Choice of Hydrologic Variables for Probabilistic Drought Classification
Publication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 3
Abstract
Management of irrigation practices takes on a more-prominent role under drought conditions. Watershed-scale drought characterization is performed using cumulative density function (CDF)-based probabilistic drought indices in this case study. To investigate the role of hydrologic variables, either singly or in combination, copulas are used for multivariate joint cumulative density functions (CDFs) combined with graphical models for probabilistic drought classification. Adopting a multivariable, multiscalar approach in the proposed framework yields a drought index that allows for examining the roles of hydrologic variables on integrated drought assessment. The methodology is demonstrated using streamflow, precipitation, and soil-moisture anomalies to develop univariate and multivariate CDF-based indices at one-month, three-month, and six-month time scales to analyze the drought events over an Indiana watershed. Drought characterization varied across the univariate, bivariate, and trivariate drought models in the case study. The multivariate models were able to capture the early onset of drought events and persistence of the drought states, features that are contributed by different components of the hydrologic cycle. While short-term drought monitoring is facilitated by one-month models, threats to long-term water storage in the watershed can be assessed better with longer-time-scale models.
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References
Al-Kaisi, M. M., et al. (2013). “Drought impact on crop production and the soil environment: 2012 experiences from Iowa.” J. Soil Water Conserv., 68(1), 19A–24A.
Bonnin, G. M., Martin, D., Lin, B., Parzybok, T., Yekta, M., and Riley, D. (2006). “Precipitation-frequency atlas of the United States.”, Vol. 2, National Weather Service, Silver Spring, MD.
Embrechts, P., Lindskog, F., and McNeil, A. (2003). “Modelling dependence with copulas and applications to risk management.” Handbook of heavy tailed distributions in finance, Elsevier, New York, 329–384.
Fan, Y., and van den Dool, H. (2004). “Climate prediction center global monthly soil moisture data set at 0.5° resolution for 1948 to present.” J. Geophys. Res., 109, D10102.
Favre, A.-C., El Adlouni, S., Perreault, L., Thiémonge, N., and Bobée, B. (2004). “Multivariate hydrological frequency analysis using copulas.” Water Resour. Res., 40(1), 1–12.
Genest, C., Rémillard, B., and Beaudoin, D. (2009). “Goodness-of-fit tests for copulas: A review and a power study.” Insurance Math. Econ., 44(2), 199–213.
Grimaldi, S., and Serinaldi, F. (2006). “Asymmetric copula in multivariate flood frequency analysis.” Adv. Water Res., 29(8), 1155–1167.
Han, F., and Liu, H. (2014). “Scale-invariant sparse PCA on high dimensional meta-elliptical data.” J. Am. Stat. Assoc., 109(505), 275–287.
Hao, Z., and AghaKouchak, A. (2013). “Multivariate standardized drought index: A parametric multi-index model.” Adv. Water Res., 57, 12–18.
Hao, Z., and AghaKouchak, A. (2014). “A nonparametric multivariate multi-index drought monitoring framework.” J. Hydrometeorol., 15(1), 89–101.
Horn, D. R. (1989). “Characteristics and spatial variability of droughts in Idaho.” J. Irrig. Drain. Eng., 111–124.
Huang, J., van den Dool, H. M., and Georgarakos, K. P. (1996). “Analysis of model-calculated soil moisture over the United States (1931-1993) and applications to long-range temperature forecasts.” J. Clim., 9(6), 1350–1362.
Ji, L., and Peters, A. J. (2003). “Assessing vegetation response to drought in the northern great plains using vegetation and drought indices.” Remote Sens. Environ., 87(1), 85–98.
Joe, H. (1997). Multivariate models and dependence concepts, Chapman & Hall, New York.
Jordan, M. I. (2004). “Graphical models.” Stat. Sci., 19(1), 140–155.
Kao, S.-C., and Govindaraju, R. S. (2008). “Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas.” Water Resour. Res., 44, W02415.
Kao, S.-C., and Govindaraju, R. S. (2010). “A copula-based joint deficit index for droughts.” J. Hydrol., 380(1-2), 121–134.
Karamouz, M., Rasouli, K., and Nazif, S. (2009). “Development of a hybrid index for drought prediction: Case study.” J. Hydrol. Eng., 617–627.
Keyantash, J. A., and Dracup, J. A. (2004). “An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage.” Water Resour. Res., 40, W09304.
