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A Novel Method for Agricultural Drought Risk Assessment

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Abstract

Climate change, increased temperatures and imbalanced precipitation distributions will potentially increase the local drought risk in certain areas. Drought assessment can identify the hidden dangers of drought and provide a theoretical basis for disaster prevention and mitigation. In this paper, a new agricultural drought risk assessment method proposed from the perspective of grain yield. The first principal components of precipitation, temperature, humidity and soil moisture represent hazard factors. The sensitive yield, which represents the sensitivity, was separated from the grain yield using a regression method. Additionally, the trend component of the grain yield represents the adaptive capacity, and the crop planting area represents exposure. Based on these definitions, the concepts of unit drought risk and regional drought risk are proposed. Four cities in Heilongjiang Province, which has the highest grain yield of any province in China, were used as application examples, and the spatial and temporal variation in the agricultural drought risk were analyzed. Application example show that the method for evaluating agricultural drought presented in this paper is reasonable in a statistical sense. The process for calculating sensitivity and adaptability shows that this method is suitable for arid and semi-arid areas, where grain yield is sensitive to hazard factors, and areas where grain yield has a certain trend.

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References

  • Abdi A, Hassanzadeh Y, Talatahari S, Fakheri-Fard A, Mirabbasi R (2017) Regional drought frequency analysis using L-moments and adjusted charged system search. J Hydroinf 19:426–442

    Article  Google Scholar 

  • Azmi M, Rudiger C, Walker JP (2016) A data fusion-based drought index. Water Resour Res 52:2222–2239. https://doi.org/10.1002/2015WR017834

    Article  Google Scholar 

  • Bazrafshan J, Hejabi S (2018) A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate. Water Resour Manag 32:2611–2624

    Article  Google Scholar 

  • Chang J, Li Y, Wang Y, Yuan M (2016) Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China. J Hydrol 540:824–834

    Article  Google Scholar 

  • Dong Z et al (2015) A novel method for quantitatively evaluating agricultural vulnerability to climate change. Ecol Indic 48:49–54

    Article  Google Scholar 

  • Draper NR, Smith H (1998) Applied regression analysis, Third edn. John Wiley & Sons, Inc., New York. https://doi.org/10.1002/9781118625590

  • Fontaine MM, Steinemann AC (2009) Assessing vulnerability to natural hazards: impact-based method and application to drought in Washington State. Natural Hazards Review 10:11–18

    Article  Google Scholar 

  • Guo H, Zhang X, Lian F, Gao Y, Lin D, Wang JA (2016) Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize. Sustainability 8:813

    Article  Google Scholar 

  • Hao Y et al (2016) Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982-2011 by using PDSI indices and agriculture drought survey data. J Geophys Res Atmos 121:2283–2298

    Article  Google Scholar 

  • Hernandez EA, Uddameri V (2014) Standardized precipitation evaporation index (SPEI)-based drought assessment in semi-arid south Texas. Environ Earth Sci 71:2491–2501

    Article  Google Scholar 

  • Ionita M, Scholz P, Chelcea S (2016) Assessment of droughts in Romania using the Standardized Precipitation Index. Nat Hazards 81:1483–1498

    Article  Google Scholar 

  • Jin J, Song Z, Cui Y, Zhou Y, Jiang S, He J (2016) Research progress on the key technologies of drought risk assessment and control. J Hydraul Eng 47:398–412

    Google Scholar 

  • Keyantash JA, Dracup JA (2004) An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour Res 40:333–341. https://doi.org/10.1029/2003wr002610

    Article  Google Scholar 

  • Kim H, Park J, Yoo J, Kim TW (2015) Assessment of drought hazard, vulnerability, and risk: A case study foradministrative districts in South Korea. J Hydro Environ Res 9:28–35

    Article  Google Scholar 

  • Li Q, Li P, Li H (2015) Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China. Stoch Env Res Risk A 29:1509–1520

    Article  Google Scholar 

  • Liu Y, Liu B, Yang X, Bai W, Wang J (2015) Relationships between drought disasters and crop production during ENSO episodes across the North China Plain. Reg Environ Chang 15:1689–1701

    Article  Google Scholar 

  • Luh J, Christenson EC, Toregozhina A (2015) Vulnerability assessment for loss of access to drinking water due to extreme weather events. Clim Chang 133:665–679

    Article  Google Scholar 

  • Mcevoy DJ, Huntington JL, Hobbins MT, Wood A, Morton C, Anderson M, Hain C (2016) The Evaporative Demand Drought Index: Part II – CONUS-wide Assessment Against Common Drought Indicators. J Hydrometeorol 17:1763–1779

    Article  Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216

    Article  Google Scholar 

  • Murthy CS, Laxman B, Sai MVRS (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

    Article  Google Scholar 

  • Nam WH, Hayes MJ, Svoboda MD, Tadesse T, Wilhite DA (2015) Drought hazard assessment in the context of climate change for South Korea. Agric Water Manag 160:106–117

