Search Results (4,260)

Climate change and rapid urbanization are increasingly disrupting urban water cycles by intensifying runoff and reducing infiltration, particularly in watersheds designated for future development. However, most existing studies have focused on fully urbanized areas, with limited attention given to semi-rural or urban–rural transition watersheds at the planning stage. In this context, the Dawoon watershed in Ulsan, Republic of Korea, represents a critical case, as it is currently undeveloped but designated for large-scale urban expansion. This study evaluates the effectiveness of Low Impact Development (LID) strategies in restoring water-cycle soundness under anticipated urbanization conditions. A hydrological model of the Dawoon watershed was developed using the Storm Water Management Model (SWMM), and multiple land-use-specific LID scenarios were designed to reflect realistic planning-stage applications. Long-term simulations were conducted to assess changes in runoff, infiltration, evapotranspiration, and overall water-cycle performance. The results indicate that urban development substantially increases surface runoff while reducing infiltration and evapotranspiration. The integrated application of LID measures significantly mitigated these impacts, reducing total runoff by approximately 3% and improving the water cycle recovery rate to nearly 99%, restoring hydrological conditions close to the pre-development state. Among the evaluated scenarios, the combined implementation of vegetated swales, infiltration–storage basins, green roofs, and permeable pavements showed the highest effectiveness. These findings highlight the importance of incorporating LID strategies at the early stages of urban planning to enhance climate resilience and prevent long-term water cycle degradation. The proposed framework provides practical guidance for setting water-cycle management targets and selecting effective LID measures in developing or peri-urban watersheds. Full article

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments. Full article

The Three-North Shelterbelt Forest Program (TNSFP) region in northern China, a critical ecological zone, has experienced significant changes in vegetation coverage and water availability under climate change. However, a comprehensive understanding of how vegetation growth responds to both water deficit and surplus remains limited. This study systematically assessed the spatiotemporal dynamics of vegetation responses to atmospheric water constraints (represented by the Standardized Precipitation Evapotranspiration Index (SPEI)) and soil moisture constraints (represented by the Standardized Soil Moisture Index (SSMI)) across the TNSFP region from 2001 to 2022. Our results revealed a compound water constraint pattern: soil moisture deficit dominated vegetation limitation across 46.41–67.88% of the region, particularly in the middle (28–100 cm) and deep (100–289 cm) layers, while atmospheric water surplus also substantially affected 37.35% of the area. From 2001 to 2022, vegetation has shown weakening correlations with atmospheric and shallow-soil moisture, but strengthening coupling with middle- and deep-soil moisture, indicating a growing dependence on deep water resources. Furthermore, the response times of vegetation to water deficit and water surplus have been reduced, indicating that vegetation growth was increasingly restricted by water deficit while being less constrained by water surplus during the period. Attribution analysis identified that air temperature exerted a stronger influence than precipitation on vegetation–water relationships over the study period. This study improved the understanding of vegetation–water interactions under combined climate and land use change, providing critical scientific support for land use-targeted adaptive management in arid and semi-arid regions. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

Understanding the spatiotemporal dynamics, interactions, and drivers of ecosystem services (ESs) is critical for ecological conservation and sustainable management in fragile sandy ecosystems. This study assessed five key ESs (water conservation, vegetation carbon sequestration, biodiversity, soil conservation, sand fixation) in the Hunshandake Sandy Land during 2000–2020, using Spearman correlation, geographically weighted regression, self-organizing maps (SOMs), and Structural Equation Modeling (SEM) to quantify trade-offs/synergies, identify ES bundles (ESBs), and clarify natural/social drivers. Results showed that all ESs fluctuated temporally with distinct spatial heterogeneity (higher in wetter, vegetated east; lower in arid, wind-erosion-prone west). Synergies dominated most ES pairs (e.g., WC-VS, WC-SC), with VS-BD showing a trade-off, WC-SF/VS-SC synergies strengthened, and WC-BD shifted from synergy to trade-off. SOMs identified six ESBs with consistent spatial patterns across decades. SEM revealed precipitation enhanced WC, evapotranspiration reduced SF/BD, temperature promoted SC but suppressed VS, elevation strongly benefited SC, NDVI was the primary driver of VS, and GDP had a slight negative effect. These findings provide insights for targeted ecological management in the study area and sustainable ES promotion in global fragile sandy landscapes. Full article

