Articles | Volume 27, issue 19
https://doi.org/10.5194/hess-27-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2023
Research article |
| 06 Oct 2023
| 06 Oct 2023
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
Dung Trung Vu
CORRESPONDING AUTHOR
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore, Singapore
Thanh Duc Dang
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL, USA
Francesca Pianosi
School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, UK
Stefano Galelli
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore, Singapore
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
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Cited articles
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission
and its capabilities for land hydrology, Springer International Publishing, 117–147, https://doi.org/10.1007/978-3-319-32449-4_6, 2016. a
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions,
Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015wr017173, 2015. a, b
Birkinshaw, S. J., O'Donnell, G. M., Moore, P., Kilsby, C. G., Fowler, H. J.,
and Berry, P. A. M.: Using satellite altimetry data to augment flow
estimation techniques on the Mekong River, Hydrol. Process., 24,
3811–3825, https://doi.org/10.1002/hyp.7811, 2010. a
Biswas, N. K., Hossain, F., Bonnema, M., Lee, H., and Chishtie, F.: Towards a
global Reservoir Assessment Tool for predicting hydrologic impacts and
operating patterns of existing and planned reservoirs, Environ. Model. Softw., 140, 105043, https://doi.org/10.1016/j.envsoft.2021.105043, 2021. a
Bonnema, M. and Hossain, F.: Inferring reservoir operating patterns across the Mekong Basin using only space observations, Water Resour. Res., 53,
3791–3810, https://doi.org/10.1002/2016wr019978, 2017. a, b
Bose, I., Jayasinghe, S., Meechaiya, C., Ahmad, S. K., Biswas, N., and Hossain, F.: Developing a baseline characterization of river bathymetry and
time-Varying height for Chindwin River in Myanmar using SRTM and
Landsat data, J. Hydrol. Eng., 26, 05021030,
https://doi.org/10.1061/(asce)he.1943-5584.0002126, 2021. a
Chow, V. T.: Open-channel hydraulic, McGraw-Hill Book Company, New York, ISBN 007085906X, ISBN 9780070859067, 1959. a
Chowdhury, A. K., Dang, T. D., Nguyen, H. T. T., Koh, R., and Galelli, S.: The Greater Mekong's climate-water-energy nexus: How ENSO-triggered
regional droughts affect power supply and CO2 emissions, Earth's Future,
9, e2020ef001814, https://doi.org/10.1029/2020ef001814, 2021. a
Chuphal, D. S. and Mishra, V.: Increased hydropower but with an elevated risk
of reservoir operations in India under the warming climate, iScience, 26, 105986, https://doi.org/10.1016/j.isci.2023.105986, 2023. a
Costa-Cabral, M. C., Richey, J. E., Goteti, G., Lettenmaier, D. P.,
Feldkötter, C., and Snidvongs, A.: Landscape structure and use, climate, and water movement in the Mekong River Basin, Hydrol. Process., 22,
1731–1746, https://doi.org/10.1002/hyp.6740, 2007. a
Critical-Infrastructure-Systems-Lab: VICRes, GitHub [code], https://github.com/Critical-Infrastructure-Systems-Lab/VICRes (last access: 29 September 2023), 2023. a
Dahiti: Database for Hydrological Time Series of Inland Waters (DAHITI), https://dahiti.dgfi.tum.de/ (last access: 29 September 2023), 2023. a
Dan, L., Ji, J., Xie, Z., Chen, F., Wen, G., and Richey, J. E.: Hydrological
projections of climate change scenarios over the 3H region of China: A
VIC model assessment, J. Geogr. Res., 117, D11102,
https://doi.org/10.1029/2011jd017131, 2012. a
Dang, T. D., Chowdhury, A. K., and Galelli, S.: On the representation of water reservoir storage and operations in large-scale hydrological models:
