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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (6): 798-815    DOI: 10.1007/s40333-024-0078-z     CSTR: 32276.14.s40333-024-0078-z
Research article     
Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China
LU Haitian1, ZHAO Ruifeng1,2,*(), ZHAO Liu3, LIU Jiaxin4, LYU Binyang5, YANG Xinyue6
1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
2Key Laboratory of Resource Environment and Sustainable Development of Oasis, Gansu Province, Lanzhou 730070, China
3School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart TAS 7005, Australia
4School of Chinese Language and Literature, Xi'an International Studies University, Xi'an 710128, China
5Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
6School of Design and Built Environment, Curtin University, Perth 6102, Australia
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Abstract  

Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas. This study took Gansu Province, China, a typical area with complex terrain and variable climate, as the research subject. Based on Google Earth Engine, we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022, and quantitatively analyzed the main causes of regional differences in surface water area. The findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to 2022. Seasonal surface water area exhibited significant fluctuations, while permanent surface water area showed a steady increase. Notably, terrestrial water storage exhibited a trend of first decreasing and then increasing, correlated with the dynamics of surface water area. Climate change and human activities jointly affected surface hydrological processes, with the impact of climate change being slightly higher than that of human activities. Spatially, climate change affected the 'source' of surface water to a greater extent, while human activities tended to affect the 'destination' of surface water. Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted. Therefore, we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water 'supply-demand' balance strategies. The study not only sheds light on the dynamics of surface water area in Gansu Province, but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.

Key words： surface water area      terrestrial water storage      Open-surface Water Detection Method with Enhanced Impurity Control method      Google Earth Engine      climate change      human activities      inland arid and semi-arid areas     
Received: 01 March 2024      Published: 30 June 2024
Corresponding Authors: *ZHAO Ruifeng (E-mail: zhaorf@nwnu.edu.cn)
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LU Haitian
ZHAO Ruifeng
ZHAO Liu
LIU Jiaxin
LYU Binyang
YANG Xinyue
Cite this article:

LU Haitian, ZHAO Ruifeng, ZHAO Liu, LIU Jiaxin, LYU Binyang, YANG Xinyue. Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China. Journal of Arid Land, 2024, 16(6): 798-815.

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http://jal.xjegi.com/10.1007/s40333-024-0078-z     OR     http://jal.xjegi.com/Y2024/V16/I6/798

Fig. 1 Overview of the Gansu Province and its five ecological functional subareas based on digital elevation model (DEM) data. DEM data were obtained from National Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) (https://cmr.earthdata.nasa.gov/search/). GP, Gannan Plateau; HH, Hexi Hinterland; YCA, Yellow River Central Area; SQM, southern Qinba Mountains; LP, Loess Plateau. Note that this map is based on the standard map (GS(2019)1652) of the Map Service System (http://bzdt.ch.mnr. gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Factor Resolution Time span Dataset Reference
Precipitation 0.05° 1990-2022 Climate Hazards Group InfraRed Precipitation with Station data Funk et al. (2015);
Huang et al. (2021b)
LST 1 km 2000-2022 MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km Sulla-Menashe et al. (2019)
Evapotranspiration 500 m 2001-2022 MOD16A2 Version 6.1 Evapotranspiration/ Latent Heat Flux product Huete et al. (1997)
FVC 250 m 2003-2022 The MYD13Q1.006 Aqua Vegetation Indices 16-Day Global 250m Huete et al. (1997)
Population 100 m 2000-2020 WorldPop Global Project Population Data Gaughan et al. (2013)
Cropland 30 m 1990-2020 CLCD Yang and Huang (2021)
Impervious surface 30 m 1990-2019 Tsinghua FROM-GLC Year of Change to Impervious Surface dataset Gong et al. (2020)
GDP 1 km 1992-2019 GRIDDED_EC-GDP Chen et al. (2022)
Table 1 Detailed description of environmental and socio-economic factors used in this study
Fig. S1 Spatial distribution of each driving factor in Gansu Province during 1985-2022. (a), precipitation; (b), land surface temperature (LST); (c), evapotranspiration; (d), fractional vegetation cover (FVC); (e), population; (f), cropland; (g), impervious surface; (h), GDP. GP, Gannan Plateau; HH, Hexi Hinterland; YCA, Yellow River Central Area; SQM, southern Qinba Mountains; LP, Loess Plateau. Note that the maps are based on the standard map (GS(2019)1652) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 2 Schematic diagram of Open-surface Water Detection Method with Enhanced Impurity Control (OWDM-EIC) method. (a), data preprocessing; (b), surface water detection rules; (c), surface water classification rules; (d), accuracy verification. NDWI, normalized difference water index; mNDWI, modified normalized difference water index; NDVI, normalized difference vegetation index; EVI, enhanced vegetation index; GAIA, annual maps of global artificial impervious area; CLCD, Landsat-derived annual China land cover dataset; WF, water frequency; JRC, Joint Research Centre.
Fig. S2 Spatial distribution of water frequency in the whole (a) and partial areas (b-f) of Gansu Province in 2022. Note that this map is based on the standard map (GS(2019)1652) of the Map Service System (http://bzdt.ch. mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Sample type Number of ground reference samples Overall accuracy (%)
Water Non-water Total
Water 1855 85 1940 99.27
Non-water 94 12,966 13,060
Table 2 Confusion matrix for accuracy assessment of OWDM-EIC using samples from the Sentinel-2A images in 2022
Sample type Number of ground reference samples Overall accuracy (%)
Water Non-water
Water 1,367,428 949,331 99.09
Non-water 1,276,405 2,301,846,362
Table 3 Confusion matrix for accuracy assessment of OWDM-EIC using the JRC dataset in 2022
Fig. 3 Trends in surface water area (SWA) and terrestrial water storage (TWS) in Gansu Province and its five ecological functional subareas from 1985 to 2022. (a), Gansu Province; (b), GP; (c), HH; (d), YCA; (e), SQM; (f), LP. The black dashed line represents the time when the mutation occurs.
Fig. 4 Trends in seasonal surface water area (SSWA) and permanent surface water area (PSWA) in Gansu Province and its five ecological functional subareas from 1985 to 2022. (a), Gansu Province; (b), GP; (c), HH; (d), YCA; (e), SQM; (f), LP.
Fig. 5 Factor detector results (indicated by q-values) for various driving factors in each subarea. (a), GP; (b), HH; (c), YCA; (d), SQM; (e), LP. X1-X8 indicate precipitation, land surface temperature, evapotranspiration, fractional vegetation cover, population, cropland, impervious surface, and gross domestic product (GDP), respectively.
Fig. 6 Interaction detection results (indicated by q-values) for various driving factors in each subarea. (a), GP; (b), HH; (c), YCA; (d), SQM; (e), LP.
Fig. 7 Relationships between SWA and precipitation (a), SWA and LST (b), SWA and human society's water consumption (WHS) (c), and TWS and WHS (d)
Fig. 8 Surface hydrological processes (a) and hydrological patterns (b) in inland arid and semi-arid areas
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