Characterizing Influence of Hydrologic Data Correlations on Climate Change Decision Variables: Evidence from Diyala River Basin in Iraq
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 3
Abstract
Water resources decision-making processes involving impacts of climate change require coherent climate datasets, which may exhibit high spatial and temporal variability. These datasets are used in models with forcing data provided by statistical weather generators that mimic observed system behavior. The datasets must conserve historical correlations, or they will lead to wrong decisions about future climate change influences. The objectives of this study are to (1) evaluate the impact of the cross, spatial, and temporal correlations in climatic datasets on the climate change decision variables and (2) examine the contributions of variability in each subcorrelation on system performance outcomes. A predeveloped nonstationary bottom-up approach was used to assess the operational rules of a multipurpose reservoir on the Diyala River basin in Iraq. The study utilizes a statistical weather generator to synthesize 10 trajectories, of 405 different climate scenarios, by varying the preserved accuracy (100%, 66%, 33%, and ≈ 0%) of cross, spatial, and temporal correlations. The results indicated that the system performance is influenced significantly when correlations were varied, with the most sensitivity to spatial correlations, followed by the cross and temporal correlations. Ignoring the spatial correlation caused a 92.2% error in system performance indicators, and cross and temporal correlations caused errors of 17.9% and 9.3%, respectively. The results also revealed that the precipitation spatial correlation was the most sensitive component of the subcorrelation effects with a 68.6% error, but the cross correlation between precipitation and wind speed only accounted for a 2.5% error. The study demonstrated that the nature of basin datasets is of paramount importance in hydrologic modeling and climate change impact assessment.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider, as indicated in the Acknowledgments.
Acknowledgments
The authors are grateful to the Iraqi Ministry of Water Resources (MoWR) for assistance in this study. Data and models were downloaded online from https://uw-hydro.github.io/code/.
References
Alodah, A., and O Seidou. 2019. “Assessment of climate change impacts on extreme high and low flows: An improved bottom-up approach.” Water 11 (6): 1236. https://doi.org/10.3390/w11061236.
Bai, P., and X. Liu. 2018. “Evaluation of five satellite-based precipitation products in two gauge-scarce basins on the Tibetan Plateau.” Remote Sens. 10 (8): 1316. https://doi.org/10.3390/rs10081316.
Betterle, A., M. Schirmer, and G. Botter. 2017. “Characterizing the spatial correlation of daily streamflows.” Water Resour. Res. 53 (2): 1646–1663. https://doi.org/10.1002/2016WR019195.
Brown, C., and R. L. Wilby. 2012. “An alternate approach to assessing climate risks.” EOS Trans. Am. Geophys. Union 93 (41): 401–402. https://doi.org/10.1029/2012EO410001.
Chaubey, I., C. T. Haan, S. Grunwald, and J. M. Salisbury. 1999. “Uncertainty in the model parameters due to spatial variability of rainfall.” J. Hydrol. 220 (1–2): 48–61. https://doi.org/10.1016/S0022-1694(99)00063-3.
Chen, J., C. Li, F. P. Brissette, H. Chen, M. Wang, and G. R. Essou. 2018. “Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling.” J. Hydrol. 560 (Mar): 326–341. https://doi.org/10.1016/j.jhydrol.2018.03.040.
Culley, S., S. Noble, A. Yates, M. Timbs, S. Westra, H. R. Maier, M. Giuliani, and A. Castelletti. 2016. “A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate.” Water Resour. Res. 52 (9): 6751–6768. https://doi.org/10.1002/2015WR018253.
Dahal, N., U. B. Shrestha, A. Tuitui, and H. R. Ojha. 2019. “Temporal changes in precipitation and temperature and their implications on the streamflow of Rosi River, Central Nepal.” Climate 7 (1): 3. https://doi.org/10.3390/cli7010003.
Dai, C., and X. S. Qin. 2019. “Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed, Canada.” Hydrol. Sci. J. 64 (13): 1616–1628. https://doi.org/10.1080/02626667.2019.1661416.
Gabellani, S., G. Boni, L. Ferraris, J. Von Hardenberg, and A. Provenzale. 2007. “Propagation of uncertainty from rainfall to runoff: A case study with a stochastic rainfall generator.” Adv. Water Resour. 30 (10): 2061–2071. https://doi.org/10.1016/j.advwatres.2006.11.015.
Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez. 2009. “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling.” J. Hydrol. 377 (1–2): 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003.
Hallegatte, S., A. Shah, R. Lempert, C. Brown, and S. Gill. 2012. Investment decision making under deep uncertainty-application to climate change. Washington, DC: World Bank. https://doi.org/10.1596/1813-9450-6193.
Khalili, M., F. Brissette, and R. Leconte. 2011. “Effectiveness of multi-site weather generator for hydrological modeling 1.” JAWRA J. Am. Water Resour. Assoc. 47 (2): 303–314. https://doi.org/10.1111/j.1752-1688.2010.00514.x.
Li, C., E. Sinha, D. E. Horton, N. S. Diffenbaugh, and A. M. Michalak. 2014. “Joint bias correction of temperature and precipitation in climate model simulations.” J. Geophys. Res.: Atmos. 119 (23): 13–153. https://doi.org/10.1002/2014JD022514.
Li, X., and V. Babovic. 2018. “A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach.” Clim. Dyn. 52: 2247–2267. https://doi.org/10.3390/cli8080093.
Li, Z. 2014. “A new framework for multi-site weather generator: A two-stage model combining a parametric method with a distribution-free shuffle procedure.” Clim. Dyn. 43 (3–4): 657–669. https://doi.org/10.1007/s00382-013-1979-2.
Li, Z., Z. Lü, J. Li, and X. Shi. 2017. “Links between the spatial structure of weather generator and hydrological modeling.” Theor. Appl. Climatol. 128 (1–2): 103–111. https://doi.org/10.1007/s00704-015-1691-8.
Mehrotra, R., J. Li, S. Westra, and A. Sharma. 2015. “A programming tool to generate multi-site daily rainfall using a two-stage semi parametric model.” Environ. Modell. Software 63 (Jan): 230–239. https://doi.org/10.1016/j.envsoft.2014.10.016.
Muthoni, F. K., V. O. Odongo, J. Ochieng, E. M. Mugalavai, S. K. Mourice, I. Hoesche-Zeledon, M. Mwila, and M. Bekunda. 2019. “Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa.” Theor. Appl. Climatol. 137 (3–4): 1869–1882. https://doi.org/10.1007/s00704-018-2712-1.
Nicótina, L., E. Alessi Celegon, A. Rinaldo, and M. Marani. 2008. “On the impact of rainfall patterns on the hydrologic response.” Water Resour. Res. 44 (12): W12401. https://doi.org/10.1029/2007WR006654.
Nijssen, B., and L. A. Bastidas. 2006. “Land-atmosphere models for water and energy cycle studies.” In Encyclopedia of hydrological sciences. Hoboken, NJ: Wiley. https://doi.org/10.1002/0470848944.hsa212.
Opalinski, N. 2018. “Response of municipal water use to weather across the contiguous US.” Ph.D. dissertation, Dept. of Civil and Environmental Engineering, Colorado State Univ.
Peleg, N., C. Skinner, S. Fatichi, and P. Molnar. 2020. “Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response.” Earth Surf. Dyn. 8 (1): 17–36. https://doi.org/10.5194/esurf-8-17-2020.
Pitman, A. J. 2003. “The evolution of, and revolution in, land surface schemes designed for climate models.” Int. J. Climatol.: J. R. Meteorol. Soc. 23 (5): 479–510. https://doi.org/10.1002/joc.893.
Pradhananga, N. S., R. B. Kayastha, B. C. Bhattarai, T. R. Adhikari, S. C. Pradhan, L. P. Devkota, A. B. Shrestha, and P. K. Mool. 2014. “Estimation of discharge from Langtang River basin, Rasuwa, Nepal, using a glacio-hydrological model.” Ann. Glaciol. 55 (66): 223–230. https://doi.org/10.3189/2014AoG66A123.
Qian, B., J. Corte-Real, and H. Xu. 2002. “Multisite stochastic weather models for impact studies.” Int. J. Climatol.: J. R. Meteorol. Soc. 22 (11): 1377–1397. https://doi.org/10.1002/joc.808.
