Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

doi:10.1007/s40333-022-0021-0

Journal of Arid Land

2022, Vol. 14

Issue (7): 771-786 DOI: 10.1007/s40333-022-0021-0 CSTR: 32276.14.s40333-022-0021-0

Research article

Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

WANG Fengjiao¹, FU Bojie², LIANG Wei¹^,³^,^*(

), JIN Zhao⁴, ZHANG Liwei¹, YAN Jianwu¹^,³, FU Shuyi⁵, GOU Fen¹

¹School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
²State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
³National Demonstration Center for Experimental Geography Education, Shaanxi Normal University, Xi'an 710119, China
⁴State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
⁵School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

Download:

HTML

PDF(2254KB)
Export: BibTeX | EndNote (RIS)

Abstract

Drought has pronounced and immediate impacts on agricultural production, especially in semi-arid and arid rainfed agricultural regions. Quantification of drought and its impact on crop yield is essential to agricultural water resource management and food security. We investigated drought and its impact on winter wheat (Triticum aestivum L.) yield in the Chinese Loess Plateau from 2001 to 2015. Specifically, we performed a varimax rotated principal component analysis on drought severity index (DSI) separately for four winter wheat growth periods: pre-sowing growth period (PG), early growth period (EG), middle growth period (MG), and late growth period (LG), resulting in three major subregional DSI dynamics for each growth period. The county-level projections of these major dynamics were then used to evaluate the growth period-specific impacts of DSI on winter wheat yields by using multiple linear regression analysis. Our results showed that the growth period-specific subregions had different major DSI dynamics. During PG, the northwestern area exhibited a rapid wetting trend, while small areas in the south showed a slight drying trend. The remaining subregions fluctuated between dryness and wetness. During EG, the northeastern and western areas exhibited a mild wetting trend. The remaining subregions did not display clear wetting or drying trends. During MG, the eastern and southwestern areas showed slight drying and wetting trends, respectively. The subregions scattered in the north and south had a significant wetting trend. During LG, large areas in the east and west exhibited wetting trends, whereas small parts in south-central area had a slight drying trend. Most counties in the north showed significant and slight wetting trends during PG, EG, and LG, whereas a few southwestern counties exhibited significant drying trends during PG and MG. Our analysis identified close and positive relationships between yields and DSI during LG, and revealed that almost all of the counties were vulnerable to drought. Similar but less strong relationships existed for MG, in which northeastern and eastern counties were more drought-vulnerable than other counties. In contrast, a few drought-sensitive counties were mainly located in the southwestern and eastern areas during PG, and in the northeastern corner of the study region during EG. Overall, our study dissociated growth period-specific and spatial location-specific impacts of drought on winter wheat yield, and might contribute to a better understanding of monitoring and early warning of yield loss.

Key words： drought severity index winter wheat crop yield principal component analysis Loess Plateau

Received: 16 January 2022 Published: 31 July 2022

Corresponding Authors: * LIANG Wei (E-mail: liangwei@snnu.edu.cn)

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WANG Fengjiao
	FU Bojie
	LIANG Wei
	JIN Zhao
	ZHANG Liwei
	YAN Jianwu
	FU Shuyi
	GOU Fen

Cite this article:

WANG Fengjiao, FU Bojie, LIANG Wei, JIN Zhao, ZHANG Liwei, YAN Jianwu, FU Shuyi, GOU Fen. Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau. Journal of Arid Land, 2022, 14(7): 771-786.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0021-0 OR http://jal.xjegi.com/Y2022/V14/I7/771

Fig. 1 Overview of the winter wheat producing counties in the Chinese Loess Plateau

Table 1 Categories for wet and dry conditions of the drought severity index (DSI)

Fig. 2 Percentages of variances explained by the first three principal components (PC1-PC3) for DSI during different growth periods of winter wheat in the Chinese Loess Plateau. PG, pre-sowing growth period; EG, early growth period; MG, middle growth period; LG, late growth period; DSI, drought severity index; RPC, rotated principal component.

