Integrated index for drought assessment based on variable fuzzy set theory: A case study in the Yellow River basin, China

It is of great importance to construct an integrated drought indicator, which is of great importance to drought risk assessment and decision-making. Given the fuzzy nature of drought, the variable fuzzy set theory was applied to develop an Integrated Drought Index (IDI) combining meteorological, hydrological, and agricultural factors across the Yellow River basin in North China. The runoff and soil moisture were derived by driving the calibrated Variable Infiltration Capacity (VIC) model with observed atmospheric forcing. Furthermore, the law of mutual change of quality and quantity was adopted to identify qualitative change points of annual IDI series in the Yellow River basin. The results indicate that: (1) the Integrated Drought Index (IDI) has a better performance compared with Standardized Precipitation Index (SPI) and Standardized streamflow Index (SSFI), and it is more sensitive and effective to capture drought onset and persistence, largely owing to its combination with the information of different drought-related variables; (2) spatially, the middle reaches has a higher drought risk than the rest portions of the Yellow River basin; seasonally, drought risk in spring and winter is larger than other seasons; overall, the IDI of the basin is dominated by an insignificantly downward trend; (3) some qualitative change points of drought were identified in the Yellow River basin, and those are primarily induced by ENSO events and the construction of dams and reservoirs. This study proposed an alternative drought indicator coupled with multivariate drought-related variables by objectively determining their weights based on the entropy weight method, which has a great value in characterizing drought. (C) 2015 Elsevier B.V. All rights reserved.

[1]  V. Singh,et al.  A review of drought concepts , 2010 .

[2]  D. Higgitt.  Continental‐Scale River Basins , 2010 .

[3]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[4]  N. Guttman.  COMPARING THE PALMER DROUGHT INDEX AND THE STANDARDIZED PRECIPITATION INDEX 1 , 1998 .

[5]  Luis S. Pereira,et al.  SPI-based drought category prediction using loglinear models , 2008 .

[6]  W. Alley.  The Palmer Drought Severity Index: Limitations and Assumptions , 1984 .

[7]  Saralees Nadarajah,et al.  Assessment of hydrological droughts for the Yellow River, China, using copulas , 2007 .

[8]  Q. Tang,et al.  A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China , 2014 .

[9]  Bart Nijssen,et al.  Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–93 , 2001 .

[10]  Fulong Chen,et al.  A New Method of Change Point Detection Using Variable Fuzzy Sets Under Environmental Change , 2014, Water Resources Management.

[11]  H. Safavi,et al.  Integrated Index for Assessment of Vulnerability to Drought, Case Study: Zayandehrood River Basin, Iran , 2014, Water Resources Management.

[12]  Yutong Chen,et al.  Spatio-temporal Changes and Frequency Analysis of Drought in the Wei River Basin, China , 2014, Water Resources Management.

[13]  Dennis P. Lettenmaier,et al.  How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting , 2008 .

[14]  Khaled H. Hamed,et al.  A modified Mann-Kendall trend test for autocorrelated data , 1998 .

[15]  D. Wilhite,et al.  CHAPfER2UNDERSTANDING THE DROUGHT PHENOMENON:THE ROLE OF DEFINITIONS , 1985 .

[16]  K. Trenberth,et al.  A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming , 2004 .

[17]  Yang Hong,et al.  Projected changes in mean and interannual variability of surface water over continental China , 2015, Science China Earth Sciences.

[18]  Qiuhong Tang,et al.  Climate change impacts on meteorological, agricultural and hydrological droughts in China , 2015 .

[19]  D. Lettenmaier,et al.  Streamflow simulation for continental‐scale river basins , 1997 .

[20]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model , 2000 .

[21]  I. Bogardi,et al.  Application of fuzzy rule-based modeling technique to regional drought , 1999 .

[22]  A. Robock,et al.  The Global Soil Moisture Data Bank , 2000 .