Kogan, F. N. (1990). “Remote sensing of weather impacts on vegetation in non-homogeneous areas.” Int. J. Remote Sens., 11(8), 1405–1419.
Maity, R., Ramadas, M., and Govindaraju, R. S. (2013). “Identification of hydrologic drought triggers from hydroclimatic predictor variables.” Water Resour. Res., 49(7), 4476–4492.
Mallya, G., Tripathi, S., Kirshner, S., and Govindaraju, R. S. (2013). “Probabilistic assessment of drought characteristics using hidden markov model.” J. Hydrol. Eng., 834–845.
Mannocchi, F., Todisco, F., and Vergni, L. (2009). “New methodology for the risk analysis and economic impact assessment of agricultural droughts.” J. Irrig. Drain. Eng., 643–653.
McKee, T. B., Doesken, N. J., and Kleist, J. (1993). “The relationship of drought frequency and duration to time scales.” Proc., 8th Conf. on Applied Climatology, Committee on Applied Climatology, American Meteorological Society, Boston, 179–183.
Palmer, W. C. (1965). “Meteorological drought.” U.S. Dept. of Commerce, Washington, DC, 58.
Palmer, W. C. (1968). “Keeping track of crop moisture conditions, nationwide: The new crop moisture index.” Weatherwise, 21(4), 156–161.
Rabiner, L. (1989). “A tutorial on hidden Markov models and selected applications in speech recognition.” Proc. IEEE, 77(2), 257–286.
Rajsekhar, D., Singh, V. P., and Mishra, A. K. (2015). “Multivariate drought index: An information theory based approach for integrated drought assessment.” J. Hydrol., 526, 164–182.
Ramadas, M., and Govindaraju, R. S. (2015). “Probabilistic assessment of agricultural droughts using graphical models.” J. Hydrol., 526, 151–163.
Richards, J., et al. (2013). “Application of the standardized precipitation index and normalized difference vegetation index for evaluation of irrigation demands at three sites in Jamaica.” J. Irrig. Drain. Eng., 922–932.
Salvadori, G., and De Michele, C. (2004). “Frequency analysis via copulas: Theoretical aspects and applications to hydrological events.” Water Resour. Res., 40, W12511.
Serinaldi, F., Bonaccorso, B., Cancelliere, A., and Grimaldi, S. (2009). “Probabilistic characterization of drought properties through copulas.” Phys. Chem. Earth, Parts A/B/C, 34(10–12), 596–605.
Shafer, B. A., and Dezman, L. E. (1982). “Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas.” Proc., 50th Annual Western Snow Conf., Western Snow, Brush Prairie, WA, 164–175.
Shiau, J. T. (2006). “Fitting drought duration and severity with two-dimensional copulas.” Water Resour. Manage., 20(5), 795–815.
Shukla, S., and Wood, A. W. (2008). “Use of a standardized runoff index for characterizing hydrologic drought.” Geophys. Res. Lett., 35.
Sims, A. P. (2002). “Adopting drought indices for estimating soil moisture: A North Carolina case study.” Geophys. Res. Lett., 29(8).
Sklar, A. (1959). “Fonction de répartition à n dimensions et leurs marges.” Publications de Institut de Statistique Université de Paris, Paris, France, 229–231 (in French).
Sohrabi, M., Ryu, J., Abatzoglou, J., and Tracy, J. (2015). “Development of soil moisture drought index to characterize droughts.” J. Hydrol. Eng., 04015025.
Steinemann, A. (2003). “Drought indicators and triggers: A stochastic approach to evaluation.” J. Am. Water Resour. Assoc., 39(5), 1217–1233.
Svoboda, M., et al. (2002). “The drought monitor.” Bull. Am. Meteorol. Soc., 83(8), 1181–1190.
Todisco, F., Vergni, L., and Mannocchi, F. (2009). “Operative approach to agricultural drought risk management.” J. Irrig. Drain. Eng., 654–664.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I. (2010). “A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index.” J. Clim., 23(7), 1696–1718.
Zhang, L., and Singh, V. P. (2006). “Bivariate flood frequency analysis using the copula method.” J. Hydrol. Eng., 150–164.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 142 • Issue 3 • March 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: May 18, 2015
Accepted: Sep 16, 2015
Published online: Nov 12, 2015
Published in print: Mar 1, 2016
Discussion open until: Apr 12, 2016
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