Abstract
Vegetation makes an outstanding contribution to the stability of ecosystems and to a certain extent reflects the state of the terrestrial ecosystem. Drought conditions greatly affect the growth and development process of vegetation due to its remarkable stochasticity and complexity. Due to the complex coupling mechanism between vegetation and drought, the research on vegetation drought risk is still limited. In this work, we focus on Northwest China and use the improved vegetation health index (VHI) and other multi-source data. We selected indicator factors based on both hazard and vulnerability, and adopt three weight determination methods, namely entropy method, critic method, and coefficient of variation method, to construct the corresponding index model, and also to establish a vegetation drought risk assessment model to quantitatively evaluate the drought risk of vegetation in northwest China. Results show that the percentage of each drought category remarkably changed during the period encompassing 1981–2020, and the vegetation drought shows deterioration in more areas of northwest China. The vegetation drought risks derived from the three weight determination methods were generally consistent, but differed for a particular vegetation type. The overall spatial distribution pattern of vegetation drought risk in Northwest China is higher in the west and lower in the east, and the vegetation in southern Qinghai and northwestern Xinjiang presents higher drought risk. This study may be used as a tool to provide quantitative basis for vegetation protection and vegetation drought management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets used in this study are available in the National Oceanic and Atmospheric Administration (NOAA, Index of/pub/corp/scsb/wguo/data/ Blended_VH_4km/geo_TIFF (noaa.gov)), the Resource and Environment Science and Data Center (http://www.resdc.cn/), the Earth Resources Observation Centre (LP DAAC, https://lpdaac.usgs.gov/product_search/), and the Harmonized World Soil Database (HWSD, https://www.fao.org/soils-portal/en/).
References
Ahmadalipour, A., Moradkhani, H., Castelletti, A., & Magliocca, N. (2019). Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment, 662, 672–686. https://doi.org/10.1016/j.scitotenv.2019.01.278
Alonso, C., Gouveia, C. M., Russo, A., & Páscoa, P. (2019). Crops’ exposure, sensitivity and adaptive capacity to drought occurrence. Natural Hazards and Earth System Sciences, 19(12), 2727–2743. https://doi.org/10.5194/nhess-19-2727-2019
Amrutha, K., Danumah, J. H., Nikhil, S., Saha, S., Rajaneesh, A., Mammen, P. C., et al. (2022). Demarcation of forest fire risk zones in Silent Valley National Park and the effectiveness of forest management regime. Journal of Geovisualization and Spatial Analysis, 6(1), 8.
Bento, V. A., Gouveia, C. M., DaCamara, C. C., Libonati, R., & Trigo, I. F. (2020). The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions. Global and Planetary Change, 190, 103198. https://doi.org/10.1016/j.gloplacha.2020.103198
Bento, V. A., Gouveia, C. M., DaCamara, C. C., & Trigo, I. F. (2018). A climatological assessment of drought impact on vegetation health index. Agricultural and forest meteorology, 259, 286–295. https://doi.org/10.1016/j.agrformet.2018.05.014
Bento, V. A., Russo, A., Vieira, I., & Gouveia, C. M. (2023). Identification of forest vulnerability to droughts in the Iberian Peninsula. Theoretical and Applied Climatology, 1–21. https://doi.org/10.1007/s00704-023-04427-y
