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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (2): 220-245    DOI: 10.1007/s40333-024-0070-7
Research article     
Influence of varied drought types on soil conservation service within the framework of climate change: insights from the Jinghe River Basin, China
BAI Jizhou1, LI Jing1,*(), RAN Hui1, ZHOU Zixiang2, DANG Hui1, ZHANG Cheng1, YU Yuyang1
1School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
2College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
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Abstract  

Severe soil erosion and drought are the two main factors affecting the ecological security of the Loess Plateau, China. Investigating the influence of drought on soil conservation service is of great importance to regional environmental protection and sustainable development. However, there is little research on the coupling relationship between them. In this study, focusing on the Jinghe River Basin, China as a case study, we conducted a quantitative evaluation on meteorological, hydrological, and agricultural droughts (represented by the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI), respectively) using the Variable Infiltration Capacity (VIC) model, and quantified the soil conservation service using the Revised Universal Soil Loss Equation (RUSLE) in the historical period (2000-2019) and future period (2026-2060) under two Representative Concentration Pathways (RCPs) (RCP4.5 and RCP8.5). We further examined the influence of the three types of drought on soil conservation service at annual and seasonal scales. The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was used to predict and model the hydrometeorological elements in the future period under the RCP4.5 and RCP8.5 scenarios. The results showed that in the historical period, annual-scale meteorological drought exhibited the highest intensity, while seasonal-scale drought was generally weakest in autumn and most severe in summer. Drought intensity of all three types of drought will increase over the next 40 years, with a greater increase under the RCP4.5 scenario than under the RCP8.5 scenario. Furthermore, the intra-annual variation in the drought intensity of the three types of drought becomes smaller under the two future scenarios relative to the historical period (2000-2019). Soil conservation service exhibits a distribution pattern characterized by high levels in the southwest and southeast and lower levels in the north, and this pattern has remained consistent both in the historical and future periods. Over the past 20 years, the intra-annual variation indicated peak soil conservation service in summer and lowest level in winter; the total soil conservation of the Jinghe River Basin displayed an upward trend, with the total soil conservation in 2019 being 1.14 times higher than that in 2000. The most substantial impact on soil conservation service arises from annual-scale meteorological drought, which remains consistent both in the historical and future periods. Additionally, at the seasonal scale, meteorological drought exerts the highest influence on soil conservation service in winter and autumn, particularly under the RCP4.5 and RCP8.5 scenarios. Compared to the historical period, the soil conservation service in the Jinghe River Basin will be significantly more affected by drought in the future period in terms of both the affected area and the magnitude of impact. This study conducted beneficial attempts to evaluate and predict the dynamic characteristics of watershed drought and soil conservation service, as well as the response of soil conservation service to different types of drought. Clarifying the interrelationship between the two is the foundation for achieving sustainable development in a relatively arid and severely eroded area such as the Jinghe River Basin.

Key words： meteorological drought      hydrological drought      agricultural drought      soil conservation service      Variable Infiltration Capacity (VIC) model      Revised Universal Soil Loss Equation (RUSLE)      Jinghe River Basin     
Received: 14 September 2023      Published: 29 February 2024
Corresponding Authors: *LI Jing (E-mail: lijing@snnu.edu.cn)
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BAI Jizhou
LI Jing
RAN Hui
ZHOU Zixiang
DANG Hui
ZHANG Cheng
YU Yuyang
Cite this article:

BAI Jizhou, LI Jing, RAN Hui, ZHOU Zixiang, DANG Hui, ZHANG Cheng, YU Yuyang. Influence of varied drought types on soil conservation service within the framework of climate change: insights from the Jinghe River Basin, China. Journal of Arid Land, 2024, 16(2): 220-245.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0070-7     OR     http://jal.xjegi.com/Y2024/V16/I2/220

