Latest journal articles

The study explores the evolving land use patterns and their implications for sustainable development in Ghana and neighboring megacities. Using 15 years of historical Land Use and Land Cover (LULC) data combined with Land-Use Harmonization datasets, the study applies the Future Land Use Simulation (FLUS) model to project future LULC dynamics under Shared Socioeconomic Pathway (SSP) scenarios in the densely urbanized Greater Accra Region (GAR) of West Africa. Analyzing historical and current land use dynamics in the GAR revealed notable shifts, notably a decrease in Rangeland and an increase in Built-up areas. Future projections of LULC under SSP scenarios show continuous expansion of Built-up areas, particularly under SSP245 (middle of the road scenario) and SSP370 (Regional Rivalry scenario). This is consistent with results from the urban growth analysis using Urban Expansion Intensity Index (UEII), indicating high-speed expansion in baseline periods and shifts towards medium to high-speed expansion under SSP245 and SSP370 with low-speed expansion under the SSP126 (Sustainability scenario). Shannon entropy analysis shows dispersed urban sprawl, especially under SSP245 and SSP370, with rapid increases in Built-up areas and declines in green areas. For instance, the analysis of the landscape metrics reveal that built-up and green areas are projected to increase and decrease up to 87% and 12% respectively, under these scenarios. The decline in urban green areas was significantly influenced by proximity to the central business district (CBD), with green spaces diminishing more as distance to the CBD decreased. Therefore, relevant local legislation, such as the 2016 Land Use and Spatial Planning Act (Act 925) must be enforced, along with integrating urban initiatives and policies that promote green areas, is essential for ensuring the sustainability of urban ecosystems for the well-being of both humans and the environment. This enables West Africa to achieve its Global commitments as reflected in the UN SDGs, towards the New Urban Agenda (NUA) and the Africa Urban Agenda 2063.

There is an urgent need to expand wastewater treatment on the African continent. To help choose appropriate technologies for this task, we evaluated the efficiency, energy and chemical demands, and costs of four wastewater treatment plants (WWTPs). These plants represent the main wastewater treatment technologies operated by the Addis Ababa Water and Sewerage Authority (AAWSA): waste stabilization pond (WSP), anaerobic baffled reactor (ABR), up-flow anaerobic sludge blanket with trickling filter (UASB-TF), and membrane bioreactor (MBR) technologies. Principal component analysis revealed that season significantly impacts the raw and treated wastewater quality (ANOSIM, R ¼ 0.3126, p ¼ 0.001), while the type of treatment plant did not significantly affect the measured effluent characteristics (ANOSIM, R ¼ 0.1235, p ¼ 0.2000). In contrast, construction and operational costs, as well as energy and chemical demands per m3 of treated wastewater, varied starkly between the WWTPs. Total costs of wastewater treatment in 2022 ranged from $0.045 to 0.546 per m3 of wastewater treated, being 6–12 times higher for MBR compared with the other WWTP technologies. Real-world performance data as reported in this study are essential for choosing appropriate technologies that meet Africa’s wastewater treatment needs.

Historically, Ethiopia has experienced recurrent droughts and floods, which may intensify due to climate change. This study has evaluated the performance of 45 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating ten extreme precipitation indices against corresponding indices from the Enhancing National Climate Services (ENACTS) during short rainy (February–May, FMAM) and main rainy (June–September, JJAS) seasons for the period 1981–2014 over Ethiopia. Ensemble mean of the top-ranking models are also evaluated against ENACTS in reproducing extreme indices over five Agro-ecological zones (AEZs) of the country. The Taylor Skill Score (TSS) was used to rank the performance of the individual CMIP6 models for JJAS and FMAM seasons with respect to ENACTS while Comprehensive Rating Metrics (RM) were used to compute the overall ranks of the models. Our results show that most CMIP6 models reasonably captured the spatial distribution of the seasonal extreme precipitation indices even though they could not reproduce the magnitude of indices, especially in the highland and high rainfall areas of the country such as Northwest and west parts of the country. However, the biases in lowland and low rainfall regions, such as the eastern and northeastern parts of the country, are smaller compared to other areas. More than 30 CMIP6 models underestimated the extreme indices with the exception of consecutive wet days which is grossly overestimated in the highland and high rainfall areas specifically in western parts of the country. Additionally, EnseMean in the tropical and desert AEZs performs particularly better in simulating extreme indices compared to other AEZs. The ensemble mean of the top-ranking models (EnseMean) generally outperformed both individual models and ensemble of all models in the representation of observed extreme indices across all metrics and seasons. Moreover, the performance of individual models is subject to variation based on the season, and the selected extreme indices. It is also noteworthy that their performance is relatively less influenced by horizontal resolution. Further evaluation, focusing on teleconnections such as ENSO and IOD, is a crucial next step for evaluating models and creating a sub-ensemble.

Groundwater is one of the most vital natural resources worldwide. However, shallow aquifers are prone to contamination, posing significant risks to human health, livestock, agricultural productivity, and economic growth. Identifying appropriate land management strategies is critical for mitigating groundwater vulnerability to pollution. This study evaluates groundwater vulnerability to pollution under various land management scenarios using the modified DRASTIC model in Bahir Dar City, Ethiopia. The analysis incorporates multiple parameters within the ArcGIS environment, including depth to water table, net recharge, aquifer characteristics, soil properties, topography, vadose zone, hydraulic conductivity, and land use/land cover (LULC). In this study, LULC was added as an additional parameter to enhance the DRASTIC model. Groundwater vulnerability to pollution was evaluated under four distinct land management scenarios: baseline, agricultural expansion, urbanization, and reforestation. A single-parameter sensitivity analysis and a map removal sensitivity analysis were performed to identify the most influential parameters affecting groundwater vulnerability under the baseline LULC conditions. The result revealed that groundwater vulnerability in Bahir Dar City under baseline conditions is primarily influenced by LULC and net recharge. The areal average groundwater vulnerability to pollution index at the baseline scenario was 184. Agricultural expansion and urbanization increased the areal average groundwater vulnerability to pollution by 4.9 % and 1.6 %, respectively, while the reforestation scenario reduced it by 1.6 %. These findings highlight the critical role of effective land management practices, such as reforestation, in mitigating groundwater susceptibility to pollution. The results also indicate that groundwater vulnerability to pollution varies across different geological formations. Therefore, given the influence of geological variability on groundwater vulnerability, incorporating geological considerations into urban expansion planning is essential for minimizing the risk of groundwater contamination.