Bento, V. A., Trigo, I. F., Gouveia, C. M., & DaCamara, C. C. (2018). Contribution of land surface temperature (TCI) to vegetation health index: A comparative study using clear sky and all-weather climate data records. Remote Sensing, 10(9), 1324. https://doi.org/10.3390/rs10091324
Campello Torres, P. H., Gonçalves, D. A., de Almeida, M., Collaço, F., Lopes dos Santos, K., Canil, K., de Sousa, C., Júnior, W., & Jacobi, P. R. (2020). Vulnerability of the São Paulo macro metropolis to droughts and natural disasters: Local to regional climate risk assessments and policy responses. Sustainability, 13(1), 114. https://doi.org/10.3390/su13010114
Cao, S., He, Y., Zhang, L., Chen, Y., Yang, W., Yao, S., & Sun, Q. (2021). Spatiotemporal characteristics of drought and its impact on vegetation in the vegetation region of Northwest China. Ecological Indicators, 133, 108420. https://doi.org/10.1016/j.ecolind.2021.108420
Cao, S., Zhang, L., He, Y., Zhang, Y., Chen, Y., Yao, S., et al. (2022). Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in Northwest China. Science of the Total Environment, 821, 153270. https://doi.org/10.1016/j.scitotenv.2022.153270
Chere, Z., Abegaz, A., Tamene, L., & Abera, W. (2022). Modeling and mapping the spatiotemporal variation in agricultural drought based on a satellite-derived vegetation health index across the highlands of Ethiopia. Modeling Earth Systems and Environment, 8(4), 4539–4552. https://doi.org/10.1007/s40808-022-01439-x
Cui, Y., & Shao, J. (2005). The role of ground water in arid/semiarid ecosystems, Northwest China. Groundwater, 43(4), 471–477. https://doi.org/10.1111/j.1745-6584.2005.0063.x
Dai, M., Huang, S., Huang, Q., Leng, G., Guo, Y., Wang, L., et al. (2020). Assessing agricultural drought risk and its dynamic evolution characteristics. Agricultural Water Management, 231, 106003. https://doi.org/10.1016/j.agwat.2020.106003
Dalezios, N., Blanta, A., Spyropoulos, N., & Tarquis, A. (2014). Risk identification of agricultural drought for sustainable agroecosystems. Natural Hazards and Earth System Sciences, 14(9), 2435–2448. https://doi.org/10.5194/nhess-14-2435-2014
Deng, H., Chen, Y., & Chen, X. (2022). Driving factors and changes in components of terrestrial water storage in the endorheic Tibetan Plateau. Journal of hydrology, 612, 128225. https://doi.org/10.1016/j.jhydrol.2022.128225
Deng, H., Pepin, N. C., Chen, Y., Guo, B., Zhang, S., Zhang, Y., et al. (2022). Dynamics of diurnal precipitation differences and their spatial variations in China. Journal of Applied Meteorology and Climatology, 61(8), 1015–1027. https://doi.org/10.1175/JAMC-D-21-0232.1
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Dikshit, A., Pradhan, B., & Alamri, A. M. (2020). Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches. Atmosphere, 11(6), 585. https://doi.org/10.3390/atmos11060585
Dunne, A., & Kuleshov, Y. (2023). Drought risk assessment and mapping for the Murray-Darling Basin. Australia. Natural Hazards, 115(1), 839–863. https://doi.org/10.1007/s11069-022-05576-5
Ekundayo, O., Okogbue, E., Akinluyi, F., Kalumba, A., & Orimoloye, I. (2021). Spatiotemporal drought assessment using vegetation health index and standardized precipitation index over Sudano-Sahelian region of Nigeria. African Geographical Review, 40(4), 412–424. https://doi.org/10.1080/19376812.2020.1841658
Fischer, G., Nachtergaele, F., Prieler, S., Van Velthuizen, H., Verelst, L., & Wiberg, D. (2008). Global agro-ecological zones assessment for agriculture (GAEZ 2008). IIASA.
