Search Results (345)

Water resource conservation and intensive utilization is an important pathway for promoting sustainable regional water resource management and high-quality development. Against the backdrop of increasing constraints on water resources, existing studies have paid insufficient attention to the multidimensional comprehensive assessment of water resource conservation and intensive utilization capacity and its underlying evolutionary mechanisms. Therefore, Heilongjiang Province was selected as the study area, and an evaluation system comprising 15 indicators was established. The game-theoretic combination weighting method, TOPSIS model, obstacle degree model, and GM(1,1) grey forecasting model were employed to comprehensively evaluate, diagnose obstacle factors, and predict the trend of water resource conservation and intensive utilization capacity in Heilongjiang Province from 2004 to 2023. The results showed that the overall capacity exhibited a fluctuating upward trend, with the comprehensive evaluation value increasing from 0.44 to 0.62. The industrial water reuse rate, effective utilization coefficient of farmland irrigation water, comprehensive water consumption rate, per capita water consumption, and ecological water use rate were the indicators with relatively high obstacle contributions. The obstacle factors exhibited distinct stage-specific characteristics: the constraining effects of efficiency-related indicators gradually weakened, whereas those of the comprehensive water consumption rate and per capita water consumption generally intensified, indicating that the factors constraining water resource conservation and intensive utilization in Heilongjiang Province underwent distinct stage-specific changes. The prediction results indicated that the capacity for water resource conservation and intensive utilization in Heilongjiang Province would continue to increase steadily in the future. However, balancing ecological water use requirements with growing water demand remains an important factor affecting sustainable water resource utilization. The evaluation–diagnosis–prediction framework developed in this study can provide a reference for the assessment and optimized management of regional water resource conservation and intensive utilization. Full article

Irrigation scheduling under seasonal water-use restrictions is a pressing challenge in water-scarce agricultural regions, where finite volumetric allocations demand careful timing and depth decisions to sustain profitability. Deep reinforcement learning (DRL) offers promise for optimizing such sequential decisions, yet existing observation designs rely on backward-looking weather statistics and omit near-term forecasts that may support the management of limited water across a growing season. This study evaluates whether augmenting the DRL agent’s observation space with seven-day precipitation and reference evapotranspiration forecasts and refactoring existing allocation information into three budget-pacing features can improve irrigation scheduling most effectively under seasonal water scarcity while retaining benefits as restrictions are relaxed. Proximal policy optimization policies were trained within the AquaCrop framework for irrigated maize in southwest Nebraska under 50, 75, 100, 125 mm and unrestricted seasonal water caps. Under the 50 mm cap, the augmented feedforward policy (FB-MLP) achieved 144.76 $/ha, which was 32.2% above the baseline policy and 22.3% above the optimized Soil Moisture Target benchmark against the validation set. Its best-run gains over the baseline across the other four scenarios averaged 2.52%, including a 1.2% improvement under unrestricted irrigation. Under the 75 mm cap, the augmented policy allocated 83.3% of its irrigation to flowering and yield formation. These findings show that the combined observation design improves irrigation scheduling most strongly where water scarcity is binding. Full article

Accurately predicting soil moisture conditions and determining the optimal sowing timing during the spring sowing period play a crucial role in ensuring high and stable grain yields. To address the limitations of existing soil moisture prediction models, i.e., the complexity and parameterization challenges of hydrological models, the weak theoretical foundations of statistical models, and the data dependence, lack of interpretability, and poor generalization of machine learning approaches, a prediction model for surface soil water content (SWC) was developed in this study. The model is based on the water balance principle and uses a stepwise correction approach with normalized functions of key influencing factors. The results are as follows: (1) The model requires readily available parameters from public databases and is driven by daily scale meteorological variables (precipitation, temperature, wind speed, and vapor pressure deficit), facilitating its integration into existing operational weather forecasts. (2) After parameterization, only the moisture exchange between surface and deep soil layers needs optimization, resulting in low computational demand. (3) A trial in Shenyang region showed that the model explains 82.1% of the SWC variance, with an RMSE of 1.7% for 1–7 day lead predictions. (4) When applied to regions without initial soil moisture observations, the model achieves satisfactory accuracy after an initial condition sensitivity period of approximately 40 days. These results provide a methodological reference for soil moisture prediction studies and offer technical support for meteorological services to integrate soil moisture forecasting into their operational frameworks. Full article

