Sustainable Agricultural Water Management Research Articles

Risks associated with wastewater reuse in agriculture: investigating the effects of contaminants in soil, plants, and insects

Rapid urbanization has heightened the urgency of the necessity for sustainable water management in agriculture. This review focuses on the impacts of using reused wastewater in agricultural practices, specifically highlighting the nutrient benefits and consequences of pollutants on important environmental elements. It investigates the impact of contaminants on agricultural ecosystems by assessing the soil composition and nutrient equilibrium. This research also examines the impact of pollution exposure on plants and insects, elucidating the behavioural adaptations and their broader ecological consequences in agricultural environments. Eventually, a comprehensive analysis was conducted to consolidate these findings, emphasizing the challenges and significance of implementing sustainable practices. This study highlights the necessity of addressing the health and environmental concerns associated with the agricultural reuse of wastewater, while also giving valuable information to guide future regulations.

Geospatial Technology for Sustainable Agricultural Water Management in India—A Systematic Review

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.

Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANNs) are proven to be suitable for predicting reference evapotranspiration (ETo) and ETa based on RS data. This study aims to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables in a large irrigation catchment. The ANN model has been applied to the two different scenarios. Data from only the 38 days of satellite overpass dates was selected in Scenario I, while in Scenario II all datasets, i.e., the 769-day data were used. An irrigation scheme, located in the Mediterranean region of Turkiye, was selected, and a total of 38 Landsat images and local climatic data collected in 2021 and 2022 were used in the ANN model. The ETa results by the ANN model for Scenarios I and II showed that the R2 values for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84) were all remarkably high. Moreover, the results of the new ANN model in two scenarios showed an acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential for obtaining daily ETa using limited climatic data and RS imagery. As a result, the suggested ANN model for daily ETa computation offers a trustworthy way to determine crop water usage in real time for sustainable water management in agriculture. It may also be used to assess how crop evapotranspiration in drought-prone areas will be affected by climate change in the 21st century.

Comparative Assessment of Crop Water Productivity of Drip Irrigated Green Pepper under Poly-House and Open-Field Conditions

Crop water productivity (CWP) is a crucial factor in determining the sustainability of agricultural practices. Drip irrigation is a method that has shown potential in improving CWP in crop production. The study aimed to compare the crop water productivity of green pepper production under poly-house and open-field environments during 2020/2021 and 2021/2022 growing seasons. The experiment was conducted during the dry seasons of 2020.2021 and 2021/2022 at the Teaching and Research Field of Federal Polytechnic, Ado-Ekiti, Nigeria. The beds were prepared, nursery was established, drip irrigation system was installed and fumigation was carried out before transplanting. The experimental design was a randomized complete block (RCBD) with nine replications in each environment. The same design was applied to both poly-house and open-field conditions. The factors are environment (poly-house and open-field) and irrigation intervals of 5, 6 and 7 days (I5, I6 and I7). Data obtained were subjected to analysis of variance and Tukey’s Honestly Significant Difference (HSD) was used for multiple comparison. Drip irrigation was applied to all plots at different irrigation intervals. The results showed that the crop water productivity under poly-house and open-field environments in 2020/2021 growing season were: I5: 29.67, I6: 36.97, I7: 20.92 and I5: 17.06, I6: 21.45, I7: 12.21 kg/m3. Similarly, in the 2021/2022 growing season, the CWP values were I5: 16.90, I6: 23.27, I7: 12.95 and I5: 9.60, I6: 14.01, I7: 6.81 kg/m3 under poly-house and open-field, respectively. The results revealed that water productivity was more efficient at low levels of water supply in the poly-house during the 2020/2021 and 2021/2022 growing seasons. The production of green pepper under poly-house increased water conservation, reduced nutrient leaching, enhanced crop productivity for sustainable agricultural practices and water management.

Water-smart farming: review of strategies, technologies, and practices for sustainable agricultural water management in a changing climate in West Africa

The global temperature increase of 1.1°C over the past decade and human population growth are already escalating competition for water in locations where water is scarce, adversely affecting agriculture. Water-smart farming was developed to address challenges associated with the availability, access and use of agricultural water. It complements practices and goals related to agricultural water in climate-smart agriculture. It encompasses several climate-responsive food cropping systems, water-saving methods, tools, and technologies to grow food sustainably. Effective implementation of water-smart farming can ameliorate the pressing burden of agricultural water competition. Therefore, the conceptual framework of water-smart farming and its principal elements or ideology are introduced in this review. The major pillars of water-smart cropping, which include conditioning and improving the soil’s capacity to capture and hold moisture, basic infrastructure for water harvesting, storage and use as nature-based cropping solutions were elaborated. Further examples of technologies and best practices in water-smart crop farming, such as breeding for drought-tolerant crop varieties, promoting dryland cropping systems, alternate wetting and drying technology, water drainage and control structures, rainwater harvesting, utilization of irrigation reservoirs, cover cropping and intercropping for enhancing soil moisture conservation were presented. The review also briefly shows the contribution of some of these best practices and adaptive technologies of water-smart cropping toward promoting water-saving methods used for growing rice, maize, legumes, cassava and peri-urban vegetables.

