Recent Conflict Early Warning Systems have found little evidence of predictive power of drought indicators for conflict prediction. However, this may result from the context-specificity of the drought-conflict relationship, as stressed in the more recent climate-conflict literature.

The present thesis assesses the local role of meteorological drought indicators for communal conflict prediction in North-Western Kenya, as a region where the narrative of resource-scarcity driven conflicts exists.

A local-scale literature review on conflict dynamics followed by a fixed-effects logistic regression modelling approach stress the importance of the spatial dimension when analysing drought-conflict relationships. The role of cross-border transhumance in linking climate variability to conflict occurrence is stressed by the lower confidence intervals and more significant effects when moving the regression analysis from the spatial delimitation of administrative units to the agency level of ethnic groups.

Differences in between ethnic groups in the obtained patterns of conflict behaviour in response to drought or water abundance are explained by their migratory behaviour along with a differentiated account of their relative drought vulnerability.

The lack of any considerable role of drought in the subsequently built quasi-replication of the WPS Global Early Warning Tool, is therefore assigned to the mismatch of administrative units as the spatial
unit of analysis in a pastoralist area, where herders frequently move their cattle to the other side of the border.

It is advocated for an ethnic-group centered approach to predicting conflict, which relaxes assumptions on spatial containment of conflict events. However, whether this alternative model specification leads
to a greater role of drought indicators in conflict prediction and better overall predictions, needs to be assessed in future work.

Empirical-statistical rainfall landslide initiation thresholds are popularly used for early warning systems to discriminate between the occurrence and non-occurrence of rainfall-induced landslides. However, the few studies that have derived landslide initiation thresholds for landslide-prone and data-scarce Rwanda rely solely on the limited in situ data. Therefore, our objective is to explore the feasibility of using satellite data and hydrological model derived data to derive both trigger and trigger-cause thresholds for landslides in Rwanda. We firstly evaluated seven precipitation products (TRMM 3B42v7, CHIRPS, PERSIANN-CDR, GLDAS 2.1, CFSv2, IMERG, and ERA5) using the rain gauge data as a reference and found that IMERG was the most suitable product for obtaining rainfall triggering conditions. We then studied the added value of incorporating the antecedent soil moisture from both a high spatial satellite data and from a distributed hydrological model following the trigger-cause framework. The results showed that the event precipitation volume E, the event duration D and the bilinear threshold E-D are the landslide initiation thresholds that accurately predict the highest number of landslide events while keeping the false and the failed alarms low. Including the antecedent soil moisture products as the causal variables -expected to account for the hillslope hydrologic processes predisposing the slopes to near failure- did not lead to any improvement with respect to the trigger only thresholds for predicting landslides in Rwanda.