Abstract
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.
Highlights
In regions with low population densities, the number and spacing of meteorological stations is often sparse [1,2]
Indigenous Knowledge Systems (IKS) are those held by indigenous peoples that are based in place, time, and embedded relationships with the culture and environment, and are socially transmitted from one generation to the [14,17,18]
This study focused on Northern soums of Ikh-Tamir [52] and Undur-Ulaan in Arkhangai aimag and the two Southern soums four soums in two aimags that are divided by the Khangai Mountains: the two of Jinst Northern
Summary
In regions with low population densities, the number and spacing of meteorological stations is often sparse [1,2]. Even in locations where terrain and climate are considered homogeneous, there can be significant differences in the climate trends observed among relatively closely spaced stations [6,7] Such factors can complicate the interpretation of station observations, especially when an evaluation of the long-term trends is needed to study climate change [8]. IKS can provide insight into climatic and hydrologic changes over time [3,8,22,23,24,25,26,27,28], especially in areas where there are limited station data [29,30,31,32,33,34]
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