Machine learning-based detection of irrigation in Vojvodina (Serbia) using Sentinel-2 data

ABSTRACT With rapid population growth and the high influence of climate change on agricultural productivity, providing enough food is the main challenge in the 21st century. Irrigation, as a hydrological artificial process, has an indispensable role in achieving that goal. However, high pressure and demand on water resources could lead to serious problems in water consumption. Knowing information about the spatial distribution of irrigation parcels is essential to many aspects of Earth system science and global change research. To extract this knowledge for the main agricultural region in Serbia located in the moderate continental area, we utilized optical satellite Sentinel-2 data and collected ground truth data needed to train the machine learning model. Both satellite imagery and ground truth data were collected for the three most irrigated crops, maize, soybean, and sugar beet during 3 years (2020–2022) characterized by different weather conditions. This data was then used for training the Random Forest-based models, separately for each crop type, differentiating irrigated and rainfed crops on the parcel level. Finally, the models were run for the whole territory of Vojvodina generating 10 m resolution maps of irrigated three crops of interest. With overall accuracy for crops per year (2020: 0.76; 2021: 0.78; 2022: 0.84) results showed that this method could be successfully used for detecting the irrigation of three crops of interest. This was confirmed by validation with the national dataset from Public Water Management Company “Vode Vojvodine” which revealed that classification maps had an accuracy of 76%. These maps further allow us to understand the spatial dynamics of the most important irrigated crops and can serve for the improvement of sustainable agricultural water management.

Sustainable agricultural water management in the Yellow River Basin, China

Sustainable agricultural water management in the Yellow River Basin, China

Towards sustainable agricultural water management

Towards sustainable agricultural water management

A Comprehensive Study of Assessing Sustainable Agricultural Water Management under Changing Climate Scenarios—A Regional Basis Study in the Western Ghats, India

The Western Ghats (WG) in South India is a biological hotspot with a cluster of small river basins and heterogeneous climate and vegetation patterns, and it is categorized under the water stress region by Central Water Commission (CWC). This study aims to evaluate the effects of climate change and land use/land cover (LULC) transformations on water balance components and irrigation water demand (IWD) across different regions of WG for a future period (2020–2050). The variable infiltration capacity model has been calibrated separately for the upper, middle, and lower regions of WG. Further, climate projections from the CMIP6 experiment (SSP2 45/SSP5 85) have been used for future projections of water balance components. The land use change shows an increase in built-up (5.79%) and a decrease in cultivable land (1.24%) by the end of 2030 from 1995. The combined impact due to climate and LULC change shows that the future rainfall/runoff increases in the lower regions of the basin by 100/36.5 mm/year through SSP 4.5. However, the summer months show an increasing water requirement in the future for the Ghats and Nilgiri regions of the basin. The present regional-based study will be useful for future agriculture water management practices in the region for sustainable development and the study can be extended to other similar regions.

Research and innovation in agricultural water management for a water‐secure world

AbstractThere is increased awareness that the current food system is unsustainable and that transformative research, development and innovation in agricultural water management (AWM) are needed to transform water and food systems under climate change. We provide an overview of research efforts, challenges, opportunities and innovations to improve water resource management and sustainability, especially in the agricultural sector. We highlight how sustainable AWM is central to balancing the needs of a growing population and increasing food demand under increasing water insecurity and scarcity, with environmental and socio‐economic outcomes. Innovative technologies are being developed to optimize water use and productivity through sustainable irrigation technologies, irrigation modernization and smart water management. However, these innovations still need to fully address equity, inequality and social justice concerning access to water, infrastructure and the delayed technological advances in the global South. This requires adopting transdisciplinary approaches, as espoused by the water–energy–food (WEF) nexus, to better anticipate and balance trade‐offs, optimize synergies and mitigate risks of maladaptation. Through such transdisciplinary approaches, AWM innovations could better consider local socio‐economic, governance, institutional and technological constraints, thus allowing for more contextualized and relevant innovations that can be scaled.

