Modeling Crop Water Productivity Using a Coupled SWAT–MODSIM Model

Leveraging remote sensing for optimised national scale agricultural water management in South Africa

Agriculture remains a critical water resources consumer in arid regions, globally, including southern Africa. The intensity of consumption, however, varies significantly depending on the adopted watering method (i.e., rainfed or irrigated) and agricultural region. Efficient agricultural water management hinges on effectively monitoring Crop Water Use (CWU) and Crop Water Productivity (CWP). This study, thus, leveraged Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data in estimating the spatio-temporal variations of CWP and CWU across irrigated and rainfed croplands in diverse South African agricultural regions between 2017 and 2022. The results showed that rainfed croplands had higher CWU in agricultural regions dominated by grains (150 mm/yr) and cattle (160 mm/yr), while irrigated croplands exhibited the highest CWU in agricultural regions with sheep rearing (175 mm/yr) and subsistence agricultural activities (160 mm/yr). However, there were no significant differences (p > 0.05) in overall CWU across all the agricultural regions. Irrigated croplands generally had higher annual CWP (>0.002 kg/mm3/yr), while rainfed croplands consistently showed low CWP especially in forestry (0.001 kg/mm3/yr) and sugar (0.0012 kg/mm3/yr) agricultural regions. There were also no significant differences in average CWP between irrigated and rainfed croplands (p > 0.05). This study demonstrates the effectiveness of national-scale remotely sensed data in monitoring the spatiotemporal variations of CWP and CWU in South Africa. The results can be used to tailor strategies to specific agricultural regions and crop types and optimise water use efficiency. This would contribute significantly to sustainable national-scale agricultural water management in South Africa.

Assessment of the effects of climate change on water balance components in the upper Erer subbasin, Ethiopia

Eastern Ethiopia watersheds are located in transition zone from Arid to semi-humid climate and in expanding to westwards the west annual rainfall is highly declining. This paper explains future hydrological response impacts under changing climate using ensemble average of the CORDEX RCMs for historical (1979-2014) and future (2024-2070) periods. The result revels the monthly average temperature varies (0.04-6.25°C) for RCP-4.5, while it varies (0.03-6.59°oC) for RCP-8.5. The monthly average rainfall to be decline by 90.71 mm and rise by 211. 22 mm for RCP-4.5, while it is going to decline by 84.97 mm and rise by 235.62 mm for RCP-8.5. The adjusted SWAT model was used to detect the changes of projected hydrological response from reference period. Balance components of the baseline period was compared to future period. The result shows the change in decrease of annual mean surface flow (4.98 %-5.63 %), groundwater flow (5.63 %-6.68 %), evapotranspiration (2.45 %-2.57 %) and water yield (5.54 %-5.21 %) to be expected from RCP-4.5 to RCP-8.5. The findings of this paper provide valuable assistance to water resource planners by enhancing their comprehension of change in climate effects at local level.

Modelling the impact of different irrigation regimes and mulching on strawberry crop growth and water use in the arsenic-contaminated Bengal basin

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.

Remote Sensing, Geophysics, and Modeling to Support Precision Agriculture—Part 2: Irrigation Management

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.

Evaluating and improving the sustainability of ecosystem services in river basins under climate change

This paper presents a new framework for evaluating the sustainability of basin-wide ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services. In this framework, the Soil and Water Assessment Tool (SWAT) and MODSIM¹ models and experts' opinions are used to evaluate the ESs. To show the applicability of the proposed framework, it is applied to the Zarrinehrud river basin under three different climate change (CC) scenarios (i.e., RCP 4.5, 6.0, and 8.5) for two different time horizons (i.e., 2020-2049 and 2020-2098). This basin is the main water supplier of the largest hypersaline lake in the Middle East, Lake Urmia. In the next step, 128 water resources management (WRM) scenarios are taken into account considering the projects defined by Urmia Lake Restoration National Committee (ULRNC). All ecosystem services are evaluated considering all WRM and CC scenarios. Finally, a group COPRAS-based decision-making approach is used to determine the best WRM scenario under climate change. The results show that WRM scenario 128 is the best scenario for improving ecosystem services in the study area. This scenario includes some projects such as allocating water to the lake from new resources, rehabilitating irrigation and draining networks, and improving cropping patterns.

Evaluation of Agricultural Water Supply and Selection of Deficient Districts in Yeongsan River Basin of South Korea Considering Supply Priority

Considering the priority of water supply, agricultural water supply capacity downstream of the Yeongsan River Basin was evaluated in this study. The water balance was analyzed using the Please check all author names carefullyMODSIM-decision support system (MODSIM-DSS) to evaluate the agricultural water supply capacity. MODSIM-DSS can also be used to analyze the watershed demand status and agricultural water supply facilities. In this study, the watershed inflow and agricultural reservoir inflow (supply) data for each watershed were obtained using the SWAT outflow data. SWAT was calibrated and validated using 16-years (2005–2020) of daily streamflow data from one water level station and two weirs, by considering water withdrawal and return flows from agricultural, domestic, and industrial water use. The coefficient of determination (R2) was analyzed as 0.50 to 0.80 at three stations. Then, water balance analyses were performed for 41 years (1980–2020) by applying the SWAT outflow results to MODSIM-DSS. The results showed that agricultural water shortages of 517.8 × 106 m3, 520.0 × 106 m3, 579.8 × 106 m3, and 517.5 × 106 m3, occurred in 1992, 1994, 1995, and 2015, respectively. The efficiencies of agricultural water supply for these years were 58.2%, 58.0%, 53.2%, and 58.2%, respectively, which were lower than the 40-year average of 84.5%.

