Irrigation Demand Research Articles

Modelling Water Management using SWAT+ : Application of Reservoirs Release Tables and the New Water Allocation Module in a Highly Managed River Basin

Abstract Incorporating the simulation of water management actions in hydrological models is paramount to enhance their reliability and usefulness. SWAT + (Soil and Water Assessment Tool) includes novelties in this aspect compared to its previous versions: the decision tables and the water allocation module provide enhanced capabilities for configuring management actions. Despite their potential, these features have not yet been applied due to their novelty. This study pioneers the use of SWAT + ’s new features applied in the Upper Tagus River Basin, a densely populated and highly regulated catchment. Irrigation, reservoir management, and various kinds of water transfers were incorporated in a SWAT + model of this basin. The implementation of management actions and their impact on the model performance were evaluated. The model accurately reproduced water demand for irrigation and water transfers, capturing both the demand volume and timing. The water sources were configured to meet most of the demand, with 73% of irrigation and 90% of water transfer requirements being satisfied. Release decision tables were configured for 31 reservoirs, resulting in an accurate simulation of outflow and storage in many of them. Incorporating management actions improved the streamflow simulation at the basin outlet, both considering the hydrograph and performance metrics (e.g., PBIAS was reduced by more than 50%). Some potential improvements in the model configuration and in the code were identified and will be addressed in future studies. This work provides a comprehensive guide to SWAT + ’s new features and the methodology employed, making it valuable for anyone working with the model.

Early-Stage Impacts of Irrigated Conservation Agriculture on Soil Physical Properties and Crop Performance in a French Mediterranean System

The Mediterranean region faces intensified climate change effects, increasing irrigation demands to sustain crop yields and increasing pressure on water resources. Adaptive management strategies such as conservation agriculture (CA) offer potential benefits for soil quality and water use efficiency. However, there is limited research on the short-term effects of this farming system under irrigated Mediterranean climatic conditions. This study aimed to explore the short-term impacts of conservation agriculture (no tillage, cover crops and crop rotation) on the soil properties, water flows and crop and water productivity in a French Mediterranean agrosystem of irrigated field crops, using a multifactorial approach. From 2021 to 2023, maize, sorghum and soybean were grown successively under either conventional tillage (CT) or conservation agriculture (CA), combined with sprinkler irrigation, subsurface drip irrigation or non-irrigated conditions. The dynamics of the surface soil properties (bulk density, penetration resistance, soil temperature), water flows (infiltration, soil evaporation) and agronomic indicators (leaf area index, crop yield, water productivity) were measured across the three cropping seasons. In the pedoclimatic conditions of the study, CA was shown to clearly impact the soil properties, water flows and crop yields, from the first year of adoption. CA practices caused an increased bulk density and soil resistance penetration, leading to decreased quasi-steady ponded infiltration in the surface horizon, particularly in the CA–subsurface drip and CA–non-irrigated conditions. These effects were also reflected in the leaf area index, crop yield and water productivity, with CA showing lower values compared to CT. Crop residues in CA reduced soil evaporation, particularly under sprinkler irrigation. However, this benefit diminished as the residues decomposed, leading to soil evaporation rates comparable to those observed in CT. Agronomic indicators were better under sprinkler irrigation than under subsurface drip irrigation. Overall, compaction emerged as a significant challenge in the adoption of CA, considering its negative impact on crop yields.

Historical trends in snowmelt used for irrigation

Abstract Many agricultural regions rely on snowmelt runoff as a source of water for irrigation, but climate change is altering runoff dynamics, making it difficult to meet increasing irrigation water demands. It remains unclear whether irrigation water shortages are systematically occurring in snow-dependent regions across the globe. Here, we study global trends in surface runoff used to meet irrigation demands by linking rainfall and snowmelt runoff data with irrigation consumption data (1985–2020). Focusing on the most snow-dependent agricultural basins, we find that surface water runoff volumes have slightly decreased and snowmelt runoff is occurring significantly earlier in time (advancing an average of 8 days). These changes, coupled with an almost universal trend of increasing irrigation water consumption, have made snowmelt runoff less able to meet irrigation needs during crop growing seasons and increased reliance on alternative water sources (interbasin transfers or reservoirs). These results highlight potential future challenges for irrigated agriculture in snow-dependent regions.

