Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 4
Abstract
The conventional hydrological modeling framework with a traditional streamflow-alone calibration approach is often challenged with the difficulty of accurately simulating the extreme flow conditions. This study explores the possibility of improving the simulation of extreme hydrological events by incorporating the antecedent soil saturation conditions using the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA). In this study, a stepwise calibration approach is adopted on the hydrological predictions for the environment (HYPE) model, based on the GRACE-derived TWSA and streamflow observations, and is compared with the conventional calibration approach based only on streamflow observations. The performance of the two modeling frameworks is demonstrated over a tropical basin in Indian subcontinent, Mahanadi Basin, for the study period of 2003–2016. Results showed that the inclusion of GRACE data in addition to the streamflow observations significantly reduced the uncertainty of sensitive parameters and improved the model realizations. The model calibrated using GRACE-derived TWSA helped to improve the model physics and provided more reliable estimates of the terrestrial water storage. Although both calibration approaches are found to be equally good in simulating the mean streamflow conditions, the GRACE-based approach successfully simulates different aspects of the extreme flow conditions. An additional calibration case of remotely sensed top-layer soil moisture instead of the GRACE-derived TWSA illustrate that better model parameterization requires complete information of both surface water and groundwater storage in the modeling framework. Overall, the study highlights the potential of GRACE-derived TWSA estimates in improving the model physics, which further helps in the modeling of extreme flows over basins subjected to frequent extreme events.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge NASA’s MEaSUREs Program (https://grace.jpl.nasa.gov) for providing gridded GRACE Tellus data. The India Meteorological Department (IMD) and Central Water Commission (CWC) of India are duly acknowledged for furnishing hydrometeorological data over Mahanadi Basin. The authors would like to thank United States Geological Survey (USGS) and the Food and Agriculture Organization (FAO) of the United Nations for providing the spatial data over the study area. The authors sincerely thank the editor and anonymous reviewers for reviewing the manuscript and providing insightful comments. The authors also express sincere thanks to Indian Institute of Technology Delhi for supporting this work.
References
Abramowitz, G., R. Leuning, M. Clark, and A. Pitman. 2008. “Evaluating the performance of land surface models.” J. Clim. 21 (21): 5468–5481. https://doi.org/10.1175/2008JCLI2378.1.
Arheimer, B., J. Dahné, C. Donnelly, G. Lindström, and J. Strömqvist. 2012. “Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea Basin—Influence of input-data quality and scale.” Hydrol. Res. 43 (4): 315–329. https://doi.org/10.2166/nh.2012.010.
Arheimer, B., J. Dahné, G. Lindström, and L. Marklund. 2011. “Multi-variable evaluation of an integrated model system covering Sweden (S-HYPE).” IAHS-AISH Publ. 345: 145–150.
Bai, P., X. Liu, and C. Liu. 2018. “Improving hydrological simulations by incorporating GRACE data for model calibration.” J. Hydrol. 557 (Feb): 291–304. https://doi.org/10.1016/j.jhydrol.2017.12.025.
Baroni, G. 2019. “A comprehensive distributed hydrological modelling intercomparison to support process representation and data collection strategies.” Water Resour. Res. 55 (2): 990–1010. https://doi.org/10.1029/2018WR023941.
Beven, K. 2001. “How far can we go in distributed hydrological modelling?” Hydrol. Earth Syst. Sci. 5 (1): 1–12. https://doi.org/10.5194/hess-5-1-2001.
Beven, K., and A. Binley. 1992. “The future of distributed models: Model calibration and uncertainty prediction.” Hydrol. Process. 6 (3): 279–298. https://doi.org/10.1002/hyp.3360060305.
Beven, K. J. 2012. “Rainfall-runoff modelling.” In Vol. 15 The primer. 2nd ed. 1–457. Chichester, UK: Wiley-Blackwell. https://doi.org/10.1002/9781119951001.
