2,691
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Impact of local versus global datasets on hydrological responses in Mahanadi River basin in India

ORCID Icon, &
Pages 856-872 | Received 21 Mar 2022, Accepted 03 Feb 2023, Published online: 24 Apr 2023

References

  • Bennett, K.E., et al., 2018. Global sensitivity of simulated water balance indicators under future climate change in the Colorado Basin. Water Resources Research, 54 (1), 132–149. doi:10.1002/2017WR020471
  • Beria, H., et al., 2017. Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale. Hydrology and Earth System Sciences, 21 (12), 6117–6134. doi:10.5194/hess-21-6117-2017
  • Bierkens, M.F.P., et al., 2015. Hyper-resolution global hydrological modelling: what is next?: “Everywhere and locally relevant” M. F. P. Bierkens et al. Invited commentary. Hydrological Processes, 29 (2), 310–320. doi:10.1002/hyp.10391
  • Bosmans, J.H.C., et al., 2017. Hydrological impacts of global land cover change and human water use. Hydrology and Earth System Sciences, 21 (11), 5603–5626. doi:10.5194/hess-21-5603-2017
  • Chen, Z., et al., 2020. Global land monsoon precipitation changes in CMIP6 projections. Geophysical Research Letters, 47 (14). doi:10.1029/2019GL086902
  • Cherkauer, K.A. and Lettenmaier, D.P., 1999. Hydrologic effects of frozen soils in the upper Mississippi River basin. Journal of Geophysical Research: Atmospheres, 104 (D16), 19599–19610. doi:10.1029/1999JD900337
  • Clark, M.P., et al., 2015. Hydrological partitioning in the critical zone: recent advances and opportunities for developing transferable understanding of water cycle dynamics. Water Resources Research, 1–28. doi:10.1002/2015WR017096.
  • Collischonn, W., et al., 2007. The MGB-IPH model for large-scale rainfall-runoff modelling. Hydrological Sciences Journal, 52 (5), 878–895. doi:10.1623/hysj.52.5.878
  • Connor, R., 2016. The United Nations world water development report 2015: water for a sustainable world. UNESCO publishing.
  • Cosby, B.J., et al., 1984. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research, 20 (6), 682–690. doi:10.1029/WR020i006p00682
  • Dai, Y., et al., 2019. A review of the global soil property maps for Earth system models. Soil, 5 (2), 137–158. doi:10.5194/soil-5-137-2019
  • Demaria, E.M., Nijssen, B., and Wagener, T., 2007. Monte Carlo sensitivity analysis of land surface parameters using the variable infiltration capacity model. Journal of Geophysical Research, 112 (11), 1–15. doi:10.1029/2006JD007534
  • Dembélé, M., et al., 2020. Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa. Hydrology and Earth System Sciences, 1–39. doi:10.5194/hess-2020-68
  • Dhanya, C.T. and Villarini, G., 2017. An investigation of predictability dynamics of temperature and precipitation in reanalysis datasets over the continental United States. Atmospheric Research, 183, 341–350. doi:10.1016/j.atmosres.2016.09.017
  • Essou, G.R.C., et al., 2016. Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling? Journal of Hydrometeorology, 17 (7), 1929–1950. doi:10.1175/JHM-D-15-0138.1
  • Faramarzi, M., et al., 2015. Setting up a hydrological model of Alberta: data discrimination analyses prior to calibration. Environmental Modelling & Software, 74, 48–65. doi:10.1016/j.envsoft.2015.09.006
  • Fisher, R.A. and Koven, C.D., 2020. Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. Journal of Advances in Modeling Earth Systems, 12 (4). doi:10.1029/2018MS001453
  • Gao, H., et al., 2010. Water budget record from Variable Infiltration Capacity (VIC) model. Algorithm Theor. Basis Doc. Terr. Water Cycle Data Rec., (Vic), 120–173 [online]. Available from: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Water+Budget+Record+from+Variable+Infiltration+Capacity+(+VIC+)+Model#2.
  • Gao, Z., et al., 2020. Comprehensive comparisons of state-of-the-art gridded precipitation estimates for hydrological applications over southern China. Remote Sensing, 12 (23), 1–20. doi:10.3390/rs12233997
  • Ghodichore, N., et al., 2018. Reliability of reanalyses products in simulating precipitation and temperature characteristics over India. Journal of Earth System Science, 127 (8), 1–21. doi:10.1007/s12040-018-1024-2
  • Gilewski, P. and Nawalany, M., 2018. Inter-comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) precipitation estimates performance for rainfall-runoff modeling in a mountainous catchment in Poland. Water (Switzerland), 10 (11), 1–23. doi:10.3390/w10111665
  • Gou, J., et al., 2020. Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China. Water Resources Research, 56 (1), 1–19. doi:10.1029/2019WR025968
  • Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology, 377 (1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003
  • Hengl, T., et al., 2017. SoilGrids250m: global gridded soil information based on machine learning. PLoS One, 12 (2), e0169748.
