Abstract
The hydrological model requires precise parameter calibration to reflect the basin characteristicsproperly. The optimum parameter values depend highly on the runoff characteristics; thus, the simulated runoff data affects the uncertainty in hydrological drought assessment using hydrological models. This study quantified the hydrological drought uncertainty based on all available calibrated parameters for 12 different observation periods. The Soil and water assessment Tool was used for hydrological modelling, and the uncertainty was analyzed using Bayesian model averaging (BMA). The hydrological drought index using the simulated runoff was analyzed to vary significantly with the optimized hydrological parameter cases. The drought index calculation using the estimated runoff showed that the number of droughts occurred frequently as the duration increased, But, it was observed that severe droughts of 21-month duration occur most frequently. The uncertainty analysis conducted with BMA revealed that for durations shorter than 18 months, the uncertainties in drought index calculations were higher than those in runoff simulations. As the duration increased, uncertainties decreased, with values of 57%, 54%, 50%, 23%, 23%, 19%, 16% and 16% for 3, 6, 9, 12, 15, 18, 21 and 24-month durations, respectively. The average uncertainty was larger for runoff estimation than for drought calculation. Still, the uncertainty variation by each PC was larger for drought calculation, ranging from 0 to 94% as the duration increased to 24 months. Therefore,the results of this study confirm that hydrological drought analysis using hydrological models contains uncertainties depending on the hydrological model parameter cases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology 333(2–4):413–430, DOI: https://doi.org/10.1016/j.jhydrol.2006.09.014
Adutwum GK, Chung ES, Shiru MS, Shahid S (2023) Joint modelling of drought severity and duration using copula theory: A case study of ghana. KSCE Journal of Civil Engineering 27(4):1850–1865, DOI: https://doi.org/10.1007/s12205-023-1423-z
Ahmed N, Wang G, Booij MJ, Xiangyang S, Hussain F, Nabi G (2022) Separation of the impact of landuse/landcover change and climate change on runoff in the upstream area of the Yangtze River, China. Water Resources Management 1–21, DOI: https://doi.org/10.1007/s11269-021-03021-z
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development 1. Journal of the American Water Resources Association 34(1):73–89, DOI: https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
Benke KK, Lowell KE, Hamilton AJ (2008) Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model. Mathematical and Computer Modelling 47(11–12):1134–1149, DOI: https://doi.org/10.1016/j.mcm.2007.05.017
Beran M, Rodier JA (1985) Hydrological aspects of drought. Studies and reports in hydrology 39. UNESCOWMO, Paris, France
Chae ST, Chung ES, Jiang J (2022) Robust siting of permeable pavement in highly urbanized watersheds considering climate change using a combination of fuzzy-TOPSIS and the VIKOR method. Water Resources Management 36(3):951–969, DOI: https://doi.org/10.1007/s11269-022-03062-y
Chang J, Li Y, Wang Y, Yuan M (2016) Copula-based drought risk assessment combined with an integrated indice in the Wei River Basin, China. Journal of Hydrology 540:824–834, DOI: https://doi.org/10.1016/j.jhydrol.2016.06.064
Duan K, Mei Y (2014) Comparison of meteorological, hydrological and agricultural drought responses to climate change and uncertainty assessment. Water Resources Management 28:5039–5054, DOI: https://doi.org/10.1007/s11269-014-0789-6
Feng K, Zhou J, Liu Y, Lu C, He Z (2019) Hydrological uncertainty processor (HUP) with estimation of the marginal distribution by a Gaussian mixture model. Water Resources Management 33:2975–2990, DOI: https://doi.org/10.1007/s11269-019-02260-5
Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: Historical development, applications, and future research directions. Transactions of the ASABE 50(4):1211–1250, DOI: https://doi.org/10.13031/2013.23637
Heim Jr RR (2002) A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society 83(8):1149–1166, DOI: https://doi.org/10.1175/1520-0477(2002)083<1149:arotdi>2.3.co;2
Her Y, Chaubey I (2015) Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty. Hydrological Processes 29(19): 4220–4237, DOI: https://doi.org/10.1002/hyp.10487
Hong X, Guo S, Zhou Y, Xiong L (2015) Uncertainties in assessing hydrological drought using streamflow drought indice for the upper Yangtze River basin. Stochastic Environmental Research and Risk Assessment 29:1235–1247, DOI: https://doi.org/10.1007/s00477-014-0949-5
Huang Y, Bárdossy A, Zhang K (2019) Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrology and Earth System Sciences 23(6):2647–2663, DOI: https://doi.org/10.5194/hess-23-2647-2019
Jiang S, Wang M, Ren L, Xu CY, Yuan F, Liu Y, Lu Y, Shen H (2019) A framework for quantifying the impacts of climate change and human activities on hydrological drought in a semiarid basin of Northern China. Hydrological Processes 33(7):1075–1088, DOI: https://doi.org/10.1002/hyp.13386
Joseph J, Ghosh S, Pathak A, Sahai AK (2018) Hydrologic impacts of climate change: Comparisons between hydrological parameter uncertainty and climate model uncertainty. Journal of Hydrology 566:1–22, DOI: https://doi.org/10.1016/j.jhydrol.2018.08.080