Blöschl, G., T. Nester, J. Komma, J. Parajka, and R. A. P. Perdigão. 2013. “The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954 and 1899 floods.” Hydrol. Earth Syst. Sci. 17 (12): 5197–5212. https://doi.org/10.5194/hess-17-5197-2013.
Braud, I., P. Ayral, C. Bouvier, F. Branger, G. Delrieu, Le C, J. Oz, G. Nord, and J. Vandervaere. 2014. “Multi-scale hydrometeorological observation and modelling for flash flood understanding.” Hydrol. Earth Syst. Sci. 18 (9): 3733–3761. https://doi.org/10.5194/hess-18-3733-2014.
Brown, J. F., T. R. Loveland, D. O. Ohlen, and Z. Zhu. 1999. “The global land-cover characteristics database: The users’ perspective.” Photogramm. Eng. Remote Sens. 65 (9): 1069–1074.
CWC (Central Water Commission). 1989. Major river basins of India—An overview, 33–36. New Delhi: Minister of Water Resources, Government of India.
Chakravorty, A., B. R. Chahar, O. P. Sharma, and C. T. Dhanya. 2016. “A regional scale performance evaluation of SMOS and ESA-CCI soil moisture products over India with simulated soil moisture from MERRA-land.” Remote Sens. Environ. 186 (Dec): 514–527. https://doi.org/10.1016/j.rse.2016.09.011.
Chen, X., D. Long, Y. Hong, C. Zeng, and D. Yan. 2017. “Improved modelling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River Basin?” Water Resour. Res. 53 (3): 2431–2466. https://doi.org/10.1002/2016WR019656.
Chen, Z., A. Hartmann, T. Wagener, and N. Goldscheider. 2018. “Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions.” Hydrol. Earth Syst. Sci. 22 (7): 3807–3823. https://doi.org/10.5194/hess-22-3807-2018.
Chow, V. T. 1978. “Stochastic modelling of watershed systems.” Adv. Hydrosci. 11: 1–93. https://doi.org/10.1016/B978-0-12-021811-0.50006-1.
Clark, M. P., et al. 2015. “Improving the representation of hydrologic processes in earth system models.” Water Resour. Res. 51 (8): 5929–5956. https://doi.org/10.1002/2015WR017096.
Coopersmith, E. J., M. H. Cosh, W. A. Petersen, J. Prueger, and J. J. Niemeier. 2015. “Soil moisture model calibration and validation: An ARS watershed on the South Fork Iowa River.” J. Hydrometeorol. 16 (3): 1087–1101. https://doi.org/10.1175/JHM-D-14-0145.1.
Coulibaly, P., F. Anctil, and B. Bobée. 2001. “Multivariate reservoir inflow forecasting using temporal neural networks.” J. Hydrol. Eng. 6 (5): 367–376. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:5(367).
Cunnane, C. 1973. “A particular comparison of annual maxima and partial duration series methods of flood frequency prediction.” J. Hydrol. 18 (3–4): 257–271. https://doi.org/10.1016/0022-1694(73)90051-6.
Das, S. K., R. K. Gupta, and H. K. Varma. 2007. “Flood and drought management through water resources development in India.” WMO Bull. 56 (3): 179–188.
Donnelly, C., K. Ernst, and B. Arheimer. 2018. “A comparison of hydrological climate services at different scales by users and scientists.” Clim. Serv. 11 (Aug): 24–35. https://doi.org/10.1016/j.cliser.2018.06.002.
Ebel, B. A., F. K. Rengers, and G. E. Tucker. 2016. “Observed and simulated hydrologic response for a first-order catchment during extreme rainfall 3 years after wildfire disturbance.” Water Resour. Res. 52 (12): 9367–9389. https://doi.org/10.1002/2016WR019110.
