Streamflow Drought Interpreted Using SWAT Model Simulations of Past and Future Hydrologic Scenarios: Application to Neches and Trinity River Basins, Texas
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 9
Abstract
In water resources and environmental management, hydrologic indexes are often valued as decision support tools because of their practical interpretability. This is true with the streamflow drought index (SDI), which is considered to be a relevant tool for assessing the availability of water resources at the watershed level. Hence, the future of freshwater resources at the watershed scale could be better understood by achieving a realistic projection of SDI. This study used a process-based watershed modeling approach to describe a framework for SDI projection. Specifically, the Soil and Water Assessment Tool (SWAT) model was used to simulate distinctly two watersheds located in the state of Texas, the Trinity and the Neches River Basins. The SWAT model was calibrated with monthly streamflow data for the period 1990–1995. The model was subsequently validated with two decades of discharge data (1996–2015). The evaluation of the SWAT performance during the calibration and validation stages showed acceptable values of efficiency criteria for both watersheds (i.e., Nash-Sutcliffe efficiency ranging from 0.56 to 0.65; index of agreement from 0.79 to 0.92). The calibrated model was used to simulate runoff for the future period 2041–2070 using inputs retrieved from a future climate scenario. However, the SDI calculation requires knowledge of the probability distribution of cumulative discharge data. A Kolmogorov-Smirnov’s goodness-of-fit analysis was conducted for both observed and simulated cumulative discharges. A lognormal distribution was considered for estimating time series of SDI. For the period 1996–2015, the SDI values recovered from the SWAT simulations matched closely with those derived directly from the observed discharge data ( for the Neches River, and for the Trinity River). This result demonstrated the capacity of the analytical procedure to capture and project realistically SDI signals. However, analysis of the statistic of the SDI patterns for the past and the future periods did not reveal any significant difference.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The author acknowledges the different institutions which released the data used in the study, including the USGS, the NOAA, the USDA, and the USEPA. The author acknowledges the Spatial Sciences Laboratory at Texas A&M Univ. for the technical support received during SWAT modeling. The author also thanks the anonymous reviewers for their useful assessments.
Disclaimer
Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture.
References
Ahmed, K. F., G. Wang, J. Silander, A. M. Wilson, J. M. Allen, R. Horton, and R. Anyah. 2013. “Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast.” Global Planet. Change 100 (Jan): 320–332. https://doi.org/10.1016/j.gloplacha.2012.11.003.
Bangdiwala, S. I. 2016. “Chi-squared statistics of association and homogeneity.” Int. J. Inj. Control Saf. Promotion 23 (4): 444–446. https://doi.org/10.1080/17457300.2016.1228144.
Ben-Zvi, A. 1987. “Indices of hydrological drought in Israel.” J. Hydrol. 92 (1–2): 179–191. https://doi.org/10.1016/0022-1694(87)90095-3.
Bucak, T., D. Trolle, H. E. Andersen, H. Thodsen, Ş. Erdoğan, E. E. Levi, N. Filiz, E. Jeppesen, and M. Beklioğlu. 2017. “Future water availability in the largest freshwater Mediterranean lake is at great risk as evidenced from simulations with the SWAT model.” Sci. Total Environ. 581 (Mar): 413–425. https://doi.org/10.1016/j.scitotenv.2016.12.149.
Chattopadhyay, S., D. R. Edwards, Y. Yu, and A. Hamidisepehr. 2017. “An assessment of climate change impacts on future water availability and droughts in the Kentucky River Basin.” Environ. Process. 4 (3): 477–507. https://doi.org/10.1007/s40710-017-0259-2.
Deo, R. C., O. Kisi, and V. P. Singh. 2017. “Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model.” Atmos. Res. 184 (Feb): 149–175. https://doi.org/10.1016/j.atmosres.2016.10.004.
