic, geological, soil, and land-use data. Climate analysis revealed a pronounced north–south rainfall gradient, with most precipitation occurring between July and September, alongside a +1 °C temperature increase over the past 42 years. Erosion susceptibility was assessed using the Revised Universal Soil Loss Equation, incorporating factors such as rainfall erosivity, soil erodibility, slope parameters, land-cover, and conservation practices. Results indicate that areas in the southern basin and those with fragile soils are most vulnerable, with rainfall erosivity being the primary driver of soil loss. Hydrological study identified flood-prone zones and characterized the regimes. These findings offer a scientific basis for targeted interventions in erosion control and flood risk reduction within the Gorgol basin. Full article

With the increasing volatility and extremity of global climate change, the frequency, intensity, and associated impacts of compound extreme climate events have escalated substantially. To investigate the temporal trends and characteristics of such events, we identified compound extreme climate events in the Huangshui River Basin, located in the northeastern Qinghai–Tibet Plateau, using daily mean temperature and precipitation records from eight meteorological stations. Compound warm–wet, warm–dry, cold–wet, and cold–dry events from 1960 to 2022 were detected based on cumulative distribution functions, and their long-term trends and intensity structures were examined. The results show that: (1) Warm–dry events dominate the basin, with an average annual frequency of 32.84 days per year, occurring frequently across all seasons; cold–dry events rank second (22.38 days per year) and are particularly frequent in winter. (2) Warm–dry events are highly concentrated in the river valley region (e.g., Minhe station), whereas cold–dry and warm–wet events mainly occur in the low-mountain areas (e.g., Huangyuan and Datong). (3) From 1960 to 2022, warm–dry and warm–wet events exhibit a highly significant increasing trend (p < 0.001), cold–dry events show a significant decreasing trend, and cold–wet events display no statistically significant trend. (4) In terms of intensity, all four types of compound events—warm–wet, warm–dry, cold–wet, and cold–dry—are dominated by weak to moderate grades. Overall, the basin is undergoing a compound-risk transition from historically “cold–dry dominated” conditions toward a regime characterized by “warm–dry predominance with emerging warm–wet events.” By identifying compound extreme climate events and analyzing their spatiotemporal variability and intensity characteristics, this study provides scientific support for disaster prevention, daily management, and risk mitigation in climate-sensitive regions. It also offers a useful reference for developing strategies to address compound extreme events induced by climate change and for implementing regional risk-prevention measures. Full article