Ehalt Macedo, H., R. Beighley, C. David, and J. T. Reager. 2019. “Using GRACE in a streamflow recession to determine drainable water storage in the Mississippi River Basin.” Hydrol. Earth Syst. Sci. 23 (8): 3269–3277. https://doi.org/10.5194/hess-23-3269-2019.
Eicker, A., M. Schumacher, J. Kusche, P. Doll, and H. M. Schmied. 2014. “Calibration/data assimilation approach for integrating GRACE data into the water GAP global hydrology model (WGHM) using an ensemble Kalman filter.” Surv. Geophys. 35 (6): 1285–1309. https://doi.org/10.1007/s10712-014-9309-8.
Entekhabi, D., et al. 2010. “The soil moisture active passive (SMAP) mission.” Proc. IEEE 98 (5): 704–716. https://doi.org/10.1109/JPROC.2010.2043918.
Espinoza, J. C., S. Chavez, J. Ronchail, C. Junquas, K. Takahashi, and W. Lavado. 2015. “Rainfall hotspots over the southern tropical Andes: Spatial distribution, rainfall intensity, and relations with large-scale atmospheric circulation.” Water Resour. Res. 51 (5): 3459–3475. https://doi.org/10.1002/2014WR016273.
Famiglietti, J. S., and M. Rodell. 2013. “Water in the balance.” Science 340 (6138): 1300–1301. https://doi.org/10.1126/science.1236460.
Famiglietti, J. S., and E. F. Wood. 1994. “Application of multiscale water and energy balance models on a tallgrass prairie.” Water Resour. Res. 30 (11): 3079–3093. https://doi.org/10.1029/94WR01499.
Franks, S. W., and K. J. Beven. 1997. “Estimation of evapotranspiration at the landscape scale: A fuzzy disaggregation approach.” Water Resour. Res. 33 (12): 2929–2938. https://doi.org/10.1029/97WR01963.
Gao, H., H. Cai, and Z. Duan. 2017. “Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows.” Hydrol. Res. 49 (1): 90–106. https://doi.org/10.2166/nh.2017.245.
Goodrich, D. C., T. J. Schmugge, T. J. Jackson, C. L. Unkrich, T. O. Keefer, R. Parry, L. B. Bach, and S. A. Amer. 1994. “Runoff simulation sensitivity to remotely sensed initial soil water content.” Water Resour. Res. 30 (5): 1393–1405. https://doi.org/10.1029/93WR03083.
Gupta, D., and C. T. Dhanya. 2020. “The potential of GRACE in assessing the flood potential of Peninsular Indian river basins.” Int. J. Remote Sens. 41 (23): 9009–9038. https://doi.org/10.1080/01431161.2020.1797218.
Gupta, H. V., S. Sorooshian, and P. O. Yapo. 1998. “Toward improved calibration of hydrologic models: Multiple and non-commensurable measures of information.” Water Resour. Res. 34 (4): 751–763. https://doi.org/10.1029/97WR03495.
Gupta, V. K., and S. Sorooshian. 1994. “Calibration of conceptual hydrologic models: Past, present and future.” In Trends in hydrology—Research trends, 329–346. Trivandrum, India: Council of Scientific Research Integration.
Gutowski, W. J., C. J. Vörösmarty, M. Person, Z. Ötles, B. Fekete, and J. York. 2002. “A coupled land-atmosphere simulation program (CLASP): Calibration and validation.” J. Geophys. Res. 107 (D16): ACL 3-1–ACL 3-17. https://doi.org/10.1029/2001JD000392.
Han, E., V. Merwade, and G. C. Heathman. 2011. “Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model.” J. Hydrol. 416–417 (Jan): 98–117. https://doi.org/10.1016/j.jhydrol.2011.11.039.
He, X., D. Lucatero, M.-E. Ridler, H. Madsen, J. Kidmose, Ø. Hole, C. Petersen, C. Zheng, and J. C. Refsgaard. 2019. “Real-time simulation of surface water and groundwater with data assimilation.” Adv. Water Resour. 127 (Jan): 13–25. https://doi.org/10.1016/j.advwatres.2019.03.004.