Fig. 3 Spatial patterns of DSI in subregions of the Chinese Loess Plateau, defined by the first three rotated principal components (RPC1-RPC3) during PG (a-c), EG (d-f), MG (g-i), and LG (j-k). PG, pre-sowing growth period; EG, early growth period; MG, middle growth period; LG, late growth period. Dark red areas indicate subregions that were identified by setting 0.6 as the threshold for the normalized loading matrices. DSI, drought severity index.

Fig. 4 Temporal dynamics and Mann-Kendal (M-K) statistics of DSI (drought severity index) in each subregion during PG (a-c), EG (d-f), MG (g-i), and LG (j-k) of winter wheat in the Chinese Loess Plateau. PG, pre-sowing growth period; EG, early growth period; MG, middle growth period; LG, late growth period. The Mann-Kendal test generated three types of statistical parameters, i.e., Z-score, UB and UF. Z-score indicated the significance of trend, and UB and UF indicated abrupt changes via their intersections. Slopes are estimated trends of RPCs. ^**, |Z|>2.58, significant trend at 99% confidence level.

Fig. 5 Trends of DSI (drought severity index) during PG (a), EG (b), MG (c), and LG (d) of winter wheat in each county of the Chinese Loess Plateau. Counties shaded by dots showed significant trends in their main DSI. PG, pre-sowing growth period; EG, early growth period; MG, middle growth period, and LG, late growth period.

Fig. 6 Spatial patterns of regression coefficients between DSI (drought severity index) and winter wheat yield anomalies during PG (a), EG (b), MG (c), and LG (d). PG, pre-sowing growth period; EG, early growth period; MG, middle growth period; LG, late growth period.


[1]	Anderson M C, Hain C, Wardlow B, et al. 2011. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. Journal of Climate, 24(8): 2025-2044. doi: 10.1175/2010JCLI3812.1

[2]	Atkinson P M, Dash J, Jeganathan C. 2011. Amazon vegetation greenness as measured by satellite sensors over the last decade. Geophysical Research Letters, 38(19): L19105, doi: 10.1029/2011GL049118. doi: 10.1029/2011GL049118

[3]	Bahri H, Annabi M, M'Hamed H C, et al. 2019. Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context. Science of the Total Environment, 692(20): 1223-1233. doi: 10.1016/j.scitotenv.2019.07.307

[4]	Bo M, Guo Y Q, Tao H B, et al. 2015. SPEIPM-based research on drought impact on maize yield in North China Plain. Journal of Integrative Agriculture, 14(4): 660-669. doi: 10.1016/S2095-3119(14)60778-4

[5]	Bognár P, Kern A, Pásztor S, et al. 2017. Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data. International Journal of Remote Sensing, 38(11): 3394-3414. doi: 10.1080/01431161.2017.1295482

[6]	Boonwichai S, Shrestha S, Babel M S, et al. 2018. Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand. Science of the Total Environment, 652(20): 189-201. doi: 10.1016/j.scitotenv.2018.10.201

[7]	Brown J F, Wardlow B D, Tadesse T, et al. 2008. The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing, 45(1): 16-46. doi: 10.2747/1548-1603.45.1.16

[8]	Chapagain R, Remenyi T A, Harris R M B, et al. 2022. Decomposing crop model uncertainty: A systematic review. Field Crops Research, 279: 108448, doi: 10.1016/j.fcr.2022.108448. doi: 10.1016/j.fcr.2022.108448

[9]	Chen J, Jönsson P, Tamura M, et al. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91(3-4): 332-344. doi: 10.1016/j.rse.2004.03.014

[10]	Chen Y, Liu T, Tian X, et al. 2015. Effects of plastic film combined with straw mulch on grain yield and water use efficiency of winter wheat in Loess Plateau. Field Crops Research, 172: 53-58. doi: 10.1016/j.fcr.2014.11.016

[11]	Dai M, Huang S, Huang Q, et al. 2020. Assessing agricultural drought risk and its dynamic evolution characteristics. Agricultural Water Management, 231: 106003, doi: 10.1016/j.agwat.2020.106003. doi: 10.1016/j.agwat.2020.106003

[12]	Dickin E, Wright D. 2008. The effects of winter waterlogging and summer drought on the growth and yield of winter wheat (Triticum aestivum L.). European Journal of Agronomy, 28(3): 234-244. doi: 10.1016/j.eja.2007.07.010

