Abstract
The frequency and intensity of extreme hydrological events (droughts and floods) have been increasing over the past few decades, which has been posing a threat to water security and agriculture production. Thus, projecting the future evolution of hydrological extremes plays a crucial role in sustainable water management and agriculture development in a changing climate. In this study, we develop the high-resolution projections of multidimensional drought characteristics and flood risks using the convection-permitting Weather Research and Forecasting (WRF) model with the horizontal grid spacing of 4 km for the Blanco and Mission River basins over South Texas. Uncertainties in model parameters are addressed explicitly, thereby leading to probabilistic assessments of hydrological extremes. Our findings reveal that the probabilistic multivariate assessments of drought and flood risks can reduce the underestimation and the biased conclusions generated from the univariate assessment. Furthermore, our findings disclose that future droughts are expected to become more severe over South Texas even though the frequency of the occurrence of droughts is projected to decrease, especially for the long-term drought episodes. In addition, South Texas region is expected to experience more floods with an increasing river discharge. Moreover, the Blanco and Mission river basins will suffer from higher flood risks as flood return periods are expected to become longer under climate change.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abera W, Formetta G, Brocca L, Rigon R (2017) Modeling the water budget of the Upper Blue Nile basin using the JGrass-NewAge model system and satellite data. Hydrol Earth Syst Sci 21:3145–3165. https://doi.org/10.5194/hess-21-3145-2017
Adamson PT, Metcalfe AV, Parmentier B (1999) Bivariate extreme value distributions: an application of the Gibbs sampler to the analysis of floods. Water Resour Res 35(9):2825–2832
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements, Irrigation and Drainage Paper No. 56. Food and Agriculture Organization of the United Nations, Rome
Carvalho KS, Wang S (2019) Characterizing the Indian Ocean sea level changes and potential coastal flooding impacts under global warming. J Hydrol 569:373–386. https://doi.org/10.1016/j.jhydrol.2018.11.072
Chebana F, Ouarda TBMJ, Bruneau P (2009) Multivariate homogeneity testing in a northern case study in the province of Quebec. Can Hydrol Process 23(12):1690–1700
Chen H, Wang S, Wang Y (2020) Exploring abrupt alternations between wet and dry conditions on the basis of historical observations and convection-permitting climate model simulations. J Geophys Res Atmos. https://doi.org/10.1029/2019JD031982
Chen X, Li F, Li J et al (2019) Three-dimensional identification of hydrological drought and multivariate drought risk probability assessment in the Luanhe River basin, China. Theor Appl Climatol. https://doi.org/10.1007/s00704-019-02780-5
Cheraghalizadeh M, Ghameshlou AN, Bazrafshan J et al (2018) A copula-based joint meteorological-hydrological drought index in a humid region (Kasilian basin, North Iran). Arab J Geosci 11:300. https://doi.org/10.1007/s12517-018-3671-7
DeChant CM, Moradkhani H (2015) On the assessment of reliability in probabilistic hydrometeorological event forecasting. Water Resour Res 51:3867–3883. https://doi.org/10.1002/2014WR016617
De Michele C, Salvadori G, Passoni G, Vezzoli R (2007) A multivariate model of sea storms using copulas. Coast Eng 54:734–751. https://doi.org/10.1016/j.coastaleng.2007.05.007
De Michele C, Salvadori G, Canossi M, Petaccia A, Rosso R (2005) Bivariate sta- tistical approach to check adequacy of dam spillway. J Hydrol Eng 10:50–57. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:1(50)
Favre A, El Adlouni S, Perreault L, Thiémonge N, Bobeé B (2004) Multivariate hydrological frequency analysis using copulas. Water Resour Res 40:W01101. https://doi.org/10.1029/2003WR002456
Flint LE, Flint AL, Mendoza J et al (2018) Characterizing drought in California: new drought indices and scenario-testing in support of resource management. Ecol Process 7:1. https://doi.org/10.1186/s13717-017-0112-6
Genest C, Favre A (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12:347–368. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347)
Goel NK, Seth SM, Chandra S (1998) Multivariate modeling of flood flows. J Hydraul Eng 124(2):146–155
Hao C, Zhang J, Yao F (2017) Multivariate drought frequency estimation using copula method in southwest China. Theor Appl Climatol 127:977–991. https://doi.org/10.1007/s00704-015-1678-5