Irrigation plays a crucial role in enhancing food production, increasing land productivity, and improving the livelihoods of smallholder farmers in Sub-Saharan Africa (SSA). Solar pumps and water harvesting ponds have emerged as promising technologies for sustainable agriculture for smallholders in SSA and beyond. The socio-economic impacts of these systems are less studied in the existing literature. This study examined the agricultural productivity of solar pump and water harvesting irrigation technologies and their impacts on income and food security among smallholder farmers in the Central Rift Valley, Lake Hawassa, and Upper Awash sub-basin areas in Ethiopia. Data were collected from 161 farming households that were selected randomly from woredas where solar pump and water harvesting pond irrigation systems had been implemented. The sample size was determined using the power calculation method. Bio-physical observation and measurements were also conducted at field levels. The benefit–cost ratio (BCR) and net water value (NWV) from the use of solar pump and water harvesting pond irrigations were analyzed to assess the viability of these systems. The household food consumption score (HFCS) and household dietary diversity score (HDDS) were calculated to measure food security, while the revenue from crop production was used to measure crop income. An endogenous switching regression model was applied to address the endogeneity nature of the adoption of the irrigation technologies. The counterfactual analysis, specifically the Average Treatment Effect on the Treated (ATT), was used to evaluate the impacts of the irrigation technologies on income and food security. Results indicate that the ATT of crop income, HFCS, and HDDS are positive and statistically significant, illustrating the role of these irrigation systems in enhancing smallholder farmers’ welfare. Moreover, smallholder farmers’ solar pump irrigation systems were found to be economically viable for few crops, with a BCR greater than 1.0 and an NWV ranging from 0.21 to 1.53 USD/m³. It was also found that bundling agricultural technologies with solar pump irrigation systems leads to enhanced agricultural outputs and welfare. The sustainable adoption and scale-up of these irrigation systems demand addressing technical and financial constraints, as well as input and output market challenges.

The global drought community and policy representatives gathered at the United Nations Convention to Combat Desertification’s 16th Conference of the Parties (UNCCD COP16) in Riyadh in December 2024 to discuss the urgent need for improvements in assessing and quantifying drought risks, in developing and implementing transformative solutions, and in boosting policy actions and investments. Only through unprecedented global cooperation can we facilitate pathways towards drought-resilient futures.

Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that shortand longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.

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.

Flooding has emerged as a significant concern in the Akaki catchment area of Ethiopia, affecting settlements and properties. Early warning systems (EWSs) are implemented to reduce flood risks, but power dynamics among at-risk communities and stakeholders have raised concerns about the reliable accessibility of warning information. We integrated a citizen science approach into existing flood EWSs to promote inclusivity, local perspectives, and equitable expertise distribution in flood early warning. It draws on primary data collected through diverse methods, alongside an extensive review of documents from the years 2021 to 2022. The analysis of qualitative data indicates the integration of citizen science into a flood EWSs delivers dependable early warning information and encourages the establishment of networks. This approach reduces dependence on external entities, enhances local decision-making capabilities, and promotes a sense of ownership, empowerment, and trust. This can transform the dynamics and responsibilities linked to flood management. However, the longer-term participation of citizen scientists in flood EWSs is challenging due to the disparity between commitment levels and benefits, lack of legal frameworks, and insufficient recognition of community diversity within policy frameworks. The research herein emphasizes the significance of understanding power dynamics and institutional capacities in integrating citizen science into flood EWSs. It offers valuable perspectives for policymakers, practitioners, and communities on participatory governance, social equity, and the resilience of communities in the face of environmental challenges.

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 Nio 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.

Early warning systems (EWS) inform decision making and planning in response to climate shocks and catastrophic disasters. However, the current disaster response mechanism falls short due to the fragmented warning, action, and finance systems, coupled with inadequate institutional collaboration, coordination and inclusive engagement for effective anticipatory action. This study addresses this challenge by introducing an Early Warning, Action and Finance (AWARE) platform to promote anticipatory action through multistakeholder engagement. Data from literature re views, expert surveys, and stakeholder workshops in Senegal, Zambia and Sri Lanka helped identify the platform’s needs and priorities. The study draws upon theories of technological frames, interpretative flexibility, boundary objects, social learning, collaborative governance and adaptive co-management to conceptualize a framework for AWARE. Results demonstrate the potential of AWARE as a boundary object that fosters social engagement, active involvement, open communication, collaboration, and shared commitment to safeguarding lives and liveli hoods. Analysis of technological frames and interpretative flexibility underscores the role of social learning in shaping the design and user features that promote multiscale institutional responses to disasters. AWARE aligns with the priorities of the Sendai Framework and emphasizes system thinking, co-production of knowledge, and the need for context-specific solutions to enhance anticipatory action. Recognizing the limitations of one-size-fits-all EWS, the AWARE framework acknowledges contextual factors as barriers to implementation. The study underscores the importance of integrated EWS and collaborative efforts to overcome implementation barriers and improve anticipatory action outcomes.

