Uncertainty Analysis of Dominating Hydrological Parameters in  Diverse Hydrometeorological Micro-Watersheds in Krishna Basin, India

Nayeemahmed  Mulla; N C Mondal

doi:10.48165/

Authors

Nayeemahmed Mulla Earth Process Modeling Group, CSIR-National Geophysical Research Institute, Hyderabad, Telangana 500007, India
N C Mondal Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India

DOI:

https://doi.org/10.48165/

Keywords:

Hydrogeological parameters, SWAT model, sensitivity, micro-watersheds, Krishna basin

Abstract

Hydrological model plays a vital role in water resources management and serves many applications, including water resources planning development and management, agriculture, and flood production. Rainfall-runoff modeling in any watershed is highly influenced by the different hydrological parameters such as Curve Number (CN_2), Groundwater Delay (GW_DEALY), Base flow (ALPHA_BF), ground water Revap (GW_REVAP), Threshold depth of water (GWQM), and ESCO. Therefore, the rainfall runoff model was simulated using the Algorithm SUFI-2 of SWAT-CUP in the Soil-Water Assessment Tool (SWAT) in two diverse hydro-meteorological watersheds of Marol (area: ~5158 km2), and Talikot (area: ~2370 km2) in Krishna basin of Southern India, where the unregulated flow exit. The results show that the R2 and NSE-values for the calibration of streamflow in Marol watershed are 0.87 and 0.84, respectively, and in the validation, 0.65 and 0.58, respectively. Similarly, for the Talikot watershed, they are 0.90 and 0.52; and 0.83 and 0.74, respectively. The curve number, groundwater delay and water available capacity of the soil are the most sensitive parameters in the Marol watershed, whereas the threshold water level in the shallow aquifer for the base flow is the additional sensitive parameter. The uncertainty ranges of the different sensitivity parameters are observed for both the watersheds. The calculated coefficient of variation shows that, among the different parameters, the GW_DELAY has relatively high uncertainty, but the curve number (CN_2) and threshold depth of water (GWQMN) parameters have relatively low uncertainties.

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References

Aawar, T. and Khare, D. (2020). Assessment of climate change impacts on streamflow through hydrological model using SWAT model: a case study of Afghanistan. Modeling Earth Systems and Environment, 6(3), 1427-1437.

Abaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H. and Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733-752

Adnan, M., Kang, S., Zhang, G., Saifullah, M., Anjum, M. N. and Ali, A. F. (2019). Simulation and Analysis of the Water Balance of the Nam Co Lake Using SWAT Model. Water, 11(7), 1383.

Anand, S., Mankin, K.R., McVay, K.A., Janssen, K.A., Barnes, P.L. and Pierzynski, G.M. (2007). Calibration and Validation of ADAPT and SWAT for Field-Scale Runoff Prediction 1. JAWRA Journal of the American Water Resources Association,

(4), 899-910.

Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., ... and Jha, M.K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.

Baffaut, C., Dabney, S.M., Smolen, M.D., Youssef, M.A., Bonta, J.V., Chu, M.L., ... and Arnold, J.G. (2015). Hydrologic and water quality modeling: Spatial and temporal considerations. Transactions of the ASABE, 58(6), 1661-1680.

Brito, D., Neves, R., Branco, M.A., Prazeres, ., Rodrigues, S., Gonçalves, M.C. and Ramos, T.B. (2019). Assessing water and nutrient long-term dynamics and loads in the Enxoé temporary river basin (Southeast Portugal). Water, 11(2), 354.

Chiew, F. and McMahon, T. (1994). Application of the daily rainfall-runoff model MODHYDROLOG to 28 Australian catchments. Journal of Hydrology, 153(1- 4), 383-416.

Chiew, F.H.S., Stewardson, M.J. and McMahon, T.A. (1993). Comparison of six rainfall-runoff modelling approaches. Journal of Hydrology, 147(1- 4), 1-36.

Chung, S., Takeuchi, J., Fujihara, M. and Oeurng, C. (2019). Flood damage assessment on rice crop in the Stung Sen River Basin of Cambodia. Paddy and Water Environment, 17(2), 255-263.

Dawdy, D.R., Lichty, R.W. and Bergmann, J.M. (1972). A rainfall-runoff simulation model for estimation of flood peaks for small drainage basins. US Government Printing Office.

Gassman, P.W., Sadeghi, A.M. and Srinivasan, R. (2015). Applications of the SWAT model special section: overview and insights. Journal of Environmental

Quality, 43(1), 1-8.

Gull Dooge, S., Ahangar, M.A. and Dar, A.M. (2017). Prediction of Stream Flow and Sediment Yield of Lolab Watershed Using SWAT Model. Hydrol Current Res, 8(1), 1-9.

Jia, Y., Kinouchi, T. and Yoshitani, J. (2005). Distributed hydrologic modeling in a partially urbanized agricultural watershed using water and energy transfer process model. Journal of Hydrologic Engineering, 10(4), 253-263.

Karatas, F.U. (2015). Estimating Sediment and Nutrient Loading in the Davis Creek Watershed Using Soil and WaterAssessment Tool (SWAT).