[13]	Ding Y, Wang F, Mu Q, et al. 2021. Estimating land use/land cover change impacts on vegetation response to drought under 'Grain for Green' in the Loess Plateau. Land Degradation & Development, 32(17): 5083-5098. doi: 10.1002/ldr.4093

[14]	Farooq M, Hussain M, Siddique K H M. 2014. Drought stress in wheat during flowering and grain-filling periods. Critical Reviews in Plant Sciences, 33(4): 331-349. doi: 10.1080/07352689.2014.875291

[15]	Fu B, Wang S, Liu Y, et al. 2017. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annual Review of Earth and Planetary Sciences, 45: 223-243. doi: 10.1146/annurev-earth-063016-020552

[16]	Geng G, Wu J, Wang Q, et al. 2016. Agricultural drought hazard analysis during 1980-2008: a global perspective. International Journal of Climatology, 36(1): 389-399. doi: 10.1002/joc.4356

[17]	Guo H, Bao A, Liu T, et al. 2018. Spatial and temporal characteristics of droughts in Central Asia during 1966-2015. Science of the Total Environment, 624: 1523-1538. doi: 10.1016/j.scitotenv.2017.12.120

[18]	Gupta V, Jain M K. 2018. Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. Journal of Hydrology, 567: 489-509. doi: 10.1016/j.jhydrol.2018.10.012

[19]	Han H, Bai J, Yan J, et al. 2021. A combined drought monitoring index based on multi-sensor remote sensing data and machine learning. Geocarto International, 36(10): 1161-1177. doi: 10.1080/10106049.2019.1633423

[20]	He L, Asseng S, Zhao G, et al. 2015. Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China. Agricultural and Forest Meteorology, 200: 135-143. doi: 10.1016/j.agrformet.2014.09.011

[21]	Hlaváčová M, Klem K, Rapantová B, et al. 2018. Interactive effects of high temperature and drought stress during stem elongation, anthesis and early grain filling on the yield formation and photosynthesis of winter wheat. Field Crops Research, 221: 182-195. doi: 10.1016/j.fcr.2018.02.022

[22]	Hu Y N, Liu Y J, Tang H J, et al. 2014. Contribution of drought to potential crop yield reduction in a wheat-maize rotation region in the North China Plain. Journal of Integrative Agriculture, 13(7): 1509-1519. doi: 10.1016/S2095-3119(14)60810-8

[23]	Huang J, Xue Y, Sun S, et al. 2015. Spatial and temporal variability of drought during 1960-2012 in Inner Mongolia, North China. Quaternary International, 355: 134-144. doi: 10.1016/j.quaint.2014.10.036

[24]	Huang J, Zhuo W, Li Y, et al. 2020. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. International Journal of Digital Earth, 13(4): 504-526. doi: 10.1080/17538947.2018.1542040

[25]	Iizumi T, Yokozawa M, Nishimori M. 2009. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agricultural & Forest Meteorology, 149(2): 333-348.

[26]	IPCC. 2021. IPCC Climate Change 2021:The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report. Cambridge: Cambridge University Press, 3-32.

[27]	Ji X, Shiran B, Wan J, et al. 2010. Importance of pre-anthesis anther sink strength for maintenance of grain number during reproductive stage water stress in wheat. Plant, Cell and Environment, 33(6): 926-942. doi: 10.1111/j.1365-3040.2010.02130.x

[28]	Jiang X, Bai J. 2020. Quantifying the impacts of drought and ecological restoration on net primary production changes in the Chinese Loess Plateau. PloS ONE, 15(9): e0238997, doi: 10.1371/journal.pone.0238997. doi: 10.1371/journal.pone.0238997

[29]	Jiao W, Wang L, McCabe M F. 2021. Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sensing of Environment, 256: 112313, doi: 10.1016/j.rse.2021.112313. doi: 10.1016/j.rse.2021.112313

[30]	Jin N, Ren W, Tao B, et al. 2018. Effects of water stress on water use efficiency of irrigated and rainfed wheat in the Loess Plateau, China. Science of the Total Environment, 642: 1-11. doi: 10.1016/j.scitotenv.2018.06.028