The world faces multiple water crises, including overextraction, flooding, ecosystem degradation and inequitable safe water access. Insufficient funding and ineffective implementation impede progress in water access, while, in part, a misdiagnosis of the causes has prioritized some responses over others (for example, hard over soft infrastructure). We reframe the responses to mitigating the world’s water crises using a ‘beyond growth’ framing and compare it to mainstream thinking. Beyond growth is systems thinking that prioritizes the most disadvantaged. It seeks to decouple economic growth from environmental degradation by overcoming policy capture and inertia and by fostering place-based and justice-principled institutional changes.

Conventional rice production through puddled transplanted rice-PTR is tillage, water, energy, and capital intensive. Furthermore, it is a major contributor to greenhouse gas (GHG) emissions. In this regard, Direct seeded rice-DSR can be a potential alternative to PTR. DSR can reduce input use and GHGs emissions, while sustaining yields. However, depending upon agroclimatic situation, DSR impact analysis on GHGs emission and yield resulted inconsistent findings, questioning whether it is better over PTR or not. To bridge this knowledge gap, we performed a meta-analysis synthesizing 876 paired measurements from 54-peer-reviewed studies to understand how DSR impacts N2O and CH4 emissions, GWP (heat-trapping potential of greenhouse gases compared to CO2), yield and C-footprint-CFP (environmental impact in CO2 eq. due to concerned activity). Compared to PTR, DSR decreased CH4 emissions by 70%, GWP by 37% and CFP by 34%, despite 85% increase in N2O emissions. However, this shift comes with a trade-off, with 11% decrease in yield. To decipher the primary factors driving these outcomes, we conducted subgroup analyses by taking assorted environmental conditions and management practices as moderators. Low to medium pH soils, zero tillage, puddled soil (wet DSR), conventional flooding, and high nitrogen rates (gt;200kg/ha) are found to be favourable for DSR with comparable yields but posing a discrepancy with environmental sustainability. Therefore, further research to evaluate DSR across agro-ecologies, management practices, are needed, to optimize yields with lower GWP and CFP.

Climate change remains a significant threat to farm households, especially in developing countries. It exacerbates their vulnerability to food insecurity by reducing agricultural productivity and raising agricultural production costs. Adoption of climate smart-agricultural (CSA) practices is a promising alternative to build resilient farm households. In this study, we assessed the impacts of adopting CSA practices on climate resilience and vulnerability among farm households in Bale-Eco Region, Ethiopia. A power calculation was used to determine the sample size, and 404 farm households were randomly selected to collect data using structured questionnaire. We estimated household climate resilience index using categorical principal component analysis, and vulnerability index using vulnerability as expected poverty approach. Endogenous switching regression model, which is conditional on the adoption of multiple CSA practices and used to control selection bias and unobserved heterogeneity, was used to assess the impacts of CSA practices on household climate resilience and vulnerability. We employed counterfactual approaches to assess the impacts. The results show that the average treatment effects for most CSA practices are statistically significant and positive for resilience, but negative for vulnerability. This provides empirical support for interventions in climate-smart agriculture, which can help farm households build resilience and reduce vulnerability. We, therefore, suggest that agricultural policies should encourage the adoption of CSA practices and provide incentive packages to farm households that promote this.

Water resources assessments are essential for effective planning in water-scarce regions such as Jordan. Such assessments require sufficient data in space and time. The WaPOR-based Water Accounting Plus (WA +) framework is relevant as it integrates remote sensing data and the Pixel-Based Soil Water Balance model to simulate a basin’s water balance. However, since it relies on remote sensing, this framework only tracks water consumption in irrigated agriculture and does not consider non-irrigation water use and its return flow. This paper modifies the WaPOR-based WA + framework to include non-irrigation manmade consumption and its return flows. The modified framework provides a more comprehensive water budget for the Amman-Zarqa (AZ) basin, presented in a modified WA + resource base sheet for 2018 through 2021. The results show that water availability in the AZ basin is highly responsive to precipitation changes. Average precipitation was approximately 926 Mm3/year between 2018 and 2020, corresponding to an average available water of 485 Mm3/year. However, a reduction in average precipitation by 28% in 2021 corresponded to a reduction in available water to 243 Mm3/year. Nevertheless, substantial groundwater outflows to neighbouring basins may indicate that available water is being overestimated. Manmade consumption increased by 18% from 2018 to 2021, and the total demand exceeded the available supply by 150%. This underscores the pressing need to investigate supply augmentation and conservation methods. Future studies could focus on improving the representation of groundwater dynamics in the modified framework by improving groundwater dynamics in PixSWAB and testing the modified framework with other remote sensing datasets.

This research explores the limitations and opportunities of Water Accounting Plus (WA+) for addressing water management issues in the MENA, focusing on Jordan. A comprehensive literature review and interview-based analysis were conducted to identify prevalent water management issues and evaluate information used in decision-making and strategy appraisals. The findings suggest that WA+ can enhance the spatio-temporal coverage of water resource assessments, refine estimates of irrigation water consumption, and facilitate demand management. Quantifying recharge and surface runoff requires integrating WA+ with hydrological models. Addressing climate change’s impact on future water resources requires integrating climate change projections with WA+.