Fung, K., Huang, Y., Koo, C., & Soh, Y. (2019). Drought forecasting: A review of modelling approaches 2007–2017. Journal of Water and Climate Change, 11(3), 771–799. https://doi.org/10.2166/wcc.2019.236
Gao, J., Jiao, K., & Wu, S. (2018). Quantitative assessment of ecosystem vulnerability to climate change: Methodology and application in China. Environmental Research Letters, 13(9), 094016. https://doi.org/10.1088/1748-9326/aadd2e
Gocic, M., & Trajkovic, S. (2013). Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global and Planetary Change, 100, 172–182. https://doi.org/10.1016/j.gloplacha.2012.10.014
Gong, Z., Zhao, S., & Gu, J. (2017). Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001–2013. Journal of Geographical Sciences, 27, 143–160. https://doi.org/10.1007/s11442-017-1369-5
Guo, E., Zhang, J., Wang, Y., Si, H., & Zhang, F. (2016). Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China. Natural hazards, 83(3), 1747–1761. https://doi.org/10.1007/s11069-016-2391-0
Hagenlocher, M., Meza, I., Anderson, C. C., Min, A., Renaud, F. G., Walz, Y., et al. (2019). Drought vulnerability and risk assessments: State of the art, persistent gaps, and research agenda. Environmental Research Letters, 14(8), 083002. https://doi.org/10.1088/1748-9326/ab225d
Han, Y., Li, Z., Huang, C., Zhou, Y., Zong, S., Hao, T., et al. (2020). Monitoring droughts in the Greater Changbai Mountains using multiple remote sensing-based drought indices. Remote Sensing, 12(3), 530. https://doi.org/10.3390/rs12030530
Hayes, M. J., Wilhelmi, O. V., & Knutson, C. L. (2004). Reducing drought risk: Bridging theory and practice. Natural Hazards Review, 5(2), 106–113. https://doi.org/10.1061/(ASCE)1527-6988(2004)5:2(106)
He, B., Wu, J., Lü, A., Cui, X., Zhou, L., Liu, M., & Zhao, L. (2013). Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China. Natural Hazards, 66, 155–166. https://doi.org/10.1007/s11069-012-0398-8
Jha, S., Das, J., Sharma, A., Hazra, B., & Goyal, M. K. (2019). Probabilistic evaluation of vegetation drought likelihood and its implications to resilience across India. Global and Planetary Change, 176, 23–35. https://doi.org/10.1016/j.gloplacha.2019.01.014
Jiang, R., Liang, J., Zhao, Y., Wang, H., Xie, J., Lu, X., & Li, F. (2021). Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China. Scientific reports, 11(1), 1–18. https://doi.org/10.1038/s41598-021-93328-z
Kang, E., Lu, L., & Xu, Z. (2007). Vegetation and carbon sequestration and their relation to water resources in an inland river basin of Northwest China. Journal of Environmental Management, 85(3), 702–710. https://doi.org/10.1016/j.jenvman.2006.09.007
Kendall, M. G. (1948). Rank correlation methods. Oxford, England: Griffin.
Kim, H., Park, J., Yoo, J., & Kim, T.-W. (2015). Assessment of drought hazard, vulnerability, and risk: A case study for administrative districts in South Korea. Journal of Hydro-environment Research, 9(1), 28–35. https://doi.org/10.1016/j.jher.2013.07.003
Kim, J. S., Park, S. Y., Hong, H. P., Chen, J., Choi, S. J., Kim, T. W., & Lee, J. H. (2020). Drought risk assessment for future climate projections in the Nakdong River Basin, Korea. International Journal of Climatology, 40(10), 4528–4540. https://doi.org/10.1002/joc.6473
Kloos, S., Yuan, Y., Castelli, M., & Menzel, A. (2021). Agricultural drought detection with MODIS based vegetation health indices in southeast Germany. Remote Sensing, 13(19), 3907. https://doi.org/10.3390/rs13193907
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78(4), 621–636. https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
Kogan, F. N. (2001). Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society, 82(9), 1949–1964. https://doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2
Kulmatiski, A., & Beard, K. H. (2013). Woody plant encroachment facilitated by increased precipitation intensity. Nature Climate Change, 3(9), 833–837. https://doi.org/10.1038/nclimate1904
Kumari, M., Kumar, D., & Vaishnavi. (2023). Dynamic drought risk assessment and analysis with multi-source drought indices and analytical hierarchy process. International Journal of Environmental Science and Technology, 20(3), 2839–2856. https://doi.org/10.1007/s13762-022-04041-x