Understanding trends and gaps in global research of crop evapotranspiration: a bibliometric and thematic review

Estimating crop evapotranspiration (ETc) is crucial for ensuring sustainable and efficient agricultural water management. Although this subject has garnered significant attention from the global scientific community, a comprehensive study encompassing the diversity, trends, and dynamics of research themes is currently lacking. To address this knowledge gap, this review employed a combined bibliometric and thematic approach to analyze bibliographic data from 1872 documents retrieved from the Web of Science™ core collection, spanning the period 1987–2022. The main findings of this review are as follows: (1) the scientific landscape is predominantly shaped by institutions from the USA and China; (2) the journal Agricultural Water Management emerged as the most prolific, with the highest number of publications and total citations; (3) a broad range of topics within ETc research were identified, with a notable emphasis on remote sensing-related subjects; (4) strategic coordination mapping revealed that ETc and reference evapotranspiration (ETo) remains an underdeveloped area of study; (5) climate change and machine learning emerged as key topics of significant scientific concern. The results suggest a need for enhanced institutional collaborations and expanded research investigations, particularly in regions grappling with agricultural water scarcity. Furthermore, research investigations should focus on ETc and ETo to fill existing knowledge gaps and advance both theoretical understanding and practical applications. Future studies should aim to contribute to the understanding of the impacts of climate change on ETc by leveraging machine learning techniques and enhancing our understanding of crop water requirements and their application in irrigation management, while also ensuring continuous updates to the existing body of knowledge to meet future challenges.

CROP WATER CONSUMPTION MODELLING AT OUTLET LEVEL BY USING REMOTE SENSING AND GIS

The enlarged demand of water due to agriculture expansion, increasing industrial and domestic needs, compounded with climate change is causing scarcity of water globally and Pakistan is also facing same conditions. Due to limited availability of water, it is necessary to adopt global best practices being employed for sustainable agricultural water management. Remote sensing techniques are being intensively used around the globe with confidence to monitor the crops growth and crop water use. The aim of the research is to develop crop water consumption monitoring system at the outlet level to regulate irrigation water distribution at farm level. Surface Energy Balance Algorithm for Land (SEBAL) is used to develop pilot case study for year 2017-18 at a distributary canal command level named 5R-Yousafwala located in Sahiwal District. Crop classification based on Landsat-8 imagery was provided by Punjab Irrigation Department. Landsat-8 and Sentinel-2 images were acquired for NDVI monitoring. Metrological data for Penman Monteith based evapotranspiration calculation was acquired from Global Land Data Assimilation System (GLDAS). Analogue cadastral maps of outlet command areas were converted to digital georeferenced format. The provided crop classification was verified after discussion with farmers during detailed field survey. In Rabi season of 2017-18, mean evapotranspiration (ET) actual was estimated about 352 mm, while in Kharif season of 2018, it was estimated as 425 mm. The crop water deficit was estimated as 36 % in rabi season and 30 % in kharif season. Irrigation share was only 37 % in Rabi season and 48% in Kharif season. Rainfall contributed 09 % in Rabi season and 30% in Kharif season. Groundwater was paramount contributor with 54 % share in rabi season while in Kharif season it contributed relatively minor share of 22%. The ET potential and ET actual fluctuate in all outlet commands due to the diverse crop cover and economic capacity to pump groundwater. The results could be proved very useful for irrigation department to monitor and reform the irrigation supplies at particular outlets by observing the crop demand, weather conditions and crop stage instead of monitoring outlet discharge only.

Determining the water footprint of sunflower in Turkey and creating digital maps for sustainable agricultural water management

Determining the water footprint of sunflower in Turkey and creating digital maps for sustainable agricultural water management

Demonstration of Center Pivot Uniformity Evaluation and Retrofit to Improve Water Use Efficiency

