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Water Resources Management

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10-05-2024

Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

Authors: Jose George, P. Athira

Published in: Water Resources Management | Issue 12/2024

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Abstract

The study focuses on the uncertainty quantification and bias correction of hydrological projections using Bayesian applications. The climate change impact assessment on streamflow has been done using Soil and Water Assessment Tool (SWAT) model in Bharathapuzha river basin, India. The uncertainty quantification has been done by using Generalised Likelihood Uncertainty Estimation (GLUE) algorithm and the ensemble spread in the streamflow projections is quantified as the total uncertainty. A Hierarchical Bayesian Algorithm is adopted in the current study to remove the systematic bias in the projections of extreme streamflow. The approach established a probabilistic correction to the projected streamflow based on the biases in daily scale hindcast streamflow simulations with the corresponding observed historical streamflow data. The procedure is applied to the ensemble streamflow predictions for the Bharathapuzha catchment and over 10 times reduction in RMSE is observed in the bias corrected streamflow. The skill of the procedure in correcting the streamflow across different terciles is studied using the concept of reliability and significant improvement is observed in the reliability of high and medium flow ranges. The average width of the ensemble streamflow simulation band for the period 2021–2030 is seen to reduce from 5560 cumec to 2188 cumec after the correction procedure is applied.

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Aadhar S, Swain S, Rath DR (2019) Application and Performance Assessment of SWAT Hydrological Model over Kharun River Basin, Chhattisgarh, India. World Environmental and Water resources Congress 2019. American Society of Civil Engineers, Reston, VA, pp 272–280CrossRef

Aawar T, Khare D (2020) Assessment of climate change impacts on streamflow through hydrological model using SWAT model: a case study of Afghanistan. Model Earth Syst Environ 6:1427–1437. https://doi.org/10.1007/s40808-020-00759-0CrossRef

Abbaspour KC, Vejdani M, Haghighat S (2000) SWAT-CUP calibration and uncertainty programs for SWAT. Trans Am Soc Agric Eng 43:1077–1087. https://doi.org/10.13031/2013.3000CrossRef

Aloui S, Mazzoni A, Elomri A et al (2023) A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: applications, feasibility, and future directions. J Environ Manage 326:116799. https://doi.org/10.1016/j.jenvman.2022.116799CrossRef

Arnold JG, Moriasi DN, Gassman PW et al (2012) SWAT: Model use, calibration, and validation. Trans ASABE 55:1491–1508CrossRef

Athira P, Nanda C, Sudheer KP (2018) A computationally efficient method for uncertainty analysis of SWAT model simulations. Stoch Environ Res Risk Assess 32:1479–1492. https://doi.org/10.1007/s00477-018-1538-9CrossRef

Bastola S, Ishidaira H, Takeuchi K (2008) Regionalisation of hydrological model parameters under parameter uncertainty: a case study involving TOPMODEL and basins across the globe. J Hydrol 357:188–206. https://doi.org/10.1016/j.jhydrol.2008.05.007CrossRef

Bastola S, Murphy C, Sweeney J (2011) The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv Water Resour 34:562–576. https://doi.org/10.1016/j.advwatres.2011.01.008CrossRef

Bennett A, Stein A, Cheng Y et al (2022) A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow. J Hydrometeorol 23:769–783. https://doi.org/10.1175/JHM-D-21-0174.1CrossRef

Bergstrom S (1991) Principles and confidence in hydrological modelling. Nord Hydrol 22:123–136. https://doi.org/10.2166/nh.1991.0009CrossRef

Beven K (1989) Changing ideas in hydrology — the case of physically-based models. J Hydrol 105:157–172. https://doi.org/10.1016/0022-1694(89)90101-7CrossRef

Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. https://doi.org/10.1016/j.jhydrol.2005.07.007

Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298. https://doi.org/10.1002/hyp.3360060305CrossRef

Breach PA, Simonovic SP, Yang Z (2016) Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact assessments of Hydro-climatic extremes. Am J Clim Chang 05:502–525. https://doi.org/10.4236/ajcc.2016.54036CrossRef

Bruno F, Cocchi D, Greco F, Scardovi E (2014) Spatial reconstruction of rainfall fields from rain gauge and radar data. Stoch Environ Res Risk Assess 28:1235–1245. https://doi.org/10.1007/s00477-013-0812-0CrossRef