Li, K., Tong, Z., Liu, X., Zhang, J., & Tong, S. (2020). Quantitative assessment and driving force analysis of vegetation drought risk to climate change: Methodology and application in Northeast China. Agricultural and forest meteorology, 282, 107865. https://doi.org/10.1016/j.agrformet.2019.107865
Li, S.-Y., Miao, L.-J., Jiang, Z.-H., Wang, G.-J., Gnyawali, K. R., Zhang, J., et al. (2020). Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015–2099. Advances in Climate Change Research, 11(3), 210–217. https://doi.org/10.1016/j.accre.2020.09.003
Lin, M., Horowitz, L. W., Xie, Y., Paulot, F., Malyshev, S., Shevliakova, E., et al. (2020). Vegetation feedbacks during drought exacerbate ozone air pollution extremes in Europe. Nature Climate Change, 10(5), 444–451. https://doi.org/10.1038/s41558-020-0743-y
Liu, X., Guo, P., Tan, Q., Zhang, F., Huang, Y., & Wang, Y. (2021). Drought disaster risk management based on optimal allocation of water resources. Natural hazards, 108, 285–308. https://doi.org/10.1007/s11069-021-04680-2
Liu, X., Zhu, X., Zhang, Q., Yang, T., Pan, Y., & Sun, P. (2020). A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications. Catena, 186, 104394. https://doi.org/10.1016/j.catena.2019.104394
Liu, Z., Li, C., Zhou, P., & Chen, X. (2016). A probabilistic assessment of the likelihood of vegetation drought under varying climate conditions across China. Scientific reports, 6(1), 35105. https://doi.org/10.1038/srep35105
Liu, Z., Menzel, L., Dong, C., & Fang, R. (2016). Temporal dynamics and spatial patterns of drought and the relation to ENSO: A case study in Northwest China. International Journal of Climatology, 36(8), 2886–2898. https://doi.org/10.1002/joc.4526
Lu, A., Ding, Y., Pang, H., Yuan, L., & Yuanqing, H. (2005). Impact of global warming on water resource in arid area of northwest China. Journal of Mountain Science, 2, 313–318. https://doi.org/10.1007/BF02918404
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245–259. https://doi.org/10.2307/1907187
Masroor, M., Sajjad, H., Rehman, S., Singh, R., Rahaman, M. H., Sahana, M., 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. https://doi.org/10.1016/j.gsf.2021.101312
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of hydrology, 391(1-2), 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Monteleone, B., Bonaccorso, B., & Martina, M. (2020). A joint probabilistic index for objective drought identification: The case study of Haiti. Natural Hazards and Earth System Sciences, 20(2), 471–487. https://doi.org/10.5194/nhess-20-471-2020
Mukherjee, S., Mishra, A., & Trenberth, K. (2018). Climate change and drought: A perspective on drought indices. Current Climate Change Reports, 4, 145–163. https://doi.org/10.1007/s40641-018-0098-x
Murthy, C., Laxman, B., & Sai, M. S. (2015). Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. International Journal of Disaster Risk Reduction, 12, 163–171. https://doi.org/10.1016/j.ijdrr.2015.01.004
Palmer, W. C. (1965). Meteorological drought (Vol. 30). US Department of Commerce.
Pedro-Monzonís, M., Solera, A., Ferrer, J., Estrela, T., & Paredes-Arquiola, J. (2015). A review of water scarcity and drought indexes in water resources planning and management. Journal of hydrology, 527, 482–493. https://doi.org/10.1016/j.jhydrol.2015.05.003
Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., et al. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and forest meteorology, 248, 215–227. https://doi.org/10.1016/j.agrformet.2017.10.001
Potter, C., Klooster, S., & Genovese, V. (2012). Net primary production of terrestrial ecosystems from 2000 to 2009. Climatic Change, 115, 365–378. https://doi.org/10.1007/s10584-012-0460-2
Qi, Y., Yu, H., Fu, Q., Chen, Q., Ran, J., & Yang, Z. (2022). Future changes in drought frequency due to changes in the mean and shape of the PDSI probability density function under RCP4. 5 scenario. Frontiers in Earth Science, 10, 386. https://doi.org/10.3389/feart.2022.857885
Qiang, Z., Yubi, Y., Yaohui, L., Zexian, L., Cunjie, Z., Dongliang, L., et al. (2015). Research progress and prospect on the monitoring and early warning and mitigation technology of meteorological drought disaster in northwest China. Advances in Earth Science, 30(2), 196. https://doi.org/10.11867/j.issn.1001-8166.2015.02.0196