Agricultural irrigation is a primary user for freshwater withdrawal. Irrigation plays an important role in crop production, as it provides the benefit of reducing the effects of prolonged dryness and erratic precipitation. Center pivot irrigation system is the most common irrigation system in agriculture. As the center pivot irrigation system ages, the system could develop a leaking joint, clogged sprinklers, and physical damage. This can cause areas of non-uniformity that can lead to under- or over-irrigated in some areas of the land, resulting in excess energy use and cost, wasting resources, and environmental impacts. Thus, it is important to evaluate the performance of a center pivot irrigation system regularly to maximize return on investments and minimize wasting resources. This study focuses on evaluating the impacts and benefits of improved center pivot irrigation distribution uniformity by performing distribution uniformity evaluations pre- and post-retrofit. This study also focused on demonstrating an unmanned aerial vehicle (UAV) to assess the performance of the center pivot irrigation system in two irrigated farmlands. The Coefficient of Uniformity (CU), Distribution Uniformity (DU), and Scheduling Coefficient (SC) were calculated based on the catch can test data. The values were utilized to evaluate water and energy savings from the improved coefficients. The team has found that replacing sprinkler packages increased the CU from 78 to 89 and the DU from 77 to 82, and reduced the SC from 1.3 to 1.2 in Field A. In Field B, replacing sprinkler packages increased the CU from 73 to 91 and the DU from 62 to 84 and reduced the SC from 1.6 to 1.2. The estimated water savings in Field A due to the reduced scheduling coefficient was approximately 151,000 liters/hectare/year, with consideration of the corn and soybean rotation field in Michigan. The estimated water savings in Field B was 608,000 liters/hectare/year. The data from this demonstration study showed the value of distribution uniformity evaluation and retrofit of irrigation systems. This information will encourage farmers and agricultural industries to consider performing more distribution uniformity evaluations, ultimately improving irrigation water use efficiency and supporting sustainable water management in agriculture.

Significance of biopolymer-based hydrogels and their applications in agriculture: a review in perspective of synthesis and their degree of swelling for water holding.

Hydrogels are three-dimensional polymer networks that are hydrophilic and capable of retaining a large amount of water. Hydrogels also can act as vehicles for the controlled delivery of active compounds. Bio-polymers are polymers that are derived from natural sources. Hydrogels prepared from biopolymers are considered non-toxic, biocompatible, biodegradable, and cost-effective. Therefore, bio-polymeric hydrogels are being extensively synthesized and used all over the world. Hydrogels based on biopolymers finds important applications in the agricultural field where they are used as soil conditioning agents as they can increase the water retention ability of soil and can act as a carrier of nutrients and other agrochemicals. Hydrogels are also used for the controlled delivery of fertilizer to plants. In this review, bio-polymeric hydrogels based on starch, chitosan, guar gum, gelatin, lignin, and alginate polymer have been discussed in terms of their synthesis method, swelling behavior, and possible agricultural application. The urgency to address water scarcity and the need for sustainable water management in agriculture necessitate the exploration and implementation of innovative solutions. By understanding the synthesis techniques and factors influencing the swelling behavior of these hydrogels, we can unlock their full potential in fostering sustainable agriculture and mitigating the challenges posed by an ever-changing environment.

Spatio-temporal estimation of green and blue water consumptions and water and land productivity using satellite remote sensing datasets and WA+ framework: A case study of the Mahi Basin, India

The agricultural activities contribute to the largest share of water consumption in the arid and semi-arid basins. In this study, we demonstrate the application of Water Accounting Plus (WA+) for estimation of the green water consumption (ETGreen) and blue water consumption (ETBlue) for assessing the water productivity (WP) and land productivity (LP) to identify the bright-spots and hot-spots at the district administrative unit level for effectively managing the scarce water resources and sustaining food security in a highly non-resilient semi-arid basin of India. The WA+ framework uses satellite remote sensing datasets from different sources for this purpose and we used the data from 2003 to 2020. The long-term average of ETGreen and ETBlue in the Mahi basin is found to be 15.8 km3/year and 12.32 km3/year, respectively. The blue water index (BWI) and green water index (GWI) in the basin vary from 0.282 to 0.598 and 0.40–0.72. We found that the BWI is highest for the districts of Gujarat, whereas, the GWI is highest for the districts of Madhya Pradesh. The long-term average of the LP and WP for both the irrigated and rainfed cereals in the basin is found as 2287.71 kg/ha & 1713.62 kg/ha and 0.721 kg/m³ & 0.483 kg/m³ , respectively from 2003 to 2020. The WP (rainfed) of all the districts of the Gujarat is comparatively lower (varying from 0.34 kg/m³ to 0.5 kg/m³) than the districts of the Madhya Pradesh (varying from 0.59 kg/m3 to 0.70 kg/m³ ) and the Rajasthan (varying from 0.48 kg/m³ to 0.73 kg/m³ ). Based on the results, we found that the Ratlam district of the Madhya Pradesh has both highest LP and WP (irrigated) as 2573.96 kg/ha and 2.14 kg/m3, respectively among all the districts of the Mahi basin, and hence it is classified as the ‘Bright spot-district’. The Anand district is found to have the lowest WP and LP as 0.44 kg/m3 and 2467.51 kg/ha, respectively and hence it is classified as the ‘hot spot-district’. For rainfed cereals, we found that the Neemuch district of Madhya Pradesh has the highest WP and LP as 0.59 kg/m3 and 1948.13 kg /ha, respectively, and the Anand district with the lowest WP as 0.34 kg/m3 and LP of 1572.21 kg/ha, respectively. Therefore, we classified the Neemach district as the ‘Bright spot-district’ and the Anand district as the hot spot- district for rainfed cereals. These findings will help develop sustainable and actionable agricultural water management plans by the policymakers and stakeholders in the basin.

IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning

Agricultural irrigation, as an important practice to protect crops from drought and promote grain yield, has a long history in China. A timely and precise dataset about the extent and dynamics of irrigated areas is necessary for water allocation and agricultural management but is scarce in China. Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. First, we generated annual nationwide training samples by strictly screening the existing irrigation maps downscaled from the statistical data. Second, we implemented locally adaptive random forest classifiers in 511 nominal 1° × 1° grid cells across China with MODIS vegetation indices, climatic factors, and topography variables. Third, we conducted nationwide pixel-wise validation of the IrriMap_CN using independent samples. The validation results based on more than 3000 ground truth points revealed that IrriMap_CN had high accuracies ranging from 77.2% to 85.9%. The time series of IrriMap_CN detected substantial expansion of irrigated areas in Xinjiang and Heilongjiang (more than 50% in total) and pronounced decreases in Sichuan, Jiangsu, and Hebei. The analyses of irrigation frequency, start time, and end time implied that North China Plain was the most intensive irrigated area; but the irrigation area showed a decreasing trend since 2000, consistent with the reduced agricultural water consumption. The annual irrigation datasets allow us to understand the spatiotemporal dynamics of irrigated croplands in China and are expected to contribute to the improvement of earth system models and facilitate sustainable agricultural water management.

Agricultural Reservoir Operation Strategy Considering Climate and Policy Changes

Agricultural water is affected by climate change and water management. Agricultural reservoirs are increasing demand on the environmental water supply because the Korean government has recently implemented an integrated water resource management policy. However, agricultural reservoirs are still in operation solely to supply agricultural water. To examine sustainable agricultural water management under climate change, we analyzed the strategy of operating regulations to efficiently distribute agricultural water as environmental water. We simulated the agricultural reservoir operation, analyzing its water supply capacity by applying operation regulations. The simulation predicted that future water supply capacity would decrease if the existing operation were maintained, and agricultural reservoir operation will be necessary in the future. The proposed reservoir operating strategy decreased the maximum water shortage and number of water shortage days compared with the existing operation with the required water supply. Our results can contribute to agricultural reservoir operation strategies and sustainable water management in response to climate change and provide decision-making guidance on water distribution for environmental use in response to water management policy changes.

A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future

Evapotranspiration (ET) is a major component of the water cycle and agricultural water balance. Estimation of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Applications of precision and digital agricultural technologies, the integration of advanced techniques including remote sensing and satellite technology, and usage of machine learning algorithms will be an advantage to enhance the accuracy of the ET estimation in agricultural water management. This paper reviews and summarizes the technical development of the available methodologies and explores the advanced techniques in the estimation of ET in agricultural water management and highlights the potential improvements to enhance the accuracy of the ET estimation to achieve precise agricultural water management.

Solar-powered irrigation system as a nature-based solution for sustaining agricultural water management in the Upper Indus Basin

Rapid deglaciation in form of lowering of glacial surface under growing warm temperatures form a major cause of disruption in meltwater flows to cryosphere-fed irrigation network which usually affects the agriculture productivity and livelihoods in the Upper Indus Basin (UIB). In such hotspots of cryosphere change and other vulnerable areas, solar powered irrigation system (SPIS) can be adopted as a nature-based solution to uplift water from the nearby glacio-fluvial streams for irrigation and domestic use. In the present study, solar energy harnessing potential was investigated in the UIB region of Pakistan based on climate and topographic suitability to adopt solar powered irrigation system (SPIS) in the region. Overall mean temperature and elevation were found suitable for harnessing solar energy in major parts of the Hindu Kush-Karakoram-Himalaya (HKH) ranges of the UIB. The slope index indicated high suitability for harnessing solar energy in about 92.5% area, while aspect exhibited high suitability in about 13.3% area and medium in 25.3% area of the HKH region. The slope-aspect (SLA) suitability was found good in about 13.5% area of the Hindu Kush, 12% of the Karakoram and 10.9% area of the Himalaya range, whereas it was observed medium in about 26.7% area of the Hindu Kush, 21.8% of the Karakoram and 22.2% area of the Himalaya range. Overall the SLA suitability was found good in about 12.1% area and medium in about 23.5% area of the three HKH ranges. However, maximum benefits of the SPIS approach can be acquired through linking it with integrated water resource management approach for sustainable agricultural water management in the region.