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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     CSTR: 32276.14.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.

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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.
[1]   Administration of Quality Supervision, Inspection and Quarantine of People's Republic of China, Standardization Administration of China. 2017. Classification of Meteorological Drought (GB/T 20481-2017). [2023-01-15]. https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=D2281945A96E8185F67EDC9E7A698049. (in Chinese)
[2]   Bai J Z, Zhou Z X, Zou Y F, et al. 2021. Watershed drought and ecosystem services: Spatiotemporal characteristics and gray relational analysis. ISPRS International Journal of Geo-Information, 10(2): 43, doi: 10.3390/ijgi10020043.
[3]   Bai J Z, Zhou Z X, Li J, et al. 2022. Predicting soil conservation service in the Jinghe River Basin under climate change. Journal of Hydrology, 615: 128646, doi: 10.1016/j.jhydrol.2022.128646.
[4]   Berdugo M, Delgado-Baquerizo M, Soliveres S, et al. 2020. Global ecosystem thresholds driven by aridity. Science, 367(6479): 787-790.
doi: 10.1126/science.aay5958 pmid: 32054762
[5]   Cao Y Q, Zhao Z M, Zhang D, et al. 2023. Applicability analysis of two comprehensive drought meteorological indexes in growing period of Maize in Liaoning Province. Pearl River, 1-16. [2023-01-24]. http://kns.cnki.net/kcms/detail/44.1037.TV.20231227.1550.002.html. (in Chinese)
[6]   Carle J. 2015. Climate Change Seen as Top Global Threat. Pew Research Centre. [2023-10-12]. https://www.pewresearch.org/global/2015/07/14/climate-change-seen-as-top-global-threat/.
[7]   Ciampalini R, Constantine J A, Walker-Springett K J, et al. 2020. Modelling soil erosion responses to climate change in three catchments of Great Britain. Science of the Total Environment, 749: 141657, doi: 10.1016/j.scitotenv.2020.141657.
[8]   Farahmand A, AghaKouchak A. 2015. A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76: 140-145.
doi: 10.1016/j.advwatres.2014.11.012
[9]   Fensham R J, Fairfax R J, Ward D P. 2009. Drought-induced tree death in savanna. Global Change Biology, 15(2): 380-387.
doi: 10.1111/gcb.2009.15.issue-2
[10]   Gampe D, Zscheischler J, Reichstein M, et al. 2021. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nature Climate Change, 11(9): 772-779.
doi: 10.1038/s41558-021-01112-8
[11]   Gazol A, Camarero J J, Jiménez J J, et al. 2018. Beneath the canopy: Linking drought-induced forest die off and changes in soil properties. Forest Ecology and Management, 422: 294-302.
doi: 10.1016/j.foreco.2018.04.028
[12]   Gou J J, Miao C Y, Samaniego L, et al. 2021. CNRD v1.0: A high-quality natural runoff dataset for hydrological and climate studies in China. Bulletin of the American Meteorological Society, 102(5): 929-947.
[13]   Han H Q, Gao H J, Huang Y, et al. 2019. Effects of drought on freshwater ecosystem services in poverty-stricken mountain areas. Global Ecology and Conservation, 17: e00537, doi: 10.1016/j.gecco.2019.e00537.
[14]   Huang J P, Yu H P, Guan X D, et al. 2015. Accelerated dryland expansion under climate change. Nature Climate Change, 6(2): 166-171.
doi: 10.1038/nclimate2837
[15]   Khan F, Pilz J, Ali S. 2021. Evaluation of CMIP5 models and ensemble climate projections using a Bayesian approach: a case study of the Upper Indus Basin, Pakistan. Environmental and Ecological Statistics, 28(2): 383-404.
[16]   Khatiwada K R, Pandey V P. 2019. Characterization of hydro-meteorological drought in Nepal Himalaya: A case of Karnali River Basin. Weather and Climate Extremes, 26: 100239, doi: 10.1016/j.wace.2019.100239.