Accurate short-term rainfall forecasting is essential for disaster mitigation. Although numerical weather prediction models are widely used, their application to short lead times is constrained by computational demands. Data-driven approaches provide an efficient alternative. To better exploit atmospheric water vapor information, this study develops a dual-branch long short-term memory (LSTM) model that integrates Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) with surface meteorological parameters for rainfall forecasting. The model processes historical rainfall and meteorological variables through separate branches. Historical rainfall characterizes precipitation persistence, while PWV, PWV variation (ΔPWV), PWV rate of change (ΔtPWV), and air temperature describe atmospheric moisture evolution and thermodynamic conditions before rainfall. The model was evaluated using hourly observations from 18 GNSS-collocated meteorological stations in Taiwan collected during 2018–2019 and compared with a rainfall history-based LSTM baseline model. Results show that the proposed model achieved accuracies of 89–91% and recalls of 88–90% for 1–3 h forecasts. Its advantages became more evident for longer lead times, with Recall and Threat Score increasing by 6–11% and 4–8%, respectively, for 2–3 h forecasts. These findings demonstrate that integrating GNSS-derived PWV with surface meteorological parameters can improve short-term rainfall forecasting. Full article

The Huaibei region is a critical grain and energy–chemical base in northern China, characterized by substantial water demand for industrial and agricultural production. Karst groundwater systems constitute the primary water supply source in this area. Under the superimposed impacts of intensive exploitation, climate change, and anthropogenic activities, karst aquifers have encountered a series of geo-environmental problems, including groundwater level decline and expansion of cones of depression. Most previous studies have predominantly focused on water quality assessment and groundwater resource quantification, yet systematic investigations into the multi-scale characteristics and driving mechanisms of karst groundwater level dynamics remain insufficient. In this study, based on long-term groundwater level and rainfall monitoring data (2014–2024) from three monitoring wells representing different types of karst aquifers, continuous wavelet transform (CWT) and wavelet coherence (WTC) approaches are introduced to identify the periodic patterns of karst groundwater levels and reveal the dominant controlling factors of groundwater level dynamics. The results demonstrate that groundwater levels in all types of karst aquifers exhibit distinct multi-scale periodic variations. The groundwater levels of HB01 and HB02 share dominant oscillation periods of 18~19 months and 9 months with regional rainfall, while the groundwater level at HB03 displays a more complex, multi-scale, periodic combination of 41 months, 18~19 months, and 9 months. Periodic variations in regional rainfall serve as the dominant controlling factor for the intra-annual and inter-annual periodic fluctuations of karst water levels, with a prominent resonance relationship identified between the two variables at dominant periodic scales. Distinct heterogeneity is observed in the response magnitude and lag time of different karst aquifer types to rainfall; specifically, the lag time of water level response to rainfall on the annual periodic scale ranges from 2.7 to 2.9 months. The correlation between annual average water level and pumping discharge is moderate for boreholes HB01 and HB03, whereas a strong correlation is detected for borehole HB02, implying that its water level regime is likely subjected to pronounced pumping disturbance. The degree of karst development, aquifer burial depth, and overlying stratum architecture are the key geological factors accounting for such heterogeneous response patterns. For the first time, this study utilizes long-term water level time series data from the karst water exploitation zone of the Huaibei Plain, complemented by synchronous precipitation and pumping records. Integrated with regional hydrogeological settings, wavelet analysis is employed to conduct an in-depth investigation into the dynamic variations in karst water levels in the Huaibei region from the perspective of groundwater recharge–discharge relationships. The results provide a scientific underpinning for the remediation of karst water over-exploitation and the optimal allocation of water resources. Specifically, pumping and artificial recharge schemes can be proactively adjusted based on periodicity forecasts. Zoned management strategies for water resources are put forward: artificial regulation and storage are recommended for zones with sensitive hydrological responses, while preventive protection is prioritized for zones with sluggish responses. By incorporating periodic characteristics and lag durations, targeted pumping strategies for dry and wet seasons can be developed, and a coupled water level–rainfall–pumping early warning system can be established to realize the long-term sustainable regulation of karst water resources. Full article