Winter and Season-Only Irrigation with Late Summer Irrigation Termination Influences Alfalfa Dry Matter Yield and Applied Water Use Efficiency

Increasing water scarcity for agricultural irrigation demands options to maximize yield with available water. Alfalfa (Medicago sativa) is a valuable crop in arid and semiarid regions and is considered a major user of irrigation water. Consequently, an area of established alfalfa was center-pivot-irrigated over two years according to one of four irrigation regimes, each with three replicates as strip plots. These were started after the last of the six harvests of the year, after seeding: winter-irrigated and throughout the growing season (winter full), winter-irrigated and terminated after the 4th harvest (winter limited), irrigated from mid-April, when canal water typically becomes available, and throughout the remainder of the growing season (season full), or typically-irrigated until the 4th harvest (season limited). Annual dry matter yield (DMY) was increased using winter irrigation compared to season-only irrigation (10.34, 8.94, 8.67, and 6.54 Mg ha−1 for winter full, winter limited, season full, and season limited, respectively, p < 0.0001, SEM 0.45). Irrigation termination after the fourth harvest with no winter irrigation significantly reduced annual applied water use efficiency (AAWUE) compared to all other treatments (9.08, 8.59, 8.82, and 7.38 kg DMY ha−1 mm−1 for winter full, winter limited, season full, and season limited, respectively; p < 0.0098, SEM = 0.38). Winter irrigation to fill the soil profile, followed by late summer irrigation termination, is feasible for increasing alfalfa productivity over season-only irrigation.

Estimating Paddy Field Water Requirements Using CROPWAT 8.0: A Case Study in Batang Anai Irrigation Area, West Sumatra, Indonesia

The accurate estimation of crop water requirements is critical for efficient water resource management, particularly in regions with limited irrigation resources. This study aims to evaluate the water requirements for rice crops using the CROPWAT 8.0 model and compare the results with the Penman Modification Calculation method, as specified in the Irrigation Planning Standards (KP-01). This research is conducted using climatological data from the Kandang IV Station located near Batang Anai Irrigation area, focusing on key factors such as effective precipitation, air temperature, humidity, wind speed, sunshine duration, and topography. The representative Soil of the local area was incorporated into the analysis. The study finds that the average evapotranspiration (ETo) using CROPWAT 8.0 was 3.09 mm/day, with the peak water demand for rice occurring at the end of August, reaching 1.51 L/s·ha. These findings align with the study’s objective of assessing irrigation demand for rice crops and offer a comparison of methodologies used to estimate water requirements. The results emphasize the need for improvements in the default crop and soil data used by CROPWAT 8.0 to better align with local agricultural conditions in Indonesia. This study contributes to the ongoing development of more accurate models for water requirement estimation and highlights the importance of region-specific calibration in irrigation planning. Further research is needed to enhance the model's functionality and to explore alternative methods for improving water use efficiency in rice farming.

Optimizing Irrigation Strategies to Improve Yield and Water Use Efficiency of Drip-Irrigated Maize in Southern Xinjiang