[17]   Kimwatu D M, Mundia C N, Makokha G O, et al. 2021. Developing a new socio-economic drought index for monitoring drought proliferation: a case study of Upper Ewaso Ngiro River Basin in Kenya. Environmental Monitoring and Assessment, 193(4): 213, doi: 10.1007/s10661-021-08989-0.
pmid: 33759015
[18]   Leal Filho W, Azeiteiro U M, Balogun A L, et al. 2021. The influence of ecosystems services depletion to climate change adaptation efforts in Africa. Science of the Total Environment, 779: 146414, doi: 10.1016/j.scitotenv.2021.146414.
[19]   Li Y Y, Chang J X, Luo L F, et al. 2019. Spatiotemporal impacts of land use land cover changes on hydrology from the mechanism perspective using SWAT model with time-varying parameters. Hydrology Research, 50(1): 244-261.
doi: 10.2166/nh.2018.006
[20]   Liang X, Xie Z H, Huang M Y. 2003. A new parameterization for surface and groundwater interactions and its impact on water budgets with the variable infiltration capacity (VIC) land surface model. Journal of Geophysical Research: Atmospheres, 108(D16): 8613, doi: 10.1029/2002JD003090.
[21]   Liu T, Zhou Z X, Zhu Q, et al. 2020. Spatiotemporal change of soil conservation service in Yanhe Watershed. Research of Soil and Water Conservation, 28(1): 93-100. (in Chinese)
[22]   Liu Y, Zhao W W, Jia L Z. 2019. Soil conservation service: concept, assessment, and outlook. Acta Ecologica Sinica, 39(2): 432-440. (in Chinese)
[23]   Mahto S S, Mishra V. 2020. Dominance of summer monsoon flash droughts in India. Environmental Research Letters, 15(10): 104061, doi: 10.1088/1748-9326/abaf1d.
[24]   Maity R, Suman M, Verma N K. 2016. Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts. Journal of Hydrology, 539: 417-428.
doi: 10.1016/j.jhydrol.2016.05.042
[25]   Maqsoom A, Aslam B, Hassan U, et al. 2020. Geospatial assessment of soil erosion intensity and sediment yield using the Revised Universal Soil Loss Equation (RUSLE) model. ISPRS International Journal of Geo-Information, 9(6): 356, doi: 10.3390/ijgi9060356.
[26]   Masroor M, Sajjad H, Rehman S, et al. 2022. Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin, India. Geoscience Frontiers, 13(2): 101312, doi: 10.1016/j.gsf.2021.101312.
[27]   Mu Q Z, Zhao M S, Kimball J S, et al. 2013. A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 94(1): 83-98.
doi: 10.1175/BAMS-D-11-00213.1
[28]   Otkin J A, Anderson M C, Hain C, et al. 2016. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agricultural and Forest Meteorology, 218-219: 230-242.
doi: 10.1016/j.agrformet.2015.12.065
[29]   Pan Y, Zhu Y H, Lü H S, et al. 2023. Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019. Agricultural Water Management, 283: 108305, doi: 10.1016/J.AGWAT.2023.108305.
[30]   Pravalie R, Sîrodoev I, Peptenatu D. 2014. Changes in the forest ecosystems in areas impacted by aridization in south-western Romania. Journal of Environmental Health Science and Engineering, 12(1): 2, doi: 10.1186/2052-336X-12-2.
pmid: 24393389
[31]   Ran H, Li J, Zhou Z X, et al. 2020. Predicting the spatiotemporal characteristics of flash droughts with downscaled CMIP 5 models in the Jinghe River basin of China. Environmental Science and Pollution Research, 27(32): 40370-40382.
doi: 10.1007/s11356-020-10036-3
[32]   Shi B L, Zhu X Y, Hu Y C, et al. 2015. Spatio-temporal variations of drought in Henan Province over a 53-year period based on standardized precipitation evapotranspiration index. Geographical Research, 34(8): 1547-1558. (in Chinese)
[33]   Sidiropoulos P, Dalezios N R, Loukas A, et al. 2021. Quantitative classification of desertification severity for degraded aquifer based on remotely sensed drought assessment. Hydrology, 8(1): 47, doi: 10.3390/hydrology8010047.
