at Google ScholarTitle data
Haag, Isabell ; Kassam, Karim-Aly ; Senftl, Thomas ; Zandler, Harald ; Samimi, Cyrus:
Measurements meet human observations : integrating distinctive ways of knowing in the Pamir Mountains of Tajikistan to assess local climate change.
In: Climatic Change.
Vol. 165
(2021)
.
- 5.
ISSN 1573-1480
DOI: https://doi.org/10.1007/s10584-021-02988-3
Project information
| Project title: |
Project's official title Project's id Ecological Calendars and Climate Adaptation in the Pamirs, ECCAP SA 775/12-1 |
|---|---|
| Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract in another language
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.