[31]	Jin Z, Liang W, Yang Y, et al. 2017. Separating vegetation greening and climate change controls on evapotranspiration trend over the Loess Plateau. Scientific Reports, 7(1): 8191, doi: /10.1038/s41598-017-08477-x. doi: /10.1038/s41598-017-08477-x

[32]	Jung C, Lee Y, Cho Y, et al. 2017. A study of spatial soil moisture estimation using a multiple linear regression model and MODIS land surface temperature data corrected by conditional merging. Remote Sensing, 9(8): 870. doi: 10.3390/rs9080870

[33]	Kogan F N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11): 91-100.

[34]	Kontgis C, Schneider A, Ozdogan M, et al. 2019. Climate change impacts on rice productivity in the Mekong River Delta. Applied Geography, 102: 71-83. doi: 10.1016/j.apgeog.2018.12.004

[35]	Lecerf R, Ceglar A, López-Lozano R, et al. 2018. Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agricultural Systems, 168: 191-202. doi: 10.1016/j.agsy.2018.03.002

[36]	Lesk C, Rowhani P, Ramankutty N. 2016. Influence of extreme weather disasters on global crop production. Nature, 529(7584): 84-87. doi: 10.1038/nature16467

[37]	Li C, Liu W, Lin W, et al. 2017. Grain yield and WUE responses to different soil water storage before sowing and water supplies during growing period for winter wheat in the Loess Tableland. Scientia Agricultura Sinica, 50(18): 3549-3560. (in Chinese)

[38]	Li Y, Zhu Y, Li D, et al. 2018. Effects of alternating drought and watering on growth, photosynthesis and yield of wither wheat. Journal of Irrigation and Drainage, 37(8): 76-82. (in Chinese)

[39]	Li Z, Zheng F L, Liu W Z, et al. 2010. Spatial distribution and temporal trends of extreme temperature and precipitation events on the Loess Plateau of China during 1961-2007. Quaternary International, 226(1-2): 92-100. doi: 10.1016/j.quaint.2010.03.003

[40]	Liang W, Zhang W, Jin Z, et al. 2020. Rapid urbanization and agricultural intensification increase regional evaporative water consumption of the Loess Plateau. Journal of Geophysical Research: Atmospheres, 125(23): e2020JD033380, doi: 10.1029/2020JD033380. doi: 10.1029/2020JD033380

[41]	Liu L, Lu R, Ding Z, et al. 2021. Analysis of climate change characteristics and circulation factors in the Loess Plateau. Journal of Earth Environment, 12(6): 615-631. (in Chinese)

[42]	Liu Q, Zhang S, Zhang H, et al. 2020. Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment, 711: 134585, doi: 10.1016/j.scitotenv.2020.134585. doi: 10.1016/j.scitotenv.2020.134585

[43]	Liu X, Pan Y, Zhu X, et al. 2018. Drought evolution and its impact on the crop yield in the North China Plain. Journal of Hydrology, 564: 984-996. doi: 10.1016/j.jhydrol.2018.07.077

[44]	Liu Z, Wu C, Liu Y, et al. 2017. Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 81-91. doi: 10.1016/j.isprsjprs.2017.05.015

[45]	Lobell D B, Burke M B. 2010. On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11): 1443-1452. doi: 10.1016/j.agrformet.2010.07.008

[46]	Lobell D B, Asseng S. 2017. Comparing estimates of climate change impacts from process-based and statistical crop models. Environmental Research Letters, 12(1): 015001, doi: 10.1088/1748-9326/015001. doi: 10.1088/1748-9326/015001

[47]	Madadgar S, AghaKouchak A, Farahmand A, et al. 2017. Probabilistic estimates of drought impacts on agricultural production. Geophysical Research Letters, 44(15): 7799-7807. doi: 10.1002/2017GL073606

[48]	McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Boston: American Meteorological Society, 179-183.

[49]	Mitchell J, Dzerdzeevskii B, Flohn H, et al. 1966. Climatic change. In: WMO Technical Note, 79 (WMO No. 195/TP. 100). World Meteorological Organization, Geneva, 79.

