Search Results (526)

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation. Full article

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

Managed aquifer recharge (MAR) is a complex and hidden process of storing surplus water under the ground surface and extracting it as, when and where needed. Evaluation of the success of any MAR project is challenging due to uncertainty in estimating the hydrogeological characteristics of the subsurface media. This paper demonstrates the use of a groundwater model (MODFLOW) to evaluate a new, large-scale regional MAR project in the agricultural heartland in Punjab, Pakistan. In this MAR project, flood waters have been diverted to the bed of an abandoned canal, where 144 recharge wells (the wells for accelerating the recharge into the aquifer) have been constructed to accelerate the recharge to the aquifer. The model was calibrated for a period of five years from October 2015 to June 2020 on a monthly stress period and the resulting water levels were simulated till 2035. The water balance components and future response of the aquifer to different scenarios up to 2035 including with and without MAR situations are presented. The model simulations showed that MAR can contribute to the replenishment of the aquifer and its potential for the case study site to contribute significantly to the management of groundwater and to enhance supplies for intensive agriculture. It was further established that MODFLOW can help in the evaluation of effectiveness of a MAR scheme. This study is unique as it evaluates a significantly large MAR project in an area where this practice has not been developed for improving groundwater access for large scale irrigation. The model provides guidelines for decision makers in the region as well as for the global community and livelihood benefits for rural communities. Full article

Intensive irrigation in arid and semi-arid regions can cause significant environmental issues, including salinity, waterlogging, and water quality deterioration. Watershed modeling helps us understand essential water balance components in these areas. This study implemented a modified SWAT (Soil and Water Assessment Tool) model tailored to capture irrigation practices within a 15,900 km² area of the Arkansas River Basin from 1990 to 2014. The model analyzed key water balance elements: surface runoff, evapotranspiration, soil moisture, lateral flow, and groundwater return flow, distinguishing between wet and dry years. Over 90% of precipitation is consumed by evapotranspiration. The average watershed water yield comprises 19% surface runoff, 39% groundwater return flow, and 42% lateral flow. Various irrigation scenarios were simulated, revealing that transitioning from flood to sprinkler irrigation reduced surface runoff by over 90% without affecting crop water availability in the intensively irrigated region of the watershed. Canal sealing scenarios showed substantial groundwater return flow reductions: approximately 15% with 20% sealing and around 57% with 80% sealing. Scenario-based analyses like these provide valuable insights for optimizing water resource management in intensively irrigated watersheds. Full article

The integration of water resources, water environment, and water ecology (hereinafter “three-water”) is essential not only for addressing the current water crisis but also for achieving sustainable development. Chaohu Lake is an important water resource and ecological barrier in the middle and lower reaches of the Yangtze River, undertaking such functions as agricultural irrigation, urban water supply, and flood control and storage. Studying the performance of “three-water” in the Chaohu Lake Basin will help to understand the pollution mechanism and governance dilemma in the lake basin. It also provides practical experience and policy references for the ecological protection and high-quality development of the Yangtze River Basin. We used the DPSIR-TOPSIS model to analyze the performance of the river–lake system in the Chaohu Lake Basin and employed an obstacle model to identify factors influencing “three-water.” The results indicated that overall governance and performance of the “three-water” in the Chaohu Lake Basin exhibited an upward trend from 2011 to 2022. Specifically, the obstacle degree of driving force decreased by 19.6%, suggesting that economic development enhanced governance efforts. Conversely, the obstacle degree of pressure increased by 34.4%, indicating continued environmental stress. The obstacle degree of state fluctuated, showing a decrease of 13.2% followed by an increase of 3.8%, demonstrating variability in the effectiveness of water resource, environmental, and ecological management. Additionally, the obstacle degree of impact declined by 12.8%, implying the reduced efficacy of governmental measures in later stages. Response barriers decreased by 5.8%. Variations in the obstacle degree of response reflected differences in response capacities. Spatially, counties and districts at the origins of major rivers and their lake outlets showed lower performance levels in “three-water” management compared to other regions in the basin. Notably, Wuwei City and Feidong County exhibited better governance performance, while Feixi County and Chaohu City showed lower performance levels. Despite significant progress in water resource management, environmental improvement, and ecological restoration, further policy support and targeted countermeasures remain necessary. Counties and districts should pursue coordinated development, leverage the radiative influence of high-performing areas, deepen regional collaboration, and optimize, governance strategies to promote sustainable development. Full article