implications on model parameterization and climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, 2020a. a, b, c, d, e
Dang, T. D., Vu, D. T., Chowdhury, A. K., and Galelli, S.: A software package
for the representation and optimization of water reservoir operations in the
VIC hydrologic model, Environ. Model. Softw., 126, 104673,
https://doi.org/10.1016/j.envsoft.2020.104673, 2020b. a, b
Dawson, C. W., Abrahart, R. J., and See, L. M.: HydroTest: Further
development of a web resource for the standardised assessment of hydrological
models, Environ. Model. Softw., 25, 1481–1482, https://doi.org/10.1016/j.envsoft.2009.01.001, 2010. a
Döll, P., Berkhoff, K., Bormann, H., Fohrer, N., Gerten, D., Hagemann, S., and Krol, M.: Advances and visions in large-scale hydrological modelling:
findings from the 11th Workshop on large-scale hydrological modelling,
Adv. Geosci., 18, 51–56, https://doi.org/10.5194/adgeo-18-51-2008, 2008. a
dtvu2205: 210520, GitHub [code and data st], https://github.com/dtvu2205/210520 (last access: 29 September 2023), 2023. a
Du, T. L. T., Lee, H., Bui, D. D., Arheimer, B., Li, H.-Y., Olsson, J., Darby, S. E., Sheffield, J., Kim, D., and Hwang, E.: Streamflow prediction in
“geopolitically ungauged” basins using satellite observations and
regionalization at subcontinental scale, J. Hydrol., 588, 125016,
https://doi.org/10.1016/j.jhydrol.2020.125016, 2020. a
Durand, M., Gleason, C. J., Garambois, P. A., Bjerklie, D., Smith, L. C., Roux, H., Rodriguez, E., Bates, P. D., Pavelsky, T. M., Monnier, J., Chen, X., Baldassarre, G. D., Fiset, J.-M., Flipo, N., d. M. Frasson, R. P., Fulton, J., Goutal, N., Hossain, F., Humphries, E., Minear, J. T., Mukolwe, M. M., Neal, J. C., Ricci, S., Sanders, B. F., Schumann, G., Schubert, J. E., and Vilmin, L.: An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope, Water Resour. Res., 52, 4527–4549, https://doi.org/10.1002/2015wr018434, 2016. a
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective
calibration approaches in hydrological modelling: A review, Hydrolog. Sci. J., 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010. a
Egüen, M., Aguilar, C., Herrero, J., Millares, A., and Polo, M. J.: On the influence of cell size in physically-based distributed hydrological modelling to assess extreme values in water resource planning, Nat. Hazards Earth Syst. Sci., 12, 1573–1582, https://doi.org/10.5194/nhess-12-1573-2012, 2012. a
Engineering ToolBox: Manning's roughness coefficients,
https://www.engineeringtoolbox.com/mannings-roughness-d_799.html (last access: 22 December 2022), 2004. a
Franchini, M. and Pacciani, M.: Comparative analysis of several conceptual
rainfall-runoff models, J. Hydrol., 122, 161–219, https://doi.org/10.1016/0022-1694(91)90178-k, 1991. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Rowland, G. H. J., Harrison, L., and Michaelsen, A. H. J.: The climate
hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015. a
Galelli, S., Dang, T. D., Ng, J. Y., Chowdhury, A. K., and Arias, M. E.:
Opportunities to curb hydrological alterations via dam re-operation in the
Mekong, Nat. Sustainabil., 5, 1058–1069, https://doi.org/10.1038/s41893-022-00971-z, 2022. a
Gao, H., Birkett, C., and Lettenmaier, D. P.: Global monitoring of large
reservoir storage from satellite remote sensing, Water Resour. Res., 48, w09504, https://doi.org/10.1029/2012wr012063, 2012. a
Gleason, C. J. and Smith, L. C.: Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry, P. Natl. Acad. Sci. USA, 111, 4788–4791, https://doi.org/10.1073/pnas.1317606111, 2014. a
