Developing of Integrated Modelling Framework to Evaluate Sustainability of Climate Smart and Drought-Resilience Agricultural Technologies by Concerning Water, Land and Food Security Network.

Prasain, suresh

doi:10.5281/zenodo.18025744

Developing of Integrated Modelling Framework to Evaluate Sustainability of Climate Smart and Drought-Resilience Agricultural Technologies by Concerning Water, Land and Food Security Network.

Prasain, suresh

Publisher: Zenodo
DOI: 10.5281/zenodo.18025744
Repository: zenodo.org
Language: en

Abstract

Australia's climate change is apparent through increasing temperatures, unpredictable rainfall, and rising sea levels, reflecting worldwide patterns (Davamani et al., 2024; Gyanendra et al., 2022; IPCC, 2014; Kashif et al., 2022). It greatly affects water resources, leading to water insecurity through a reduction in stream flow and a drop in groundwater levels by 1 m/year, as stream flow and groundwater recharge are largely dependent on yearly and seasonal rainfall (Mahdavi and Samani, 2024; BOM, 2023; Cook et al., 2022; Maheshwari, 2021). Consequently, it increases drought and negatively impacts the agricultural system by diminishing crop yields, elevating pests and diseases, and lowering the average profits of farms (Paswan et al., 2024; Alexandra, 2022; CCA, 2023). It poses a considerable risk to land use alteration in the agricultural sector, including ongoing intensification of land use for farming, shifting agricultural areas, increasing desertification, and greater dependence on unsustainable farming practices (Jayaramu et al., 2024b, 2024a; Zhang et al., 2024; Mewett et al., 2013; Millar and Roots, 2012), which has become a primary factor in the rising agricultural water demand alongside the rapid growth of the population (Salim et al., 2024; Cook et al., 2022; Maheshwari, 2021). Consequently, changes in climate and land use negatively affect the sustainability of water and food security in Australia (Doble et al., 2024; Cook et al., 2022) due to a rising demand paired with diminishing water resource availability (both surface and groundwater) with increasing drought and desertification in cultivated areas (Jayaramu et al., 2024b; Xue et al., 2024; Dhungana et al., 2024; Mensah et al., 2022b, 2022a). This trend is evident in major iconic water resources and aquifers across Australia, including the Murray-Darling River Basin, Lake Eyre Basin, Great Artesian Basin, Perth Groundwater, and Daly River Basin (CSIRO, 2024; Crosbie et al., 2012, 2010; Maheshwari, 2021; Smerdon et al., 2012) since the early 1900s. Nonetheless, specific details regarding the present and upcoming effects of climate and land use changes on water quantity (stream flow, water yield, recharge rate, depth, and storage yield) as well as an assessment of agricultural sustainability in relation to water and food security remain unclear (Mondal et al., 2024; Crosbie et al., 2013; Smerdon et al., 2012). In addition to this, there is a shortage of developing and implementing effective methodologies and models for combining demand-supply analysis to enhance the sustainability of agricultural systems regarding water and food security in the context of climate change and land use in Australia and globally (Davamani et al., 2024; Dao et al., 2024; Cook et al., 2022; Al Atawneh et al., 2021). On this context, this paper focuses on developing an effective integrating model that can be used for measuring possible climate-smart and drought-resilience agricultural practices while considering sustainable water and food security under changing climate and land use scenarios of Australia. A critical and comprehensive literature review has been conducted to evaluate and analyse modelling framework use in our topic by using the PRISMA-P framework and the Double Diamond Approach for the selection of review and empirical research articles (1990-2025), which are sourced from Web of Science, Scopus, ScienceDirect, and Google Scholar. Finally, 875 (75 reviews and 800 empirical) articles have been selected for our review study. From this study, we classify this time period (1990-2025) such a that 1990s represented the era of foundational knowledge of the topic, the 2000s marked the introduction of climate and land use modelling and GIS tools to this research, the 2010s signified the emergence of integrated and coupled models, and the 2020s highlight the ascendancy of AI/ML/DL, use of big data for integrating of Crop models, MCDMs in to GSBMs for policy link, and use of hybrid models (SWBMS-HNMs, SWBMS/HNMs-WQBMs and hydro–crop-climate–land models ) in sustainable use of water in agriculture by using climate smart and drought resilience agricultural practices. In this context, various Soil and Water Balance Models (SWBMs) combined with Hydrogeological and Numerical models (HNMs) like the SWAT-MODFLOW model (Bamal et al., 2024; Zeydalinejad and Nassery, 2023; Mensah et al., 2022b), the WETSPASS-MF-OWHM model (Jasechko et al., 2024; Soltani et al., 2023; Page et al., 2023; Soundala and Saraphirom, 2022; Hamdi and Goita, 2023), the SWAT-MODFLOW-WEAP model (Abbas et al., 2022; Dehghanipour et al., 2019), and the