A methodology to characterise human water use with application in the data-scarce tropical Andes

In the tropical Andes and adjacent lowlands, widespread pressure on water availability driven by natural processes, human activities and climate change is considerably exacerbated by increasing water demand. Urban population growth and irrigation and hydropower capacity expansion lead to growing allocation and competition over water resources that often intensify water stress and conflict potentials.

A better grasp of local water use and complex intertwining of stakeholders is urgently needed in the tropical Andes and beyond. However, meaningful assessments at detailed spatiotemporal scales are hampered by insufficient data quantity and quality and limited modelling approaches. To overcome these limitations, we propose a blended approach collecting in-situ, statistical and remote sensing data at various source and scale levels. These data are then downscaled to a locally gridded dataset on a monthly basis using an allocation routine that defines the closest feasible abstraction point for domestic and agricultural demand. Water use and demand estimates are validated by (semi-)structured interviews using mixed qualitative-quantitative methods. We put particular emphasis on the variability between rural and urban households, small and large-scale agriculture, and on unravelling the complex interlinks of sectoral and upstream-downstream water use. A major challenge represents the exploration of future pathways of water demand which is entailed by large uncertainties associated with political instabilities, institutional turnover, climate change, social conflicts, and economic volatility in the tropical Andes. To address this challenge, we discuss smart and flexible model approaches and a recurrent revision of projected datasets under changing conditions.

Our improvement of locally relevant sectoral water use estimates builds an important baseline for integration into water balance studies and decision-making. The UNDESA stresses the urgent need for investing into collecting and producing consistent socio-economic data in developing countries in order to visualize the most vulnerable and to overcome inequalities. We consider that our contribution is a step forward to informing local and national decision-making for improved water management and long-term achievement of the SDGs.