Qu, S., Wang, L., Lin, A., Zhu, H., & Yuan, M. (2018). What drives the vegetation restoration in Yangtze River basin, China: Climate change or anthropogenic factors? Ecological Indicators, 90, 438–450. https://doi.org/10.1016/j.ecolind.2018.03.029
Qu, Y., Zhang, X., Zeng, J., Li, Z., & Lv, J. (2023). Historical drought events in the early years of Qing Dynasty in Shanxi based on hydrological reconstructions. Water, 15(5), 995. https://doi.org/10.3390/w15050995
Raji, S. A., Odunuga, S., & Fasona, M. (2022). Spatially explicit scenario analysis of habitat quality in a tropical semi-arid zone: Case study of the sokoto–rima basin. Journal of Geovisualization and Spatial Analysis, 6(1), 11. https://doi.org/10.1007/s41651-022-00106-0
Running, S., & Zhao, M. (2019). MOD17A3HGF MODIS/Terra net primary production gap-filled yearly L4 global 500 m SIN grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A3HGF
Saini, M., Dutta, V., & Joshi, P. K. (2021). Reassessment of drought management policies for India: Learning from Israel, Australia, and China. Environmental Sustainability, 4(4), 671–689. https://doi.org/10.1007/s42398-021-00208-3
Savari, M., Damaneh, H. E., & Damaneh, H. E. (2022). Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. International journal of disaster risk reduction, 67, 102654. https://doi.org/10.1016/j.ijdrr.2021.102654
Shahid, S., & Behrawan, H. (2008). Drought risk assessment in the western part of Bangladesh. Natural Hazards, 46(3), 391–413. https://doi.org/10.1007/s11069-007-9191-5
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
Tang, J., Zeng, J., Zhang, L., Zhang, R., Li, J., Li, X., et al. (2020). A modified flexible spatiotemporal data fusion model. Frontiers of Earth Science, 14, 601–614. https://doi.org/10.1007/s11707-019-0800-x
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Wang, Q., Qi, J., Wu, H., Zeng, Y., Shui, W., Zeng, J., & Zhang, X. (2020). Freeze-Thaw cycle representation alters response of watershed hydrology to future climate change. Catena, 195, 104767. https://doi.org/10.1016/j.catena.2020.104767
Wang, Q., Wu, J., Li, X., Zhou, H., Yang, J., Geng, G., et al. (2017). A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model. International Journal of Biometeorology, 61, 685–699. https://doi.org/10.1007/s00484-016-1246-4
Wang, Q., Zeng, J., Leng, S., Fan, B., Tang, J., Jiang, C., et al. (2018). The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century. Frontiers of Earth Science, 12, 818–833. https://doi.org/10.1007/s11707-018-0697-9
Wang, Q., Zeng, J., Qi, J., Zhang, X., Zeng, Y., Shui, W., et al. (2021). A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018. Earth System Science Data, 13(2), 331–341. https://doi.org/10.5194/essd-13-331-2021
Wilhite, D. A., Hayes, M. J., Knutson, C., & Smith, K. H. (2000). Planning for drought: Moving from crisis to risk management 1. JAWRA Journal of the American Water Resources Association, 36(4), 697–710. https://doi.org/10.1111/j.1752-1688.2000.tb04299.x
Wu, X., Zhang, R., Bento, V. A., Leng, S., Qi, J., Zeng, J., & Wang, Q. (2022). The effect of drought on vegetation gross primary productivity under different vegetation types across China from 2001 to 2020. Remote Sensing, 14(18), 4658. https://doi.org/10.3390/rs14184658
Wu, Z., Yu, L., Zhang, X., Du, Z., & Zhang, H. (2019). Satellite-based large-scale vegetation dynamics in ecological restoration programmes of Northern China. International Journal of Remote Sensing, 40(5-6), 2296–2312. https://doi.org/10.1080/01431161.2018.1519286
Xu, K., Wang, X., Jiang, C., & Sun, O. J. (2020). Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity. Forest Ecosystems, 7(03), 315–326. https://doi.org/10.1186/s40663-020-00239-y
Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., & Wu, S. (2018). China’s multi-period land use land cover remote sensing monitoring data set (CNLUCC). Resource and Environmental Science Data Registration and Publication System. https://doi.org/10.12078/2018070201
Yang, H., Hu, D., Xu, H., & Zhong, X. (2020). Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China. Environmental Science and Pollution Research, 27, 14915–14932. https://doi.org/10.1007/s11356-020-08006-w
