International Water Management Institute

view full article: https://rdcu.be/ehEgq The interaction between climate change and agricultural intensification contributes to biodiversity loss, while widespread degradation of land and water undermine food system productivity. Agroecological principles aim to guide food systems transformation but rarely refer to water or aquatic foods, which are critical elements of nutritious, sustainable and equitable food systems. Here we examine the principles and frameworks presented in agroecological literature and suggest rephrasing of six of the principles to incorporate water, aquatic foods and land- to seascapes. We recommend three cross-sectoral actions that leverage aquatic features in agroecosystems to facilitate more effective transition pathways towards sustainable food systems.

This chapter assesses the variations and options for improving water productivity to address water risks and insecurity in South Asian countries. The water productivity indicators of focus are physical water productivity (PWP)—the production per unit of water use, and economic water productivity (EWP), the value of production per unit of water use. A significant potential exists to increase PWP in many South Asian countries and regions with no water scarcity. These regions require increased access to water. However, increasing EWP should take precedence under water-scarce conditions. The latter may require reducing water-intensive crop areas and diversifying to less water-intensive crops.

Sri Lanka Government’s ambitious decision to ban synthetic agrochemicals, including chemical fertilizers (and pesticides), in April 2021 made it the first nation in the world to embark on a full-scale transition to – as the Government called it—organic farming, and address concerns about human health and the environment. Previous policies had envisioned a gradual shift, but the sudden ban caught agriculture off guard. Declining foreign exchange reserves to import chemical fertilizers and coinciding peak fertilizer prices appeared to support the timing of the move. However, the ensuing rush for organic fertilizers failed to meet the national demand, resulting in severe losses in rice and export-oriented plantation crops. Facing decreasing yields and food insecurity, the government lifted the ban in November 2021. The events raised critical questions about the necessity and feasibility of such a drastic transition and alternative ways. To explore the general feasibility of transitioning toward organic fertilizers, this study considered the actual and potential availability of biomass to “replace” chemical fertilizers at the national scale as was envisioned by the Government. The analysis focused on the four main national crops and showed that in none of the selected scenarios, Sri Lanka’s actual and potentially available organic fertilizer could supply rice- and plantation-based agrosystems with sufficient nitrogen, not to mention other crops or nutrients. The Government will in every scenario, including one that assumes a stepwise transition, remain compelled to spend significantly on importing organic fertilizer to maintain the required crop yields, which would cost the Government more foreign currency than purchasing chemical fertilizer. Even more costly is purchasing rice to close the national production gap, as Sri Lanka eventually did at the end of its nationwide experiment, which resulted in major food security concerns.

Risk and vulnerability assessment in food systems has become indispensable in the context of climate change as the implications are significant for the food supply chain stakeholders and food security. The goal of this study was to identify food system vulnerabilities, and elements contributing to vulnerabilities and propose practical interventions to increase food system resilience to climate change. This study concentrated on the upcountry vegetable supply chains. Heavy rain and landslides were identified as the key climatic hazards affecting upcountry vegetable-producing districts Nuwara Eliya and Badulla. Severe adverse climate events have affected vegetable production and post-harvest handling in both districts. There was a significant correlation between the severity of the climate hazard and vegetable production. The case study conducted with upcountry vegetable farmers and supply chain actors confirmed these findings. In general, the climate change adaptation measures along the supply chain were limited. The research reveals an association between climate hazards and agricultural production and distribution networks. The lack of reliable data was a major challenge for undertaking risk and vulnerability assessments of supply chains to facilitate interventions to build resilient food supply chains.

