Response of Gross Primary Productivity (GPP) of the Desert Steppe Ecosystem in the Northern Foothills of Yinshan Mountain to Extreme Climate

Shuixia Zhao, Mengmeng Zhang (), Yingjie Wu, Enliang Guo (), Yongfang Wang, Shengjie Cui and Tomasz Kolerski
Additional contact information
Shuixia Zhao: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Mengmeng Zhang: College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
Yingjie Wu: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Enliang Guo: College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
Yongfang Wang: College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
Shengjie Cui: Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Tomasz Kolerski: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland

Land, 2025, vol. 14, issue 4, 1-18

Abstract: The desert steppe ecosystem at the Northern Foothills of the Yinshan Mountains (NFYS) is characterized by its fragility and heightened sensitivity to global climate change. Understanding the response and lag effects of Gross Primary Productivity (GPP) to climate change is imperative for advancing ecological management and fostering sustainable development. The spatiotemporal dynamics of chlorophyll fluorescence-based GPP data and its responses to precipitation, temperature, and extreme climate from 2001 to 2023 were analyzed. The random forest model and the partial least squares regression model were employed to further elucidate the response mechanisms of GPP to extreme climate, with a specific focus on the lag effect. The findings revealed that the GPP in the NFYS exhibited distinct regional characteristics, demonstrating a predominantly increasing trend over the past 23 years. The region has experienced a warming and drying trend, marked by a decrease in the intensity and frequency of extreme precipitation events, and an increase in extremely high temperatures and consecutive hot days, except a slight, albeit insignificant, increase in precipitation in the northeastern part. GPP exhibits varying degrees of lag, ranging from one to three months, in response to both normal and extreme climatic conditions, with a more immediate response to extreme temperatures than to precipitation. The influence of different climatic conditions on the lag effects of GPP can amplify the negative effects of extreme temperatures and the positive impact of extreme precipitation. The anticipated trend towards a warmer and more humid climate is projected to foster an increase in GPP. This research is of great theoretical and practical significance for deeply understanding the adaptation mechanisms of ecosystems under the context of climate change, optimizing desertification control strategies, and enhancing regional ecological resilience.

Keywords: gross primary productivity; desert steppe; extreme climate; northern foothills of the Yinshan Mountains (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/4/884/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/4/884/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:4:p:884-:d:1636221

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().