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F.,
Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Macedo, H. E.,
Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M. E.,
Meng, J., Mulligan, M., Nilsson, C., Olden, J. D., Opperman, J. J., Petry,
P., Liermann, C. R., Sáenz, L., Salinas-Rodríguez, S., Schelle, P., Schmitt, R. J. P., Snider, J., Tan, F., Tockner, K., Valdujo, P. H., van Soesbergen, A., and Zarfl, C.: Mapping the world's free-flowing rivers, Nature, 59, 215–221, https://doi.org/10.1038/s41586-019-1111-9, 2019. a
Haddeland, I., Skaugen, T., and Lettenmaier, D. P.: Anthropogenic impacts on
continental surface water fluxes, Geophys. Res. Lett., 33, l08406,
https://doi.org/10.1029/2006gl026047, 2006. a
Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Florke, M., Hanasaki, N.,
Konzmann, M., and Ludwig, F.: Global water resources affected by human
interventions and climate change, P. Natl. Acad. Sci. USA, 111, 3251–3256,
https://doi.org/10.1073/pnas.1222475110, 2014. a
Hagemann, M. W., Gleason, C. J., and Durand, M. T.: BAM: Bayesian
AMHG-Manning inference of discharge using remotely sensed stream width,
slope, and height, Water Resour. Res., 53, 9692–9707, https://doi.org/10.1002/2017wr021626, 2017. a
Hecht, J. S., Lacombe, G., Arias, M. E., Dang, T. D., and Pimanh, T.:
Hydropower dams of the Mekong River Basin: A review of their
hydrological impactss, J. Hydrol., 568, 285–300, https://doi.org/10.1016/j.jhydrol.2018.10.045, 2019. a
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan,
M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U.,
Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013. a
Huang, Q., Long, D., Du, M., Han, Z., and Han, P.: Daily continuous river
discharge estimation for ungauged basins using a hydrologic model calibrated
by satellite altimetry: Implications for the SWOT mission, Water Resour. Res., 56, e2020wr027309, https://doi.org/10.1029/2020wr027309, 2020. a
International Rivers: The environmental and social impacts of Lancang dams,
https://archive.internationalrivers.org/sites/default/files/attached-files/ir_lancang_dams_researchbrief_final.pdf
(last access: 22 April 2023), 2014. a
Johnson, K.: China commits to share year-round water data with Mekong River Commission, Reuters,
https://www.reuters.com/article/us-mekong-river/china-commits-to-share-year-round-water-data-with-mekong
(last access: 22 December 2022), 2020. a
JPL: SWOT: Surface Water and Ocean Topography,
https://swot.jpl.nasa.gov/ (last access: 22 December 2022), 2022. a
Kabir, T., Pokhrel, Y., and Felfelani, F.: On the precipitation-induced
uncertainties in process-based hydrological modeling in the Mekong River
Basin, Water Resour. Res., 58, e2021wr030828, https://doi.org/10.1029/2021wr030828, 2022. a, b, c
Khan, S. I., Hong, Y., Vergara, H. J., Gourley, J. J., Brakenridge, G. R.,
Groeve, T. D., Flamig, Z. L., Policelli, F., and Yong, B.: Microwave satellite data for hydrologic modeling in ungauged basins, IEEE Geosci. Remote Sens. Lett., 9, 663–667, https://doi.org/10.1109/lgrs.2011.2177807, 2012. a
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94jd00483, 2014. a
Lima, F. N., Fernandes, W., and Nascimento, N.: Joint calibration of a
hydrological model and rating curve parameters for simulation of flash flood
in urban areas, Brazil. J. Water Resour., 24, https://doi.org/10.1590/2318-0331.241920180066, 2019. a, b
Liu, G., Schwartz, F. W., Tseng, K.-H., and Shum, C. K.: Discharge and
water-depth estimates for ungauged rivers: Combining hydrologic, hydraulic,
and inverse modeling with stage and water-area measurements from satellites,