The Akaki River, in Ethiopia, becomes a source of antimicrobial-resistant (AMR) pathogens and genes that are spreading to receiving water. Water quality monitoring (WQM) is limited in Akaki, and the available evidence is based on short-term monitoring of inconsistent sampling sites and water quality parameters. Therefore, we designed a suitable WQM plan for the Big Akaki River receiving wastewater from rural, urban, and peri-urban areas. WQM plan was designed by employing multiple approaches including literature review, field observations, spatial analysis, and pollutant “hotspot” identification. Information was extracted through a systematic review of 48 articles, selected through a screening process, to guide the selection of suitable monitoring sites. Field observation was used to inspect previously sampled sites and identify pollution sources and exposure routes to antibiotic-resistant bacteria and zoonotic pathogens. For validation, water samples were collected from 40 sites identified from the literature review and field observation, and results were refined during a stakeholder consultation workshop. Hotspots were identified based on chemical oxygen demand, dissolved oxygen, ammonia, and extended-spectrum βeta-lactamase (ESβL)-producing Escherichia coli and Salmonella enteritidis/Shigella flexneri data. Cluster analysis of the water quality data categorized the 40 sites into three groups, and the number of sites for future monitoring to 20, including possible pollutant hotspots, reference sites, known pollution sources, exposure routes, and availability of river discharge data. The WQM plan will help AMR and zoonotic pathogens monitoring and mitigation in the study sites. Our approach can be replicated to design WQM plans for other rivers.

The Limpopo River Basin (LRB), a transboundary river basin extending over Botswana, Mozambique, South Africa, and Zimbabwe, is highly vulnerable to drought. This manuscript analyzes drought conditions in the LRB using Earth Observation (EO) datasets and key drought indices such as the Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI). The year 2023, marked by the El Niño phenomenon, exacerbated dry conditions, resulting in prolonged water shortages and reduced agricultural output. Approximately 37% of the basin has been experiencing drought since the 2023–2024 cropping season, impacting ecosystems and crop yields. The present manuscript presents a comprehensive analysis of drought conditions in the LRB and applications of the Digital Twin platform for the LRB to support resource allocation for agricultural planning. Integrating multiple near real-time datasets, the platform enables policymakers to visualize and analyze drought conditions, enhancing decision-making for sustainable resource management and food security in the basin.

This paper introduces and applies iGain4Gains, an Excel-based model, to reveal how changes to water conservation and allocation, and irrigation technology, can produce four nexus gains. These gains are; reduced aggregate water consumption, sustained crop production, lower carbon emissions, and enhanced water availability for nature. We developed the model with limited data and hypothetical future scenarios from the Amman–Zarqa basin in Jordan. Given its significant irrigation and urban water demands and difficult decisions regarding future water allocation and nexus choices, this basin is a highly appropriate case study. The paper’s primary aim is to demonstrate the iGains4Gains nexus model rather than to build an accurate hydrological model of the basin’s water resources. The model addresses two critical questions regarding increased irrigation efficiency. First, can irrigation efficiency and other factors, such as irrigated area, be applied to achieve real water savings while maintaining crop production, ensuring greenhouse gas emission reductions, and ‘freeing’ water for nature? Second, with the insight that water conservation is a distributive/allocative act, we ask who between four paracommoners (the proprietor irrigation system, neighbouring irrigation systems, society, and nature) benefits hydrologically from changes in irrigation efficiency? Recognising nexus gains are not always linear, positive and predictable, the model reveals that achieving all four gains simultaneously is difficult, likely leading to trade-offs such as water consumption rebounds or increased carbon emissions. Demonstrated by its use at a workshop in Jordan in February 2024, iGains4Gains can be used by students, scientists and decision-makers, to explore and understand nexus trade-offs connected to changes in irrigation management. The paper concludes with recommendations for governing water and irrigated agriculture in basins where large volumes of water are withdrawn and depleted by irrigation.

Groundwater is a crucial resource to support surface water bodies via groundwater discharge. In this study, we applied two methods of estimating global environmentally critical groundwater discharge, defined as the flux of groundwater to streamflow necessary to maintain a healthy environment, from 1960 to 2010: the Presumptive Standard stipulates that a standard proportion of groundwater discharge should be maintained at all timesteps, while the Q* is a low‐flow index that focuses on critical periods. We calculated these critical flow thresholds using simulated natural groundwater discharge, and estimated violations of the thresholds when human‐impacted groundwater discharge dropped too low. Our global assessment of the frequency and severity of violations over all timesteps in our study period showed that the Presumptive Standard estimated more frequent and severe violations than the Q*, but that the spatial patterns were similar for both methods. During low‐flow periods, when the relative importance of groundwater to support streamflow is greatest, both methods estimated similar magnitudes of violation frequency and severity. We further compared our results to a method of estimating environmentally critical streamflow, Variable Monthly Flow, which does not explicitly consider groundwater. From the differences in violation frequency between these groundwater‐centric and surface water‐centric methods, we evaluated the influence of including groundwater contributions to streamflow in environmental flow assessments. Our results show that including groundwater in such assessments is particularly important for regions with high groundwater demands in the drier climates of the world, while it is less important for regions with low groundwater demands and more humid climates.