Kashimbiri, N., Mtalo, F., Mwanuzi, F., Mondal, N.C. and Singh, V.S. (2009). Modelling the impact of urbanization on groundwater using System Dynamic Technique: a case study of Arusha Municipal Well Field in Northeastern Tanzania. Tanzania Journal of Engineering and Technology, 3(1), 67-80.

Khalid, K., Ali, M.F., Rahman, N.F.A., Mispan, M.R., Haron, S.H., Othman, Z. and Bachok, M.F. (2016). Sensitivity analysis in the watershed model using the SUFI-2 algorithm. Procedia

Engineering, 162, 441-447.

Krause, P., Boyle, D.P. and Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89-97.

Loague Sharad, K. and Green, R.E. (2007). Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology, 7(1-2), 51-73.

Mondal, N. C., Singh, V.P. and Ahmed, S. (2012). Entropy-based approach for assessing natural recharge in unconfined aquifers from Southern India. Water Resources Management, 26(9), 2715-2732

Mondal, N.C. and Ahmed, S. (2015). Landscape entropy approach to demarcating pathways for water oozing in a desert area in India. Current Science, 109(1), 148-157.

Mondal, N.C. and Singh, V.P. (2012). Chloride migration in groundwater for a tannery belt in Southern India. Environmental Monitoring and Assessment, 184(5), 2857-2879.

Mondal, N.C., Adike, S., Singh, V.S., Ahmed, S. and Jayakumar, K.V. (2017). Determining shallow aquifer vulnerability by the DRASTIC model and hydrochemistry in granitic terrain, Southern India. Journal of Earth System Science, 126(6), 1-23.

Mondal, N.C., Singh, V.P. and Sankaran, S. (2011). Groundwater flow model for a tannery belt in Southern India. Journal of Water Resource and Protection, 3(2), 85-97.

Prasanchum, H., Sirisook, P. and Lohpaisankrit, W. (2020). Flood Risk Areas Simulation Using Swat and Gumbel Distribution Method in Yang Catchment,

Northeast Thailand. Geographia Technica, pp.29-39.

Rangarajan, R., Mondal, N.C., Singh, V.S. and Singh, S.V. (2009). Estimation of natural recharge and its relation with aquifer parameters in and around Tuticorin town, Tamil Nadu, India. Current Science, 97(2), 217-226.

Sarwade, D.V., Singh, V.S., Puranik, S.C. and Mondal, N.C. (2007). Comparative study of analytical and numerical methods for estimation of aquifers parameters: a case study in basaltic terrain, Journal of The Geological Society of India, 70(6), 1039-1046.

Setti, S., Maheswaran, R., Sridhar, V., Barik, K.K., Merz, B. and Agarwal, A. (2020). Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling.

Atmosphere, 11(11), 1252.

Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M. and Xu, C. (2015). Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. Journal of Hydrology, 523, 739-757.

Srinivas, G. and Gopal, M.N. (2017). Hydrological modeling of Musi River Basin, India and sensitive parameterization of streamflow using SWAT CUP. J. Hydrogeol. Hydrol. Eng, 6, 2-11.

Tyagi, J.V., Rai, S.P., Qazi, N. and Singh, M.P. (2014). Assessment of discharge and sediment transport from different forest cover types in lower Himalaya using Soil and Water Assessment Tool (SWAT). International Journal of Water Resources and Environmental Engineering, 6(1), 49-66.

Vallam, P., Qin, X.S. and Yu, J.J. (2014). Uncertainty quantification of hydrologic model. APCBEE Procedia, 10, 219-223.

Varade, A.M., Khare, Y.D., Mondal, N.C., Muley, S., Wankawar, P. and Raut, P. (2013). Identification of water conservation sites in a watershed (WRJ-2) of Nagpur district, Maharashtra using Geographical Information System (GIS) technique. Journal of the Indian Society of Remote Sensing, 41(3), 619-630.

Venkatarao, A.V., Mondal, N.C. and Ahmed, S. (2019). Investigating

groundwater recharge potential zones using a cross-correlation technique in a part of Deccan Volcanic Province (DVP), Central India. Environmental Earth Sciences, 78(24), 1-12.

Vilaysane, B., Takara, K., Luo, P., Akkharath, I. and Duan, W. (2015). Hydrological stream flow modeling for calibration and uncertainty analysis using SWAT model in the Xedone river basin, Lao PDR. Procedia Environmental Sciences, 28, 380-390.

Wible, T. (2014). Enhanced watershed modeling and data analysis with a fully coupled hydrologic model and cloud based flow analysis (Doctoral dissertation, Colorado State University).

Yang, J., Reichert, P., Abbaspour, K.C., Xia, J. and Yang, H. (2008). Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology, 358(1-2), 1- 23.

Zhang, Z., Lu, W., Chu, H., Cheng, W. and Zhao, Y. (2014). Uncertainty analysis of hydrological model parameters based on the bootstrap method: A case study of the SWAT model applied to the Dongliao River Watershed, Jilin Province, Northeastern China. Science China Technological Sciences, 57(1), .219-229.