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Accurate irrigated area (IA) mapping is essential for hydrological and climate modeling. However, existing IA mapping approaches typically rely on persistently irrigated or non‐irrigated samples, which has reduced sensitivity to year‐to‐year IA variability. Here, we develop a Categorical Triple Collocation (CTC)‐based sampling framework that identifies both continuously and intermittently irrigated pixels, thereby improving the representation of IA temporal dynamics in training samples. Coupled with machine learning, this framework produces annual 500‐m IA maps across China for 2000–2022. Compared with conventional sampling strategies, the proposed approach reduces IA mapping error substantially, with RMSE decreasing from 16.6% to 8.3%. It also captures interannual IA changes driven by large‐scale agricultural policy shifts, which conventional approaches fail to resolve. These results demonstrate the robustness of the CTC‐based sampling framework for IA mapping, which may directly support water management and Earth system modeling in intensively managed agricultural regions.

Correctly calculating the timing and amount of crop irrigation is crucial for capturing irrigation effects on surface water and energy budgets and land‐atmosphere interactions. This study incorporated a dynamic irrigation scheme into the Noah with multiparameterization land surface model and investigated three methods of determining crop growing season length by agriculture management data. The irrigation scheme was assessed at field scales using observations from two contrasting (irrigated and rainfed) AmeriFlux sites near Mead, Nebraska. Results show that crop‐specific growing‐season length helped capture the first application timing and total irrigation amount, especially for soybeans. With a calibrated soil‐moisture triggering threshold (IRR_CRI), using planting and harvesting dates alone could reasonably predict the first application for maize. For soybeans, additional constraints on growing season were required to correct an early bias in the first modeled application. Realistic leaf area index input was essential for identifying the leaf area index‐based growing season. When transitioning from field to regional scales, the county‐level calibrated IRR_CRI helped mitigate overestimated (underestimated) total irrigation amount in southeastern Nebraska (lower Mississippi River Basin). In these two heavily irrigated regions, irrigation produced a cooling effect of 0.8–1.4 K, a moistening effect of 1.2–2.4 g/kg, a reduction in sensible heat flux by 60–105 W/m2, and an increase in latent heat flux by 75–120 W/m2. Most of irrigation water was used to increase soil moisture and evaporation, rather than runoff. Lacking regional‐scale irrigation timing and crop‐specific parameters makes transferring the evaluation and parameter‐constraint methods from field to regional scales difficult. Key Points A dynamic irrigation scheme was incorporated into Noah‐MP, using soil moisture availability and crop growing season as two major triggers Crop‐specific growing season length helped capture the first application timing and total irrigation amount, especially for soybeans It was imperative to calibrate the soil moisture trigger when transitioning irrigation modeling from field to regional scales

Indian river basins are intensively managed with country-specific agricultural practices of cultivating submerged paddy and uncontrolled groundwater (GW) irrigation. Numerical experiments with the state-of-the-art land surface models, such as variable infiltration capacity (VIC), without incorporating region-specific practices, could be misleading. Here, we coupled VIC with 2D GW model AMBHAS, incorporating India-specific irrigation practices and crop practices, including submerged paddy fields. We performed numerical experiments to understand the causal factors of GW depletion in the northwest Indo-Gangetic plain (IGP). We identify widespread flood irrigation and cultivation of water-intensive paddy as critical drivers of the declining GW scenario. Our numerical experiments suggest that the introduction of drip irrigation reduces GW depletion in the northwest, but does not change the sign of GW level trends. The GW levels in the non-paddy fields of the middle IGP are less sensitive to irrigation practices due to the high return flow to GW for flood irrigation.

Wetting pattern geometry is useful in determining the spacing between emitters and the irrigation time in drip irrigation systems. This research aimed to generate an empirical model to estimate the width and depth of the wetting front in surface drip irrigation based on experimental tests in a cube-shaped container with transparent walls in soils with a sandy clay loam texture, with hydraulic conductivities from 2.316 to 3.945 cm h−1, and organic matter contents from 1.7 to 2.8%, and different irrigation conditions: discharge rates of 1.44, 2.90, 3.00, 3.75, and 4.00 L h−1, initial moisture levels between permanent wilting point and field capacity, and irrigation times from 0.58 to 9.50 h. The experimental conditions and the strategy for measuring the wetting front and soil moisture are detailed so the experiment is verifiable. The proposed model performed better than five other empirical models, with average values of 3 cm for the root mean square error and 0.88 for the Nash and Sutcliffe efficiency coefficient. The generated model is efficient and simple and can be a very useful tool for the design and operation of surface drip irrigation systems in soils with conditions similar to those of this study.

Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected & validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 & 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

An adequate representation of the water infiltration process in the soil allows improving the efficiency in application and the uniformity of surface irrigation. The Green-Ampt model has shown to be a good representation of the process, and researchers from the United States Department of Agriculture (USDA) determined the values of their parameters for USA soils, which are shown in tables or through functional relationships. This information is used as a reference in several parts of the world, although there is no certainty that they are representative of the local soils, as is the case in Mexico. In this study, the parameters of the Green-Ampt equation were determined and evaluated in some soils of agricultural importance in Mexico. The parameters were obtained in four manners: one of them applied a methodology adapted from Brooks and Corey to quantify the wetting front capillary pressure head and used an permeameter under constant hydraulic head to determine the saturated hydraulic conductivity, and the other three consisted in taking them from three studies reported by the USDA. The values of the parameters suggested in Mexico drastically underestimated the results with relative errors (RE) in the range of -49.0 to -94.0%. The most representative ones were those obtained with the methodology proposed in this research, with RE of -15.0 to 6.0%.

