Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980–1994, 1998–2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998–2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980–1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA–2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.
Changes in climate can be favorable as well as detrimental for natural and anthropogenic systems. Temperatures in Central Asia have risen significantly within the last decades whereas mean precipitation remains almost unchanged. However, climatic trends can vary greatly between different subregions, across altitudinal levels, and within seasons. Investigating in the seasonally and spatially differentiated trend characteristics amplifies the knowledge of regional climate change and fosters the understanding of potential impacts on social, ecological, and natural systems. Considering the known limitations of available climate data in this region, this study combines both high-resolution and long-term records to achieve the best possible results. Temperature and precipitation data were analyzed using Climatic Research Unit (CRU) TS 4.01 and NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B43. To study long-term trends and low-frequency variations, we performed a linear trend analysis and compiled anomaly time series and regional grid-based trend maps. The results show a strong increase in temperature, almost uniform across the topographically complex study site, with particular maxima in winter and spring. Precipitation depicts minor positive trends, except for spring when precipitation is decreasing. Expected differences in the development of temperature and precipitation between mountain areas and plains could not be detected.
Seasonal rounds are deliberative articulations of a community’s sociocultural relations with their ecological system. The process of visualizing seasonal rounds informs transdisciplinary research. We present a methodological approach for communities of enquiry to engage communities of practice through context-specific sociocultural and ecological relations driven by seasonal change. We first discuss historical précis of the concept of seasonal rounds that we apply to assess the spatial and temporal communal migrations and then describe current international research among Indigenous and rural communities in North America and Central Asia by the creation of a common vocabulary through mutual respect for multiple ways of knowing, validation of co-generated knowledge, and insights into seasonal change. By investigating the relationship between specific biophysical indicators and livelihoods of local communities, we demonstrate that seasonal rounds are an inclusive and participatory methodology that brings together diverse Indigenous and rural voices to anticipate anthropogenic climate change.
In mountain environments dimensions of climate change are unclear because of limited availability of meteorological stations. However, there is a necessity to assess the scope of local climate change, as the livelihood and food systems of subsistence-based communities are already getting impacted. To provide more clarity about local climate trends in the Pamir Mountains of Tajikistan, this study integrates measured climate data with community observations in the villages of Savnob and Roshorv. Taking a transdisciplinary approach, both knowledge systems were considered as equally pertinent and mutually informed the research process. Statistical trends of temperature and snow cover were retrieved using downscaled ERA5 temperature data and the snow cover product MOD10A1. Local knowledge was gathered through community workshops and structured interviews and analysed using a consensus index. Results showed, that local communities perceived increasing temperatures in autumn and winter and decreasing amounts of snow and rain. Instrumental data records indicated an increase in summer temperatures and a shortening of the snow season in Savnob. As both knowledge systems entail their own strengths and limitations, an integrative assessment can broaden the understanding of local climate trends by (i) reducing existing uncertainties, (ii) providing new information, and (iii) introducing unforeseen perspectives. The presented study represents a time-efficient and global applicable approach for assessing local dimensions of climate change in data-deficient regions.
Across Central Asia, surface temperatures have been significantly rising due to anthropogenic climate change, resulting in detrimental consequences for local communities (Finaev et al., 2016;Haag, Jones, et al., 2019;Hu et al., 2014). One direct consequence is food insecurity. Based on the Climate and Food Vulnerability Index, Tajikistan has high food insecurity, while being ranked among the countries least responsible for greenhouse gas emissions (Ware & Kramer, 2019). High elevations with poor accessibility and elevated susceptibility to natural hazards exacerbates the vulnerability of certain villages in the mountains of Central Asia. These communities are usually characterized by a low diversification of livelihoods, primarily being subsistence-based farmers and herders (Gentle & Maraseni, 2012;Kohler et al., 2010;Manandhar et al., 2018). Neighboring villages in the Bartang Valley of Tajikistan, Savnob and Roshorv, historically managed to ensure food security by aligning the timing of their seasonal agropastoral activities with the occurrence of biotic indicators. Knowledge about seasonal activities and their relation to indicators is embodied in the communities' ecological calendars (Kassam et al., 2011).
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