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Across the globe, station-based meteorological data are analyzed to estimate the rate of change in precipitation. However, in sparsely populated regions, like Mongolia, stations are few and far between, leaving significant gaps in station-derived precipitation patterns across space and over time. We combined station data with the observations of herders, who live on the land and observe nature and its changes across the landscape. Station-based trends were computed with the Mann–Kendall significance and Theil–Sen rate of change tests. We surveyed herders about their observations of changes in rain and snowfall amounts, rain intensity, and days with snow, using a closed-ended questionnaire and also recorded their qualitative observations. Herder responses were summarized using the Potential for Conflict Index (PCI2), which computes the mean herder responses and their consensus. For one set of stations in the same forest steppe ecosystem, precipitation trends were similar and decreasing, and the herder-based PCI2 consensus score matched differences between stations. For the other station set, trends were less consistent and the PCI2 consensus did not match well, since the stations had different climates and ecologies. Herder and station-based uncertainties were more consistent for the snow variables than the rain variables. The combination of both data sources produced a robust estimate of climate change uncertainty.

Merging Indigenous Knowledge Systems and Station Observations to Estimate the Uncertainty of Precipitation Change in Central Mongolia