Chen X, Hao Z, Devineni N, Lall U (2014) Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical bayesian modeling. Hydrol Earth Syst Sci 18:1539–1548. https://doi.org/10.5194/hess-18-1539-2014CrossRef

Chen H, Cifelli ROB, Chandrasekar V, Ma Y (2019) A flexible bayesian approach to bias correction of radar-derived precipitation estimates over complex terrain: Model design and initial verification. J Hydrometeorol 20:2367–2382. https://doi.org/10.1175/JHM-D-19-0136.1CrossRef

Chen Y, Wang L, Shi X et al (2023) Impact of Climate Change on the hydrological regimes of the Midstream section of the Yarlung Tsangpo River Basin Based on SWAT Model. Water (Switzerland) 15. https://doi.org/10.3390/w15040685

Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrol Process 24:1133–1148. https://doi.org/10.1002/hyp.7568CrossRef

Dams J, Nossent J, Senbeta TB et al (2015) Multi-model approach to assess the impact of climate change on runoff. J Hydrol 529:1601–1616. https://doi.org/10.1016/j.jhydrol.2015.08.023CrossRef

Drissia TK (2019) Spatial and temporal variation of water stress in Bharathapuzha River Basin, Kerala, India. J Inst Eng Ser A 100:167–175. https://doi.org/10.1007/s40030-018-0336-1CrossRef

Gardini A, Trivisano C, Fabrizi E (2020) Bayesian inference for quantiles of the log-normal distribution. Biometrical J 62:1997–2012. https://doi.org/10.1002/bimj.201900386CrossRef

George J, Athira P (2022) Process informed selection of climate models for climate change impact assessment in the Western Coast of India. Theor Appl Climatol 805–828. https://doi.org/10.1007/s00704-022-04197-z

George J, Athira P (2023) A multi-stage Stochastic Approach for Statistical Downscaling of Rainfall. Water Resour Manag. https://doi.org/10.1007/s11269-023-03615-9CrossRef

George J, Athira P (2024) A model output statistic-based probabilistic approach for statistical downscaling of temperature. Theor Appl Climatol. https://doi.org/10.1007/s00704-024-04860-7CrossRef

Githui F, Gitau W, Mutua F, Bauwens W (2009) Climate change impact on SWAT simulated streamflow in western Kenya. Int J Climatol 29:1823–1834. https://doi.org/10.1002/joc.1828CrossRef

Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003CrossRef

Hashino T, Bradley AA, Schwartz SS (2007) Evaluation of bias-correction methods for ensemble streamflow volume forecasts. Hydrol Earth Syst Sci 11:939–950. https://doi.org/10.5194/hess-11-939-2007CrossRef

Hattermann FF, Vetter T, Breuer L et al (2018) Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ Res Lett 13. https://doi.org/10.1088/1748-9326/aa9938

Herrera PA, Marazuela MA, Hofmann T (2022) Parameter estimation and uncertainty analysis in hydrological modeling. Wiley Interdiscip Rev Water 9:1–23. https://doi.org/10.1002/wat2.1569CrossRef

Hunt KMR, Fletcher JK (2019) The relationship between Indian monsoon rainfall and low-pressure systems. Clim Dyn 53:1859–1871. https://doi.org/10.1007/s00382-019-04744-xCrossRef

IPCC (2021) Climate Change 2021 The Physical Science Basis WGI

John J, Chithra NR, Thampi SG (2019) Prediction of land use/cover change in the Bharathapuzha river basin, India using geospatial techniques. Environ Monit Assess 191. https://doi.org/10.1007/s10661-019-7482-4

Jones RN (2000) Managing uncertainty in climate change projections - issues for impact assessment: an editorial comment. Clim Change 45:403–419. https://doi.org/10.1023/a:1005551626280CrossRef

Kim TJ, Kwon HH, Lima C (2018) A bayesian partial pooling approach to mean field bias correction of weather radar rainfall estimates: application to Osungsan weather radar in South Korea. J Hydrol 565:14–26. https://doi.org/10.1016/j.jhydrol.2018.07.082CrossRef

