Predicting Climate Change in Kyrgyzstan

Date: 18 March 2019
Other languages: Русский язык |

“Human civilisation depends greatly on ecosystems, and the services they provide such as soils, vegetation, precipitation and temperature regimes,” said Dr. Maksim Kulikov, Research Fellow at the University of Central Asia’s (UCA) Mountain Societies Research Institute (MSRI). “All these factors are in constant interaction, and human impact can affect the balance in ecosystems. It is important to understand how and to what extent the natural resources can sustainably be used without severe consequences.” 

UCA’s MSRI held a Public Lecture in Bishkek on the “Impact Assessment of Grazing and Climatic Factors on Vegetation in Kyrgyzstan” in March 2019. Delivered by Dr. Maksim Kulikov, he presented outputs of his research on an assessment of the interactions between soil, vegetation and climatic factors, quantifying them to better predict climate change scenarios.

Watch the full lecture at: 

Dr. Kulikov presented a unique digital model, developed to assess soil erosivity and erodibility depending on grazing pressure and climate factors. It identified five distinct vegetation clusters with different patterns of vegetation and climate interactions. Dr. Kulikov noted that the tool can be useful for pasture committees and practitioners who work in Natural Resources Management. The regression analysis concluded that most of Kyrgyzstan’s area is more prone to seasonal redistribution of temperature and precipitation, than annual growth based on trends. Areas with artificial irrigation systems also indicated a much lower dependence on climatic factors, which can be used to measure climate change adaptation.

The research also investigated the impact of existing grazing practices on rangelands’ soil and vegetation resources. It integrated extensive field expeditions on the Fergana ridge through collection of soil samples, vegetation and social data, as well as laboratory analysis of soil, statistical analysis, and modelling using remotely sensed and direct observation data.

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