Since farmers in the inland valley region of the Niger Delta mostly rely on experience rather than empirical evidence when it comes to irrigation, flood irrigation being the most popular technique, the region’s agricultural sector needs more efficient water management. In order to better understand the intricate hydrodynamics of water flow through the soil subsurface, this study aimed to develop a soil column laboratory experimental setup for soil water infiltration. The objective was to measure the soil water content and soil matric potential at 10 cm intervals to study the soil water characteristic curve as a relationship between the two hydraulic parameters, mimicking drip soil subsurface micro-irrigation. A specially designed cylindrical vertical soil column rig was built, and an EQ3 equitensiometer of Delta-T Devices was used in the laboratory as a precision sensor to measure the soil matric potential Ψ (kPa), and the volumetric soil water content θ (%) was measured using a WET150 sensor of Delta-T Devices. The relationship between the volumetric soil water content and the soil matric potential resulted in the generation of the soil water characteristic curve. Two separate monoliths of undisturbed soil samples from Ivrogbo and Oleh in the Nigerian inland valley of the Niger Delta, as well as a uniformly packed sample of soil from Aberdeen, UK, for comparison, were used in gravity-driven flow experiments. In each case, tests were performed once on the monoliths of undisturbed soil samples. In contrast, the packed sample was subjected to an experiment before being further agitated to simulate ploughing and then subjected to an infiltration experiment, resulting in a total of four samples. The Van Genuchten model of the soil water characteristic curve was used for the verification of the experimental results. Comparing the four samples’ volumetric soil water contents and soil matric potentials at various depths revealed a significant variation in their behaviour. However, compared to the predicted curve, the range of values was narrower. Compared to n = 2 in the Van Genuchten curve, the value of n at 200 mm depth was found to be 15, with θr of 0.046 and θs of 0.23 for the packed soil sample, resulting in a percentage difference of 86.7%. Additionally, n = 10 for the ploughed sample resulted in an 80% difference, yet θr = 0.03 and θs = 0.23. For the Ivrogbo sample and the Oleh sample, the range of the matric potential was relatively too small for the comparison. The pre-experiment moisture content of the soil samples was part of the cause of this, in addition to differences in the soil types. Furthermore, the data revealed a remarkable agreement between the measured behaviour and the projected technique of the soil water characteristic curve. Full article

The high water demand of rice cultivation is mainly due to flood irrigation, which requires large volumes not only to meet evapotranspiration needs, but also due to losses from percolation, lateral seepage, and surface runoff. In addition to lowering water use efficiency, surface runoff may transport nutrients. This study aimed to calibrate and validate a daily water balance model to estimate surface runoff losses across three rice-growing seasons. During the first two seasons, different model components were calibrated by comparing simulated and observed water depths. In the final season, the calibrated model was validated using direct runoff measurements obtained from weirs and flowmeters. Results showed strong agreement between model estimates and direct measurements of water depth and surface runoff. Linear regression models showed good fit, with coefficients of determination (R²) above 0.80 for water depth and 0.79 for runoff. A validated daily water balance model, combined with periodic monitoring of water depth, proved to be a reliable tool for estimating surface runoff during the rice-growing season. Total runoff—from irrigation, rainfall, and final drainage—represented between 7.5% and 18% of the total water input. This approach offers a practical tool for improving irrigation water management and understanding runoff-driven nutrient transport. Full article

Waterborne illnesses, including those caused by Salmonella, are an increasing public health challenge, particularly in developing countries. Potential sources of salmonellosis include fruits and vegetables irrigated/treated with surface water, leading to human infections. Salmonella causes millions of gastroenteritis cases annually, but early detection through routine water quality surveillance is time-consuming, requires specialized equipment, and faces limitations, such as coverage gaps, delayed data, and poor accessibility. Climate change-driven extreme events such as floods and droughts further exacerbate variability in water quality. In this context, remote sensing offers an efficient and cost-effective alternative for environmental monitoring. This study evaluated the potential of Sentinel-2 satellite imagery to predict Salmonella occurrence in the Maipo and Mapocho river basins (Chile) by integrating spectral, microbiological, climatic, and land use variables. A total of 1851 water samples collected between 2019 and 2023, including 704 positive samples for Salmonella, were used to develop a predictive model. Predicting Salmonella in surface waters using remote sensing is challenging for several reasons. Satellite sensors capture environmental proxies (e.g., vegetation cover, surface moisture, and turbidity) but not pathogens. Our goal was to identify proxies that reliably correlate with Salmonella. Twelve spectral indices (e.g., NDVI, NDWI, and MNDWI) were used as predictors to develop a predictive model for the presence of the pathogen, which achieved 59.2% accuracy. By spatially interpolating the occurrences, it was possible to identify areas with the greatest potential for Salmonella presence. NDWI and AWEI were most strongly correlated with Salmonella presence in high-humidity areas, and spatial interpolation identified the higher-risk zones. These findings reveal the challenges of using remote sensing to identify environmental conditions conducive to the presence of pathogens in surface waters. This study highlights the methodological challenges that must be addressed to make satellite-based surveillance an accessible and effective public health tool. By integrating satellite data with environmental and microbiological analyses, this approach can potentially strengthen low-cost, proactive environmental monitoring for public health decision-making in the context of climate change. Full article