[50]	Mu Q, Zhao M, 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

[51]	Nalder I A, Wein R W. 1998. Spatial interpolation of climatic Normals: test of a new method in the Canadian boreal forest. Agricultural and Forest Meteorology, 92(4): 211-225. doi: 10.1016/S0168-1923(98)00102-6

[52]	Naseer M A, Hussain S, Nengyan Z, et al. 2022. Shading under drought stress during grain filling attenuates photosynthesis, grain yield and quality of winter wheat in the Loess Plateau of China. Journal of Agronomy and Crop Science, 208(2): 255-263. doi: 10.1111/jac.12563

[53]	Palmer W C. 1965. Meteorological Drought. Washington DC: US Department of Commerce Weather Bureau, 1-56.

[54]	Portela M, Zelenáková M, Santos J, et al. 2017. Comprehensive characterization of droughts in Slovakia. International Journal of Environmental Science and Development, 8(1): 25-29. doi: 10.18178/ijesd.2017.8.1.915

[55]	Potopová V, Štěpánek P, Možný M, et al. 2015. Performance of the standardised precipitation evapotranspiration index at various lags for agricultural drought risk assessment in the Czech Republic. Agricultural and Forest Meteorology, 202: 26-38. doi: 10.1016/j.agrformet.2014.11.022

[56]	Qin Z, Tang H, Li W, et al. 2014. Modelling impact of agro-drought on grain production in China. International Journal of Disaster Risk Reduction, 7: 109-121. doi: 10.1016/j.ijdrr.2013.09.002

[57]	Raziei T, Saghafian B, Paulo A A, et al. 2009. Spatial patterns and temporal variability of drought in western Iran. Water Resources Management, 23(3): 439-455. doi: 10.1007/s11269-008-9282-4

[58]	Richman M B. 1986. Rotation of principal components. Journal of Climatology, 6(3): 293-335. doi: 10.1002/joc.3370060305

[59]	Salehnia N, Salehnia N, Saradari A, et al. 2020. Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological and drought indicators through pooled panel data and statistical downscaling. Ecological Indicators, 111: 105991, doi: 10.1016/j.ecolind.2019.105991. doi: 10.1016/j.ecolind.2019.105991

[60]	Sandholt I, Rasmussen K, Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2-3): 213-224. doi: 10.1016/S0034-4257(01)00274-7

[61]	Savitzky A, Golay M J. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627-1639. doi: 10.1021/ac60214a047

[62]	Sen P K. 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324): 1379-1389. doi: 10.1080/01621459.1968.10480934

[63]	Shi H, Wang G. 2015. Impacts of climate change and hydraulic structures on runoff and sediment discharge in the middle Yellow River. Hydrological Processes, 29(14): 3236-3246. doi: 10.1002/hyp.10439

[64]	Shi H, Chen J, Wang K, et al. 2018. A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China. Science of the Total Environment, 616: 363-375.

[65]	Srivastava R, Panda R, Halder D. 2017. Effective crop evapotranspiration measurement using time-domain reflectometry technique in a sub-humid region. Theoretical and Applied Climatology, 129(3): 1211-1225. doi: 10.1007/s00704-016-1841-7

[66]	Tian H, Huang N, Niu Z, et al. 2019. Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sensing, 11(7): 820. doi: 10.3390/rs11070820

[67]	Wan W, Liu Z, Li K, et al. 2021. Drought monitoring of the maize planting areas in Northeast and North China Plain. Agricultural Water Management, 245: 106636, doi: 10.1016/j.agwat.2020.106636. doi: 10.1016/j.agwat.2020.106636

[68]	Wang F, Liang W, Fu B, et al. 2020. Changes of cropland evapotranspiration and its driving factors on the loess plateau of China. Science of the Total Environment, 728: 138582, doi: 10.1016/j.scitotenv.2020.138582. doi: 10.1016/j.scitotenv.2020.138582

[69]	Wang G Z, Lu J S, Chen K Y, et al. 2014. Exploration of method in separating climatic output based on HP filter. Chinese Journal of Agrometeorology, 35(2): 195-199. (in Chinese)