Her, Y., and I. Chaubey. 2015. “Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty.” Hydrol. Processes 29 (19): 4220–4237. https://doi.org/10.1002/hyp.10487.
India-WRIS (India-Water Resources Information System). 2019. “Stream flow discharge data for the period 2003–2016.” Accessed May 6, 2019. http://indiawris.gov.in/wris/.
Jena, P. P., C. Chatterjee, G. Pradhan, and A. Mishra. 2014. “Are recent frequent high floods in Mahanadi Basin in eastern India due to increase in extreme rainfalls?” J. Hydrol. 517 (Sep): 847–862. https://doi.org/10.1016/j.jhydrol.2014.06.021.
Jenkinson, A. F. 1955. “The frequency distribution of the annual maximum (or minimum) values of meteorological elements.” Q. J. R. Meteorol. Soc. 81 (348): 158–171. https://doi.org/10.1002/qj.49708134804.
Khakbaz, B., B. Imam, K. Hsu, and S. Sorooshian. 2012. “From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models.” J. Hydrol. 418–419 (Feb): 61–77. https://doi.org/10.1016/j.jhydrol.2009.02.021.
Landerer, F. W., and S. C. Swenson. 2012. “Accuracy of scaled GRACE terrestrial water storage estimates.” Water Resour. Res. 48 (4): W04531. https://doi.org/10.1029/2011WR011453.
Lehner, B., et al. 2011. Global reservoir and dam database, version 1 (GRanDv1): Dams, revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center. https://doi.org/10.7927/H4N877QK.
Lehner, B., and P. Döll. 2004. “Development and validation of a global database of lakes, reservoirs and wetlands.” J. Hydrol. 296 (1–4): 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028.
Lehner, B., K. Verdin, and A. Jarvis. 2008. “New global hydrography derived from spaceborne elevation data.” EOS Trans. Am. Geophys. Union 89 (10): 93–94. https://doi.org/10.1029/2008EO100001.
Li, D., Z. Liang, B. Li, X. Lei, and Y. Zhou. 2018. “Multi-objective calibration of MIKE SHE with SMAP soil moisture datasets.” Hydrol. Res. 50 (2): 644–654. https://doi.org/10.2166/nh.2018.110.
Li, H., M. Sivapalan, and F. Tian. 2012. “Comparative diagnostic analysis of runoff generation processes in Oklahoma DMIP2 basins: The Blue River and the Illinois River.” J. Hydrol. 418–419 (Feb): 90–109. https://doi.org/10.1016/j.jhydrol.2010.08.005.
Liang, X., Z. Xie, and M. Huang. 2003. “A new parameterization for surface and groundwater interactions and its impact on water budgets with the variable infiltration capacity (VIC) land surface model.” J. Geophys. Res. 108 (D16): 8613. https://doi.org/10.1029/2002JD003090.
Lindström, G., C. Pers, J. Rosberg, J. Strömqvist, and B. Arheimer. 2010. “Development and testing of the HYPE (hydrological predictions for the environment) water quality model for different spatial scales.” Hydrol. Res. 41 (3–4): 295–319. https://doi.org/10.2166/nh.2010.007.
Liu, Y., W. Wang, and Y. Hu. 2017. “Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River Basin.” J. Hydrol. Hydromech. 65 (2): 123–133. https://doi.org/10.1515/johh-2017-0011.
Liu, Z., and E. Todini. 2002. “Towards a comprehensive physically-based rainfall-runoff model.” Hydrol. Earth Syst. Sci. 6 (5): 859–881. https://doi.org/10.5194/hess-6-859-2002.
Livneh, B., and D. P. Lettenmaier. 2012. “Multi-criteria parameter estimation for the unified land model.” Hydrol. Earth Syst. Sci. 16 (8): 3029–3048. https://doi.org/10.5194/hess-16-3029-2012.