The contradiction between increased irrigation demand and water scarcity in arid regions has become more acute for crops as a result of global climate change. This highlights the urgent need to improve crop water use efficiency. In this study, four irrigation volumes were established for drip-irrigated maize under plastic mulch: 2145 m3 ha−1 (W1), 2685 m3 ha−1 (W2), 3360 m3 ha−1 (W3), and 4200 m3 ha−1 (W4). The effects of these volumes on soil moisture, maize growth, water consumption, crop coefficients, and yield were analyzed. The results showed that increasing the irrigation volume led to a 2.86% to 8.71% increase in soil moisture content, a 24.56% to 47.41% increase in water consumption, and a 3.43% to 35% increase in the crop coefficient. Maize plant height increased by 16.34% to 42.38%, ear height by 16.85% to 51.01%, ear length by 2.43% to 28.13%, and yield by 16.96% to 39.24%. Additionally, soil temperature was reduced by 1.67% to 5.67%, and the maize bald tip length decreased by 6.62% to 48%. The irrigation water use efficiency improved by 6.57% to 28.89%. A comprehensive evaluation using the TOPSIS method demonstrated that 3360 m3 ha−1 of irrigation water was an effective irrigation strategy for increasing maize yield under drip irrigation with plastic mulch in the southern border area. Compared to 4200 m3 ha−1, this strategy saved 840 m3 ha−1 of irrigation water, increased the irrigation water use efficiency by 23.96%, and resulted in only a 0.84% decrease in yield. The findings of this study provide a theoretical foundation for optimizing production benefits in the context of limited water resources.

Optimized Evaluations of Irrigation Profits and Balance of Irrigation Water Demand and Supply Under Climate Change

Optimizing irrigation profit is essential for enhancing agricultural productivity and ensuring sustainable water resource management, especially under the uncertainties caused by climate variability. It signifies that maximizing irrigation profits has become the primary consideration of current agricultural irrigation systems. By discussing the change in irrigation profits with irrigation fraction f, which represents the proportion of irrigated farmland per unit area of cultivated land, a new method of qualitative diagnosis of irrigation supply and demand balance was proposed by means of the optimal irrigation fraction f0 corresponding to the objective function yf and the correlation coefficient ρ between irrigation water demand anomalies D^ and irrigation water withdrawal anomalies I^ (ρD^,I^=0). It will serve as a new attempt to optimize irrigation profits. We argue that the optimal objective function yf0 represents the state of equilibrium of irrigation supply and demand. This study proposes a reverse optimization idea, which includes two comparison methods, to estimate the comprehensively optimal irrigation fraction f0 that maximizes irrigation profits and irrigation profit per unit area while minimizing risks associated with climatic anomalies. By demonstrating that f0 performs exceptionally well in meeting the objectives of maximizing irrigation profits and minimizing risks across various climatic scenarios, we suggest a robust method for optimizing irrigation profits, which simply calculates ρD^,I^f=0 because ρD^,I^ is the function of f. Since f0 could capture the optimal solution to the greatest extent. It can serve as a simple and effective solution to determine the optimal objective function yf0 and diagnose the state of balance between irrigation water demand and supply. This method offers practical implications for water resource management and agricultural planning in the face of climate change.

Towards sustainable agricultural water management in Poland – How to meet water demand for supplemental irrigation?

The global challenge of water scarcity, particularly in agriculture, demands urgent attention due to the overexploitation of water resources and the escalating impacts of climate change. This study focuses on the unique challenges faced by Poland, experiencing increasing concerns related to droughts. It explores the utilization of supplemental irrigation, specifically in the context of Central Europe, where a distinctive approach known as supplemental irrigation is employed. The study emphasizes the need for sustainable water management practices and investigates the potential of small water retention measures (SWRMs), such as ponds and drainage water management, as solutions to enhance water availability in agriculture. A macro-scale water balance study is conducted using the Soil and Water Assessment Tool (SWAT) to estimate spatio-temporal variability of water demand for supplemental irrigation in Poland. The highest demand, approximately 2.5 billion m3 (for arable lands) and 1.3 billion m3 (for grasslands), occurred during the exceptionally dry year of 2015, characterized by severe agricultural drought effects. The study also assesses the efficiency of SWRMs in meeting irrigation demands at national level. The results highlight a paradox in their effectiveness during critical periods, specifically in dry years when water demands are the highest. The outcomes of the model experiment underscored concerns about the insufficiency of meeting the water needs of irrigated agriculture solely through the construction of small retention facilities during very dry years. The outcomes of this research contribute to a better understanding of irrigation water demands in temperate climate region, support evidence-based practices for sustainable water management, and inform policymakers and stakeholders involved in water governance.