Improved freshwater resource management requires the implementation of widespread, effective, and timely water quality monitoring. Conventional monitoring methods are often inhibited by financial, infrastructural, and human capacity limitations, especially in developing regions. This study aimed to validate the citizen-scientist-operated transparency or clarity tube (hereafter “clarity tube”) for measuring water clarity as a proxy for total suspended solids (TSS) concentration, a critical quality metric in river systems and wastewater treatment works (WWTW) effluent in Southern Africa. Clarity tubes provided a relatively accurate and precise proxy for TSS in riverine lotic systems and WWTW effluent, revealing significant inverse log- linear relationships between clarity and TSS with r 2 = 0.715 and 0.503, respectively. We demonstrate that clarity-derived estimates of TSS concentration (TSScde) can be used to estimate WWTW compliance with WWTW effluent TSS concentration regulations. The measurements can then be used to engage with WWTW management, potentially affecting WWTW performance. Overall, these findings demonstrate the usefulness of clarity tubes as low-cost, accessible, and easy-to-use citizen science tools for high spatial and temporal resolution water quality monitoring, not only in rivers in Southern Africa but also in WWTW effluent for estimating compliance, with strong global relevance to the sustainable development goals (SDGs).

Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R2 of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R2 =0.83) and with low-cost measurements (R2 = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.

Central Asia faces substantial water scarcity due to increasing water demand driven by rapid urbanization, population growth, economic development, and inefficiency of irrigated agriculture. These developments are compounded by the effects of climate change, such as rising temperatures, loss of glacier mass and increased frequency of extreme events, including droughts. The region’s escalating water scarcity is causing disputes and straining rural livelihoods. Moreover, these challenges drive migration, creating considerable societal impacts. However, these issues remain underexplored in climate change research, making the region a global blind spot in climate adaptation and migration studies. We advocate for innovative research pathways that scrutinize smallholder adaptation strategies, examine the nexus between climate change, water scarcity, and mobility, and investigate tensions and cooperation over water resources. We conclude by emphasizing that substantial investments in inter- and transdisciplinary collaboration, improved data availability and quality, and strengthening of research and institutional capacities are essential to advance interdisciplinary climate impact research in Central Asia. Such efforts are vital for addressing existing knowledge gaps and enhancing evidence-based policymaking to improve the region’s position in current and future debates on climate change and sustainable development.

To address the growing electricity demand driven by population growth and economic development while mitigating climate change, West and Central African countries are increasingly prioritizing renewable energy as part of their Nationally Determined Contributions (NDCs). This study evaluates the implications of climate change on renewable energy potential using ten downscaled and bias-adjusted CMIP6 models (CDFt method). Key climate variables—temperature, solar radiation, and wind speed—were analyzed and integrated into the Teal-WCA platform to aid in energy resource planning. Projected temperature increases of 0.5–2.7 C (2040–2069) and 0.7–5.2 C (2070–2099) relative to 1985–2014 underscore the need for strategies to manage the rising demand for cooling. Solar radiation reductions (~15 W/m2 ) may lower photovoltaic (PV) efficiency by 1–8.75%, particularly in high-emission scenarios, requiring a focus on system optimization and diversification. Conversely, wind speeds are expected to increase, especially in coastal regions, enhancing wind power potential by 12–50% across most countries and by 25–100% in coastal nations. These findings highlight the necessity of integrating climate-resilient energy policies that leverage wind energy growth while mitigating challenges posed by reduced solar radiation. By providing a nuanced understanding of the renewable energy potential under changing climatic conditions, this study offers actionable insights for sustainable energy planning in West and Central Africa.

Freshwater pollution is, together with climate change, one of today’s most severe and pervasive threats to the global environment. Comprehensive and spatially explicit scenarios covering a wide range of constituents for freshwater quality are currently scarce. In this Global Perspective paper, we propose a novel model-based approach for five water quality constituents relevant for human and ecosystem health (nitrogen, biochemical oxygen demand, anthropogenic chemicals, fecal coliform, and arsenic). To project the driving forces and consequences for emissions and impacts, a set of common data based on the same assumptions was prepared and used in different large-scale water quality models including all relevant demographic, socioeconomic, and cultural changes, as well as threshold concentrations to determine the risk for human and ecosystem health. The analysis portrays the strong links among water quality, socio-economic development, and lifestyle. Internal consistency of assumptions and input data is a prerequisite for constructing comparable scenarios using different models to support targeted policy development.

The major concern of applying citizen science in water resources is the quality of the data. However, there are limited scientific studies addressing this concern and showing the data value. In this study, we established a citizen science program in the Akaki catchment which hosts Addis Ababa, Ethiopia. Citizen scientists monitored river stage at multiple gauging sites for multiple years. We evaluated the quality of citizen science data through a systematic quality control. Reference data was obtained from neighboring stations of the citizen science program and professionals while the evaluation involved graphical inspections and statistical methods. The quality-controlled data were applied to evaluate the spatial and temporal variation of rainfall-runoff relationships. Initially, large numbers of suspicious data were detected using single station data but that was significantly reduced when the data of multiple sites were compared. Further comparison against professional data revealed excellent agreement with high correlation coefficient (r gt;0.95), and low centered root mean square error (RMSE) lt;0.03–0.08 mm. The citizen science data indicated a large difference in rainfall-runoff relationship over the dominantly urban and rural sub-catchments. The citizen science data allowed comparison of runoff coefficient and base flow index for recent and historical periods where recent streamflow data is unavailable from a formal data source. This study illustrates the immense value of (i) multiple data quality assessment steps for building confidence on the quality of citizen science data, and (ii) citizen science for enhancing our understanding of rainfall-runoff relationships and change in a rapidly urbanizing catchment.