[70]	Wang Q, Wu J, Lei T, et al. 2014. Temporal-spatial characteristics of severe drought events and their impact on agriculture on a global scale. Quaternary International, 349: 10-21. doi: 10.1016/j.quaint.2014.06.021

[71]	Wang S, Mo X, Liu Z, et al. 2017. Understanding long-term (1982-2013) patterns and trends in winter wheat spring green-up date over the North China Plain. International Journal of Applied Earth Observation and Geoinformation, 57: 235-244. doi: 10.1016/j.jag.2017.01.008

[72]	Wang S, Mo X, Hu S, et al. 2018. Assessment of droughts and wheat yield loss on the North China Plain with an aggregate drought index (ADI) approach. Ecological Indicators, 87: 107-116. doi: 10.1016/j.ecolind.2017.12.047

[73]	Wang X, Chen J, Ge J, et al. 2021. The different root apex zones contribute to drought priming induced tolerance to a reoccurring drought stress in wheat. The Crop Journal, 9(5): 1088-1097. doi: 10.1016/j.cj.2020.11.008

[74]	Wang Y, Zhang T, Chen X, et al. 2018. Spatial and temporal characteristics of droughts in Luanhe River basin, China. Theoretical and Applied Climatology, 131(3): 1369-1385. doi: 10.1007/s00704-017-2059-z

[75]	Wu D, Li Z, Zhu Y, et al. 2021. A new agricultural drought index for monitoring the water stress of winter wheat. Agricultural Water Management, 244: 106599, doi: 10.1016/j.agwat.2020.106599. doi: 10.1016/j.agwat.2020.106599

[76]	Wu X, Tang Y, Li C, et al. 2018. Characterization of the rate and duration of grain filling in wheat in southwestern China. Plant Production Science, 21(4): 358-369. doi: 10.1080/1343943X.2018.1518722

[77]	Xu P, Zhou T, Zhao X, et al. 2018. Diverse responses of different structured forest to drought in Southwest China through remotely sensed data. International Journal of Applied Earth Observation and Geoinformation, 69: 217-225. doi: 10.1016/j.jag.2018.03.009

[78]	Yang F, Zhang Q, Wang R, et al. 2014. Evapotranspiration measurement and crop coefficient estimation over a spring wheat farmland ecosystem in the Loess Plateau. PloS ONE, 9(6): e100031, doi: 10.1371/journal.pone.0100031. doi: 10.1371/journal.pone.0100031

[79]	Yao Y, Li Y, Yang J, et al. 2015. Change of terrestrial surface drought and wet conditions in the growth period on Loess Plateau. Chinese Agricultural Science Bulletin, 387(24): 187-194. (in Chinese)

[80]	Yu H, Zhang Q, Sun P, et al. 2019. Impacts of drought intensity and drought duration on winter wheat yield in five provinces of North China plain. Acta Geographica Sinica, 79(1): 87-102.

[81]	Zhang C, Wang J, Lei T, et al. 2018. Spatiotemporal evolution of vegetation cover and surface humidity since implementing the Grain for Green Project in the Loess Plateau. Arid Zone Research, 35(6): 1468-1476. (in Chinese)

[82]	Zhang J, Mu Q, Huang J. 2016. Assessing the remotely sensed Drought Severity Index for agricultural drought monitoring and impact analysis in North China. Ecological Indicators, 63: 296-309. doi: 10.1016/j.ecolind.2015.11.062

[83]	Zhang X, Yamaguchi Y. 2014. Characterization and evaluation of MODIS-derived Drought Severity Index (DSI) for monitoring the 2009/2010 drought over southwestern China. Natural Hazards, 74(3): 2129-2145. doi: 10.1007/s11069-014-1278-1

[84]	Zhou Y, Xiao X, Zhang G, et al. 2017. Quantifying agricultural drought in tallgrass prairie region in the US Southern Great Plains through analysis of a water-related vegetation index from MODIS images. Agricultural and Forest Meteorology, 246: 111-122.