Hydrological Sustainability of Dam-Based Water Resources in a Mediterranean Basin Undergoing Climate Change

The Flumendosa dams are a key part of the water resources system of the island of Sardinia. The analysis of a long-term (1922–2022) hydrological database showed that the Flumendosa basin has been affected by climate change since the middle of the last century, associated with a decrease in winter precipitation and annual runoff (Mann–Kendall τ = −0.271), reduced by half in the last century, and an increase in the mean annual air temperature (Mann–Kendall τ = +0.373). We used a spatially distributed ecohydrological model and a water resources management model (WARGI) to define the economic efficiency and the optimal water allocation in the water system configurations throughout the evaluation of multiple planning and management rules for future climate scenarios. Using future climate scenarios, testing land cover strategies (i.e., forestation/deforestation), and optimizing the use of water resources, we predicted drier future scenarios (up to the end of the century) with an alarming decrease in water resources for agricultural activities, which could halt the economic development of Sardinia. In the future hydrological conditions (2024–2100), irrigation demands will not be totally satisfied, with up to 74% of future years being in deficit for irrigation, with a mean deficit of up to 52% for irrigation.

Research on Irrigation Grade Discrimination Method Based on Semantic Segmentation

As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased.

A comprehensive approach to an urban water reuse system to meet irrigation demand and conserve water: City of Cape Coral, FL, USA

ABSTRACT Maximizing the reuse of domestic wastewater is a critical objective, spurred by the pressing need to avoid water loss, reduce environmental discharges, and manage irrigation water demand. In the City of Cape Coral (FL, USA), thousands of old-fashioned and inefficient irrigation systems have been installed over the past 50 years. The city has adopted year-round external water use restrictions, but demands continue to increase. In 2020, peak 24-h irrigation water demands exceeded 177,914 m3. In response to recurrent dry seasons and growing demand over the past 20 years, the city has diversified its water resources. The city uses brackish groundwater with reverse osmosis treatment to produce drinking water and reuses 100% of its wastewater effluent for irrigation by blending the treated wastewater with fresh water from 483 km of freshwater canals. To promote efficient irrigation water use among consumers, a new approach has been adopted to automate private irrigation systems. The Environmental Resource Assessment and Management System Integrated Urban Water Model was used to simulate water management scenarios. The simulation showed that increasing automatic irrigation efficiency can save millions of cubic meters of irrigation water and help the city meet the future build-out peak-day demand of 219,758 m3/d, as estimated by the Blaney–Criddle method.

Effect of alternate wetting and drying technique (AWD) on water productivity of paddy in major irrigation schemes – A case study in Mahaweli System B

This report investigates the effectiveness of the Alternate Wetting and Drying (AWD) technique on water productivity and yield in paddy cultivation within Mahaweli System B, located in Sri Lanka's dry zone. With increasing water scarcity, particularly during the Yala season, farmers struggle to meet irrigation demands using traditional methods. The study compares AWD with conventional irrigation practices, focusing on its ability to reduce water consumption while maintaining or increasing rice yields. Two paddy fields were selected for trials, and the results show that fields using AWD consumed significantly less water, averaging 3.7 feet per acre, compared to 5.3 feet in conventionally irrigated fields. Moreover, AWD fields produced an average yield of 6.04 tons per hectare, an increase of over 100 kg compared to the control fields, which yielded 5.04 tons per hectare. The technique also reduced the frequency of irrigation events, offering a practical solution to water management challenges in the region. These findings indicate that AWD can enhance water productivity without sacrificing crop yield, making it a promising strategy for areas facing water scarcity. However, further studies across multiple seasons and varied locations are recommended to validate its long-term benefits and adaptability.