Accelerating the renovation of existing residential buildings is essential for achieving European climate targets, but aggregate renovation rates do not show whether interventions are sufficiently deep, well targeted, or cost-effective. This paper develops a transparent scenario framework for prioritising renovation strategies at local building stock scale. The workflow combines a literature-based screening of renovation indicators and implementation conditions, semi-structured expert interviews used for qualitative triangulation, and a typology-based bottom-up model of the South Tyrolean residential stock. The stock model uses census and provincial floor-area data, representative space heating and domestic hot water demand values from SINFONIA and previous South Tyrolean studies, and static end-state renovation assumptions. Three scenarios are compared: deep renovation of priority high-demand clusters, medium renovation of the same clusters, and light renovation of the whole stock. The baseline model estimates residential heating and domestic hot water demand at approximately 1990 GWh/year. The selected priority clusters account for about 56% of floor area and nearly 59% of baseline demand. Under the central assumptions, targeted deep renovation would reduce demand by approximately 704 GWh/year (35%), targeted medium renovation by 352 GWh/year (18%), and whole-stock light renovation by 299 GWh/year (15%). Indicative CO₂ reductions are reported separately and are proportional to the same final energy reduction assumptions because carrier switching is not modelled. A static cost-effectiveness screening using Italy-level energy-related renovation cost data is reported as an order-of-magnitude range rather than a local investment forecast. The results show that renovation depth and target selection should be considered jointly, and that the framework is transferable where typological stock data and representative energy demand values are available. Full article

Daily groundwater-level prediction in arid inland basins is driven by complex meteorological–hydrological conditions, water supply, pumping, and irrigation demand. Using data from the Zhangye Basin (2018–2025), this study selected 10 representative wells from 51 candidates to build a one-day-ahead framework with a 60-day input window. Dempster–Shafer evidence theory fused five criteria (Pearson, Spearman, lagged correlation, mutual information, and tree-model importance) to screen external variables. Long short-term memory network (LSTM), temporal convolutional network (TCN), and Transformer served as first-level sequence models; extreme gradient boosting (XGBoost) as the second-level stacking learner; and SHapley Additive exPlanations (SHAP) to quantify feature contributions. Dempster–Shafer evidence theory (D-S evidence theory) results indicated that groundwater pumping proxy variable (GPV), irrigation water-demand intensity proxy variable (IWD), surface-water supply proxy variable (SWS), canal-diversion proxy variable (CDV), air temperature (AT), runoff, vapor pressure deficit (VPD), and canal irrigation supply–demand coupling intensity (CISDCI) exhibited high process-representation relevance. During the 90-day test period, Stacking achieved the lowest RMSE for six of 10 wells. Regional average RMSE, MAE, and NSE values were 0.1596 m, 0.0772 m, and 0.9326 for the Zhangye group, and 0.0185 m, 0.0133 m, and 0.9177 for the Gaotai group. SHAP showed historical groundwater-level data dominated contributions, accounting for 64.17% and 43.96% in the Zhangye and Gaotai groups, respectively, and indicating model dependence rather than direct hydrological causality. This framework provides a cautious reference for short-term groundwater forecasting and input selection under the given data conditions. Full article