Surface water is essential for agricultural, domestic and industrial production worldwide. Monitoring surface dynamics is crucial for sustainable ecosystems and global water resources. Importance of monitoring surface water dynamics is even more pronounced in the semi‐arid regions worldwide. An analysis of surface water extent and volume change patterns has been conducted, comparing these dynamics with alterations in precipitation patterns within a basin in Central Bundelkhand, a semi‐arid region in the Central India prone to droughts. To map the waterbodies, we leveraged Sentinel‐1 SAR data using an automated mapping framework and utilised DEM dataset to extract bathymetry using interpolation with modifications using water persistence. Analysis revealed a lag in surface water peak water level with respect to accumulated rainfall by 2–3 months. Furthermore, we have categorised the water bodies into small, medium and large by surface area and found that smaller water bodies show a higher intra‐annual variance, while medium and large water bodies show a lower intra‐annual variance. The findings suggest that smaller communities reliant on smaller water bodies are at a higher risk from climate variability in the region and a delay in attaining peak surface storage across the basin causes further challenges to water management.

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5–0.9, NSE of 0.6–0.9, and KGE of 0.5–0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³·year⁻¹, with contributions to oceanic basins: 1000.0 km³·year⁻¹ to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³·year⁻¹ to the South Atlantic, largely from the Congo River; 214.7 km³·year⁻¹ to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³·year⁻¹ to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.

Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topographic surveys, hydrological analyses, and environmental assessments with high-end instrument requirements. In a country like India, where the hydropower sector is mushrooming rapidly, implementing these techniques on such a large scale is challenging. Alternatively, cloud-based tools like Google Earth Engine (GEE), G-res, and Earth Observation (EO) data related to biophysical and climatic conditions with in-situ reservoir water levels provide an opportunity to quantify GHG emissions from LHRs efficiently. In the present study, Maithon, one of the oldest LHRs in India, situated in a tropical climatic zone, has been studied by integrating site-specific parameters to estimate GHG emissions. The results from this study, which show that at the mean operating level (146.31 m) of the reservoir, net GHG emission is 1,024 − 1,271 gCO2e/m2/yr (with a 95% confidence interval), are of significant importance. This study highlights the GHG emissions varying greatly between the full reservoir level (786 gCO2e/m2/yr) and near the dead storage level (3,855 gCO2e/m2/yr), indicating the role of reservoir operating level in mitigating GHG emissions to achieve global goals like net zero emissions. There has been limited work globally using the G-res tool, and this is the first comprehensive study of initial GHG emission estimation of a tropical reservoir using G-res and GEE incorporating updated high-resolution land use land cover and Sentinel-1 images.

Feminist research approaches in agriculture are considerably underutilized. In this chapter, we suggest a few key reasons to help explain their lack of use in agriculture. We also provide background on what constitutes feminist research in agriculture through a review of the literature. Using a case study approach, we highlight the important and unique characteristics that define feminist research approaches in agriculture. The case studies provide examples of how researchers working in agriculture can gradually adopt key feminist research principles. We argue that to transform agrifood systems to be more inclusive, equitable, and sustainable, feminist approaches must be used in all research in agriculture. The chapter concludes by discussing what is needed to increase the use of feminist research approaches in agriculture, recognizing that resistance to change is inevitable and requires commitment at the top to spearhead efforts to institutionalize feminist approaches within agricultural research organizations.

A major challenge for agricultural water management (AWM) in the 21st century is to feed a growing population in the face of increasing intersectoral resource competition, evolving diets, degradation, pandemics, geopolitical conflicts and climate change. This has to be achieved within the planetary boundaries and without compromising the livelihood and environmental (ecosystem) objectives linked to water, including provisioning, supporting and regulating services. This paper uses a systems and nexus lens to unravel the centrality and complexities in AWM, with particular emphasis on the interconnected dimensions and objectives of AWM, as well as its practices and technologies. AWM exists beyond water and food with linkages to human and environmental well‐being. AWM needs to catalyse transformation and integrate approaches across systems, users and scales to meet its objectives in a changing climate. It must provide perspectives beyond productivity, managing water risks and safeguarding food security – as important as these are – and integrate our understanding of the interconnected climate, land, water, food and ecosystems to address planetary health outcomes. By doing so, AWM could catalyse contextualised, equitable, innovative solutions that acknowledge local socio‐economic and institutional structures and limitations while catalysing sustainable development and climate resilience.