This paper mainly aims to illustrate an irrigation management tool to simulate scheduling of district-level water needs over the course of an irrigation season. The tool is mostly based on a daily model for simulating flow of water (and solutes) in heterogeneous agri-environmental systems (called FLOWS-HAGES). The model produces information on the daily evolution of: soil water contents and pressure potentials in the soil profile; water uptake and actual evapotranspiration; stress periods for each crop; return fluxes to the groundwater and their quality in terms of solute concentrations (e.g., nitrates). FLOWS-HAGES provides a daily list of hydrants to be operated according to water or crop-based criteria. The daily optimal sequence of hydrant use may thus be established by passing the volumes to be delivered on to the model for simulating the hydraulics of the irrigation network, in order to ensure that the discharges flowing inside the network of distribution pipes are delivered under optimal pressure head distribution in the system. All the above evaluations can be carried out in a stochastic framework to account for soil heterogeneity and climate changes. To illustrate the potential of FLOWS-HAGES, a case study was considered for a selected sector of the Irrigation District 10 in the “Sinistra Ofanto” irrigation system (southern Italy, Apulia region). In a 139 ha area (Sector 6 of the Irrigation District), soil profiles were analyzed for characterization of hydraulic properties variability. Hydraulic properties were determined by a combination of field and laboratory measurements. Model simulations were validated by comparing soil water storage simulated and measured by a sensor based on electromagnetic induction technique. Irrigation water volumes and frequency calculated by the model were compared to the volumes actually supplied by the farmers. Compared to the farmers behavior, the model simulates more frequent irrigations with lower irrigation volumes. Finally, some indexes of irrigation performance were calculated for each farm under study. The resulting maps provide useful information on the spatial distribution of farmer behavior, indicating the abuse or underuse of water as well as the fraction of the water lost by drainage following the irrigation method applied.

The sustainability of the Hetao Irrigation System, located in the water scarce upper Yellow River basin, is a priority considering the need for water saving, increased water productivity, and higher farmers’ incomes. The upgrading of basin irrigation, the main irrigation method, is essential and includes the adoption of precise land levelling, cut-off management, improved water distribution uniformity, and adequate irrigation scheduling. With this objective, the current study focuses on upgrading wheat basin irrigation through improved design using a decision support system (DSS) model, which considers land parcels characteristics, crop irrigation scheduling, soil infiltration, hydraulic simulation, and environmental and economic impacts. Its use includes outlining water saving scenarios and ranking alternative designs through multi-criteria analysis considering the priorities of stakeholders. The best alternatives concern flat level basins with a 100 and 200 m length and inflow rates between 2 and 4 L s−1 m−1. The total irrigation cost of designed projects, including the cost of the autumn irrigation, varies between 2400 and 3300 Yuan ha−1; the major cost component is land levelling, corresponding to 33–46% of total irrigation costs. The economic land productivity is about 18,000 Yuan ha−1. The DSS modelling defined guidelines to be applied by an extension service aimed at implementing better performing irrigation practices, and encouraged a good interaction between farmers and the Water Users Association, thus making easier the implementation of appropriate irrigation management programs.

Water for irrigation in the Canterbury region of New Zealand is becoming an increasingly precious commodity, as it is in many other areas of the world. Adequate use of this resource will define the economical and environmental future of the region. Current irrigation systems, even under best management practices, over-apply water, as they do not account for spatial variability of crop water needs in fields. Over-application of water is wasteful and has environmental and economical repercussions. Water requirements are determined by crop evapotranspiration (ET). Key factors affecting ET in Canterbury are wind and solar radiation. Both of these are significantly affected by windbreaks, resulting in variability in ET and water requirements across a field. Understanding the variability in ET caused by windbreaks will enable for the correct application of water through precision irrigation systems. A theoretical model was developed to estimate savings in irrigation by accounting for windbreaks in the Canterbury region. Windbreaks reduce evapotranspiration and therefore crops/pasture behind windbreaks needs less water than those in other parts of the field. Results for a case study in Canterbury show that windbreaks can potentially reduce the annual on-farm water consumption by 10 to 20%, while still maintaining ideal crop/pasture yields. In the short term, the application of precision irrigation systems in fields with windbreaks can have the farm level benefits of improved water use and reduced nitrogen/phosphorus leaching. In the long term this could translate directly into cost savings because of a potential decrease in energy used for irrigation (running pumps, etc.).

Regulation of river systems has led to the development of irrigated agriculture and other uses of this engineered additional water supply. One of the main environmental drawbacks from regulation of river systems has been a shift in the seasonality of flow of these rivers. One such river is the River Murray, Australia. A common concern for the River Murray is a decreased incidence of winter and spring flooding of floodplains. A lesser known problem is the increased incidence of unseasonal flooding of the Barmah-Millewa Forest (B-MF) around the Barmah Choke on the River Murray. These flood events have often been said to be created by 'rain rejection events'. Rain rejection events are the rejection of advanced irrigation orders by irrigators due to rainfall on their properties meaning this previously requested water goes unutilised. This paper investigates the causes of unutilised irrigation orders (UIO) and takes a holistic view to investigate the variables affecting unseasonal flooding of the B-MF. It is concluded from this research that unseasonal flooding of the B-MF can be linked to UIO but there are other more significant factors; inflow from Ovens River particularly during December, River Murray flow at Albury and the available airspace in the River Murray at Tocumwal. UIO were found to be linked to the previous day's UIO, rainfall and the advanced order volume placed four days prior.