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Study region: Akaki is a headwater catchment of the Awash River Basin in Ethiopia and hosts Addis Ababa. The catchment remains ungauged despite providing diverse ecosystem services. Study focus: Studies typically assume that calibrated model parameters remain valid outside the calibration period despite many catchments undergoing anthropogenic change. We evaluated the value of citizen science data to improve a rainfall-runoff model performance in the urbanizing Akaki catchment. Five and nineteen people trained as citizen scientists monitored river stage and rainfall data, respectively, for three years. Rating curves were developed for the conversion of stage data into river flow data. A HEC-HMS model, calibrated for a historical period, was evaluated using citizen science data. Next, we assessed the effectiveness of progressively updating model parameters using a recent land use land cover map and citizen science data. Then, the HEC-HMS model was recalibrated at the catchment outlet and validated at multiple sub-catchments’ outlets. Finally, the model was recalibrated at multiple sub-catchments’ outlets using citizen science data. New hydrological insights for the region: the HEC-HMS model of the Akaki, which was calibrated for a historical period, does not capture the current rainfall-runoff response. However, the model simulations were made useful by revisiting the model calibration at multiple sub-catchments’ outlets using citizen science data. Overall, this article demonstrates the value of citizen science data for model calibration and validation.

Freshwater ecosystems are important for directly ensuring a range of benefits and services that sustain local livelihoods and help mitigate climate change. However, freshwater ecosystems are threatened by anthropogenic and natural pressures affecting their ability to sustainably provide these services. Managing freshwater ecosys tems is mainly challenged by the complexity of the drivers of their degradation and by the difficulty of balancing the need for short-term socioeconomic development and protecting and restoring ecosystems to support longterm, sustainable development. Addressing such challenges requires an integrated and systematic approach, catchment-wide management and meaningful engagement and collaboration among stakeholders. The present study was conducted in Lake Ziway, Rift Valley Lakes Basin, Ethiopia. The overarching objective was to conserve the freshwater ecosystem through co-planning and designing management options for the buffer zone and entire catchment. The study engaged local communities through multiple approaches including inception and vali dation workshops, reconnaissance surveys, key informant interviews and focus group discussions. It also employed quantitative data collection methods to characterize the freshwater ecosystem and entire catchment. The participatory approach employed in this study identified multiple management options including tree-based forest and landscape restoration measures, integrated soil and water conservation, and multiple buffer zone restoration measures. The identified and validated management options are aimed at improving the functionality of the freshwater ecosystem in the face of increased need of economic development and climate change. Furthermore, the study provided key recommendations to support the processes of policy development and the effective implementation of suggested and validated management options.

Introduction: Cities located in lower income countries are global flood risk hotspots. Assessment and management of these risks forms a key part of global climate adaptation efforts. City scale flood risk assessments necessitate flood hazard information, which is challenging to obtain in these localities because of data quality/scarcity issues, and the complex multi-source nature of urban flood dynamics. A growing array of global datasets provide an attractive means of closing these data gaps, but their suitability for this context remains relatively unknown. Methods: Here, we test the use of relevant global terrain, rainfall, and flood hazard data products in a flood hazard and exposure assessment framework covering Addis Ababa, Ethiopia. To conduct the tests, we first developed a city scale rain-on-grid hydrodynamic flood model based on local data and used the model results to identify buildings exposed to flooding. We then observed how the results of this flood exposure assessment changed when each of the global datasets are used in turn to drive the hydrodynamic model in place of its local counterpart. Results and discussion: Results are evaluated in terms of both the total number of exposed buildings, and the spatial distribution of exposure across Addis Ababa. Our results show that of the datasets tested, the FABDEM global terrain and the PXR global rainfall data products provide the most promise for use at the city scale in lower income countries.

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 km³year-1, with contributions to oceanic basins: 1000.0 km³year-1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³year-1 to the South Atlantic, largely from the Congo River; 214.7 km³year-1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³year-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.

This study interrogates the state of social-ecological landscapes (SEL) in West Africa, focusing on two case studies: the Mankran SEL in Ghana (case study 1) and the Doma–Rutu SEL in Nigeria (case study 2). Using a mix of methods, the assessment was framed by the Drivers Pressure State Impact Response (DPSIR) model tailored for SEL evaluation (DPSIR-SEL). In the Mankran landscape, land use patterns shifted significantly from 2008 to 2018, with cash crop cultivation peaking at 30% in 2015 before declining to 14.5% by 2018. Water quality assessments in the Mankran micro-watershed indicated that several parameters, including Total Suspended Solids (TSS) at 914.41 1974 mg/L, lead at 18.73 17.26 g/L, and arsenic at 53.41 86.66 g/L, exceeded World Health Organization (WHO) standards, raising concerns about potential contamination. In contrast, the Doma–Rutu landscape in Nigeria experienced land use and land cover (LULC) changes from 2000 to 2022, characterized by the expansion of residential and agricultural areas alongside modifications to natural water bodies and vegetation. Water quality issues have emerged, with elevated levels of electrical conductivity, total dissolved solids, and salinity. Furthermore, Focus Group Discussions (FGDs) revealed persistent herder-farmer conflicts in Nigeria, which have historically constrained crop production due to various environmental and social factors. The intertwined challenges faced by both the Mankran and Doma–Rutu landscapes underscore the urgent need for sustainable and inclusive resource management, adaptive land-use strategies, and proactive measures to safeguard water quality.

Financial inclusion is recognized as a vital driver of sustainable development and serves as a fundamental pillar of climate action. It is crucial to enhance the climate resilience of smallholder farmers in the face of severe and unpredictable climate shocks, which disproportionately affect them. However, the level of financial inclusion in Ethiopia remains low, and its impact on the climate resilience of smallholder farmers has not been thoroughly examined using rigorous model and comprehensive dataset. This study investigates the impact of financial inclusion on the climate resilience of rural households, using a large data set from the Ethiopian Socio-Economic Survey. The principal component analysis was applied to construct a climate resilience index. The financial inclusion was measured using an index that encompasses three dimensions: penetration, availability, and usage. In order to address the endogenous nature of financial inclusion, an instrumental variable approach was employed, using the distance to the nearest financial institution and religion as instrumental variables. The results demonstrated a positive and significant impact of financial inclusion on the climate resilience of rural households. Therefore, the government should strengthen the provision of essential financial and related infrastructures in rural Ethiopia to improve access to financial products and services. Furthermore, it is essential for policymakers to initiate and implement financial sector reforms that ensure the availability of affordable and tailored financial services. These reforms should also prioritize the development of climate-resilient agricultural finance, thereby contributing to the achievement of climate action goal of sustainable development.