    Article  Google Scholar 

  • Patel NR, Parida BR, Venus V, Saha SK, Dadhwal VK (2012) Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data. Environ Monit Assess 184:7153–7163

    Article  Google Scholar 

  • Pei W, Fu Q, Liu D, Li TX, Cheng K (2016) Assessing agricultural drought vulnerability in the Sanjiang Plain based on an improved projection pursuit model. Nat Hazards 82:683–701

    Article  Google Scholar 

  • Rahmat SN, Jayasuriya N, Bhuiyan M (2015) Assessing droughts using meteorological drought indices in Victoria, Australia. Hydrol Res 46:463–476

    Article  Google Scholar 

  • Rajsekhar D, Singh VP, Mishra AK (2014) Multivariate drought index: An information theory based approach for integrated drought assessment. J Hydrol 526:164–182

    Article  Google Scholar 

  • Sehgal VK, Dhakar R (2016) Geospatial approach for assessment of biophysical vulnerability to agricultural drought and its intra-seasonal variations Environ Monit Assess 188:197

  • Vicente-Serrano SM et al (2015) The Westerly Index as complementary indicator of the North Atlantic oscillation in explaining drought variability across Europe. Clim Dyn 47:1–19

    Google Scholar 

  • Wang H et al. (2016a) Monitoring winter wheat drought threat in Northern China using multiple climate-based drought indices and soil moisture during 2000–2013 Agricultural & Forest Meteorology s 228–229:1–12

  • Wang L, Li F, Wang W, Liu W (2016b) Drought-resistant afforestation techniques for soil moisture conservation by using surface covering in Western Heilongjiang Province. Science of Soil & Water Conservation 14:147–154

    Google Scholar 

  • Wilhite DA, Hayes MJ, Knutson C, Smith KH (2000) Planning for drought: moving from crisis to risk management. Jawra Journal of the American Water Resources Association 36:697–710

    Article  Google Scholar 

  • Wu D, Yan DH, Yang GY, Wang XG, Xiao WH (2013) Assessment on agricultural drought vulnerability in the Yellow River basin based on a fuzzy clustering iterative model. Nat Hazards 67:919–936

    Article  Google Scholar 

  • Xu K, Yang D, Yang H, Li Z, Qin Y, Shen Y (2015) Spatio-temporal variation of drought in China during 1961–2012: A climatic perspective. J Hydrol 526:253–264

    Article  Google Scholar 

  • Yuan XC, Wang Q, Wang K (2015) China’s regional vulnerability to drought and its mitigation strategies under climate change: data envelopment analysis and analytic hierarchy process integrated approach. Mitig Adapt Strateg Glob Chang 20:162–172

    Article  Google Scholar 

  • Zhang B, Wu P, Zhao X, Wang Y, Gao X, Cao X (2013) A drought hazard assessment index based on the VIC–PDSI model and its application on the Loess Plateau, China. Theor Appl Climatol 114:125–138

    Article  Google Scholar 

  • Zhang D, Wang G, Zhou H (2011) Assessment on agricultural drought risk based on variable fuzzy sets model. Chin Geogr Sci 21:167–175

    Article  Google Scholar 

  • Zhang J, Zhao Y, Ding Z (2014) Research on the Relationships Between Rainfall and Meteorological Yield in Irrigation District. Water Resour Manag 28:1689–1702

    Article  Google Scholar 

  • Zhou YL, Zhou P, Jin JL (2014) Establishment of hydrological drought index based on sources of regional water supply and its application to drought frequency analysis for Kunming. J Hydraul Eng 45:1038–1047

    Google Scholar 

  • Zhu Y, Wang W, Singh VP, Liu Y (2016) Combined use of meteorological drought indices at multi-time scales for improving hydrological drought detection. Sci Total Environ 571:1058–1068

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported by funds from the Philosophy and Social Science Research Project of Heilongjiang Province (No. 17GLC120), the National Natural Science Foundation of China (Nos. 51479032, 51579044, and 51609039) and the Northeast Agricultural University Young Talents Foundation Project of Northeast Agricultural University (No. 18QC66).

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Authors and Affiliations

  1. College of Science, Northeast Agricultural University, Harbin, 150030, China

    Wei Pei & Kun Cheng

  2. School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Changjiang Street No. 600, Harbin, Heilongjiang, 150030, People’s Republic of China

    Qiang Fu, Dong Liu, Tianxiao Li & Song Cui

  3. Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Qiang Fu

  4. Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Qiang Fu

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  1. Wei Pei
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  2. Qiang Fu
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  3. Dong Liu
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  4. Tianxiao Li
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  5. Kun Cheng
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  6. Song Cui
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Correspondence to Qiang Fu.

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Pei, W., Fu, Q., Liu, D. et al. A Novel Method for Agricultural Drought Risk Assessment. Water Resour Manage 33, 2033–2047 (2019). https://doi.org/10.1007/s11269-019-02225-8

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  • DOI: https://doi.org/10.1007/s11269-019-02225-8

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