Kang HY, Chae ST, Chung ES (2023) Quantifying medium-sized city flood vulnerability due to climate change using multi-criteria decision-making techniques: Case of Republic of Korea. Sustainability 15(22):16061, DOI: https://doi.org/10.3390/su152216061
Karlsson IB, Sonnenborg TO, Refsgaard JC, Trolle D, Børgesen CD, Olesen JE, Jeppesen E, Jensen KH (2016) Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change. Journal of Hydrology 535:301–317, DOI: https://doi.org/10.1016/j.jhydrol.2016.01.069
Kim JH, Sung JH, Chung ES, Kim SU, Son M, Shiru MS (2021) Comparison of projection in meteorological and hydrological droughts in the cheongmicheon watershed for RCP4. 5 and SSP2–4.5. Sustainability 13(4):2066, DOI: https://doi.org/10.3390/su13042066
Kim JH, Sung JH, Shahid S, Chung ES (2022) Future hydrological drought analysis considering agricultural water withdrawal under SSP scenarios. Water Resources Management 36(9):2913–2930, DOI: https://doi.org/10.1007/s11269-022-03116-1
Konapala G, Mishra A (2020) Quantifying climate and catchment control on hydrological drought in the continental United States. Water Resources Research 56(1):e2018WR024620, DOI: https://doi.org/10.1029/2018wr024620
Ma Q, Xiong L, Li Y, Li S, Xu CY (2018) Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China. Science of the Total Environment 618:1298–1313, DOI: https://doi.org/10.1016/j.scitotenv.2017.09.235
Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resources Management 23:881–897, DOI: https://doi.org/10.1007/s11269-008-9305-1
Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute
Qi J, Lee S, Zhang X, Yang Q, McCarty GW, Moglen GE (2020) Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US. Journal of Hydrology 581: 124415, DOI: https://doi.org/10.1016/j.jhydrol.2019.124415
Raymond C, Horton RM, Zscheischler J, Martius O, AghaKouchak A, Balch J, Bowen SG, Camargo SJ, Hess J, Kornhuber K, Oppenheimer M, Ruane AC, Wahl T, White K (2020) Understanding and managing connected extreme events. Nature Climate Change 10(7):611–621, DOI: https://doi.org/10.1038/s41558-020-0790-4
Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resources Research 46(5), DOI: https://doi.org/10.1029/2009wr008328
Roushangar K, Ghasempour R, Alizadeh F (2022) Uncertainty assessment of the integrated hybrid data processing techniques for short to long term drought forecasting in different climate regions. Water Resources Management 1–24, DOI: https://doi.org/10.1007/s11269-021-03027-7
Saharwardi MS, Kumar P (2022) Future drought changes and associated uncertainty over the homogenous regions of India: A multimodel approach. International Journal of Climatology 42(1):652–670, DOI: https://doi.org/10.1002/joc.7265
Shin MJ, Jung Y (2022) Using a global sensitivity analysis to estimate the appropriate length of calibration period in the presence of high hydrological model uncertainty. Journal of Hydrology 607:127546, DOI: https://doi.org/10.1016/j.jhydrol.2022.127546
Song YH, Shahid S, Chung ES (2022) Differences in multi-model ensembles of CMIP5 and CMIP6 projections for future droughts in South Korea. International Journal of Climatology 42(5):2688–2716, DOI: https://doi.org/10.1002/joc.7386
Tate EL, Gustard A (2000) Drought definition: A hydrological perspective. Springer Netherlands 23–48, DOI: https://doi.org/10.1007/978-94-015-9472-1_3
Teweldebrhan AT, Burkhart JF, Schuler TV (2018) Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches. Hydrology and Earth System Sciences 22(9):5021–5039, DOI: https://doi.org/10.5194/hess-22-5021-2018
Vallam P, Qin XS, Yu JJ (2014) Uncertainty quantification of hydrologic model. APCBEE Procedia 10:219–223, DOI: https://doi.org/10.1016/j.apcbee.2014.10.042
Van Loon AF (2015) Hydrological drought explained. Wiley Interdisciplinary Reviews: Water 2(4):359–392, DOI: https://doi.org/10.1002/wat2.1085
Wang HM, Chen J, Xu CY, Zhang J, Chen H (2020) A framework to quantify the uncertainty contribution of GCMs over multiple sources in hydrological impacts of climate change. Earth’s Future 8(8): e2020EF001602, DOI: https://doi.org/10.1029/2020ef001602
Xie K, Liu P, Zhang J, Wang G, Zhang X, Zhou L (2021) Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy. Journal of Hydrology 598:125772, DOI: https://doi.org/10.1016/j.jhydrol.2020.125772
Xu Z, Godrej AN, Grizzard TJ (2007) The hydrological calibration and validation of a complexly-linked watershed-reservoir model for the Occoquan watershed, Virginia. Journal of Hydrology 345(3–4):167–183, DOI: https://doi.org/10.1016/j.jhydrol.2007.07.015
Zhao C, Brissette F, Chen J, Martel JL (2020) Frequency change of future extreme summer meteorological and hydrological droughts over North America. Journal of Hydrology 584:124316, DOI: https://doi.org/10.1016/j.jhydrol.2019.124316
Acknowledgments
This study was supported by the Research Program funded by SeoulTech (Seoul National University of Science and Technology).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Kim, J.H., Chung, ES., Song, J.Y. et al. Quantifying Uncertainty in Hydrological Drought Index Using Calibrated SWAT Model. KSCE J Civ Eng 28, 2066–2076 (2024). https://doi.org/10.1007/s12205-024-1029-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-024-1029-0