The interactions between iron oxides and organic carbon within the particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions in paddy soils remain insufficiently understood, yet they are likely crucial for unlocking the carbon sequestration potential of these systems. In this study, we investigated the distribution of soil iron oxides and organic carbon within POM and MAOM fractions following 10 years of continuous irrigation and organic amendment management. We also examined the relationship between iron oxide transformation and these two SOC (soil organic carbon) fractions. Our results demonstrated that, under both flooded irrigation and controlled irrigation regimes, straw return or manure application effectively enhanced soil carbon sequestration, as evidenced by increases in both POM-C (POM-associated organic carbon) and MAOM-C (MAOM-associated organic carbon) contents. Meanwhile, exogenous carbon inputs promoted the transformation of crystalline iron oxides into short-range ordered iron oxides and iron oxide colloids, thereby enhancing the activation and complexation degree of soil iron oxides and facilitating the formation of Fe-bound organic carbon. Further regression analysis revealed that the activation degree of iron oxides had a stronger influence on POM-C, whereas the complexation degree had a greater effect on MAOM-C. This implies that exogenous carbon inputs are effective in promoting soil carbon sequestration in both flooded and water-saving irrigated rice paddies and that iron oxide transformation plays a key role in mediating this effect. Full article

Small reservoirs in hilly areas serve as critical water conservancy infrastructure, playing an essential role in flood control, irrigation, and regional water security. However, dam-break events pose significant risks to downstream urban areas, threatening the sustainability and resilience of cities. This study takes Guangyuan City as a case study and employs numerical simulation methods—including dam-break modeling, hydrological modeling, and hydrodynamic modeling—to analyze the impact of dam-break floods on downstream urban regions. The results reveal that dam failure in small reservoirs can cause peak flood velocities exceeding 15 m/s, severely endangering urban infrastructure, ecosystems, and public safety. Additionally, for reservoirs with large catchment areas, dam-break floods combined with rainfall-induced flash floods may create compound disaster effects, intensifying urban flood risks. These findings underscore the importance of sustainable reservoir management and integrated flood risk strategies to enhance urban resilience and reduce disaster vulnerability. This research contributes to sustainable development by providing scientific insights and practical support for flood risk mitigation and resilient infrastructure planning in mountainous regions. Full article

Given the worsening global climate change that drives drought frequency and irrigation water shortages, implementing water-conserving practices like alternate wetting and drying (AWD) is now critically urgent. Biochar is widely used for soil carbon sequestration. However, there is limited information on the effects of biochar on soil organic carbon (SOC) and its labile fractions in paddy fields, especially under AWD. A two-year field experiment was conducted with two irrigation regimes (CF: continuous flooding irrigation; AWD) as the main plots and 0 (B₀) and 20 t ha⁻¹ (B₁) biochar as sub-plots. AWD had no effect on the SOC and particulate organic carbon (POC) content, but increased the dissolved organic carbon (DOC), microbial biomass carbon (MBC), easily oxidizable organic carbon (EOC), light fraction organic carbon (LFOC), and carbon pool management index (CPMI) at 0–10 cm depths, by 24.4–56.4%, 12.6–17.7%, 9.2–16.8%, 25.6–28.1%, and 11.3–18.6%, respectively. Biochar increased SOC while also increasing DOC, MBC, EOC, LFOC, POC, and CPMI at 0–20 cm depths, by 18.4–53.3%, 14.7–70.2%, 17.4–22.3%, 10.2–27.6%, 95.2–188.3%, 46.6–224%, and 5.6–27.2, respectively, making SOC more labile under AWD. Our results highlight that biochar still holds great potential for improving soil quality and carbon sequestration under AWD, and the combination of biochar and AWD can achieve the synergistic optimization of the food–water–carbon sequestration trade-off, which is beneficial to sustainable agricultural production. Full article

Despite the essential role of phosphorus (P) in rice growth, P-use efficiency (PUE) remains low due to limited bioavailable P in soils and an over-reliance on chemical fertilizers, leading to resource waste and environmental risks, such as eutrophication. This study investigates whether and how alternating wetting and moderate drying (AWMD) irrigation promotes P absorption and transport in rice. This study was conducted over two years using a pot experiment. Conventional flooding (CF) irrigation was applied throughout the growing season, while AWMD irrigation was imposed from two weeks after transplanting to one week before harvest. AWMD improved shoot biomass by 8.7–9.4% and the photosynthetic rate by 12–15%, significantly enhanced PUE, and optimized root traits and enzyme activities related to P uptake. It also promoted leaf acid phosphatase and ribonuclease activities, facilitating P remobilization to grains. In conclusion, AWMD enhanced the ability of roots to absorb P and optimized the redistribution of P between vegetative organs and grains, synergistically increasing grain yield and PUE in rice. Full article

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article