Fig. 1 Overview of the Jinghe River Basin based on the Digital Elevation Model (DEM)
Category/Indicator Unit Data time Resolution Source
DEM m 2009 30 m Geospatial Data Cloud (http://www.gscloud.cn/)
Land use / 2000, 2005, 2010, 2015, and 2018 1 km Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/)
Soil data / 2009 1 km Harmonized World Soil Database (http://data.casnw.net/portal/metadata/
navigator)
NDVI / 2000-2019 250 m MODIS13Q1 (https://search.earthdata.nasa.gov/search)
Daily runoff m3/s 2006-2015 / Yellow River Conservancy Commission of the Ministry of Water Resource (http://www.yrcc.gov.cn)
Meteorological observation data Daily precipitation mm 1976-2019 / China National Meteorological Science Data Center (http://data.cma.cn/)
Daily minimum temperature °C 1976-2019
Daily maximum temperature °C 1976-2019
Daily wind speed m/s 1976-2019
NEX-GDDP dataset Daily mean precipitation rate at surface kg/(m2•s) 1976-2005
2026-2060		 0.25° NASA Center for Climate Simulation (https://www.nccs.nasa.gov/services/data-
collections/land-based-products/nex-gddp)
Daily minimum near-surface air temperature K 1976-2005
2026-2060
Daily maximum near-surface air temperature K 1976-2005
2026-2060
Vegetation library file / / / veglib.LDAS (https://vic.readthedocs.io/en/develop/index.html)
Table 1 Detailed description of data used in the study
Parameter Description Unit Range Optimized value
infilt Variable infiltration curve parameter (binfilt) / 0.00-0.40 0.19
Ds Fraction of Dsmax where non-linear baseflow begins / 0.00-1.00 0.05
Dsmax Maximum velocity of baseflow mm/h 0-30 10
Ws Fraction of maximum soil moisture where non-linear baseflow occurs / 0.00-1.00 0.50
d1 Thickness of the first soil layer m / 0.1
d2 Thickness of the second soil layer m / 0.5
d3 Thickness of the third soil layer m / 2.0
Table 2 Main parameters and optimized values of the Variable Infiltration Capacity (VIC) model
Fig. 2 Comparison of simulated and observed monthly runoff as well as precipitation at Zhangjiashan Hydrological Station from 2006 to 2015
Grade Type SI value
1 No drought SI> -0.5
2 Mild drought -1.0<SI≤ -0.5
3 Moderate drought -1.5<SI≤ -1.0
4 Severe drought -2.0<SI≤ -1.5
5 Special drought SI≤ -2.0
Table 3 Drought classification criteria based on the standardized index (SI)
Land use type Cultivated land (paddy field) Forest Grassland Water Urban Bare land
P value 0.150 1.000 1.000 0.000 0.000 1.000
Slope range <5° 5°-10° 10°-15° 15°-20° 20°-25° >25°
P value 0.100 0.221 0.305 0.575 0.705 0.800
Table 4 Soil conservation measure factor (P) values for different land use types and slope ranges
Fig. 3 Spatial distribution of drought intensity of annual-scale meteorological drought (a, d, and j), hydrological drought (b, e, and h), and agricultural drought (c, f, and i) during the historical period (2000-2019) and future period (2026-2060) under the RCP4.5 and RCP8.5 scenarios in the Jinghe River Basin. RCP, Representative Concentration Pathway; SPI, Standardized Precipitation Index; SRI, Standardized Runoff Index; SSMI, Standardized Soil Moisture Index. The SPI12, SRI12, and SSMI12 correspond to the annual-scale meteorological, hydrological, and agricultural droughts, respectively.
Fig. 4 Spatial distribution of multi-year average of meteorological (a-d), hydrological (e-h), and agricultural (i-l) drought intensity at the seasonal scale from 2000 to 2019 in the Jinghe River Basin. The SPI3, SRI3, and SSMI3 correspond to the seasonal-scale meteorological, hydrological, and agricultural droughts, respectively.
Fig. 5 Inter-annual changes in total soil conservation in the Jinghe River Basin from 2000 to 2019
Fig. 6 Spatial distribution of multi-year average soil conservation modulus from 2000 to 2019 in the Jinghe River Basin
Fig. 7 Temporal variations in annual total soil conservation in spring (a), summer (b), autumn (c), and winter (d) from 2000 to 2019 in the Jinghe River Basin
Fig. 8 Spatial distribution of multi-year average soil conservation modulus in spring (a), summer (b), autumn (c), and winter (d) during 2000-2019 in the Jinghe River Basin
Season Climate scenario Relative change of average seasonal soil conservation (%)
2030s 2040s 2050s 2060s
Spring RCP4.5 -14.0 21.6 -22.6 -40.3
RCP8.5 -1.3 -21.8 -25.4 -35.9
Summer RCP4.5 -54.4 -30.0 -34.3 -22.6
RCP8.5 -43.8 -22.0 -49.4 -9.1
Autumn RCP4.5 -62.6 -56.8 -59.6 -75.3
RCP8.5 -45.5 -66.3 -47.8 -61.9
Winter RCP4.5 200.1 -13.7 -98.8 -99.4
RCP8.5 -99.4 -83.4 -98.5 -98.2
Table 5 Relative changes of average seasonal soil conservation in the 2030s, 2040s, 2050s, and 2060s under the RCP4.5 and RCP8.5 scenarios compared to the seasonal soil conservation in 2019
Fig. 9 Degree of change in average annual soil conservation in severe drought years relative to the historical period (2000-2019) (a-c) and future period (2026-2060) under the RCP4.5 (d-f) and RCP8.5 (g-i) scenarios for meteorological, hydrological, and agricultural droughts. The SPI12, SRI12, and SSMI12 correspond to the annual-scale meteorological, hydrological, and agricultural droughts, respectively.
Fig. 10 Degree of change in average seasonal soil conservation in severe drought years relative to the historical period (2000-2019) under meteorological drought (a-d), hydrological drought (e-h), and agricultural drought (i-l). The SPI3, SRI3, and SSMI3 correspond to the seasonal-scale meteorological, hydrological, and agricultural droughts, respectively.
Fig. S1 Verification results of the NEX-GDDP dataset used in the study for monthly precipitation (a), monthly minimum temperature (b), and monthly maximum temperature (c) from 1976 to 2005. NEX-GDDP, NASA Earth Exchange Global Daily Downscaled Projections; r, Pearson's correlation coefficient.
Fig. S2 Spatial distribution of multi-year average of meteorological drought intensity (a-d and m-p), hydrological drought intensity (e-h and q-t), and agricultural drought intensity (i-l and u-x) at the seasonal scale under the RCP4.5 and RCP8.5 scenarios in the future period (2026-2060). RCP, representative concentration pathway. The SPI3, SRI3, and SSMI3 correspond to the seasonal-scale meteorological, hydrological, and agricultural droughts, respectively.
Fig. S3 Degree of change in average seasonal soil conservation in severe drought years relative to the future period (2026-2060) under the RCP4.5 and RCP8.5 scenarios for meteorological drought (a-d and m-p), hydrological drought (e-h and q-t), and agricultural drought (i-l and u-x). The SPI3, SRI3, and SSMI3 correspond to the seasonal-scale meteorological, hydrological, and agricultural droughts, respectively.
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