Eckhardt, K., and J. G. Arnold. 2001. “Automatic calibration of a distributed catchment model.” J. Hydrol. 251 (1–2): 103–109. https://doi.org/10.1016/S0022-1694(01)00429-2.
Franke, T. M., T. Ho, and C. A. Christie. 2012. “The chi-square test: Often used and more often misinterpreted.” Am. J. Eval. 33 (3): 448–458. https://doi.org/10.1177/1098214011426594.
Gassman, P. W., A. M. Sadeghi, and R. Srinivasan. 2014. “Applications of the SWAT model special section: Overview and insights.” J. Environ. Qual. 43 (1): 1–8. https://doi.org/10.2134/jeq2013.11.0466.
Geza, M., and J. E. McCray. 2008. “Effects of soil data resolution on SWAT model stream flow and water quality predictions.” J. Environ. Manage. 88 (3): 393–406. https://doi.org/10.1016/j.jenvman.2007.03.016.
Homer, C. G., J. A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. D. Herold, J. D. Wickham, and K. Megown. 2015. “Completion of the 2011 National Land Cover Database for the conterminous United States—Representing a decade of land cover change information.” Photogramm. Eng. Remote Sens. 81 (5): 345–354. https://doi.org/10.1016/S0099-1112(15)30100-2.
Kroll, C. N., and R. M. Vogel. 2002. “Probability distribution of low streamflow series in the United States.” J. Hydrol. Eng. 7 (2): 137–146. https://doi.org/10.1061/(ASCE)1084-0699(2002)7:2(137).
Legates, D. R., and G. J. McCabe Jr. 1999. “Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation.” Water Res. 35 (1): 233–241. https://doi.org/10.1029/1998WR900018.
Lopes, R. H. 2011. “Kolmogorov-Smirnov test.” In International encyclopedia of statistical science, 718–720. Berlin: Springer.
McCuen, R. H., Z. Knight, and A. G. Cutter. 2006. “Evaluation of the Nash-Sutcliffe efficiency index.” J. Hydrol. Eng. 11 (6): 597–602. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597).
McKee, T. B., N. J. Doesken, and J. Kleist. 1993. “The relationship of drought frequency and duration to time scales.” In Vol. 17 of Proc., 8th Conf. on Applied Climatology, 179–183. Boston: American Meteorological Society.
Mearns, L. O., et al. 2012. “The North American regional climate change assessment program overview of Phase I results.” Bull. Am. Meteorol. Soc. 93 (9): 1337–1362. https://doi.org/10.1175/BAMS-D-11-00223.1.
Mishra, A. K., and V. R. Desai. 2006. “Drought forecasting using feed-forward recursive neural network.” Ecol. Modell. 198 (1): 127–138. https://doi.org/10.1016/j.ecolmodel.2006.04.017.
Montgomery, D. R., G. E. Grant, and K. Sullivan. 1995. “Watershed analysis as a framework for implementing ecosystem management.” J. Am. Water Resour. Assoc. 31 (3): 369–386. https://doi.org/10.1111/j.1752-1688.1995.tb04026.x.
Mukundan, R., D. E. Radcliffe, and L. M. Risse. 2010. “Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment.” J. Soil Water Conserv. 65 (2): 92–104. https://doi.org/10.2489/jswc.65.2.92.
Nakicenovic, N., J. Alcamo, G. Davis, B. De Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, T. Y. Jung, and T. Kram. 2000. Special report on emissions scenarios: A special report of Working Group III of the Intergovernmental Panel on Climate Change, 599. Cambridge, UK: Cambridge University Press.
Nalbantis, I., and G. Tsakiris. 2009. “Assessment of hydrological drought revisited.” Water Resour. Manage. 23 (5): 881–897. https://doi.org/10.1007/s11269-008-9305-1.
Poff, N. L., J. D. Allan, M. B. Bain, J. R. Karr, K. L. Prestegaard, B. D. Richter, R. E. Sparks, and J. C. Stromberg. 1997. “The natural flow regime.” BioScience 47 (11): 769–784. https://doi.org/10.2307/1313099.