Kimani MW, Hoedjes JCB, Su Z (2018) Bayesian Bias correction of satellite rainfall estimates for climate studies. Remote Sens 10:1–18. https://doi.org/10.3390/rs10071074CrossRef

Li C, Fang H (2021) Assessment of climate change impacts on the streamflow for the Mun River in the Mekong Basin, Southeast Asia: using SWAT model. CATENA 201:105199. https://doi.org/10.1016/j.catena.2021.105199CrossRef

Lima CHR, Lall U (2010) Spatial scaling in a changing climate: a hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow. J Hydrol 383:307–318. https://doi.org/10.1016/j.jhydrol.2009.12.045CrossRef

Loveland TR, Reed BC, Ohlen DO et al (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21:1303–1330. https://doi.org/10.1080/014311600210191CrossRef

Lozano JS, Bustamante GR, Hales RC et al (2021) A streamflow bias correction and performance evaluation web application for geoglows ecmwf streamflow services. Hydrology 8. https://doi.org/10.3390/HYDROLOGY8020071

Ma Y, Chandrasekar V (2021) A hierarchical bayesian Approach for Bias correction of NEXRAD Dual-Polarization Rainfall estimates: Case Study on Hurricane Irma in Florida. IEEE Geosci Remote Sens Lett 18:568–572. https://doi.org/10.1109/LGRS.2020.2983041CrossRef

Massey FJ (1951) The Kolmogorov-Smirnov Test for Goodness of Fit. J Am Stat Assoc 46:68. https://doi.org/10.2307/2280095CrossRef

Mehrishi P, Kundu A, Thakur IS (2022) An Appraisal of Kerala Flood-2019. J Indian Soc Remote Sens 50:1563–1567. https://doi.org/10.1007/s12524-022-01544-5CrossRef

Mishra V, Shah HL (2018) Hydroclimatological Perspective of the Kerala Flood of 2018. J Geol Soc India 92:645–650. https://doi.org/10.1007/s12594-018-1079-3CrossRef

Mohsen Hassan Abdalla E, Alfredsen K, Merete Muthanna T (2022) Towards improving the calibration practice of conceptual hydrological models of extensive green roofs. J Hydrol 607:127548. https://doi.org/10.1016/j.jhydrol.2022.127548CrossRef

Nachtergaele F, Velthuizen H, Van, Verelst L (2023) Harmonized World Soil Database version 2.0. FAO; International Institute for Applied Systems Analysis (IIASA);

Neitsch SL, Arnold JG, Kiniry JR et al (2002) Soil and Water Assessment Tool user’s Manual. TWRI Rep TR-192 412

Pai DS, Sridhar L, Rajeevan M et al (2014) Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65:1–18CrossRef

Pokhrel P, Robertson DE, Wang QJ (2013) A bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions. Hydrol Earth Syst Sci 17:795–804. https://doi.org/10.5194/hess-17-795-2013CrossRef

Pratt JW, Gibbons JD (1981) Kolmogorov-Smirnov Two-Sample Tests. pp 318–344

Ravindranath A, Devineni N, Lall U et al (2019) Streamflow Reconstruction in the Upper Missouri River Basin Using a novel bayesian network model. Water Resour Res 55:7694–7716. https://doi.org/10.1029/2019WR024901CrossRef

Reggiani P, Renner M, Weerts AH, Van Gelder PAHJM (2009) Uncertainty assessment via bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system. Water Resour Res 45:1–14. https://doi.org/10.1029/2007WR006758CrossRef

Rehana S, Mujumdar PP (2014) Basin Scale Water resources systems modeling under cascading uncertainties. Water Resour Manag 28:3127–3142. https://doi.org/10.1007/s11269-014-0659-2CrossRef

Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void-filling interpolation methods for SRTM data. Int J Geogr Inf Sci 21:983–1008. https://doi.org/10.1080/13658810601169899CrossRef

Samavati A, Babamiri O, Rezai Y, Heidarimozaffar M (2023) Investigating the effects of climate change on future hydrological drought in mountainous basins using SWAT model based on CMIP5 model. Stoch Environ Res Risk Assess 37:849–875. https://doi.org/10.1007/s00477-022-02319-7CrossRef

Schepen A, Zhao T, Wang QJ, Robertson DE (2018) A bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments. Hydrol Earth Syst Sci 22:1615–1628. https://doi.org/10.5194/hess-22-1615-2018