Evaluating the resilience of water resources management scenarios using the evidential reasoning approach: The Zarrinehrud river basin experience

This paper introduces a new methodology for quantifying the total resilience of water resources management scenarios. The climate change impacts on water supply and demand have been investigated using a calibrated soil and water assessment tool (SWAT) and a MODSIM water allocation model. Several criteria have been defined to measure five aspects of water resources systems resilience. The first aspect defines resilience as system strength against crossing a performance threshold (reliability). In the second aspect, if the system crosses the performance threshold, the recovery rate of the system after a disturbance is evaluated. The violation from the performance threshold has been measured as the third aspect (vulnerability), which considers the failure's severity. The fourth aspect is the resilience under extreme events with unknown occurrence probability, which includes four sub-criteria, namely rapidity, robustness, resourcefulness, and redundancy (4 R). Finally, the fifth criterion considers the ecological condition of the system (ecological index). To compare water resources management scenarios (alternatives), an analytical evidential reasoning-based (ER) approach has been used. To show the applicability of the proposed methodology, it has been applied to the Zarrinehrud river basin, which is the leading water supplier of Lake Urmia in Iran. As one of the largest saline lakes globally, this lake has been suffering from drastic desertification and salinization in the past two decades. The grade-based results of the performance criteria are synthesized into a grade-based total resilience criterion to facilitate the comparison of water resources management scenarios. It is shown that a scenario which results in 40% reduction in agricultural water demand until 2023 has the highest resilience and an acceptable construction and operational cost.

A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use

Socioeconomic development in watersheds lead to land-use changes, which can alter water and sediment inflows into reservoirs, leading to uncertainty in water supply reliability. A modelling framework coupling the Soil and Water Assessments Tool (SWAT) and the @RISK genetic algorithm optimisation tool was developed to optimise water allocation and estimate water supply reliability under uncertainty in future land-use. The multi-purpose Nuicoc reservoir in Vietnam was used as a case study. Modelling results showed that an expansion of the urban areas by 10% and conversion of 5% of the forest to agricultural areas produced the highest water releases for downstream demands of all simulated scenarios, with 5 Mcm/year greater water releases than the baseline for the case where sedimentation was not considered. However, when sedimentation was considered, it generated the greatest decrease in water releases, with 6.25 Mcm/year less than the baseline. Additionally, it was determined that spatial distribution of land-use significantly affect sediment inflows into the reservoir, highlighting the importance of targeted sediment management. This demonstrates the usefulness of the proposed framework for decision-makers in assessing the impact of possible land-use changes on the reservoir operation.

Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT's database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75-80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

Estimation of Water Budget Components of the Sakarya River Basin by Using the WEAP-PGM Model

The use of water resources has increased with rapid population growth, industrial development, and agricultural activities. Besides, the problem might increase with the potential climate change impacts on water quantity. Thus, sustainable use of water resources becomes crucial. Modeling studies provide scientific support to the analysis of water resource problems and develop strategies for current and potential problems for the sustainable management of water resources. In this study, WEAP-PGM (Water Evaluation and Planning System—Plant Growth Model) was applied to the Sakarya River Basin in Turkey, where almost 50% of the area is agricultural land. The main goals in the study are compiling/integrating available data from different sources in a data-scarce region for hydrological models, and estimating the water budget components of Sakarya River Basin on an annual basis as well as investigating the applicability of WEAP-PGM. General model performance ratings indicated that model simulations represent streamflow variations at acceptable levels. Model results revealed that, runoff is 4747 million m3, flow to groundwater is 3065 million m3 and evapotranspiration is 23,011 million m3. This model setup can be used as a baseline for calculating the crop yields under climate change in the context of water-food-energy nexus in the further studies.

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R2) = 0.787 and Nash–Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models.

Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

Estimating Changes in the Green Water Productivity of Cropping Systems in Northern Shaanxi Province in China’s Loess Plateau

Water shortages limit agricultural production in the world’s arid and semi-arid regions. The Northern region of China’s Shaanxi Province, in the Loess Plateau, is a good example. Raising the water productivity of rainfed grain production in this region is essential to increase food production and reduce poverty, thereby improving food security. To support efforts to increase crop water productivity (CWP), we accounted for limitations of most existing studies (experimental studies of specific crops or hydrological modeling approaches) by using actual field data derived from statistical reports of cropping patterns. We estimated the CWPs of nine primary crops grown in four counties in Northern Shaanxi from 1994 to 2008 by combining statistics on the cultivated area and yields with detailed estimates of evapotranspiration based on daily meteorological data. We further calculated both the caloric CWP of water (CCWP) and the CWP of productive water (i.e., water used for transpiration). We found that regional CWP averaged 6.333 kg mm–1 ha–1, the CCWP was 17,683.81 cal mm–1 ha–1, the CWP of productive green water was 8.837 kg mm–1 ha–1, and the CCWP of productive green water was 24,769.07 cal mm–1 ha–1. Corn, sorghum, and buckwheat had the highest CWP, and although potatoes had the largest planted area and relatively high CWP, they had a low CCWP.