Water Resour. Res., 51, 6017–6035, https://doi.org/10.1002/2015wr016971, 2015. a
Lohmann, D., Nolte-Holube, R., and Raschke, E.: A large-scale horizontal
routing model to be coupled to land surface parametrization schemes, Tellus A, 48, 708–721, https://doi.org/10.3402/tellusa.v48i5.12200, 1996. a
Lohmann, D., Raschke, E., Nijssen, B., and Lettenmaier, D. P.: Regional scale
hydrology: I. Formulation of the VIC-2L model coupled to a routing
model, Hydrolog. Sci. J., 43, 131–141, https://doi.org/10.1080/02626669809492107, 1998. a
MRC: The flow of the Mekong, Mekong River Commission Secretariat, Vientiane, https://www.mrcmekong.org/assets/Publications/report-management-develop/MRC-IM-No2-the-flow-of-the-mekong.pdf (last access: 22 December 2022), 2009. a
MRC – Mekong River Commission: Discharge Time-series, https://portal.mrcmekong.org/time-series/discharge/ (last access: 22 December 2022), 2022. a
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in
Earth system models – Part 1: Problem definition and representation of
water demand, Hydrol. Earth Syst. Sci., 19, 33–61, https://doi.org/10.5194/hess-19-33-2015, 2015a. a
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in
Earth system models – Part 2: Representation of water supply and allocation and opportunities for improved modeling, Hydrol. Earth Syst. Sci., 19, 63–90, https://doi.org/10.5194/hess-19-63-2015, 2015b. a
Papa, F., Bala, S. K., Pandey, R. K., Durand, F., Gopalakrishna, V. V., Rahman, A., and Rossow, W. B.: Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal, J. Geophys. Res., 117, c11021, https://doi.org/10.1029/2012jc008158, 2012. a
Park, D. and Markus, M.: Analysis of a changing hydrologic flood regime using
the Variable Infiltration Capacity model, J. Hydrol., 515, 627–280, https://doi.org/10.1016/j.jhydrol.2014.05.004, 2014. a
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Model. Softw., 79,
214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a
Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B.:
Evolutionary multiobjective optimization in water resources: The past,
present, and future, Adv. Water Resour., 51, 438–456, https://doi.org/10.1016/j.advwatres.2012.01.005, 2013. a, b
Ren-Jun, Z.: The Xinanjiang model applied in China, J. Hydrol., 135, 371–381, https://doi.org/10.1016/0022-1694(92)90096-e, 1992. a
Shin, S., Pokhrel, Y., Yamazaki, D., Huang, X., Torbick, N., Qi, J.,
Pattanakiat, S., Ngo-Duc, T., and Nguyen, T. D.: High resolution modeling of
river-floodplain-reservoir inundation dynamics in the Mekong River Basin, Water Resour. Res., 56, e2019wr026449, https://doi.org/10.1029/2019wr026449, 2020. a
Steyaert, J. C., Condon, L. E., Turner, S. W. D., and Voisin, N.: ResOpsUS, a dataset of historical reservoir operations in the contiguous United
States, Sci. Data, 9, 1–8, https://doi.org/10.1038/s41597-022-01134-7, 2022. a
Sun, W., Fan, J., Wang, G., Ishidaira, H., Bastola, S., Yu, J., Fu, Y. H.,
Kiem, A. S., Zuo, D., and Xu, Z.: Calibrating a hydrological model in a
regional river of the Qinghai–Tibet plateau using river water width
determined from high spatial resolution satellite images, Remote Sens. Environ., 214, 100–114, https://doi.org/10.1016/j.rse.2018.05.020, 2018. a
Tarpanelli, A., Paris, A., Sichangi, A. W., OLoughlin, F., and Papa, F.: Water resources in Africa: The role of earth observation data and hydrodynamic modeling to derive river discharge, Surv. Geophys., 44, 97–122,
https://doi.org/10.1007/s10712-022-09744-x, 2022. a
Tatsumi, K. and Yamashiki, Y.: Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters, Agr. Water Manage., 159, 92–106, https://doi.org/10.1016/j.agwat.2015.05.011, 2015. a