SWAT-MODFLOW-GRACE-FO model (Mundetia et al., 2024; Hamdi et al., 2020; Mohamed et al., 2017) have been employed for assessing quantitative water supply metrics (stream flow, water yield, and groundwater storage). Similarly, the WEAP-MABIA model (Chapagain et al., 2025; Rhymee et al., 2024; Guan and Mascaro, 2023), the CROPWAT Model (Casella et al., 2019; Al-Najar, 2011), and the ARIMA-ETS-NNAR model integrates with GIS (Rajballie et al., 2022), are utilized for water demand analysis. Certain Crop Growth Simulation Models (CGSMs) such as DSSAT, APSIM, ABM, and AUACROP, integrated with SWBMs or HNM (Siad et al., 2019), including the Hydrus 1D-DSSAT model (Shelia et al., 2018), DSSAT-RZWQM model (Ma et al., 2006), DSSAT-SWAP model (Dokoohaki et al., 2016), DSSAT-ABM model (Phetheet et al., 2021) and SWAT-MODFLOW-AQUACROP model (Hu et al., 2025) were utilized to assess adaptive capacity, policy effectiveness, and agricultural system resilience through the simulation of crop growth, yield, and water needs. Techniques for Multi-Criteria Decision-Making (MCDM) like AHP, TOPSIS, EWM, and VIKOR (Paul and Roy, 2024; Mundetia et al., 2024; Yuan et al., 2024) are integrated with several groundwater vulnerability or sustainability assessment methods (Chandra and Sahoo, 2023; Bordbar et al., 2023; Bhattacharya et al., 2020) including the DRASTIC-AHP/TOPSIS/VIKOR model, GRACE-AHP/TOPSIS/EWM model, GLADI-AHP/TOPSIS/EWM model, AQUIVAL-AHP/TOPSIS model, and the SWAT-MODFLOW-WEAP-AHP/TOPSIS/VIKOR/EWM model (Paul and Roy, 2024; Mundetia et al., 2024; Hanifehlou et al., 2022) applied for evaluating water sustainability across various climatic and land use scenarios. These separate studies have shown the effectiveness of hydrological modelling (e.g., SWAT-MODFLOW), water demand simulation (e.g., WEAP-MABIA), crop modelling (e.g., DSSAT, APSIM), and decision-making tools (e.g., VIKOR). However, there is a lack of thorough and cohesive studies that integrate these tools into a unified methodological framework. Additionally, prior studies inform us in developing a comprehensive integrated modelling framework that combines these tools (SWBMs, HNMs, CGSMs, and MCDMs) into a unified methodological structure by incorporating hydrological, agronomic, climate, land, and socio-economic elements to assess the long-term impacts of climate and land changes, trade-offs, and evaluate the effectiveness of climate-smart and drought-resilient agricultural adaptation technologies (Sahoo et al., 2025; Regmi and Paudel, 2024; Thottadi and Singh, 2024) regarding water and food security (Dzvene et al., 2025; Davamani et al., 2024; Xue et al., 2024; Salim et al., 2024). This research develops an innovative integrated model by the combination of WETSPASS/SWAT-MODFLOW (for water supply), WEAP-MABIA (for water demand), DSSAT (for agricultural productivity), and VIKOR (for sustainability ranking) to assess climate-smart and drought-resilient adaptation technologies in a comprehensive manner. It incorporates climate projections, land use dynamics, hydrological processes, and crop growth responses using the Integrating Modelling Framework, in which the WETSPASS/SWAT-MODFLOW will be used for quantifying groundwater and surface water availability, WEAP-MABIA will be employed for assessing water demand for agricultural use, and WEAP-DSSAT-VIKOR will be utilized for evaluating the sustainability of climate-smart and drought-adaptation technologies using the MCDM tool. The Decision Support System for Agrotechnology Transfer (DSSAT) model is selected as the core crop simulation tool for this research due to its broad applicability, strong integration capabilities, and suitability for evaluating climate-smart and drought agricultural adaptation technologies (Sahoo et al., 2025; Chandra and Sahoo, 2023; Dokoohaki et al., 2016). DSSAT supports a wide range of crops commonly cultivated in Australia and is capable of simulating crop growth, yield, and water use under varying climate and land use scenarios. It has been widely used in conjunction with water management models, such as WEAP, and decision-support tools like VIKOR, making it ideal for the integrated assessment framework adopted in this study. While APSIM, a model developed in Australia, offers robust capabilities for dryland and rotational cropping systems (Siad et al., 2019), DSSAT is preferred here due to its interoperability, multi-crop flexibility, and established application in sustainability assessments that involve both agronomic and hydrological parameters. In conclusion, the integrating modelling framework has been developed to evaluate the sustainability of climate smart and drought resilience agricultural adaptation technologies concerning water and food security under changing climate and land use scenarios in Australia by achieving the following Outputs: creating GIS-based maps of 1. water availability, 2. water demand, and 3. crop yield potential; 4. developing sustainability zoning maps grounded in climate-smart and drought-resilient agricultural technology considering climate-land use change scenarios; and 5. formulating policy recommendations for climate-resilient agriculture.