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. Water governance is crucial to ensure the wellbeing of both people and their food security. However, Cambodia’s water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, weak coordination among relevant agencies, and limited involvement of local communities. This study examines water governance across different sectors, from centralized to community-based natural resources management, and explores the opportunities that can be done to improve water governance. This study undertakes the literature and case studies of farmer water user communities (FWUCs), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia. This study concludes that water governance has been challenged by FWUCs competing for water resources to intensify rice production at the expense of increased pesticides and fertilizer uses, which undermine the fishery productivity, degrade the natural resources in rivers and water bodies, and increase water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future.

Despite the government’s active promotion of rice production, a significant portion of the population still faces food insecurity. While existing literature often highlights the success of achieving rice surplus, few studies delve into the connections between rice surplus and food security, and critically analyze why food security is persistent. In addressing this issue, the study investigates the underlying causes of food insecurity amidst the government’s efforts to increase rice production. The study entails a comprehensive review of existing literature and an examination of food security in three provinces in the Cambodian Mekong Delta. It concludes that while rice intensification has led to increased rice production available for consumption, challenges persist in terms of access to and utilization of rice for food consumption. These challenges are attributed to increased production costs with minimal profit margins, indebtedness, migration, land grabs, water conflicts, and lack of institutional integration in food systems, compounded by the effects of climate change and environmental degradation. As a result, a farming household with one hectare of farmland cannot produce enough rice for food consumption. Consequently, young people opt to sell off their land to settle debts and seek employment opportunities in urban and overseas areas.

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.

Efficient agricultural water management (AWM) practices enhance crop water productivity and promote climate change adaptation and resilience initiatives, particularly in smallholder farming systems. Approximately 90% of sub-Saharan Africa’s (SSA) agriculture is rainfed under smallholder farmers who constitute about 60% of the continent’s population and depend on agriculture for their livelihoods. While AWM is central to the African agenda, knowledge of AWM is fragmented, making it challenging to operationalize regional initiatives at country levels. Therefore, this study sought to review the status of AWM practices and technologies in Africa and provide guidelines, scenarios, and investment plans to guide the prioritization and operationalization of the African Union’s irrigation development and AWM (AU-IDAWM) initiative. The initiative proposes four developmental pathways; 1—improved water control and watershed management in rain-fed farming, 2—farmer-led irrigation, 3—irrigation scheme development and modernization, and 4—unconventional water use for irrigation. The preferred reporting items for systematic reviews and meta-analyses approach guided the systematic literature review. The study indicates that most agricultural production systems are mainly under pathways 1 and 2, which dictate the subsequent AWM practices. Pathway 4 had isolated adoption in North Africa. SSA exhibited overlaps in opportunities for AWM, whereas North Africa had green energy and strong extension services. The challenges were unique to each geopolitical region. Policy-related issues affected North Africa, whilst low investment in AWM dominated West Africa. Poor institutional coordination plagued East Africa, whilst low access to extension services affected Southern Africa. The Central African region was undermined by poor management practices that culminated in soil salinity in the agricultural lands. Targeted and scalable investments across interventions are necessary to potentially improve AWM uptake and subsequent food security in the continent. Also, institutional setups are essential in coordinating efforts towards achieving AWM. Extension services are essential information dissemination platforms for adopting effective climate-smart agriculture.

Digitalisation in agriculture is transforming the way farming is practised worldwide, and its potential benefits for smallholder farmers in sub-Saharan Africa (SSA) are particularly promising. Yet, scientific evidence on the digital technologies utilised by smallholder food producers in SSA and the associated challenges still needs improvement. This review attempts to provide a thorough overview of the technologies currently being employed by smallholder farmers in SSA while also exploring the associated challenges and opportunities. Through a systematic literature search, 27 relevant studies were analysed to identify the region-wise current technologies, challenges, and opportunities. Results show that various digital technologies are employed, including digital extension services and digital marketing of agricultural products. These technologies improve access to information and markets and enhance productivity. However, challenges hinder widespread adoption. Limited internet connectivity, low digital literacy, inadequate infrastructure, and affordability issues impede progress. Gender disparities further limit the equitable distribution of digitalisation benefits. Despite these challenges, significant opportunities arise from adopting digital technologies. The potential advantages are market access, better decision-making capabilities, and increased income and livelihoods. Digitalisation offers transformative possibilities for smallholder farmers in SSA. Overcoming barriers such as limited connectivity and low digital literacy is crucial. By harnessing the opportunities digital technologies can offer, the livelihoods of smallholder farmers can be uplifted, contributing to the growth of agriculture in SSA.

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegalapos;s 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 % decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (Rsquared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegalapos;s resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Solar irrigation pumps (SIPs) are gradually replacing diesel pumps in relatively water-intensive agricultural production systems and geographies to reduce carbon emissions from food systems. However, beyond its climate change mitigation potential and fulfillment of Nationally Determined Contributions commitments, the adoption of solar irrigation also has direct co-benefits for farmers. Taking the case of Bangladesh and anchored on primary data collected among solar and diesel pump users, this article analyses the role access to solar irrigation has on household and farm-level outcomes. The propensity score matching and inverse probability matching approaches identify a positive effect of SIP access on food security and profitability from dry-season paddy for adopters. Different transmission channels are explored to explain these co-benefits. Lower costs of irrigation and labor are identified as the strongest pathways. This analysis strengthens the case for investments toward the solarization of agriculture in developing countries where it delivers significant development co-benefits in addition to climate change mitigation benefits. The valuation of the farmers’ co-benefits, along with global climate mitigation impacts, also highlights the potential role of such programs toward climate justice.