[34]   Sun W Y, Shao Q Q, Liu J Y. 2013. Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau. Journal of Geographical Sciences, 23(06): 1091-1106.
doi: 10.1007/s11442-013-1065-z
[35]   Terwayet Bayouli O, Zhang W C, Terwayet Bayouli H. 2023. Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia. Journal of Arid Land, 15(11): 1269-1289.
doi: 10.1007/s40333-023-0110-8
[36]   Wang D Y, Zhang W, Lu C J, et al. 2022. Construction and precision evaluation of comprehensive drought index based on meteorological and remote sensing vegetation information. Geomatics and Information Science of Wuhan University, doi: 10.13203/j.whugis20220237. (in Chinese)
[37]   Wen K G, Ding Y H. 2008. Chinese Dictionary of Meteorological Hazards. Comprehensive Volume. Beijing: Meteorological Press, 1-948. (in Chinese)
[38]   Wood E F, Lettenmaier D P, Zartarian V G. 1992. A land-surface hydrology parameterization with subgrid variability for general circulation models. Journal of Geophysical Research: Atmospheres, 97(D3): 2717-2728.
doi: 10.1029/91JD01786
[39]   Wu Q, Jiang X W, Xie J, et al. 2018. Multimodel superensemble prediction of air temperature in southwestern China during 2020-2050 based on CMIP5 data. Journal of Arid Meteorology, 36(6): 971-978. (in Chinese)
[40]   Xie Z H, Su F G, Liang X, et al. 2003. Applications of a surface runoff model with horton and dunne runoff for VIC. Advances in Atmospheric Sciences, 20(2): 165-172.
doi: 10.1007/s00376-003-0001-z
[41]   Yang X L, Liu G S, Yang X G, et al. 2005. The modification of palmer drought severity model for Gansu Loess Plateau. Journal of Arid Meteorology, 23(2): 8-12. (in Chinese)
[42]   Yu Y Y, Li J, Zhou Z X, et al. 2022. Spatial pattern optimization of ecosystem services based on Bayesian networks: A case of the Jing River Basin. Arid Land Geography, 45(4): 1268-1280. (in Chinese)
[43]   Zeng P, Sun F Y, Liu Y Y, et al. 2020. Future river basin health assessment through reliability-resilience-vulnerability: Thresholds of multiple dryness conditions. Science of the Total Environment, 741: 140395, doi: 10.1016/j.scitotenv.2020.140395.
[44]   Zhang H B, Gu L, Xin C, et al. 2016. Investigation on the spatial-temporal variation of drought characteristics in Jinghe River Basin. Journal of North China University of Water Resources and Electric Power (Natural Science Edition), 37(3): 1-10. (in Chinese)
[45]   Zhang S N, Wu Y P, Sivakumar B, et al. 2019. Climate change-induced drought evolution over the past 50 years in the southern Chinese Loess Plateau. Environmental Modelling & Software, 122: 104519, doi: 10.1016/j.envsoft.2019.104519.
[46]   Zhang S B, Chen J. 2021. Uncertainty in projection of climate extremes: A comparison of CMIP5 and CMIP6. Journal of Meteorological Research, 35(4): 646-662.
doi: 10.1007/s13351-021-1012-3
[47]   Zhang Y Q, Zheng H X, Zhang X Z, et al. 2023. Future global streamflow declines are probably more severe than previously estimated. Nature Water, 1(3): 261-271.
doi: 10.1038/s44221-023-00030-7
[48]   Zheng T, Zhou Z X, Zou Y F, et al. 2021. Analysis of spatial and temporal characteristics and spatial flow process of soil conservation service in Jinghe Basin of China. Sustainability, 13(4): 1794, doi: 10.3390/SU13041794.
[49]   Zhou Y, Li N, Ji Z H, et al. 2013. Temporal and spatial patterns of droughts based on Standard Precipitation Index (SPI) in Inner Mongolia during 1981-2010. Journal of Natural Resources, 28(10): 1694-1706. (in Chinese)
doi: 10.11849/zrzyxb.2013.10.005
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