  • Her, Y., et al., 2019. Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions. Scientific Reports, 9 (1), 1–22. doi:10.1038/s41598-019-41334-7
  • Huffman, G.J., et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8 (1), 38–55. doi:10.1175/JHM560.1
  • Huffman, G.J. et al., 2018. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4.
  • Huffman, G.J., Bolvin, D.T., and Nelkin, E.J., January 2015. Day 1 IMERG final run release notes. 1–9 [online]. Available from: https://pmm.nasa.gov/sites/default/files/document_files/IMERG_FinalRun_Day1_release_notes.pdf.
  • Jiang, L. and Yu, L., July 2018. Analyzing land use intensity changes within and outside protected areas using ESA CCI-LC datasets. Global Ecology and Conservation, 20. doi:10.1016/j.gecco.2019.e00789
  • Jin, L., et al., 2018. Simulating climate change and socio-economic change impacts on flows and water quality in the Mahanadi River system, India. Science of the Total Environment, 637–638, 907–917. doi:10.1016/j.scitotenv.2018.04.349
  • Joseph, J., et al., 2018. Hydrologic impacts of climate change: comparisons between hydrological parameter uncertainty and climate model uncertainty. Journal of Hydrology, 566 (September), 1–22. doi:10.1016/j.jhydrol.2018.08.080
  • Kneis, D., Chatterjee, C., and Singh, R., 2014. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrology and Earth System Sciences, 18 (7), 2493–2502. doi:10.5194/hess-18-2493-2014
  • Knoben, W.J.M., Freer, J.E., and Woods, R.A., 2019. Technical note: inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences, 23 (10), 4323–4331. doi:10.5194/hess-23-4323-2019
  • Krpec, P., Horáček, M., and Šarapatka, B., February 2020. A comparison of the use of local legacy soil data and global datasets for hydrological modelling a small-scale watersheds: implications for nitrate loading estimation. Geoderma, 377, 114575. doi:10.1016/j.geoderma.2020.114575
  • Liang, X., et al., 1994. A simple hydrologically based model of land surface water and energy fluxes for GSMs. Journal of Geophysical Research, 99 (D7), 14415–14428. doi:10.1029/94JD00483
  • Liu, Y., et al., 2011. Impacts of land-use and climate changes on hydrologic processes in the Qingyi River Watershed, China. Journal of Hydrologic Engineering, 18 (11), 1495–1512. doi:10.1061/(asce)he.1943-5584.0000485
  • Lohmann, D.A.G., Nolte-Holube, R., and Raschke, E., 1996. A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus A, 48 (5), 708–721. doi:10.3402/tellusa.v48i5.12200
  • Mahto, S.S. and Mishra, V., 2019. Does ERA-5: outperform other reanalysis products for hydrologic applications in India? Journal of Geophysical Research: Atmospheres, 124 (16), 9423–9441. doi:10.1029/2019JD031155
  • Mishra, V., et al., 2020. Does comprehensive evaluation of hydrological models influence projected changes of mean and high flows in the Godavari River basin? Climatic Change, 163 (3), 1187–1205. doi:10.1007/s10584-020-02847-7
  • Mishra, N., Aggarwal, S.P., and Dadhwal, V.K., 2008. Macroscale hydrological modelling and impact of land cover change on stream flows of the Mahanadi River basin. A Master thesis submitted to Andhra University, Indian Institute of Remote Sensing (National Remote Sensing Agency) Dept. of Space, Govt. of India.
  • Mujumdar, P.P., 2015. Share data on water resources. Nature, 521 (7551), 151–152.
  • Muñoz-Sabater, J., et al., 2021. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13 (9), 4349–4383.
  • Naha, S., Rico-Ramirez, M.A., and Rosolem, R., 2021. Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India. Hydrology and Earth System Sciences, 25 (12), 6339–6357. doi:10.5194/hess-25-6339-2021
  • Pai, D.S., 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), 1–18. doi:10.54302/mausam.v65i1.851
  • Prakash, S., et al., 2018. A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. Journal of Hydrology, 556, 865–876. February. doi:10.1016/j.jhydrol.2016.01.029.