Lo, M. H., and J. S. Famiglietti. 2010. “Effect of water table dynamics on land surface hydrologic memory.” J. Geophys. Res. 115 (D22): D22118. https://doi.org/10.1029/2010JD014191.
López, P. L., E. H. Sutanudjaja, J. Schellekens, G. Sterk, and M. F. P. Bierkens. 2017. “Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products.” Hydrol. Earth Syst. Sci. 21 (6): 3125–3144. https://doi.org/10.5194/hess-21-3125-2017.
Massari, C., L. Brocca, T. Moramarco, Y. Tramblay, and J. F. Didon-Lescot. 2014. “Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall.” Adv. Water Resour. 74 (Dec): 44–53. https://doi.org/10.1016/j.advwatres.2014.08.004.
Merz, R., G. Bloschl, and J. Parajka. 2006. “Spatio-temporal variability of event runoff coefficients.” J. Hydrol. 331 (3–4): 591–604. https://doi.org/10.1016/j.jhydrol.2006.06.008.
Moore, C., and J. Doherty. 2005. “Role of the calibration process in reducing model predictive error.” Water Resour. Res. 41 (5): W05020. https://doi.org/10.1029/2004WR003501.
Moriasi, D. N., M. W. Gitau, N. Pai, and P. Daggupati. 2015. “Hydrologic and water quality models: Performance measures and evaluation criteria.” Am. Soc. Agric. Biol. Eng. 58 (6): 1763–1785. https://doi.org/10.13031/trans.58.10715.
Nachtergaele, F. O., et al. 2012. “Harmonized world soil database (version 1.2).” In Proc., Food and Agriculture Organization of the UN, Int. Institute for Applied Systems Analysis, ISRIC. Laxenburg, Austria: World Soil Information, Institute of Soil Science, Chinese Academy of Sciences, Joint Research Centre of the EC.
Nash, J. E., and J. V. Sutcliffe. 1970. “River flow forecasting through conceptual models part I—A discussion of principles.” J. Hydrol. 10 (3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6.
Ning, S., H. Ishidaira, and J. Wang. 2015. “Calibrating a hydrologic model by step-wise method using GRACE TWS and discharge data.” J. Jpn. Soc. Civ. Eng., Ser. B1 (Hydraul. Eng.) 71 (4): 85–90. https://doi.org/10.2208/jscejhe.71.I_85.
Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su. 2007. “Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data.” J. Geophys. Res. 112 (D7): D07103. https://doi.org/10.1029/2006JD007522.
Onyutha, C. 2016. “Influence of hydrological model selection on simulation of moderate and extreme flow events: A case study of the Blue Nile Basin.” Adv. Meteorol. 2016 (7148326): 28. https://doi.org/10.1155/2016/7148326.
Onyutha, C., and P. Willems. 2013. “Uncertainties in flow-duration-frequency relationships of high and low flow extremes in Lake Victoria Basin.” Water 5 (4): 1561–1579. https://doi.org/10.3390/w5041561.
Onyutha, C., and P. Willems. 2015. “Empirical statistical characterization and regionalization of amplitude–duration–frequency curves for extreme peak flows in the Lake Victoria Basin, East Africa.” Hydrol. Sci. J. 60 (6): 997–1012. https://doi.org/10.1080/02626667.2014.898846.
Pai, D. S., L. Sridhar, M. Rajeevan, O. P. Sreejith, N. S. Satbhai, and B. Mukhopadhyay. 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.
Pan, S., L. Liu, Z. Bai, and Y. Xu. 2018. “Integration of remote sensing evapotranspiration into multi-objective calibration of distributed hydrology–soil–vegetation model (DHSVM) in a humid region of China.” Water 10 (12): 1841. https://doi.org/10.3390/w10121841.