Geospatial sciences (GS) include a wide range of applications, from environmental monitoring to infrastructure development , as well as location-based analysis and services. Notably, graph theory algorithms have emerged as indispensable tools in GS because of their capability to model and analyse spatial relationships efficiently. This article underscores the critical role of graph theory applications in addressing real-world geospatial challenges, emphasising their significance and potential for future innovations in advanced spatial analytics, including the digital twin concept. The analysis shows that researchers from 58 countries have contributed to exploring graph theory and its application over 37 years through more than 700 research articles. A comprehensive collection of case studies has been showcased to provide an overview of graph theory's diverse and impactful applications in advanced geospatial research across various disciplines (transportation, urban planning, environmental management, ecology, disaster studies and many more) and their linkages to the United Nations Sustainable Development Goals (UN SDGs). Thus, the interdisciplinary nature of graph theory can foster an understanding of the association among different scientific domains for sustainable resource management and planning.

Drought, a consequence of prolonged precipitation deficiencies, is a significant hazard exacerbated by climate change. Highly susceptible to extreme climatic events, Sri Lanka faces drought as its most prominent hazard, necessitating comprehensive assessments. This study focuses on the escalating impact of hydrological drought intensified by climate change on the Maduru Oya and Kirindi Oya dry zone basins in Sri Lanka, crucial due to their vulnerability to altered hydroclimatic dynamics. Monitoring hydrological droughts in these regions is paramount for ensuring a reliable water supply for irrigation and other purposes. The research utilizes the Streamflow Drought Index for the monitoring of hydrological droughts. It considers six CMIP6 (Sixth Phase of the Coupled Model Intercomparison Project) Global Climate Models, with the CNRM-HR-1 model chosen as the preferred model. Two future Shared Socio-economic Pathway scenarios, SSP1-2.6 and SSP5-8.5, were selected to project future climatic conditions. The Random Forest algorithm was utilized to predict future streamflow in the two selected sub-basins. The hydrological drought assessment reveals the heightened vulnerability of the Padiyathalawa sub-basin in the Maduru Oya basin, with a notable rise in moderate hydrological drought occurrences under both future scenarios. Conversely, the Wellawaya sub-basin in the Kirindi Oya basin exhibits susceptibility to frequent moderate hydrological droughts, along with an 80% increase in severe drought occurrences under the SSP5-8.5. Consequently, both basins are projected to face water scarcity in the future. This underscores the importance of implementing measures to ensure a reliable water supply, given the substantial impact of climate change on watershed hydrology.

Precise estimation of irrigated areas is essential for effective water management, increased production, environmental conservation, and conflict resolution. Nonetheless, discrepancies frequently exist between estimated and actual irrigated areas. To address the data gaps in actual irrigation areas within Ethiopia, we utilized high-resolution remote sensing imagery. However, the accuracy of these images under varying climatic and landscape conditions was not fully substantiated. We conducted a comparative analysis between global irrigation map and local irrigated region maps within two distinct watersheds. Field data was gathered to both train and assess by employing random forest supervised classification algorithm. This algorithm was then applied to create accurate irrigation maps using high-resolution Sentinel-1 data for the Bilate and Gumara watersheds. During the irrigation seasons, maps of irrigated regions were produced using time-series imagery. Additionally, we employed maps indicating lands suitable for surface irrigation and applied post-processing techniques to refine the actual irrigated areas. The resulting accuracy was comparably high for both watersheds, with values of 88% and 87%. The kappa coefficients were 0.74 and 0.73, respectively, indicating a very good level of agreement. However, there were significant discrepancies between the global irrigation map and the local irrigated regions map in terms of spatial distribution and the extent of irrigation. This discrepancy necessitates further analysis of both products to decipher the underlying causes of their differences. We recommend for additional studies encompassing diverse watershed characteristics to improve irrigation area mapping via remote sensing. Our findings also validate the effectiveness of post-processing techniques in remote sensing applications.