Hao Z, AghaKouchak A (2013) Multivariate Standardized Drought Index: a parametric multi-index model. Adv Water Resour 57:12–18. https://doi.org/10.1016/j.advwatres.2013.03.009
Hao Z, Singh VP (2015) Drought characterization from a multivariate perspective: a review. J Hydrol 527:668–678. https://doi.org/10.1016/j.jhydrol.2015.05.031
Hong SY, Pan HL (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339. https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res Atmos 113:D13103. https://doi.org/10.1029/2008JD009944
Jimenez PA, Dudhia J, Gonzalez-Rouco JF, Navarro J, Montavez JP, Garcia-Bustamante E (2012) A revised scheme for the WRF surface layer formulation. Mon Weather Rev 140:898–918. https://doi.org/10.1175/MWR-D-11-00056.1
Kao SC, Govindaraju RS (2008) Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas. Water Resour Res 44:W02415. https://doi.org/10.1029/2007WR006261
Kao SC, Govindaraju RS (2010) A copula-based joint deficit index for droughts. J Hydrol 380:121–134. https://doi.org/10.1016/j.jhydrol.2009.10.029
Karri S, Dash SK, Panda SK et al (2018) Relative role of sea surface temperature and snow on Indian summer monsoon seasonal simulation using a GCM. Arab J Geosci 11:210. https://doi.org/10.1007/s12517-018-3559-6
Kavianpour M, Seyedabadi M, Moazami S (2018) Spatial and temporal analysis of drought based on a combined index using copula. Environ Earth Sci 77:769. https://doi.org/10.1007/s12665-018-7942-0
Kay AL, Rudd AC, Davies HN et al (2015) Use of very high resolution climate model data for hydrological modelling: baseline performance and future flood changes. Climatic Change 133:193. https://doi.org/10.1007/s10584-015-1455-6
Kwon H, Lall U (2016) A copula-based nonstationary frequency analysis for the 2012–2015 drought in California. Water Resour Res 52:5662–5675. https://doi.org/10.1002/2016WR018959
Lange S, Rockel B, Volkholz J et al (2015) Regional climate model sensitivities to parametrizations of convection and non-precipitating subgrid-scale clouds over South America. Clim Dyn 44:2839. https://doi.org/10.1007/s00382-014-2199-0
Li K, Huang G, Wang S (2019) Market-based stochastic optimization of water resources systems for improving drought resilience and economic efficiency in arid regions. J Clean Prod 233:522–537. https://doi.org/10.1016/j.jclepro.2019.05.379
Liu C et al (2017) Continental-scale convection-permitting modeling of the current and future climate of North America. Clim Dyn 49:71–95. https://doi.org/10.1007/s00382-016-3327-9
Liu C, Ikeda K, Thompson G, Rasmussen R, Dudhia J (2011) Highresolution simulations of wintertime precipitation in the Colorado Headwaters region: sensitivity to physics parameterizations. Mon Weather Rev 139:3533–3553. https://doi.org/10.1175/MWR-D11-00009.1
Lloyd-Hughes B, Saunders MA (2002) A drought climatology for Europe. Int J Climat 22:571–1592. https://doi.org/10.1002/joc.846
Miah MG, Abdullah HM, Jeong C (2017) Exploring standardized precipitation evapotranspiration index for drought assessment in Bangladesh. Environ Monit Assess 189:547. https://doi.org/10.1007/s10661-017-6235-5
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Moore RJ (1985) The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J 30:273–297. https://doi.org/10.1080/02626668509490989
Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manage 23:881–897. https://doi.org/10.1007/s11269-008-9305-1
Niu GY et al (2011) The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J Geophys Res Atmos 116:D12109. https://doi.org/10.1029/2010JD015139
Prein AF et al (2015) A review on regional convection-permitting climate modeling: demonstrations, prospect, and challenges. Rev Geophys 53:323–361. https://doi.org/10.1002/2012RG000457
Parry M, Canziani O, Palutikof J et al (2007) Climate change 2007: impacts, adaptation and vulnerability; Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge
Razavi S, Gupta HV (2016) A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application. Water Resour Res 52:440–455. https://doi.org/10.1002/2015WR017559
Raziei T, Saghafian B, Paulo AA et al (2009) Spatial patterns and temporal variability of drought in western Iran. Water Resour Manage 23:439. https://doi.org/10.1007/s11269-008-9282-4
Roy T, Gupta HV, Serrat-Capdevila A, Valdes JB (2017) Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model. Hydrol Earth Syst Sci 21:879–896. https://doi.org/10.5194/hess-21-879-2017
Sadegh M, Ragno E, AghaKouchak A (2017) Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework. Water Resour Res 53:5166–5183. https://doi.org/10.1002/2016WR020242
