Enhancing urban resilience through water ecosystem services in the arid region of Northwest China

doi:10.1016/j.jaridl.2026.03.005

Journal of Arid Land

2026, Vol. 18

Issue (3): 429-451 DOI: 10.1016/j.jaridl.2026.03.005 CSTR: 32276.14.JAL.20250500

Research article

Enhancing urban resilience through water ecosystem services in the arid region of Northwest China

ZHOU Yuxuan¹, HE Jia¹^,²^,^*(

), WANG Shoufeng¹

¹College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
²Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Xinjiang Normal University, Urumqi 830054, China

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Abstract

Within the context of global climate change and rapid urbanization, increasing urban resilience (UR) is especially important in the arid region of Northwest China (ANC), where fragile ecosystems and an uneven water distribution create significant sustainability challenges. In this study, a coupled UR-water ecosystem services (WESs) framework was developed on the basis of 1-km resolution remote sensing data for the 2000-2020 period obtained from the Landsat series, Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS), and Global Precipitation Measurement (GPM), among other sources. Within the framework, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was incorporated to provide a WES indicator system. Moreover, entropy weighting was employed to quantify both UR and WES indicators; the coupling coordination degree (CCD) model was used to measure the coupled relationship between UR and WESs; an extreme gradient boosting (XGBoost)-SHapley Additive exPlanations (SHAP) interpretation approach was adopted to identify key drivers and underlying mechanisms; and Geographically Weighted Regression (GWR) was applied to capture spatial distribution characteristics of major driving factors. The results indicated that UR steadily increased from 4.60×10^-3 to 10.24×10^-3, whereas WESs followed an inverted V-shaped trend, with a peak value observed in 2010 (11.84×10^-3). The CCD remained consistently low (mean: 0.0166-0.0246) and exhibited considerable spatial heterogeneity. Notably, the degree of coordination was greater in the oasis and mountain core areas but significantly lower at desert areas. XGBoost-SHAP model analysis revealed six key drivers influencing various states of the CCD between UR and WESs systems. The contributions of these factors could be ranked as follows: water yield (WY; 24.30%)>farmland area per capita (FP; 21.10%)>gross domestic product (GDP) per capita (GDPC; 19.00%)>soil retention (SR; 14.90%)>population density (PD; 5.42%)>water purification (WP; 4.40%). In contrast, in UR system, WP (53.66%) and SR (31.62%) served as the dominant drivers. Moreover, the dominant drivers shifted from a combination of natural and socioeconomic factors in State I (sustainable high resilience) to primarily socioeconomic factors in State III (unsustainable low resilience). SR and WP exerted positive moderating effects, whereas socioeconomic factors such as GDPC and PD exerted inhibitory effects on the coordination relationship. This research highlights that UR in the ANC region is limited mainly by water scarcity, weak feedback loops, and spatial variability, emphasizing the need for tailored intervention strategies.

Key words： urban resilience water ecosystem services (WESs) coupling coordination degree Extreme Gradient Boosting (XGBoost) SHapley Additive exPlanations (SHAP) Northwest China arid region

Received: 09 October 2025 Published: 31 March 2026

Corresponding Authors: *HE Jia (E-mail: hejiahj@xjnu.edu.cn)

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Cite this article:

ZHOU Yuxuan, HE Jia, WANG Shoufeng. Enhancing urban resilience through water ecosystem services in the arid region of Northwest China. Journal of Arid Land, 2026, 18(3): 429-451.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.03.005 OR http://jal.xjegi.com/Y2026/V18/I3/429

Fig. 1 Distribution of altitude in the arid region of Northwest China (ANC). DEM, digital elevation model.

Table 1 Urban resilience (UR) data sources

Table 2 Water ecosystem service (WES) data sources

Fig. 2 Urban resilience (UR)-water ecosystem services (WESs) coupling analysis framework. PD, population density; PGR, population growth rate; UPS, urban population size; URA, urbanization rate; GDPC, gross domestic product (GDP) per capita; NLAI, nighttime light aggregation index; NLI, nighttime light intensity; FE, fossil fuel carbon dioxide (CO₂) emissions; FP, farmland area per capita; PRE, precipitation; PET, potential evapotranspiration; LULC, land use/land cover; CCD, coupling coordination degree; XGBoost-SHAP, Extreme Gradient Boosting-SHapley Additive exPlanations; GWR, Geographically Weighted Regression.

Fig. 3 Spatiotemporal distribution of UR indicators in the ANC region from 2000 to 2020. (a-c), population resilience (POP); (d-f), economic resilience (ECO); (g-i), environmental resilience (ENV); (j-l), overall UR.

Fig. 4 Spatiotemporal distribution of WES indicators in the ANC region from 2000 to 2020. (a-c), water yield (WY); (d-f), soil retention (SR); (g-i), WP (also reflects the situation of nitrogen export); (j-l), overall WESs.

Table 3 Pearson correlation coefficients (r) between UR and WES indicators

Fig. 5 Spatiotemporal distribution of CCD values between UR and WES indicators in the ANC region. (a), 2000; (b), 2005; (c), 2010; (d), 2015; (e), 2020; (f), mean of 2000-2020.

Fig. 6 Spatiotemporal distribution of the CCD values between the UR and individual WES indicators in the ANC region. (a), WY-POP; (b), WY-ECO; (c), WY-ENV; (d), SR-POP; (e), SR-ECO; (f), SR-ENV; (g), WP-POP; (h), WP-ECO; (i), WP-ENV.

Fig. 7 Temporal variation of mean CCD between UR and individual WES indicators. (a), WY-POP; (b), WY-ECO; (c), WY-ENV; (d), SR-POP; (e), SR-ECO; (f), SR-ENV; (g), WP-POP; (h), WP-ECO; (i), WP-ENV.

Fig. 8 SHAP feature summary and importance plots for CCD. (a), State I area; (b), State II area; (c), State III area; (d), State IV area. The grey vertical line in the figure indicates a SHAP value of zero, serving as a reference baseline indicating that the feature does not contribute to model predictions.

Fig. 9 SHAP dependency plots for CCD. (a-c), State I area; (d-f), State II area; (g-i), State III area; (j-l), State IV area.

Fig. 10 SHAP dependency plots for the WESs (WY, SR, and WP) on UR. (a), POP; (b), ECO; (c), ENV; (d), UR.

Fig. 11 Spatial distribution of the major driving factors based on the GWR model. (a), WY; (b), SR; (c), WP; (d), FP; (e), GDPC; (f), PD.


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