In semi-arid areas like Southeastern Anatolia, where agricultural productivity and water supply are extremely climate-sensitive, drought is a significant environmental and socioeconomic problem. Comprehensive assessment of drought and soil moisture dynamics is fundamental to sustainable agriculture and water security in semi-arid regions. This study analyzes drought patterns across seven provinces in the Southeastern Anatolia (GAP) region of Türkiye (Adıyaman, Diyarbakır, Gaziantep, Kilis, Mardin, Siirt, and Şanlıurfa) from 1963 to 2022, employing four drought indices (SPI, SPEI, CZI, and RDI) at multiple timescales (1-, 3-, and 12-month) to support evidence-based strategies for sustainable water and agricultural resource management. A more thorough evaluation is made possible by this multi-index and multi-scale method, which is rarely used concurrently at the provincial level. Additionally, the drought characterization was validated and enhanced through the analysis of ERA5-Land soil moisture data (1950–2022). According to the findings, the provinces with the lowest median index values and the highest frequency of extreme drought episodes are Diyarbakır and Şanlıurfa. The SPEI-12 (THW) median values showed a neutral long-term drought–wetness balance with seasonal changes, ranging from −0.0714 (Adıyaman) to 0.188 (Şanlıurfa). Particularly after 2009, soil moisture levels decreased to as low as 2–3 mm during the summer, indicating heightened evapotranspiration stress. RDI-12’s reliability in long-term drought evaluation was confirmed by its strongest correlation with other indices (r = 0.87–0.97). According to spatial research, the frequency of moderate droughts in the southwest was as high as 39%, whilst the eastern provinces experienced severe and intense droughts as high as 8%. However, with frequency above 53%, wet occurrences were more common in the east, particularly in Siirt. By clarifying long-term drought and soil moisture patterns, this study provides essential insights for sustainable irrigation planning and agricultural water allocation in the GAP region. Full article

Evapotranspiration (ET) data products has greatly facilitated the hydrological research in complex basins, and various ET datasets have been produced and applied. The applicability and reliability of ET dataset is significant for regional studies. Therefore, this study compared ET datasets from multisource remote sensing (GLEAM, MOD16, GLASS, PML-V2, Han, Chen and Ma), machine learning (Jung) and reanalysis products (ERA5-Land, MERRA2) for the Yarlung Zangbo River basin (YZB). ET was estimated using the terrestrial water balance (TWB) and was taken as baseline for comparisons of different ET datasets in terms of spatial distribution and temporal variation. Results indicate that (1) the TWB-based ET estimates are rational with acceptable uncertainties; (2) the multi-source ET datasets exhibit good correlations with TWB-ET across the entire basin (r = 0.78–0.90) in term of annual variation, with GLEAM-ET performing the best (r = 0.88, RMSE = 14.24 mm, Rbias = 18.55%); (3) Spatially, PML-ET and Ma-ET show higher consistency with TWB-ET, and temporally, MOD16-ET and GLASS-ET better capture the changing trend; (4) A comprehensive evaluation using the linear weighted method reveals that GLASS-ET and GLEAM-ET perform relatively well in all aspects and are reliable datasets for ET research in the YZB. These findings provide a scientific basis for ET estimation and data selection in the YZB, offering important references for ET analysis and hydrological research. Full article

Accurately estimating reference crop evapotranspiration (ET₀) is essential for agricultural water-resource management, yet the traditional Penman–Monteith (PM) method requires multiple meteorological variables and is difficult to apply in data-sparse regions. To explore more data-efficient alternatives, this study systematically evaluates several machine-learning (ML) models capable of capturing nonlinear relationships, using daily observations from 698 meteorological stations across China. In addition, we incorporate SHapley Additive exPlanation (SHAP), a game-theory-based interpretability approach, to quantify the contribution of input variables at both national and regional scales. The results show that the Random Forest (RF) model performs best (coefficient of determination, R² = 0.957; mean absolute percentage error, MAPE = 9.214%), significantly outperforming multiple linear regression and approaching the accuracy of the PM method. SHAP analysis indicates that maximum temperature, sunshine duration, and month are the most influential factors nationwide. Geographic variables contribute less overall but become important in specific regions, such as Southwest China. The study also reveals pronounced spatial heterogeneity in the drivers of ET₀, highlighting the necessity of regionalized interpretations. Furthermore, sensor-reduction experiments demonstrate that reasonable estimation accuracy can be maintained even without radiation or wind-speed observations, offering guidance for low-cost monitoring scenarios. Overall, this study provides transparent model comparisons for ML-based ET₀ estimation, uncovers regional differences in controlling factors, and offers practical insights for designing meteorological monitoring strategies in data-limited environments. Full article