Panda, D., A. Kumar, S. Ghosh, and R. Mohanty. 2013. “Streamflow trends in the Mahanadi River Basin (India): Linkages to tropical climate variability.” J. Hydrol. 495 (Jul): 135–149. https://doi.org/10.1016/j.jhydrol.2013.04.054.
Parajka, J., and G. Blöschl. 2008. “Spatio-temporal combination of MODIS images—Potential for snow cover mapping.” Water Resour. Res. 44 (3): W03406. https://doi.org/10.1029/2007WR006204.
Pechlivanidis, I. G., and B. Arheimer. 2015. “Large-scale hydrological modelling by using modified PUB recommendations: The India-HYPE case.” Hydrol. Earth Syst. Sci. 19 (11): 4559–4579. https://doi.org/10.5194/hess-19-4559-2015.
Pechlivanidis, I. G., B. Arheimer, C. Donnelly, Y. Hundecha, S. Huang, V. Aich, L. Samaniego, S. Eisner, and P. Shi. 2017. “Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions.” Clim. Change 141 (3): 467–481. https://doi.org/10.1007/s10584-016-1723-0.
Penna, D., I. van Meerveld, O. Oliviero, G. Zuecco, R. S. Assendelft, G. Dalla Fontana, and M. Borga. 2015. “Seasonal changes in runoff generation in a small forested mountain catchment.” Hydrol. Processes 29 (8): 2027–2042. https://doi.org/10.1002/hyp.10347.
Pickands, J., III. 1975. “Statistical inference using extreme order statistics.” Ann. Stat. 3 (1): 119–131. https://doi.org/10.1214/aos/1176343003.
Pierong, R., and M. Takman. 2014. Evaluation of the hydrological model India-HYPE With focus on precipitation driving data and regionalization quality. Uppsala, Sweden: Committee of Tropical Ecology, Uppsala Univ.
Rai, P. K., B. R. Chahar, and C. T. Dhanya. 2017. “GIS-based SWMM model for simulating the catchment response to flood events.” Hydrol. Res. 48 (2): 384–394. https://doi.org/10.2166/nh.2016.260.
Rakovec, O., R. Kumar, S. Attinger, and L. Samaniego. 2016. “Improving the realism of hydrologic model functioning through multivariate parameter estimation.” Water Resour. Res. 52 (10): 7779–7792. https://doi.org/10.1002/2016WR019430.
Rasmussen, J., H. Madsen, K. H. Jensen, and J. C. Refsgaard. 2015. “Data assimilation in integrated hydrological modelling using ensemble Kalman filtering: Evaluating the effect of ensemble size and localization on filter performance.” Hydrol. Earth Syst. Sci. 19 (7): 2999–3013. https://doi.org/10.5194/hess-19-2999-2015.
Ray, K., P. Pandey, C. Pandey, A. P. Dimri, and K. Kishore. 2019. “On the recent floods in India.” Curr. Sci. 117 (2): 204–218. https://doi.org/10.18520/cs/v117/i2/204-218.
Reager, J. T., and J. S. Famiglietti. 2009. “Global terrestrial water storage capacity and flood potential using GRACE.” Geophys. Res. Lett. 36 (23): 2–7. https://doi.org/10.1029/2009GL040826.
Reager, J. T., A. C. Thomas, E. A. Sproles, M. Rodell, H. K. Beaudoing, B. Li, and J. S. Famiglietti. 2015. “Assimilation of GRACE terrestrial water storage observations into a land surface model for the assessment of regional flood potential.” Remote Sens. 7 (11): 14663–14679. https://doi.org/10.3390/rs71114663.
Reager, J. T., B. F. Thomas, and J. S. Famiglietti. 2014. “River basin flood potential inferred using GRACE gravity observations at several months lead time.” Nat. Geosci. 7 (8): 588–592. https://doi.org/10.1038/ngeo2203.