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unisq.edu.au CRICOS: QLD 00244B, NSW 02225M TEQSA: PRV12081 This content is protected and may not be shared, uploaded, or distributed Suresh Prasain a a School of Surveying and Engineering, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Email: [email protected] Developing of Integrated Modelling Framework to Evaluate Sustainability of Climate Smart and DroughtResilience Agricultural Technologies by Concerning Water Land and Food Security Network. Introduction Climate Change-CC (increasing temperatures, unpredictable rainfall, changing pattern of ENSO, IDO, SAM and rising sea levels) and Land Use Change (LUC) affect resource nexus by effecting the sustainability of WaterLand-Food (WLF) security networking system (Chapagain et al., 2024; Davamani et al., 2024). There is lack of developing integrative and comprehensive modelling methodologies to combine demand and supply analysis of water to enhance the sustainability of WLF security systems in the context of Climate and Land Use Change-CLUC (Dao et al., 2024; Cook et al., 2022) Literature review inform need to develop a comprehensive integrated modelling framework that combines SWBMs, HNMs, CGSMs, and MCDMs models into a unified methodological structure by incorporating hydrological, agronomic, climate, land, and socio-economic elements together (Sahoo et al., 2025; Regmi and Paudel, 2024) Research Objective Major aim is to develop effective integrating modelling framework that can be used for evaluating of possible climate smart and drought resilience agricultural practices with considering WLF security system under changing scenarios of CC and LUC in Australia. Specific Objectives 1. To estimate water balance components under changing scenarios CLUC. 2. To estimate agricultural water demand and assess extraction patterns across these scenarios. 3. To evaluate the demand-supply gap and water policy impacts in WLF security concerns. 4. To examine climate-smart and drought resilience adaptation strategies for maximizing crop yield while minimizing water use Current and future meteorological data: temperature, wind speed, relative humidity, solar radiation and rainfall. Preparation of Adaptation Strategies based on DemandSupply of water to adopt future drought. Research Objective 3 Physical characteristics Data: DEM map, User Soil Maps, elevation, aspect, slope and topographic features GIS based Maps. Land Use Change Data: climate change impact to Land Use Maps Plant Growth and Management Data: Plant rooting depth, crop yield indexes, NDVI and Leaf Area Index (LAI) Maps. Hydrological and Hydrogeologic Data: Geological information, Surface flow and aquifers parameters, initial groundwater head. SWAT+ Model SWAT-MODFLOW Model WEAP-MABIA Preparation of Spatial Based Inputs Files Simulated Water Balance Data Observed Groundwater/ Surface water data. Water supply/availability data: depth, flow and storage yield Analysis Agricultural Water Demand Analysis Calibrated and simulated GW supply/availability data by NSE, MSE, RMSE, PBias. MARE. Calibrated and simulated GW demand data by NSE, MSE, RMSE, PBias. MARE. Observed (Surface/Subsurface) supply and demand data. Research Objective 1 Measurements of sustainability indexes and developing of susceptibility zoning maps by using WEAP and VIKOR models Research Objective 2 Agricultural Infrastructure Development and Demographic Data. Examine of Sustainability of agricultural climate resilience adaptation technologies and analysis with existing and future strategies and policies by using DSSATWEAP-VIKOR model. Research Objective 4 Figure: Integrating Modelling Farmwork for evaluating of the sustainability of climate smart and drought resilience agricultural adaptation technologies concerning WLE security under CLUC scenarios. Methodology Using PRISMA-P framework with Double Diamond Approach (DDA) for the selection of review and empirical research articles from Web of Science, Scopus, ScienceDirect, and Google Scholar database for a Systematic Literature Review (SLR) to analysis modelling system on this topic. Comprehensive review from selected 875 (75 review and 800 empirical) articles show that 1990s represented the era of foundational knowledge, the 2010s signified the emergence of integrated and coupled models, and the 2020s highlight the ascendancy of AI/ML/DL, use of big data for integrating of Crop models, MCDMs integrated into CSBMs for policy link, and use of hybrid models (SWBMs-HNMs, SWBMS/HNMs-WQBMs/CSBMs and hydro–crop-climate–land models ). Abbreviations: AI/ML/DL: Artificial Intelligence/Machine Learning/Deep Learning; CC: Climate Change; LUC: Land Use change; CLUC: Climate and Land Use Change; SWBMs: Soil and Water Balance Models; HNMs: Hydrological and Numerical based Models; WQBMs: Water Quality Based Models; CSBMs: Crop Simulation Based Models; SLR: Systematic Literature Review; PRISMA-P: Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. Result and Conclusion Develops an innovative integrated modeling framework by the combination of SWAT+/SWAT-MODFLOW (for water supply), WEAPMABIA (for water demand), DSSAT (for agricultural productivity), and VIKOR (for sustainability ranking) to assess climate-smart and droughtresilient adaptation technologies in a comprehensive manner to address WLE security nexus under scenarios of CLUC . Possible Outputs: creating GIS-based maps of 1. water availability, 2. water demand, and 3. crop yield potential; 4. developing sustainability zoning maps for climate-smart and drought-resilient agricultural technologies to address WLE security nexus by considering CLUC scenarios; and 5. formulating policy recommendations for applying climate and drought resilient agriculture technologies.