Todini, E.: The ARNO rainfall–runoff model, J. Hydrol., 175, 339–382, https://doi.org/10.1016/S0022-1694(96)80016-3, 1996. a
USGS – United States Geological Survey: Space Shuttle Radar Topography Mission (SRTM) DEM, https://earthexplorer.usgs.gov/ (last access: 22 December 2022), 2022. a
van Vliet, M. T. H., Wiberg, D., Leduc, S., and Riahi, K.: Power-generation
system vulnerability and adaptation to changes in climate and water resources, Nat. Clim. Change, 6, 375–380, https://doi.org/10.1038/nclimate2903, 2016. a
Vegad, U. and Mishra, V.: Ensemble streamflow prediction considering the
influence of reservoirs in India, Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, 2022. a
Vu, D. T.: Codes and data of satellite observations reveal 13 years of
reservoir filling strategies, operating rules, and hydrological alterations
in the Upper Mekong River Basin, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6299041, 2022. a
Vu, D. T., Dang, T. D., Galelli, S., and Hossain, F.: Satellite observations
reveal 13 years of reservoir filling strategies, operating rules, and
hydrological alterations in the Upper Mekong River basin, Hydrol. Earth Syst. Sci., 26, 2345–2364, https://doi.org/10.5194/hess-26-2345-2022, 2022. a, b, c, d, e, f
Wagener, T. and Pianosi, F.: What has Global Sensitivity Analysis ever
done for us? A systematic review to support scientific advancement and to
inform policy-making in earth system modelling, Earth-Sci. Rev., 194, 1–18, https://doi.org/10.1016/j.earscirev.2019.04.006, 2019. a
Wi, S., Ray, P., M.C.Demaria, E., Steinschneider, S., and Brown, C.: A
user-friendly software package for VIC hydrologic model development,
Environ. Model. Softw., 98, 35–53, https://doi.org/10.1016/j.envsoft.2017.09.006, 2017. a
WLE Mekong: Greater Mekong dam observatory,
https://wle-mekong.cgiar.org/changes/our-research/greater-mekong-dams-observatory/
(last access: 22 December 2022), 2022. a
Xiong, J., Guo, S., and Yin, J.: Discharge estimation using integrated
satellite data and hybrid model in the Midstream Yangtze River, Remote
Sens., 13, 2272, https://doi.org/10.3390/rs13122272, 2021. a
Xue, X., Zhang, K., Hong, Y., and Gourley, J. J.: New multisite cascading
calibration approach for hydrological models: Case study in the Red
River Basin using the VIC model, J. Hydrol. Eng., 21, 05015019, https://doi.org/10.5194/hess-23-3735-2019, 2015.
a
Yeste, P., Ojeda, M. G.-V., Gámiz-Fortis, S. R., Castro-Díez, Y., and
Esteban-Parra, M. J.: Integrated sensitivity analysis of a macroscale
hydrologic model in the north of the Iberian Peninsula, J. Hydrol., 590, 125230, https://doi.org/10.1016/j.jhydrol.2020.125230, 2020. a, b
Yun, X., Tang, Q., Wang, J., Liu, X., Zhang, Y., Lu, H., Wang, Y., Zhang, L.,
and Chen, D.: Impacts of climate change and reservoir operation on streamflow
and flood characteristics in the Lancang-Mekong River Basin, J. Hydrol., 590, 125472, https://doi.org/10.1016/j.jhydrol.2020.125472, 2020. a
Zhai, K., Wu, X., Qin, Y., and Du, P.: Comparison of surface water extraction
performances of different classic water indices using OLI and TM
imageries in different situations, Geospat. Inform. Sci., 18, 34–42, https://doi.org/10.1080/10095020.2015.1017911, 2015. a
Zhang, S., Gao, H., and Naz, B. S.: Monitoring reservoir storage in South
Asia from multisatellite remote sensing, Water Resour. Res., 50, 8927–8943, https://doi.org/10.1002/2014wr015829, 2014. a
Short summary
The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
The calibration of hydrological models over extensive spatial domains is often challenged by the...