The Hailiutu River Basin in northern China represents a semi-arid area where groundwater-dependent ecosystems (GDEs) play a critical role in maintaining regional vegetation structure and ecological stability. This study investigated the spatiotemporal dynamics of GDEs and their relationship with water conditions using trend analysis, partial correlation, and Random Forest models over the period of 2002–2022. The results show that vegetation activity (NDVI) increased at a rate of 0.0052/yr in GDEs. Precipitation exhibited a basin-wide upward trend of 0.735 mm/yr, while SPEI increased at 0.0207/yr. In contrast, groundwater storage declined markedly at −11.19 mm/yr, highlighting a persistent reduction in water availability that poses a significant risk to the stability of GDEs. Both partial correlation analysis and the random forest model consistently showed strong ecohydrological interactions between vegetation and groundwater. Vegetation dynamics are primarily driven by groundwater availability, especially in groundwater-dependent ecosystems. Conversely, groundwater variations are most strongly influenced by vegetation. The results indicate that precipitation and the standardized precipitation–evapotranspiration index (SPEI) are the primary positive drivers of interannual NDVI variability, whereas groundwater plays a critical role in sustaining GDEs. Field observations of key species confirm the dependence of GDEs on groundwater, and vegetation dynamics are regulated by climate and groundwater; however, ongoing groundwater decline may threaten ecosystem stability. These findings demonstrate that vegetation transpiration exerts the dominant influence on groundwater variations, while groundwater simultaneously constrains vegetation growth, particularly in areas where declining groundwater storage anomalies (GWSAs) coincide with reduced NDVI. The results emphasize that continuous groundwater depletion threatens vegetation–groundwater sustainability, highlighting the need for balanced groundwater and vegetation management in arid regions. Full article

Understanding the local-scale impacts of climate change is critical for protecting water resources and ecosystems in vulnerable agricultural regions. This study investigates the Canagagigue Creek Watershed (CCW) in Southern Ontario, Canada, which is an area vital to the Grand River Basin yet threatened by sediment runoff, making it an ecologically sensitive area. We applied an integrated Statistical Downscaling Model (SDSM) and Soil and Water Assessment Tool (SWAT) (version 2012) approach under the IPCC A2 scenario to project impacts for the period 2025–2044. The results reveal a fundamental hydrological shift, and evapotranspiration is projected to claim nearly 70% of annual precipitation, leading to a ~30% reduction in total water yield. Seasonally, the annual streamflow peak is projected to shift from March to April, indicating a transition from a snowmelt-dominated to a rainfall-influenced system, while extended low-flow periods increase drought risk. Crucially, sediment yield at the watershed outlet is projected to decrease by 7.9–10.5%. The concomitant reduction in streamflow implies a weakened sediment transport capacity. However, this points to a heightened risk of increased in-stream deposition, which would pose a dual threat, (a) elevating flood risk through channel aggradation and (b) creating a long-term sink for agricultural pollutants that degrades water quality. By linking SDSM and SWAT, this study moves beyond generic predictions, providing a targeted blueprint for climate-resilient land and water management that addresses the complex, interacting challenges of water quantity. Full article

Climate change is increasingly threatening the sustainability of coffee farming in northern Peru, particularly in the Amazonas region, where coffee cooperatives serve as vital socioeconomic hubs for thousands of families. This study analyzed historical climate data from 1979 to 2024 to project trends up to 2030, integrating local perceptions from coffee producers to identify trends, anomalies, and future scenarios within four coffee cooperatives in northern Peru. We examined variables such as precipitation, temperature, evapotranspiration, and wind speed using nonparametric statistical analyses and SARIMA time-series models. The findings indicate a steady increase in maximum and average temperatures, alongside greater irregularity in precipitation. Specifically, the Bagua Grande and COOPARM cooperatives are experiencing precipitation deficits, while the Alta Montaña and Ocumal cooperatives are facing excess rainfall. Additionally, we project an increase in evapotranspiration by 2030. Surveys conducted with coffee growers reveal a consensus regarding irregular rainfall patterns; however, there is less recognition of the rising temperature trends. This discrepancy emphasizes the importance of combining scientific data with local knowledge to develop more effective adaptation strategies at the cooperative level. We conclude that enhancing climate training and cooperative management is essential for improving the resilience of regional coffee farming. Full article

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration ($ET$) being a key ecohydrological process. However, monitoring $ET$ over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of $ET$ remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based $ET$ models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on $ET$ patterns. Our results showed a good agreement between modeled and observed $ET$ using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on $ET$, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor $ET$ in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions. Full article

