The Climate Risk Informed Decision Analysis (CRIDA) framework incorporates the uncertainties of climate change that impact project planning, socioeconomic justification, and engineering design into a step-wise and collaborative planning process to guide a technical analyst to low-regret risk- and cost-effective solutions; Research has been carried out to demonstrate and improve, through additional guidelines, the usability of CRIDA, in a pilot for the Limari basin in Chile. The added guidelines (1) offer the analyst numerically based justifications for analytical decisions to ensure a more structured application of CRIDA and (2) improves on co-design aspects by incorporating stakeholder risk perceptions and opinions explicitly in the process. The Limari Basin has experienced an increase in drought frequency and severity over the last decades. A strategic approach for adaptation is recommended through CRIDA based on an evaluation of the future risk to climate change and the confidence in this analysis and a subsequent systematic assessments of adaptation options. The resulting strategy requires the increase of water supply robustness by adding new water sources that can be implemented in combination with flexible measures for managing demand (i.e. implementing agricultural meshes and improving irrigation efficiency) in parallel or in series to create adaptation pathways. The study demonstrated the functionality of CRIDA. While the added guidelines required more processing time, subjectivity in the method is reduced thus also reducing possible bias introduced by the analyst. In addition, overall acceptability of the proposed strategies is improved by incorporating stakeholder risk perceptions and opinions explicitly in the process.

The Morgan-Morgan-Finney (MMF) model is identified as the most appropriate hillslope soil erosion model for high resolution global application with the objective of identifying relative erosion risk areas and modelling the effect of land cover changes on erosion at a catchment scale. This is justified by its simple model structure, low input requirements, semi-empirical basis and distributed application. However, transferability to global application is hindered by its reliance on empirical data for input requirements. This paper proposes methodologies of generating input data based on remotely sensed products which improves the spatial and temporal accuracy of the input rasters. The MMF model is further redesigned by coupling it with the IHE Delft Institute for Water Education inhouse hydrological model, WaterPix, to replace runoff calculations in order to improve its treatment of infiltration and by applying the model in monthly timesteps to analyse erosional differences within the seasonal crop calendar. The redesigned MMF model algorithm is coded in python and applied over a headwater in the Ganga basin located in the Madhya Pradesh state of India. The model produces realistic erosion rates and distributions and provides additional information on the spatial and temporal variation of erosion in the study area. However, validation of the redesigned model with field data needs to be prioritized before it is utilized.