[85]	Zhou Z, Shi H, Fu Q, et al. 2020. Assessing spatiotemporal characteristics of drought and its effects on climate-induced yield of maize in Northeast China. Journal of Hydrology, 588: 125097, doi: 10.1016/j.jhydrol.2020.125097. doi: 10.1016/j.jhydrol.2020.125097

[1]	SUN Hui, ZHAO Yunge, GAO Liqian, XU Mingxiang. Reasonable grazing may balance the conflict between grassland utilization and soil conservation in the semi-arid hilly areas, China[J]. Journal of Arid Land, 2024, 16(8): 1130-1146.

[2]	MEN Huan, DING Hua, DENG Yahong, MU Huandong, HE Nainan, SUN Pushuo, LI Zhixu, LIU Yan. Rock mechanical characteristics and landscape evolutionary mechanism of the slit-type Danxia landform on the Chinese Loess Plateau[J]. Journal of Arid Land, 2024, 16(10): 1327-1343.

[3]	WANG Jing, WEI Yulu, PENG Biao, LIU Siqi, LI Jianfeng. Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau, China[J]. Journal of Arid Land, 2024, 16(1): 131-145.

[4]	GAO Yalin, QI Guangping, MA Yanlin, YIN Minhua, WANG Jinghai, WANG Chen, TIAN Rongrong, XIAO Feng, LU Qiang, WANG Jianjun. Regulation effects of water and nitrogen on yield, water, and nitrogen use efficiency of wolfberry[J]. Journal of Arid Land, 2024, 16(1): 29-45.

[5]	ZHAO Xiaohan, HAN Dianchen, LU Qi, LI Yunpeng, ZHANG Fangmin. Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index[J]. Journal of Arid Land, 2023, 15(8): 920-939.

[6]	MA Xinxin, ZHAO Yunge, YANG Kai, MING Jiao, QIAO Yu, XU Mingxiang, PAN Xinghui. Long-term light grazing does not change soil organic carbon stability and stock in biocrust layer in the hilly regions of drylands[J]. Journal of Arid Land, 2023, 15(8): 940-959.

[7]	ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.

[8]	SUN Liquan, GUO Huili, CHEN Ziyu, YIN Ziming, FENG Hao, WU Shufang, Kadambot H M SIDDIQUE. Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China[J]. Journal of Arid Land, 2023, 15(1): 34-51.

[9]	LIU Yulin, LI Jiwei, HAI Xuying, WU Jianzhao, DONG Lingbo, PAN Yingjie, SHANGGUAN Zhouping, WANG Kaibo, DENG Lei. Carbon inputs regulate the temperature sensitivity of soil respiration in temperate forests[J]. Journal of Arid Land, 2022, 14(9): 1055-1068.

[10]	WANG Yaobin, SHANGGUAN Zhouping. Formation mechanisms and remediation techniques for low-efficiency artificial shelter forests on the Chinese Loess Plateau[J]. Journal of Arid Land, 2022, 14(8): 837-848.

[11]	SUN Dingzhao, LIANG Youjia, PENG Shouzhang. Scenario simulation of water retention services under land use/cover and climate changes: a case study of the Loess Plateau, China[J]. Journal of Arid Land, 2022, 14(4): 390-410.

[12]	LI Panpan, WANG Bing, YANG Yanfen, LIU Guobin. Effects of vegetation near-soil-surface factors on runoff and sediment reduction in typical grasslands on the Loess Plateau, China[J]. Journal of Arid Land, 2022, 14(3): 325-340.

[13]	WU Huining, CUI Qiaoyu. High-frequency climatic fluctuations over the past 30 ka in northwestern margin of the East Asian monsoon region, China[J]. Journal of Arid Land, 2022, 14(12): 1331-1343.

[14]	Hushiar HAMARASH, Rahel HAMAD, Azad RASUL. Meteorological drought in semi-arid regions: A case study of Iran[J]. Journal of Arid Land, 2022, 14(11): 1212-1233.

[15]	LING Xinying, MA Jinzhu, CHEN Peiyuan, LIU Changjie, Juske HORITA. Isotope implications of groundwater recharge, residence time and hydrogeochemical evolution of the Longdong Loess Basin, Northwest China[J]. Journal of Arid Land, 2022, 14(1): 34-55.

Viewed

Full text

Abstract

Cited

Shared

Discussed