Advancing drought risk analysis and management: a case study of Kenya

In 2019, scientists from African countries called for more research on drought and better drought forecasting and management (Padma, 2019). Between 2020 and 2023, the Horn of Africa had experienced the worst drought in 40 years, with severe consequences related to reduced agricultural productivity and high food prices (Okoth, 2024). In this presentation, we will showcase the drought research done within the DOWN2EARTH project with a case study in Kenya.

Agro-pastoral livelihoods in the Horn of Africa (HoA) are acutely exposed to climate variability due to the predominance of rain-fed systems. Yet drought risk emerges from more than rainfall deficits—it reflects interacting biophysical processes, socio-economic vulnerability, and institutional response capacity. We advance an integrated, impact-based and adaptation-informed framework by combining statistical risk modelling across Kenya’s arid and semi-arid lands (ASALs) with a coupled socio-hydrological and agent-based simulation of human–water interactions.

First, using Spearman correlations and Random Forest regression, we link drought hazards to observed societal impacts and identify distinct timescale sensitivities: short (2–6 months) precipitation deficits align with increased household water trekking distances, while medium-to-long drought indices (5–24 months) better explain declines in milk production and increases in malnutrition. Clustering counties by vulnerability profiles improves predictive skill. Socio-economic clustering best captures water access outcomes, whereas environmental clustering better explains agricultural and nutrition impacts. Extending to probabilistic risk via Random Forest hindcasts (1984–2014) yields Average Annual Loss (AAL) and Probable Maximum Loss (PML) estimates, highlighting spatial heterogeneity: high water-access risk in northwestern Kenya and elevated livestock, milk, and malnutrition risk in eastern and southeastern counties. Priority adaptation pathways include sanitation and safe water access, poverty reduction, and small-scale water infrastructure.

Second, the ADOPT‑AP framework couples the DRYP hydrological model with a behavioural agent model to simulate bounded-rational adaptation and policy scenarios. Sensitivity analysis identifies irrigation abstraction as the dominant driver of both drought hazard and adaptation uptake. Replacing upstream commercial farms with communities or forests increases downstream streamflow and groundwater, modestly improving water access and production in drought years. During the 2020–2023 drought, doubling extension access marginally boosts low-cost measure adoption but not capital-intensive options, underscoring finance constraints; scaling water harvesting improves milk and reduces water trekking but has mixed crop effects and downstream hydrological trade-offs.

Together, these results demonstrate how vulnerability-informed, spatially targeted interventions and dynamic adaptation modelling can be used to strengthen early warning, guide equitable water governance, and build long-term resilience. However, improving drought management requires more than research. Early warnings are for example often not acted upon because of cultural values or limited resources. We therefore advocate for more transdisciplinary research, co-creation of drought adaptation solutions, and strengthening connections between communities and formal governance actors.

References

Padma, T. V. (2019). African nations push UN to improve drought research. Nature, 573(7774).

Okoth, D. (2024). The cost of African drought. Nature Africa, doi.org/10.1038/d44148-024-00075-0.