Resolving the conflict between the need for electricity generation upstream and the water supply for economic development downstream in the Ca River Basin, Vietnam

ABSTRACT Vietnam has vigorously developed hydroelectric reservoirs in main river basins with hydroelectricity reserves to produce electricity and exploit and use water resources efficiently and minimally – risks to meet socio-economic development requirements – in the downstream area. The study has shown evidence that the water level's daily mean value of the Ca River is increasingly reduced. However, the number of occurrences of the daily minimum flow was more than that of the period when it was not hydropower reservoirs and was affected by climate change. The study calculated and built five scenarios for water use in the downstream areas. These scenarios are calculated based on the water demand for domestic, industry, irrigation, and water sea level. A simulation model was developed for optimal hydropower generation of reservoir system upstream and forced to discharge downstream with five scenarios. This study's results demonstrated that four upstream reservoirs could operate with scenario 3 corresponding HNam Dan = 0.7 m. The results suggested options for the operation of hydroelectric reservoirs and simultaneously developed scenarios for taking water for agricultural irrigation downstream of the Ca River Basin with water level for irrigation drains and domestic water factories. The benefit of electricity in four reservoirs in scenario 3 reached 1,009 billion Vietnamese Dong (VND).

Persistent Water Scarcity Due To High Irrigation Demand in Arid China: A Case Study in the North Slope of the Tianshan Mountains

AbstractWater scarcity is a critical threat in arid regions in China due to dry climate and rising human water demand. The sustainability of a recent wetter trend and its impact on future water security remain uncertain. This case study focuses on a hotspot region, the North Slope of the Tianshan Mountains (NSTM), to assess water scarcity in the coming decades (2030–2050) under two climate scenarios. To this end, we developed an integrated agro‐hydrological model to simulate historical and future hydrological processes and crop water dynamics in arid regions. Our results indicate nonsignificant increases in precipitation (around 3%) and evident rising temperatures (0.9–1.5°C) in the NSTM compared to the present‐day (2011–2020) climate. This translates to a projected increase in water availability (5.6%–11.2%) during 2030–2050, with slightly larger increases (6.3%–14%) in glacier runoff. However, the spatial mismatch between precipitation increases and water demand makes this potential gain largely offset by rising irrigation water demand (over 7%) if cropland remains constant from 2020 onwards. As a result, the current annual water deficit (3.3 km3) is likely to increase by 5%–11%, with 32% of NSTM basins facing persistent water scarcity. Most croplands are at high risk of groundwater depletion and 17%–34% of basins will experience intensified water scarcity. These findings highlight the urgent need for comprehensive water management strategies, including improved irrigation efficiency and exploration of alternative water sources, to ensure water security and sustainable development in arid China facing a changing climate.

Harvesting the sun twice: Energy, food and water benefits from agrivoltaics in East Africa

Food, energy and water insecurity are concomitant challenges facing many communities in East Africa. Agrivoltaic systems – agriculture integrated with photovoltaic panels – address all three challenges, providing low carbon electricity, food production and water conservation on the same land area. Agrivoltaics have proven benefits for the food-energy-water nexus in the USA, Europe and Asia, but research is lacking in sub-Saharan Africa, where energy access remains low, and climate change and water scarcity threaten food systems. This study presents evidence for concomitant electricity generation, food production and water conservation from agrivoltaic systems in Tanzania and Kenya, demonstrating the viability of these systems for both grid-tied agribusinesses and rural, off-grid communities. Performance of some crops improved under agrivoltaics, generating higher incomes for farmers and agribusinesses while reducing energy bills and/or enhancing energy supply. Crop survivability during a warm period was greater under the agrivoltaic system, indicating potential for climate change resilience. Panel shading reduced irrigation demand, thus some crops achieved greater yields while needing less water input. Rainwater harvesting from panel runoff further reduced irrigation needs. Combining energy infrastructure with agriculture enhanced land productivity for all crops at both sites. Agrivoltaics, whether grid-tied or off-grid, could address multiple Sustainable Development Goals in East Africa simultaneously by contributing to energy security, climate change-resilient food production, and water conservation in the region.