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.

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.

Southern Africa faces significant impacts of El Nio primarily in the form of droughts. Zambia is not an exception. Standardized Precipitation Index (SPI), rainfall anomaly and Vegetation Condition Index (VCI) are robust indicators for drought studies due to their distinct and complementary roles. Our results reveal severe meteorological drought conditions in Zambia using SPI and rainfall anomaly. VCI values have declined in the cropping season due to vegetation stress induced by water deficit conditions. Low rainfall leads to widespread deterioration of crop production, with approximately 40.46% of the country experiencing drought conditions in 2023–2024. The Central, Eastern, Southern, Lusaka, and Copperbelt provinces showed lower VCI values in March and April 2024, indicating poor crop health and drought-like conditions. On the other hand, low rainfall has substantially influenced hydropower reservoirs. Significant surface water loss is observed in the hydropower reservoirs such as Itezhi Tezhi Dam (117.40 sq. km), Mita Hills Dam (25.72 sq. km) and in parts of Lake Kariba (58.72 sq. km) between December 2023 and April 2024. This loss has disrupted industries relying on water resources and hindered hydropower generation, leaving substantial portions of the population without electricity for extended periods. The present study aims to explore the power of open access Earth Observation data and cloud analytics to evaluate the extent and multi-sectoral impact of the recent drought in Zambia. Results highlight the upcoming challenges the country might face in food and nutrition and the critical need for stakeholder involvement and policy design to mitigate future crises and strengthen vulnerable communities.

Climate change, population growth, rapid urbanization, shifting dietary patterns, and economic development pose significant challenges to food security, particularly in the Global South. Addressing these challenges involves efforts aimed at sustainable agricultural intensification (SAI), especially for smallholder farmers in marginalized regions. However, knowledge gaps persist regarding smallholder farmers’ access to water for SAI, particularly in arid and semi-arid agroecological zones. This study investigates smallholder farmers’ access to water for SAI in the Guinea and Sudan Savannah Agroecological Zones (SSAZ) of Ghana. Data were collected from 698 smallholder farmers across 25 communities using a structured questionnaire and geospatial techniques. The analysis employed cost distance analysis, factor analysis, and multinomial-ordered logistic regression. Findings indicate that the average distance travelled to access water from a dam or a river was 11 km and 9 km, respectively. Most respondents reported low to moderate water access for SAI. Key factors influencing water access included soil type, vegetation, and the distances to dams and rivers. To improve water access, it is recommended that smallholder farmers be educated on effective soil and water conservation techniques. Additionally, both government and non-governmental organizations should focus on building community-level dams to increase water availability for sustainable agricultural intensification.

In the search for promising pathways for equitable and transformative climate adaptation in low-income rural areas, the present study focuses on resilience to more variable and less predictable availability of precipitation and water resources. Equitable water governance is conceptualized as formal and informal polycentric decision-making that narrows infrastructure inequities and ensures equitable water resources allocation. Focusing on recognitional and procedural equity, vulnerable women’s and men’s community-based water tenure is starting point. Partnering with government in Zambia and with the Water Integrity Network in Kenya, field research in three communities in rural Zambia and in rural communities and a small town sharing several schemes in Kenya is conducted. These studies recognized horizontal polycentricity of community-based water tenure by identifying common features: communities’ age-old drought resilience coping strategies to meet daily domestic water needs and daily or seasonal small-scale productive water needs, by tapping water from surface- and groundwater sources through multi-purpose infrastructure, both self-financed and publicly supported, and by ‘sharing’ water ‘in’ within the community and ‘sharing out’ with neighbouring communities and powerful third parties. Procedural equity implies inclusive, locally-led planning, design and implementation in polycentric vertical governance with governments and other support agencies from local to national level and vice versa. The four interventions studied were the installation of a a solar-powered borehole for multiple uses and local government’s institutional framework for potential replication in Zambia, and post-construction support of small water systems and a planned megadam in Kenya. Further research on similar transformative approaches elsewhere is recommended.

To achieve their normative health, environmental and social objectives, water and sanitation services must be safely managed, inclusive and climate resilient. Meeting these imperatives presents a need and opportunity for innovative thinking about water and sanitation service systems. Circular economy concepts are being applied across a multitude of product and service sectors with the aim to facilitate regenerative flows of resources. Given the dependence on water resources, associated climate risks, and the generation of reusable waste products in water and sanitation service delivery, circular economy concepts can be usefully leveraged to drive sustainability outcomes. This article contributes a heuristic in the form of a conceptual framework for applying circular economy concepts in the design and delivery of water and sanitation services in diverse Global South contexts. The framework seeks to drive multiple outcomes relevant to water and sanitation initiatives: safely managed services, social inclusion, and climate resilience. Co-developed by an international research team applying a theoretical multiplicity approach and collaborative sensemaking, the heuristic takes the form of a suite of eight adapted circular economy ‘R strategies’ for water and sanitation. The R strategies were selected and articulated to reflect theory-based principles of circular economy, climate resilience and inclusion. They are intended to prompt thinking and action in pursuit of safely managed, climate resilient, inclusive water and sanitation services that align with the broader sustainability directions that circular economy narratives aspire to. The heuristic offers a conceptually rigorous, practical tool that can support collaborative, deliberative processes to realise the potential benefits of circularity in water and sanitation service systems.