This study compares the performance of empirical and hybrid deep learning (DL) models in estimating daily potential evapotranspiration (PET) in the Nakdong River Basin (NRB), South Korea, with the FAO-56 Penman–Monteith (PM) method as a reference. Two empirical models, Priestley–Taylor (P-T) and Hargreaves–Samani (H-S), and two DL models, a standalone Long Short-Term Memory (LSTM) network and a hybrid Convolutional Neural Network Bidirectional LSTM with an attention mechanism, were trained on a meteorological dataset (1973–2024) across 13 meteorological stations. Four input combinations (C1, C2, C3, and C4) were tested to assess the model’s robustness under varying data availability conditions. The results indicate that empirical models performed poorly, with a basin-wide RMSE of 5.04–5.79 mm/day and negative NSE (−10.37 to −13.99), and are therefore poorly suited to NRB. In contrast, DL models achieved significant improvements in accuracy. The hybrid CNN-BiLSTM Attention Mechanism (C1) produced the highest performance, with R² = 0.820, RMSE = 0.672 mm/day, NSE = 0.820, and KGE = 0.880, which was better than the standalone LSTM (R² = 0.756; RMSE = 0.782 mm/day). The generalization of heterogeneous climates was also verified through spatial analysis, in which the NSE at the station level consistently exceeded 0.70. The hybrid DL model was found to be highly accurate in representing the temporal variability and seasonal patterns of PET and is therefore more suitable for operational hydrological modeling and water-resource planning in the NRB. Full article

This study investigates the propagation of climate model variability to coastal groundwater systems under the high-emission RCP8.5 scenario, focusing on the Almyros Basin in Greece. Using Med-CORDEX bias-corrected climate projections, an Integrated Modelling System (IMS) combines UTHBAL (surface hydrology) and MODFLOW (groundwater hydrology) to simulate future conditions, including precipitation, temperature, evapotranspiration, groundwater recharge, water balance, and seawater intrusion (as a quantity). The analysis quantifies both central tendencies and inter-model spread, revealing substantial declines in groundwater recharge and intensified seawater intrusion, while highlighting the uncertainty introduced by climate model projections. These findings provide critical insights for adaptive water resource management and planning in Mediterranean coastal aquifers under climate change. Full article

Water and nitrogen are the key factors restricting the productivity improvement of onion in the Hexi Oasis. Unreasonable water and fertilizer management not only increases input costs, but also causes environmental pollution of farmland soil, thereby affecting the sustainable development of agriculture. To explore the effects of the water–nitrogen interaction and optimized combination schemes on onion yield, water–nitrogen use efficiency, and economic benefits under mulched drip irrigation in the Hexi Oasis, a four-year (2020–2023) water–nitrogen coupling regulation experiment was conducted at the Yimin Irrigation Experimental Station in Minle County, Hexi Corridor. The onion was used as the test crop and three irrigation levels were established, based on reference crop evapotranspiration (ET_c): low water (W1, 70% ET_c), medium water (W2, 85% ET_c), and sufficient water (W3, 100% ET_c), as well as high nitrogen N3 (330 kg·ha⁻¹), medium nitrogen N2 (264 kg·ha⁻¹), and low nitrogen N1 (198 kg·ha⁻¹). Meanwhile, no nitrogen application N0 (0 kg·ha⁻¹) was set as the control at three irrigation levels. This study analyzed the effects of different water and nitrogen supply conditions on onion quality, yield, water–nitrogen use efficiency, and economic benefits. A water–nitrogen economic benefit coupling model was established to optimize water–nitrogen combination schemes targeting different economic objectives. The results revealed that medium-to-high water–nitrogen combinations were beneficial for improving onion quality, while excessive irrigation and nitrogen application inhibited bulb quality accumulation. Both yield and economic benefits increased with the increasing amount of irrigation, whereas excessive nitrogen application showed a diminishing yield-increasing effect, simultaneously increasing farm input costs and ultimately reducing the economic benefits. In the four-year experiment, the N3W3 treatment in 2020 achieved the highest yield, economic benefits, and net profit, reaching 136.93 t·ha⁻¹, 20,376.3 USD·ha⁻¹, and 14,320.8 USD·ha⁻¹, respectively, with no significant difference from the N2W3 treatment. From 2021 to 2023, the N2W3 treatment achieved the highest yield, economic benefits, and net profit, averaging 130.87 t·ha⁻¹, 28,449.5 USD·ha⁻¹, and 21,881.5 USD·ha⁻¹, respectively. Lower irrigation and nitrogen application rates mutually restricted the water and nitrogen utilization, resulting in low water use efficiency, irrigation water use efficiency, nitrogen partial factor productivity, and nitrogen agronomic use efficiency. The relationship between the irrigation amount, nitrogen application rate, and the economic benefits of onion fits a bivariate quadratic regression model. This model predicts that onion’s economic benefits are highly correlated with the actual economic benefits, with analysis revealing a parabolic trend in economic benefits as water and nitrogen inputs increase. By optimizing the model, it was determined that when the irrigation amount reached 100%, the ET_c and nitrogen application rate was 264 kg·ha⁻¹, and the economic benefits were close to the target range of 27,000–29,000 USD·ha⁻¹; this can be used as the optimal water and nitrogen management model and technical reference for onion in the Hexi Oasis irrigation area, which can not only ensure high yield and quality but also improve the use efficiency of water and nitrogen. Full article