In this study, daily reference evapotranspiration (ET₀) values for the period 2025–2030 for Lake Burdur, located in the Mediterranean climate zone and within the Burdur closed basin, were estimated using nested architecture focused on high accuracy. The ET₀ target corresponds to the FAO-56 Penman–Monteith reference evapotranspiration variable provided by the Open-Meteo Historical Weather API, and it is treated throughout as a standardized measure of atmospheric evaporative demand rather than as actual lake-surface evaporation or basin water loss. For this purpose, daily mean air temperature, relative humidity, shortwave surface radiation, and evapotranspiration data for the period 1984–2024 were obtained from the Open-Meteo platform. In the first stage of the study (Model 1), separate SARIMAX (statistical), XGBoost (machine learning), and LSTM (deep learning) models were applied for temperature, relative humidity, and radiation series; the model with the highest validation mean for each variable was selected. Accordingly, LSTM (Mean $R^2$ = 0.967) was determined to be the most successful model for temperature, SARIMA(X) (Mean $R^2$ = 0.812) for relative humidity, and XGBoost (Mean $R^2$ = 0.845) for the radiation variable, which is non-linear, has strong autocorrelation, and exhibits distinct seasonality. In the second stage (Model 2), these best climate predictions were used as independent variables for evapotranspiration, and LSTM provided the highest success for evapotranspiration (Mean $R^2$ = 0.941). Trend analyses revealed that the increase in temperature and evapotranspiration and the decrease in relative humidity observed in the past period will continue in the near future. The uncertainty analysis conducted using the Monte Carlo/resampling approach on historical data showed that the 95% prediction intervals largely protected the upward trend in evapotranspiration against random fluctuations. These intervals reflect residual-based uncertainty under the fitted model rather than the full predictive uncertainty of future basin evapotranspiration. The findings indicate that designing model selection appropriate to the structure of the variables within a nested prediction framework significantly improves forecast accuracy and can provide a viable decision support input for sustainable water management in Mediterranean basins experiencing water scarcity. Full article

This discussion paper examines the evolution of freshwater distribution networks from smart to intelligent and ultimately meta-intelligent or wise systems, highlighting the transition from human-supervised operation to autonomous adaptive management. Smart systems integrate monitoring, automation and remote control through information technologies. Intelligent systems extend these capabilities by adding predictive analytics, demand forecasting and automated operational optimization. Wise systems further evolve through adaptive learning mechanisms that allow continuous self-improvement while minimizing dependence on operators. Evidence from Greek water utilities demonstrates practical applications and operational outcomes. The analysis discusses implementation challenges including investment costs, system complexity, data governance and resilience. Finally, the paper proposes design principles for scalable adaptive water networks applicable to utilities with different sizes, resources and levels of technological maturity. Full article

Short-term water demand forecasting is a key issue for the management of smart water networks, particularly in the context of remote control and active regulation. This study analyses a real-world dataset of water demand coefficients, collected at 15 min intervals, from a municipality in Southern Italy serving approximately 73,000 inhabitants. The proposed model, based on Gaussian Process Regression (GPR) with a Rational Quadratic kernel (RQ), is compared with a statistical benchmark constructed using average patterns for each time slot by the application of the Gauss Distribution. The results show a reduction in RMSE and MAE and a better ability to track the daily dynamics of demand using the GPR approach. Full article

Climate change is intensifying water scarcity in the Mediterranean region, placing the Antalya province of Türkiye at significant risk due to declining water availability, rapid population growth, and intense tourism activities which increase seasonal demand. This study forecasts population and urban water demand until 2050 and evaluates several water loss reduction scenarios for the city’s drinking water distribution network. In developing the forecasted water demand, the analysis incorporates several water loss reduction scenarios. These include a baseline scenario maintaining current water loss levels, a moderate improvement scenario aligned with Türkiye’s national regulatory targets, and an advanced scenario achieving international best practices. Results show that reducing water losses, caused mainly by aging infrastructure, pressure fluctuations, and leaks, can substantially decrease total water demand. Improved network efficiency is therefore essential for maintaining long-term water security and supporting climate change adaptation efforts in Antalya. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

Although South Africa has an extensive water infrastructure, it continues to face significant water scarcity due to its semi-arid climate, increasing urbanisation, ageing infrastructure, and pollution. These challenges, coupled with climate change and increasing water demand, have led to inefficiencies across the water value chain, particularly in rural areas. This review paper evaluates the current adoption of predictive analytics in South Africa’s water management system through a systematic literature review. It identifies the current applications, implementation gaps, and key system components that are suitable candidates to enhance efficiency, resource planning, and long-term sustainability in the sector. The findings show that while predictive models are being applied in urban systems for demand forecasting and proactive maintenance, only 15% of the reviewed studies address their actual adoption in rural or under-resourced contexts. This underscores the need for more inclusive development strategies to ensure equitable water service delivery. Although strides have been made in research and innovation, a major barrier is the slow transition from research to operational deployment, which hinders the full realisation of these technologies’ benefits that are essential for water supply sustainability and availability. Full article