Salvadori G, De Michele C (2015) Multivariate real-time assessment of droughts via copula-based multi-site Hazard Trajectories and Fans. J Hydrol 526:101–115. https://doi.org/10.1016/j.jhydrol.2014.11.056
Sexton D, Rowell D, Folland C et al (2001) Detection of anthropogenic climate change using an atmospheric GCM. Clim Dyn 17:669. https://doi.org/10.1007/s003820000141
Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35:226–236. https://doi.org/10.1029/2007GL032487
Song X, Song S, Sun W, Mu X, Wang S, Li J, Li Y (2015) Recent changes in extreme precipitation and drought over the Songhua River Basin, China, during 1960–2013. Atmos Res 157:137–152. https://doi.org/10.1016/j.atmosres.2015.01.022
Tabari H, Nikbakht J, Hosseinzadeh Talaee P (2013) Hydrological drought assessment in northwestern Iran based on streamflow drought index (SDI). Water Resour Manage 27:137. https://doi.org/10.1007/s11269-012-0173-3
Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon Wea Rev 136:5095–5115. https://doi.org/10.1175/2008MWR2387.1
Vicente-Serrano SM, López-Moreno JI, Beguería S, Lorenzo-Lacruz J, Azorin-Molina C, Morán-Tejeda E (2011) Accurate computation of a streamflow drought index. J Hydrol Eng 17:318–332. https://doi.org/10.1061/(ASCE)HE.1943-5584.000043
Wagner S, Berg P, Schädler G et al (2013) High resolution regional climate model simulations for Germany: Part II—projected climate changes. Clim Dyn 40:415. https://doi.org/10.1007/s00382-012-1510-1
Wang S, Ancell BC, Huang GH, Baetz BW (2018) Improving robustness of hydrologic ensemble predictions through probabilistic pre- and post-processing in sequential data assimilation. Water Resour Res 54:2129–2151. https://doi.org/10.1002/2018WR022546
Wang S, Wang Y (2019) Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations. Clim Dyn 53:1613–1636. https://doi.org/10.1007/s00382-019-04702-7
Wang S, Zhu J (2020) Amplified or exaggerated changes in perceived temperature extremes under global warming. Clim Dyn 54:117–127. https://doi.org/10.1007/s00382-016-3327-9
Waseem M, Ajmal M, Lee JH et al (2016) Multivariate drought assessment considering the antecedent drought conditions. Water Resour Manage 30:4221. https://doi.org/10.1007/s11269-016-1416-5
Xiao M, Zhang Q, Singh VP, Chen X (2016) Probabilistic forecasting of seasonal drought behaviors in the Huai River basin, China. Theor Appl Climatol 128:667–677. https://doi.org/10.1007/s00704-016-1733-x
Yang ZL et al (2011) The community Noah land surface model with multiparameterization options (Noah–MP): 2. Evaluation over global river basins. J Geophys Res Atmos 116:D1211. https://doi.org/10.1029/2010JD015140
Yue S (2000) The bivariate lognormal distribution to model a multivariate flood episode. Hydrol Process 14:2575–2588
Yue S (2001) A bivariate gamma distribution for use in multivariate flood frequency analysis. Hydrol Process 15:1033–1045
Zhang B, Wang S, Wang Y (2019) Copula-based convection-permitting projections of future changes in multivariate drought characteristics. J Geophys Res Atmos. https://doi.org/10.1029/2019JD030686
Zhang D, Yan D, Lu F, Wang Y, Feng J (2015) Copula-based risk assessment of drought in Yunnan Province, China. Nat Hazards 75:2199–2220. https://doi.org/10.1007/s11069-014-1419-6
Zhu J, Wang S, Huang G (2019) Assessing climate change impacts on human-perceived temperature extremes and underlying uncertainties. J Geophys Res Atmos 124:3800–3821. https://doi.org/10.1029/2018JD029444
Zuo D, Cai S, Xu Z, Li F, Sun W, Yang X, Kan G, Liu P (2016) Spatialtemporal patterns of drought at various time scales in Shandong Province of eastern China. Theor Appl Climatol 131:271–284. https://doi.org/10.1007/s00704-016-1969-5
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 51809223) and the Hong Kong Polytechnic University Start-up Grant (Grant No. 1-ZE8S). The daily rainfall and runoff observations for Blanco and Mission river basins were collected from the U.S. MOPEX dataset. The PRISM dataset was produced by the PRISM Climate Group at Oregon State University and the CFSR product was provided by the NCEP. We would like to express our sincere gratitude to the editor and anonymous reviewers for their constructive comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Qing, Y., Wang, S., Zhang, B. et al. Ultra-high resolution regional climate projections for assessing changes in hydrological extremes and underlying uncertainties. Clim Dyn 55, 2031–2051 (2020). https://doi.org/10.1007/s00382-020-05372-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-020-05372-6