  • Rawls, W.J., Gimenez, D., and Grossman, R., 1998. Use of soil texture, bulk density, and slope of the water retention curve to predict saturated hydraulic conductivity. Transactions of the ASAE, 41 (4), 983. doi:10.13031/2013.17270
  • Reynolds, C.A., Jackson, T.J., and Rawls, W.J., 2000. Estimating soil water-holding capacities by linking the food and agriculture organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resources Research, 36 (12), 3653–3662. doi:10.1029/2000WR900130
  • Rodell, M., et al., 2005. Evaluation of 10 methods for initializing a land surface model. Journal of Hydrometeorology, 6 (2), 146–155. doi:10.1175/JHM414.1
  • Rodriguez, D.A. and Tomasella, J., 2016. On the ability of large-scale hydrological models to simulate land use and land cover change impacts in Amazonian basins. Hydrological Sciences Journal, 61 (10), 1831–1846. doi:10.1080/02626667.2015.1051979
  • Rodríguez, E., et al., 2020. Combined use of local and global hydro meteorological data with hydrological models for water resources management in the Magdalena - Cauca Macro Basin – Colombia. Water Resources Management, 34 (7), 2179–2199. doi:10.1007/s11269-019-02236-5
  • Shah, R. and Mishra, V., 2014a. Evaluation of the reanalysis products for the Monsoon season droughts in India. Journal of Hydrometeorology, 15 (4), 1575–1591. doi:10.1175/jhm-d-13-0103.1
  • Shah, R. and Mishra, V., 2014b. Evaluation of the reanalysis products for the Monsoon season Droughts in India. Journal of Hydrometeorology, 15 (4), 1575–1591. doi:10.1175/jhm-d-13-0103.1
  • Sharifi, E., Steinacker, R., and Saghafian, B., 2016. Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: preliminary results. Remote Sensing, 8 (2). doi:10.3390/rs8020135
  • Sivasena Reddy, A. and Janga Reddy, M., 2015. Evaluating the influence of spatial resolutions of DEM on watershed runoff and sediment yield using SWAT. Journal of Earth System Science, 124 (7), 1517–1529. doi:10.1007/s12040-015-0617-2
  • Sterling, S.M., Ducharne, A., and Polcher, J., 2013. The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3 (4), 385–390. doi:10.1038/nclimate1690
  • Strömqvist, J., et al., September 2009. Using recently developed global data sets for hydrological predictions. IAHS-AISH Publication, 333, 121–127.
  • Sungmin, O., et al., 2017. Evaluation of GPM IMERG early, late, and final rainfall estimates using WegenerNet gauge data in southeastern Austria. Hydrology and Earth System Sciences, 21 (12), 6559–6572. doi:10.5194/hess-21-6559-2017
  • Tang, G., et al., 2020. Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sensing of Environment, 240, 111697. September. doi:10.1016/j.rse.2020.111697.
  • Tarawneh, E., Bridge, J., and Macdonald, N., 2016. A pre-calibration approach to select optimum inputs for hydrological models in data-scarce regions. Hydrology and Earth System Sciences, 20 (10), 4391–4407. doi:10.5194/hess-20-4391-2016
  • Van Den Hurk, B., et al., 2011. Acceleration of land surface model development over a decade of glass. Bulletin of the American Meteorological Society, 92 (12), 1593–1600. doi:10.1175/BAMS-D-11-00007.1
  • Voisin, N., Wood, A.W., and Lettenmaier, D.P., 2008. Evaluation of precipitation products for global hydrological prediction. Journal of Hydrometeorology, 9 (3), 388–407. doi:10.1175/2007JHM938.1
  • Wood, E.F., et al., 2011. Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resources Research, 47 (5), doi:10.1029/2010WR010090
  • Yanto, L.B., Kasprzyk, J., and Kasprzyk, J., 2017. Hydrological model application under data scarcity for multiple watersheds, Java Island, Indonesia. Journal of Hydrology: Regional Studies, 9, 127–139. doi:10.1016/j.ejrh.2016.09.007
  • Yeste, P., et al., 2020. Integrated sensitivity analysis of a macroscale hydrologic model in the North of the Iberian Peninsula. Journal of Hydrology, 590 (September 2019), 125230. doi:10.1016/j.jhydrol.2020.125230
  • Zeng, X., 2002. Global vegetation root distribution for land modeling. Journal of Hydrometeorology, 2 (5), 525–530. doi:10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2
  • Zhao, R.J., et al., The Xinanjiang model hydrological forecasting proceedings Oxford symposium, IASH, 1980.
  • Zubieta, R., et al., 2016. Hydrological modeling of the Peruvian-Ecuadorian Amazon basin using GPM-IMERG satellite-based precipitation dataset. Hydrology and Earth System Sciences, 1–21. doi:10.5194/hess-2016-656