[23]  Michael K. Ng,et al.  An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data , 2007, IEEE Transactions on Knowledge and Data Engineering.

[24]  U. Sommer,et al.  Global warming benefits the small in aquatic ecosystems , 2009, Proceedings of the National Academy of Sciences.

[25]  L. Bauwens,et al.  UNIVERSITY OF SOUTHERN CALIFORNIA Center for Applied Financial Economics (CAFE) Forecasting A Long Memory Process Subject to Structural Breaks , 2013 .

[26]  J. Valdes,et al.  Nonparametric Approach for Estimating Return Periods of Droughts in Arid Regions , 2003 .

[27]  Chen Shou-yu,et al.  Variable sets and the theorem and method of optimal decision making for water resource system , 2012 .

[28]  A. Aghakouchak,et al.  Multivariate Standardized Drought Index: A parametric multi-index model , 2013 .

[29]  E. Wood,et al.  Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off‐line simulation of the terrestrial hydrologic cycle , 2007 .

[30]  Eric F. Wood,et al.  One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model , 1996 .

[31]  Lei Jiang-qu.  Variable fuzzy evaluation on comprehensive divisions of drought in the Wei River basin , 2014 .

[32]  D. Burn,et al.  Nonparametric methods for drought severity estimation at ungauged sites , 2012 .

[33]  Eric F. Wood,et al.  A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part II: Application to Land Surface Modeling with Satellite Rainfall Forcing , 2009 .

[34]  J. Ballester,et al.  Rescaled range analysis of the asymmetry of solar activity , 1996 .

[35]  Xinwang Liu,et al.  Parameterized defuzzification with maximum entropy weighting function - Another view of the weighting function expectation method , 2007, Math. Comput. Model..

[36]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.

[37]  S. Brown,et al.  Evaluating Uncertainties in the Projection of Future Drought , 2008 .

[38]  Guobin Fu,et al.  Hydro-Climatic Trends of the Yellow River Basin for the Last 50 Years , 2004 .

[39]  H. D. Orville.  AMS Statement on Meteorological Drought , 1990 .

[40]  Shengzhi Huang,et al.  Copulas-based probabilistic characterization of the combination of dry and wet conditions in the Guanzhong Plain, China , 2014 .

[41]  Rao S. Govindaraju,et al.  A copula-based joint deficit index for droughts. , 2010 .

[42]  Jonathan R. M. Hosking,et al.  Spatial Comparability of the Palmer Drought Severity Index , 1992 .

[43]  J. Hosking.  Modeling persistence in hydrological time series using fractional differencing , 1984 .

[44]  Jun Xia,et al.  The spatial and temporal analysis of dry spells in the Yellow River basin, China , 2012, Stochastic Environmental Research and Risk Assessment.

[45]  D. Wilhite.  Drought as a natural hazard : Concepts and definitions , 2000 .

[46]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[47]  Dawen Yang,et al.  Spatio-temporal variation of drought in China during 1961–2012: A climatic perspective , 2015 .

[48]  Nicolas R. Dalezios,et al.  Severity-duration-frequency analysis of droughts and wet periods in Greece , 2000 .

[49]  S. Shukla,et al.  Use of a standardized runoff index for characterizing hydrologic drought , 2008 .

[50]  Guo Yu,et al.  Variable Fuzzy Sets and its Application in Comprehensive Risk Evaluation for Flood-control Engineering System , 2006 .

[51]  V. Singh,et al.  Evaluation and application of the SPDI-JDI for droughts in Texas, USA , 2015 .

[52]  J. Dracup,et al.  Risk and Reliability in Predicting Droughts: The Use of Prehistoric Tree-Ring Data , 1991 .

[53]  Yoshiki Saito,et al.  Interannual and seasonal variation of the Huanghe (Yellow River) water discharge over the past 50 years: Connections to impacts from ENSO events and dams , 2006 .

[54]  Sergio M. Vicente-Serrano,et al.  Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin , 2005 .