The International Water Management Institute (IWMI) is a non-profit international water management research organisation under the CGIAR with its headquarters in Colombo, Sri Lanka, and offices across Africa and Asia. Research at the Institute focuses on improving how water and land resources are managed, with the aim of underpinning food security and reducing poverty while safeguarding the environment.Its research focuses on: water availability and access, including adaptation to climate change; how water is used and how it can be used more productively; water quality and its relationship to health and the environment; and how societies govern their water resources. In 2012, IWMI was awarded the prestigious Stockholm Water Prize Laureate by Stockholm International Water Institute for its pioneering research, which has helped to improve agricultural water management, enhance food security, protect environmental health and alleviate poverty in developing countries.IWMI is a member of CGIAR, a global research partnership that unites organizations engaged in research for sustainable development, and leads the CGIAR Research Program on Water, Land and Ecosystems. IWMI is also a partner in the CGIAR Research Programs on: Aquatic Agricultural Systems (AAS); Climate Change, Agriculture and Food Security (CCAFS); Dryland Systems; and Integrated Systems for the Humid Tropics.
Rights of wetlands shifts the people-wetlands relationship towards one recognizing the intrinsic rights and living beingness of wetlands, embodying reciprocity, kinship, and gratitude. Recognition of wetland rights occurs within the broader Rights of Nature movement, which has often been led by Indigenous Peoples and local communities from many cultural traditions. Intrinsic wetland rights centre on the right to exist and incorporate seven other rights, forming a holistic framework that enables preservation of wetland ecosystem integrity. These rights provide the basis for a wetland’s thriving existence. The eight rights are: • the right to exist. • their natural place in the landscape; • natural and connected hydrologic regimes; • natural climatic conditions; • naturally occurring biodiversity; • natural ecosystem processes; • natural water, soil, and air quality (i.e., structural integrity and form); and. • regeneration and a naturally determined future. Moral and ethical responsibilities towards these rights include fostering harmonious relationships in and with wetlands, respecting interdependence of all wetland components, safeguarding and fostering conditions enabling rights of wetlands, and acting in a precautionary manner when faced with risks of harm to wetlands. Legal and policy responsibilities include acknowledging plural worldviews, recognising legal standing and political orientations to represent and defend wetland rights, providing adequate means for wetland representation and defending and advocating for wetlands, and using best available science to restore degraded wetlands and support regeneration, thereby supporting harmonious societal relationships with wetlands. Opportunities for implementation of rights of wetlands at a variety of scales (individual, community, national, and global) are explored, and training/guidance resources are referenced.
Different types of techniques used for removal of heavy metals from water.
While climate change poses increasing risks to cocoa production, the adoption of irrigation in Ghana's cocoa sector remains low. In this context, this study examined farmers' preferences for climate and loan financing information for solar irrigation adoption. A discrete choice experiment was conducted with cocoa farmers across seven regions of Ghana. In treatment groups, we varied the availability of major season consecutive dry days information, while both groups received major season rainfall amount information, forecast spread, and financial conditions for solar irrigation adoption. The climate information projected conditions over a five-year horizon. Using mixed logit and latent class models, we found that farmers responded strongly to loan costs per month and the fraction of their farm that would be irrigated. In contrast, farmers showed limited sensitivity to climate and forecast spread information. We found that farmers were not willing to pay for rainfall amount and consecutive dry days information. We did not detect a significant difference in cocoa farmer preferences for solar irrigation when additional dry days information was provided. However, we did detect that consecutive dry days information was used during decision making. Preferences were shaped by perceptions of climate change and education levels, and not by stated attribute non-attendance. The findings highlight the importance of financial support and that transmission of climate information to farmers and actual use of this information for decision making is complex and requires a context-specific combination of climate and behavioural sciences.
In the present era, climate change coupled with population explosion, industrial advancement and upsurge in agricultural activities has put a tremendous pressure on freshwater resources leading to water crisis. To manage this ongoing issue, rainwater harvesting (RWH) at potential sites is emerging as an ecofriendly strategy for sufficing the agricultural and other domestic requirements. In this perspective, it is imperative to identify the potential sites for harvesting the excess rainwater which otherwise flows as a surface runoff. In the present study, suitable sites for rainwater harvesting have been identified in block Balachaur of District SBS Nagar Punjab (India) using GIS and multi-criteria decision-making techniques. The analytical hierarchy process (AHP) and Fuzzy-AHP techniques have been used in GIS environment to identify the most suitable sites for RWH. This study advances current GIS-based rainwater harvesting (RWH) assessment approaches by integrating both AHP and Fuzzy-AHP within a multi-criteria geospatial decision framework. Eight criterion layers including runoff depth, soil type, slope, stream order, drainage density, geology, LULC and distance to roads have been used. AHP and Fuzzy-AHP have been used to assign the weight to each layer and then weighted overlay analysis (for AHP) and fuzzy overlay (for Fuzzy-AHP) was done in ArcGIS to generate the RWH site suitability maps. The AHP-based RWH site suitability map revealed that about 7.34
This study evaluates the performance of seven Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and their Ensemble mean in reproducing historical maximum (Tmax) and minimum (Tmin) temperatures across five agro-ecological zones (AEZs) of Ethiopia during 1995–2014. The assessment covers daily to annual time scales using observational and GCM datasets. Model performance was evaluated using Percent Bias (PBIAS), Root Mean Square Error (RMSE), and correlation coefficient (r), while the Comprehensive Rating Index (CRI) was applied for ranking. Results show substantial spatial and temporal variability in model performance. For Tmax, the Ensemble mean and EC-Earth3-veg performed best at the daily scale, while EC-Earth3-veg, MPI-ESM1-2-LR and BCC-CSM2-MR excelled at the monthly scale across most AEZs. Seasonally, top-performing models included Ensemble mean, MPI-ESM1-2-LR and MRI-ESM2-0 during Bega (October-January), EC-Earth3-veg, CNRM-CM6-1 and Ensemble mean during Belg (February-May) and CNRM-CM6-1 and MPI-ESM1-2-LR during Kiremt (June-September). For Tmin, CNRM-CM6-1, BCC-CSM2-MR and Ensemble mean ranked highest at both daily and monthly scales in most AEZs. At the annual scale, MRI-ESM2-0, Ensemble mean, MPI-ESM1-2-LR and CNRM-CM6-1 excel for Tmax, while EC-Earth3-veg, BCC-CSM2-MR and Ensemble mean lead for Tmin across most AEZs. MIROC6 consistently exhibited the weakest performance for both Tmax and Tmin across most AEZs and time periods. Thus, the Ensemble mean of all evaluated models does not consistently rank among the top three performers across all AEZs and time scales. The identified best-performing CMIP6 models provide valuable tools for assessing climate change impacts and developing region-and time-specific adaptation strategies in Ethiopia. Given the variability in GCM performance across AEZs and time scales, national or large-scale studies would benefit from using the Ensemble mean derived from the best-performing models. This study provides crucial insights into the strengths and weaknesses of different GCMs, supporting evidence-based decision-making and enhancing efforts to build climate resilience and adaptation strategies in the region.