Schneider SH, Kuntz-Duriseti K (2002) Uncertainty and climate change policy. Clim Chang Policy Surv 53–87

Schoups G, Van De Giesen NC, Savenije HHG (2008) Model complexity control for hydrologic prediction. Water Resour Res 44:1–14. https://doi.org/10.1029/2008WR006836CrossRef

Setegn SG, Srinivasan R, Melesse AM, Dargahi B (2009) SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia. https://doi.org/10.1002/hyp.7457. Hydrol Process 2274:n/a-n/a

Shen M, Chen J, Zhuan M et al (2018) Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. J Hydrol 556:10–24. https://doi.org/10.1016/j.jhydrol.2017.11.004CrossRef

Smith MD (2011) The ecological role of climate extremes: current understanding and future prospects. J Ecol 99:651–655. https://doi.org/10.1111/j.1365-2745.2011.01833.xCrossRef

Sreelatha K, AnandRaj P (2022) Regional evaluation of global climate models for precipitation, maximum and minimum temperature over southern-part of India. ISH J Hydraul Eng 28:449–462. https://doi.org/10.1080/09715010.2020.1779137CrossRef

Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 248. https://doi.org/10.1002/asl.232. :n/a-n/a

Tang X, Zhang J, Wang G et al (2021) Uncertainty analysis of swat modeling in the lancang river basin using four different algorithms. Water (Switzerland) 13:1–21. https://doi.org/10.3390/w13030341CrossRef

Taye MT, Ntegeka V, Ogiramoi NP, Willems P (2011) Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin. Hydrol Earth Syst Sci 15:209–222. https://doi.org/10.5194/hess-15-209-2011CrossRef

Thirel G, Andréassian V, Perrin C (2015) On the need to test hydrological models under changing conditions. Hydrol Sci J 60:1165–1173. https://doi.org/10.1080/02626667.2015.1050027CrossRef

Towler E, Rajagopalan B, Summers RS, Yates D (2010) An approach for probabilistic forecasting of seasonal turbidity threshold exceedance. Water Resour Res 46:1–10. https://doi.org/10.1029/2009WR007834CrossRef

Varughese A, Hajilal G (2017) Analysis of historical Climate Change trends in Bharathapuzha River Basin, Kerala, India. Nat Environ Pollut Technol 16:237–242

Viola MR, de Mello CR, Chou SC et al (2015) Assessing climate change impacts on Upper Grande River Basin hydrology, Southeast Brazil. Int J Climatol 35:1054–1068. https://doi.org/10.1002/joc.4038CrossRef

Wang QJ, Robertson DE, Chiew FHS (2009) A bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour Res 45:1–18. https://doi.org/10.1029/2008WR007355CrossRef

Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42. https://doi.org/10.1029/2005WR004065

Wu L, Liu X, Chen J et al (2022) Overcoming equifinality: time-varying analysis of sensitivity and identifiability of SWAT runoff and sediment parameters in an arid and semiarid watershed. Environ Sci Pollut Res 29:31631–31645. https://doi.org/10.1007/s11356-022-18573-9CrossRef

Yang J, Reichert P, Abbaspour KC et al (2008) Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J Hydrol 358:1–23. https://doi.org/10.1016/j.jhydrol.2008.05.012CrossRef

Yoshida T, Hanasaki N, Nishina K et al (2022) Inference of parameters for a global hydrological model: identifiability and predictive uncertainties of climate-based parameters. Water Resour Res 58. https://doi.org/10.1029/2021WR030660

Zhao F, Wu Y, Qiu L et al (2018) Parameter uncertainty analysis of the SWAT model in a mountain-loess transitional watershed on the Chinese Loess Plateau. Water (Switzerland) 10:1–16. https://doi.org/10.3390/w10060690CrossRef

Zhong R, Zhao T, He Y, Chen X (2019) Hydropower change of the water tower of Asia in 21st century: a case of the Lancang River hydropower base, upper Mekong. Energy 179:685–696. https://doi.org/10.1016/j.energy.2019.05.059CrossRef

Title: Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment
Authors: Jose George
P. Athira
Publication date: 10-05-2024
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 12/2024
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-024-03876-y

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Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

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