Slack, J. R., and J. M. Landwehr. 1992. Hydro-climatic data network (HCDN); a US Geological Survey streamflow data set for the United States for the study of climate variations. Washington, DC: USGS.
Smakhtin, V. U. 2001. “Low flow hydrology: A review.” J. Hydrol. 240 (3): 147–186. https://doi.org/10.1016/S0022-1694(00)00340-1.
Sohoulande Djebou, D. C. 2015. “Integrated approach to assessing streamflow and precipitation alterations under environmental change: Application in the Niger River Basin.” J. Hydrol. Reg. Stud. 4 (Sep): 571–582. https://doi.org/10.1016/j.ejrh.2015.09.004.
Sohoulande Djebou, D. C. 2017. “Bridging drought and climate aridity.” J. Arid Environ. 144 (Sep): 170–180. https://doi.org/10.1016/j.jaridenv.2017.05.002.
Sohoulande Djebou, D. C. 2018. “Toward an integrated watershed zoning framework based on the spatio-temporal variability of land-cover and climate: Application in the Volta river basin.” Environ. Dev. 28 (Dec): 55–66. https://doi.org/10.1016/j.envdev.2018.09.006.
Sohoulande Djebou, D. C., and V. P. Singh. 2016. “Entropy-based index for spatiotemporal analysis of streamflow, precipitation, and land-cover.” J. Hydrol. Eng. 21 (11): 05016024. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001429.
Tabari, H., J. Nikbakht, and P. H. Talaee. 2013. “Hydrological drought assessment in Northwestern Iran based on streamflow drought index (SDI).” Water Resour. Manage. 27 (1): 137–151. https://doi.org/10.1007/s11269-012-0173-3.
Tidwell, V. C., H. D. Passell, S. H. Conrad, and R. P. Thomas. 2004. “System dynamics modeling for community-based water planning: Application to the Middle Rio Grande.” In Vol. 66 of Aquatic sciences, 357–372. New York: New York Academy of Sciences.
Vicente-Serrano, S. M., J. I. López-Moreno, S. Beguería, J. Lorenzo-Lacruz, C. Azorin-Molina, and E. Morán-Tejeda. 2012. “Accurate computation of a streamflow drought index.” J. Hydrol. Eng. 17 (2): 318–332. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000433.
Vrugt, J. A., C. J. F. ter Braak, M. P. Clark, J. M. Hyman, and B. A. Robinson. 2008. “Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation.” Water Resour. Res. 44 (12): 1–15. https://doi.org/10.1029/2007WR006720.
Wang, X., and A. M. Melesse. 2006. “Effects of STATSGO and SSURGO as inputs on SWAT model’s snowmelt simulation.” J. Am. Water Resour. Assoc. 42 (5): 1217–1236. https://doi.org/10.1111/j.1752-1688.2006.tb05296.x.
Wehner, M. F. 2013. “Very extreme seasonal precipitation in the NARCCAP ensemble: Model performance and projections.” Clim. Dyn. 40 (1–2): 59–80. https://doi.org/10.1007/s00382-012-1393-1.
Wilhite, D. A., and M. H. Glantz. 1985. “Understanding: The drought phenomenon: The role of definitions.” Water Int. 10 (3): 111–120. https://doi.org/10.1080/02508068508686328.
Willmott, C. J. 1981. “On the validation of models.” Phys. Geogr. 2 (2): 184–194. https://doi.org/10.1080/02723646.1981.10642213.
Zabaleta, A., M. Meaurio, E. Ruiz, and I. Antigüedad. 2014. “Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, northern Spain.” J. Environ. Qual. 43 (1): 235–245. https://doi.org/10.2134/jeq2012.0209.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 21, 2017
Accepted: Apr 18, 2019
Published online: Jun 27, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 27, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.