This study compares the performance of satellite precipitation products (SPPs) and soil moisture-based rainfall products (SM2RPPs) in capturing rainfall patterns in Burkina Faso, West Africa. The findings indicate that SPPs outperform SM2RPPs at daily to annual timescales. The decomposition of total bias reveals that hit bias are dominant, but are larger in magnitude in SM2RPPs. Systematic errors are the major component of hit biases, suggesting they could be further treated through bias-correction. Wavelet analysis indicates that SPPs and SM2RPPs well capture seasonal and annual rainfall variability. All products struggle to accurately represent the most extreme rainfall indices, with SPPs performing better than SM2RPPs. For meteorological droughts, RFE2 and CHIRPS show the strongest correlations with observed SPI values across 1- to 12-month timescales. Although SM2RPPs perform less than SPPs in Burkina Faso, they appear mature for hydrometeorological applications and could further benefit from bias-correction to attain similar or better performance than SPPs.

This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana’s contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

This study examines the experiences of local government councilors between 2013–2020 in four provinces of Pakistan. The study addresses three key questions: Firstly, it investigates the differences and commonalities in the local government structures across provinces and explores the potential for synchronization to enhance the implementation of Sustainable Development Goals (SDGs) at the local level. Secondly, it examines the main challenges local councilors face in effectively delivering basic services, including water, sanitation, health, education, waste management, and transport, within both rural and urban contexts. Lastly, the study analyses the necessary changes or reforms at the provincial level within the existing local government system in rural and urban areas to ensure the delivery of basic services in alignment with SDGs. The findings reveal that a powerful bureaucracy and political influence at the intermediate to upper levels of the government hierarchy exert significant control over administrative, fiscal, and political.

Global demand for sustainable agri-food products creates opportunities for smallholder farmers. But on-farm verification of sustainable practices is costly, making third-party certification inaccessible to many poor farmers. Digital technologies for tracing agri-food products from farm to fork could help: novel traceability systems, based on blockchain technology, can make data about on-farm practices transparently available to downstream stakeholders, enabling them to verify sustainability claims without external audits. There is limited understanding, however, of how such digital tracing systems fit local needs and capacities in smallholder contexts. Through a grounded theory approach, we explore the potential of digital traceability for sustainability monitoring in a case study in the Ucayali region of Peru. Based on interviews with local cocoa sector stakeholders, we identify two major challenges: first, setting up digital traceability systems requires investments that do not seem justified by corresponding increases in income. And second, relying on farmer-supplied data creates the risk of inaccurate information due to insufficient capacity as well as incentives for providing distorted data. Our findings suggest that strong cooperation along the supply chain is needed to ensure adequate return on farmer-side costs and investments. Focusing on sustainability metrics at the community level, in addition to the farm level, may be promising, as these metrics can be reliably collected by external stakeholders and add value beyond existing third-party certification standards. Our case study provides recommendations for local policy makers and supply chain stakeholders to develop inclusive digital sustainability tracing systems with smallholder farmers.

The study reviewed the applications of the water–energy–food (WEF) nexus for knowledge generation and decision-making in the Global South. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol identified 336 studies from the Web of Science and Scopus datasets. One hundred eighty-five articles applied WEF nexus tools to improve the understanding of WEF nexus interactions and to show the potential of nexus applications. The other articles (151) focused on nexus applications to guide planning and decision support for resource allocation and policy formulation. Environment, climate, ecosystems, land, and socioeconomics were other popular nexus dimensions, while waste and economy were considered to a lesser extent. Limitations associated with nexus applications included unavailability of data, uncertainties from data sources, scale mismatch and bias. The inability of nexus tools to capture the complex realities of WEF interactions is hindering adoption, especially for policy formulations and investment planning. Data limitations could be solved using a sound scientific basis to correct uncertainties and substitute unavailable data. Data gaps can be bridged by engaging stakeholders, who can provide local and indigenous knowledge. Despite the limitations, applying nexus tools could be useful in guiding resource management. Limitations associated with nexus applications included – investment planning. Plausible pathways for operationalising the WEF nexus are discussed.

Hydrological Ecosystem Services (HES) are crucial components of environmental sustainability and provide indispensable benefits. The present study identifies critical hot and cold spots areas of HES in the Aglar watershed of the Indian Himalayan Region using six HES descriptors, namely water yield (WYLD), crop yield factor (CYF), sediment yield (SYLD), base flow (LATQ), surface runoff (SURFQ), and total water retention (TWR). The analysis was conducted using weightage-based approaches under two methods: (1) evaluating six HES descriptors individually and (2) grouping them into broad ecosystem service categories. Furthermore, the study assessed pixel-level uncertainties that arose because of the distinctive methods used in the identification of hot and cold spots. The associated synergies and trade-offs among HES descriptors were examined too. From method 1, 0.26% area of the watershed was classified as cold spots and 3.18% as hot spots, whereas method 2 classified 2.42% area as cold spots and 2.36% as hot spots. Pixel-level uncertainties showed that 0.57 km2 and 6.86 km2 of the watershed were consistently under cold and hot spots, respectively, using method 1, whereas method 2 identified 2.30 km2 and 6.97 km2 as cold spots and hot spots, respectively. The spatial analysis of hot spots showed consistent patterns in certain parts of the watershed, primarily in the south to southwest region, while cold spots were mainly found on the eastern side. Upon analyzing HES descriptors within broad ecosystem service categories, hot spots were mainly in the southern part, and cold spots were scattered throughout the watershed, especially in agricultural and scrubland areas. The significant synergistic relation between LATQ and WYLD, and sediment retention and WYLD and trade-offs between SURFQ and HES descriptors like WYLD, LATQ, sediment retention, and TWR was attributed to varying factors such as land use and topography impacting the water balance components in the watershed. The findings underscore the critical need for targeted conservation efforts to maintain the ecologically sensitive regions at watershed scale.