Yin, C., Chen, X., Luo, M., Meng, F., Sa, C., Bao, S., et al. (2023). Quantifying the contribution of driving factors on distribution and change of net primary productivity of vegetation in the Mongolian Plateau. Remote Sensing, 15(8), 1986. https://doi.org/10.3390/rs15081986
Zeng, J., Zhang, R., Qu, Y., Bento, V. A., Zhou, T., Lin, Y., et al. (2022). Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather, 35, 100412. https://doi.org/10.1016/j.wace.2022.100412
Zeng, J., Zhang, R., Tang, J., Liang, J., Li, J., Zeng, Y., et al. (2021). Ecological sustainability assessment of the carbon footprint in Fujian Province, southeast China. Frontiers of Earth Science, 15, 12–22. https://doi.org/10.1007/s11707-020-0815-3
Zeng, J., Zhou, T., Qu, Y., Bento, V. A., Qi, J., Xu, Y., et al. (2023). An improved global vegetation health index dataset in detecting vegetation drought. Scientific Data, 10(1), 338. https://doi.org/10.1038/s41597-023-02255-3
Zeng, Z., Wu, W., Li, Z., Zhou, Y., Guo, Y., & Huang, H. (2019). Agricultural drought risk assessment in Southwest China. Water, 11(5), 1064. https://doi.org/10.3390/w11051064
Zhang, F., Chen, Y., Zhang, J., Guo, E., Wang, R., & Li, D. (2019). Dynamic drought risk assessment for maize based on crop simulation model and multi-source drought indices. Journal of Cleaner Production, 233, 100–114. https://doi.org/10.1016/j.jclepro.2019.06.051
Zhang, R., Bento, V. A., Qi, J., Xu, F., Wu, J., Qiu, J., et al. (2023). The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018. Big Earth Data, 1–26. https://doi.org/10.1080/20964471.2022.2148331
Zhang, R., Qi, J., Leng, S., & Wang, Q. (2022). Long-term vegetation phenology changes and responses to preseason temperature and precipitation in Northern China. Remote Sensing, 14(6), 1396. https://doi.org/10.3390/rs14061396
Zhang, R., Wu, X., Zhou, X., Ren, B., Zeng, J., & Wang, Q. (2022). Investigating the effect of improved drought events extraction method on spatiotemporal characteristics of drought. Theoretical and Applied Climatology, 1–14. https://doi.org/10.1007/s00704-021-03838-z
Zhao, J., Zhang, Q., Zhu, X., Shen, Z., & Yu, H. (2020). Drought risk assessment in China: Evaluation framework and influencing factors. Geography and Sustainability, 1(3), 220–228. https://doi.org/10.1016/j.geosus.2020.06.005
Zhu, Y., Chen, Y., Ren, L., Lü, H., Zhao, W., Yuan, F., & Xu, M. (2016). Ecosystem restoration and conservation in the arid inland river basins of Northwest China: Problems and strategies. Ecological Engineering, 94, 629–637. https://doi.org/10.1016/j.ecoleng.2016.06.107
Funding
This work was supported by the open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research), Grant NO: IWHR-SKL-KF202007, the Natural Science Foundation of Fujian Province (2021J01627), and the National Natural Science Foundation of China (41601562).
Author information
Authors and Affiliations
Contributions
The conception and design of the study was performed by Huixia Chen and Qianfeng Wang. Material preparation, analysis, and the first draft of the manuscript were performed by Huixia Chen. Virgílio A. Bento reviewed and edited the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Ethics approval
All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Competing interests
The authors declare no competing interests.
Research involving human participants and/or animals
This article does not contain any studies with human participants and animals performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Highlight
• Improved VHI and multi-weight methods were used to assess vegetation drought risk.
• Vegetation drought level changed evidently, with more areas deteriorated.
• The drought risk derived from different methods presents overall consistency, but varied with vegetation types.
• The vegetation drought risk in Northwest China is higher in the west and lower in the east.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, H., Wang, Q., Bento, V.A. et al. Vegetation drought risk assessment based on the multi-weight methods in Northwest China. Environ Monit Assess 195, 1148 (2023). https://doi.org/10.1007/s10661-023-11747-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-023-11747-z