Remote Sensing of Residential Landscape Irrigation in Weber County, Utah: Implications for Water Conservation, Image Analysis, and Drone Applications

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning.

A new optimized procedure for circular WasteWater sustainability: Coastal cities supporting agricultural rural communities

This proposal introduces a novel methodology that addresses the increasing irrigation demands driven by climate change and urban growth. Traditionally water-scarce areas are now facing severe water deficits, while wastewater volumes from treatment plants, often discharged into the sea, contribute to pollution. The proposed hybrid system strategy innovatively reallocates 33 hm3 of water annually to agricultural communities, employing a zero-discharge approach to prevent marine pollution. Evaluated from energetic, environmental, and social perspectives, this methodology shows a remarkable cost-benefit ratio exceeding 12, showing its feasibility. It features technical indicators for optimizing water distribution and regulatory components, applied effectively to 28,424 ha of farmland. This strategy meets 24.1 % of the irrigation needs in these regions while safeguarding coastal areas from degradation. Crucially, it integrates 11.3 GWh of renewable energy annually, underscoring its sustainability and enhancing its replicability for other water-deficient regions.

Water saving based on moisture observations: scheduling drip irrigation regimes for tomatoes under greenhouse conditions in Tajikistan

This study aimed to examine the efficiency of different drip irrigation regimes in spring film greenhouses for early tomato harvesting. The field (small-plot) experiment included designing and testing the watering technology suitable for rural Tajikistan. Irrigation, soil moisture, and tomato growth were followed for several seasons under four irrigation pilots. The experiments showed that the most effective drip irrigation regime for Elpida hybrid tomato was carrying it out while regulating soil moisture within 75-85%, with the irrigation demand of 4,978 m3/ha and irrigation norm of 99.0 m3/ha, also contributing to better water efficiency. The volume of irrigation water for the production of one unit of tomato crop in the second experiment was 5.73 m3/ha, i.e. 14.18% less than in the control plot. The study showed that the tomato evapotranspiration coefficient tends to increase as the threshold of soil mois-ture before and after irrigation grows. The maximum net yield amounted to 1,342 thou. Somoni/ha – 1.9 times or 52.7% higher compared to the control plot. The research findings can guide individual farmers and production facilities, as well as the overall development of agrarian economies like Tajikistan.

Crop Coefficients and Irrigation Demand in Response to Climate-Change-Induced Alterations in Phenology and Growing Season of Vegetable Crops

This study investigates the effects of climate change on the irrigation demand of vegetable crops caused by alteration of climate parameters affecting evapotranspiration (ET), plant development, and growing periods in Central Europe. Utilizing a model framework comprising two varying climate scenarios (RCP 2.6 and RCP 8.5) and two regional climate models (COSMO C-CLM and WETTREG 2013), we calculate the daily crop water balance (CWBc) as a measure for irrigation demand based on reference ET and the temperature-driven duration of crop coefficients until 2100. Our findings for onion show that rising temperatures may shorten cultivation periods by 5 to 17 days; however, the irrigation demand may increase by 5 to 71 mm due to higher ET. By reaching the base temperatures for onion growth earlier in the year, cultivation start can be advanced by up to 30 days. Greater utilization of winter soil moisture reduces the irrigation demand by up to 21 mm, though earlier cultivation is restricted by frost risks. The cultivation of thermophilic crops, however, cannot be advanced to the same extent, as shown for bush beans, and plants will transpire more strongly due to longer dry periods simulated for summer. The results underscore the need for adaptive crop and water management strategies to counteract the simulated changes in phenology and irrigation demand of vegetable crops. Therefore, special consideration must be given to the regional-specific and model- and scenario-dependent simulation results.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.