Razmkhah, H., A. M. AkhoundAli, F. Radmanesh, and B. Saghafian. 2016. “Evaluation of rainfall spatial correlation effect on rainfall-runoff modeling uncertainty, considering 2-copula.” Arabian J. Geosci. 9 (4): 323. https://doi.org/10.1007/s12517-016-2392-z.
Srivastav, R. K., and S. P. Simonovic. 2015. “Multi-site, multivariate weather generator using maximum entropy bootstrap.” Clim. Dyn. 44 (11–12): 3431–3448. https://doi.org/10.1007/s00382-014-2157-x.
Steinschneider, S., and C. Brown. 2013. “A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments.” Water Resour. Res. 49 (11): 7205–7220. https://doi.org/10.1002/wrcr.20528.
Steinschneider, S., S. Wi, and C. Brown. 2015. “The integrated effects of climate and hydrologic uncertainty on future flood risk assessments.” Hydrol. Processes 29 (12): 2823–2839. https://doi.org/10.1002/hyp.10409.
Syed, K. H., D. C. Goodrich, D. E. Myers, and S. Sorooshian. 2003. “Spatial characteristics of thunderstorm rainfall fields and their relation to runoff.” J. Hydrol. 271 (1–4): 1–21. https://doi.org/10.1016/S0022-1694(02)00311-6.
Taner, M. Ü., P. Ray, and C. Brown. 2019. “Incorporating multidimensional probabilistic information into robustness-based water systems planning.” Water Resour. Res. 55 (5): 3659–3679. https://doi.org/10.1029/2018WR022909.
Verdin, A., B. Rajagopalan, W. Kleiber, G. Podestá, and F. Bert. 2019. “BayGEN: A Bayesian space-time stochastic weather generator.” Water Resour. Res. 55 (4): 2900–2915. https://doi.org/10.1029/2017WR022473.
Waheed, S. Q., N. S. Grigg, and J. A. Ramirez. Forthcoming. “Nonstationary-probabilistic framework to assess the water resources system vulnerability: Long-time robust plan and timing.” J. Water Resour. Plann. Manage. https://doi.org/10.22541/au.158575719.90657634.
Waheed, S. Q., N. S. Grigg, and J. A. Ramirez. 2020a. “Development of a parametric regional multivariate statistical weather generator for risk assessment studies in areas with limited data availability.” Climate 8 (8): 93. https://doi.org/10.3390/cli8080093.
Waheed, S. Q., N. S. Grigg, and J. A. Ramirez. 2020b. “Variable infiltration capacity model sensitivity, parameter uncertainty, and data augmentation for the Diyala River basin in Iraq.” J. Hydrol. Eng. 25 (9): 04020040. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001975.
Weaver, C. P., R. J. Lempert, C. Brown, J. A. Hall, D. Revell, and D. Sarewitz. 2013. “Improving the contribution of climate model information to decision making: The value and demands of robust decision frameworks.” Wiley Interdiscip. Rev. Clim. Change 4 (1): 39–60. https://doi.org/10.1002/wcc.202.
Whateley, S., S. Steinschneider, and C. Brown. 2014. “A climate change range-based method for estimating robustness for water resources supply.” Water Resour. Res. 50 (11): 8944–8961. https://doi.org/10.1002/2014WR015956.
Wood, E. F., M. Sivapalan, K. Beven, and L. Band. 1988. “Effects of spatial variability and scale with implications to hydrologic modeling.” J. Hydrol. 102 (1–4): 29–47. https://doi.org/10.1016/0022-1694(88)90090-X.
Zhang, J., D. Han, Y. Song, and Q. Dai. 2018. “Study on the effect of rainfall spatial variability on runoff modeling.” J. Hydroinf. 20 (3): 577–587. https://doi.org/10.2166/hydro.2018.129.
Zhou, L., Y. Meng, C. Lu, S. Yin, and D. Ren. 2020. “A frequency-domain nonstationary multi-site rainfall generator for use in hydrological impact assessment.” J. Hydrol. 585: 124770. https://doi.org/10.1016/j.jhydrol.2020.124770.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 26 • Issue 3 • March 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 1, 2020
Accepted: Oct 16, 2020
Published online: Jan 4, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 4, 2021
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.