Seibert, J. 2000. “Multi-criteria calibration of a conceptual runoff model using a genetic algorithm.” Hydrol. Earth Syst. Sci. 4 (2): 215–224. https://doi.org/10.5194/hess-4-215-2000.
Shahrban, M., J. P. Walker, Q. J. Wang, and D. E. Robertson. 2018. “On the importance of soil moisture in calibration of rainfall–runoff models: Two case studies.” Hydrol. Sci. J. 63 (9): 1292–1312. https://doi.org/10.1080/02626667.2018.1487560.
Shepard, D. 1968. “A two-dimensional interpolation function for irregularly-spaced data.” In Proc., 1968 23rd ACM National Conf. Association for Computing Machinery, 517–524. New York: Association for Computing Machinery. https://doi.org/10.1145/800186.810616.
Singh, A., J. T. Reager, and A. Behrangi. 2019a. “Estimation of hydrological drought recovery based on GRACE water storage deficit.” Hydrol. Earth Syst. Sci. Discuss. 1–23. https://doi.org/10.5194/hess-2019-590.
Singh, A. K., A. S. Jasrotia, A. K. Taloor, B. S. Kotlia, V. Kumar, S. Roy, P. K. C. Ray, K. K. Singh, A. K. Singh, and A. K. Sharma. 2017. “Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology.” Quat. Int. 444 (Part A): 191–200. https://doi.org/10.1016/j.quaint.2017.04.014.
Singh, A. K., J. N. Tripathi, B. S. Kotlia, K. K. Singh, and A. Kumar. 2019b. “Monitoring groundwater fluctuations over India during Indian summer monsoon (ISM) and northeast monsoon using GRACE satellite: Impact on agriculture.” Quat. Int. 507 (Feb): 342–351. https://doi.org/10.1016/j.quaint.2018.10.036.
Song, S., and W. Wang. 2019. “Impacts of antecedent soil moisture on the rainfall- runoff transformation process based on high- resolution observations in soil tank experiments.” Water 11 (2): 296. https://doi.org/10.3390/w11020296.
Srivastava, A. K., M. Rajeevan, and S. R. Kshirsagar. 2009. “Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region.” Atmos. Sci. Lett. 254: 249–254. https://doi.org/10.1002/asl.232.
Sun, Z., X. Zhu, Y. Pan, and J. Zhang. 2017. “Assessing terrestrial water storage and flood potential using GRACE data in the Yangtze River Basin, China.” Remote Sens. 9 (10): 1011. https://doi.org/10.3390/rs9101011.
Swenson, S. C. 2012. “GRACE monthly land water mass grids NETCDF RELEASE 5.0. Version. 5.0.” Accessed May 6, 2019. https://doi.org/10.5067/TELND-NC005.
Swenson, S. C., and J. Wahr. 2006. “Post-processing removal of correlated errors in GRACE data.” Geophys. Res. Lett. 33 (8): L08402. https://doi.org/10.1029/2005GL025285.
Tanner, J. L., and D. A. Hughes. 2015. “Surface water—groundwater interactions in catchment scale water resources assessments—understanding and hypothesis testing with a hydrological model.” Hydrol. Sci. J. 60 (11): 1880–1895. https://doi.org/10.1080/02626667.2015.1052453.
Tanouchi, H., J. Olsson, G. Lindström, A. Kawamura, and H. Amaguchi. 2019. “Improving urban runoff in multi-basin hydrological simulation by the HYPE model using EEA urban atlas: A case study in the Sege River Basin, Sweden.” Hydrology 6 (1): 28. https://doi.org/10.3390/hydrology6010028.
Tapley, B. D., S. Bettadpur, J. C. Ries, P. F. Thompson, and M. M. Watkins. 2004. “GRACE measurements of mass variability in the Earth system.” Science 305 (5683): 503–505. https://doi.org/10.1126/science.1099192.
Ter Braak, C. J. F. 2006. “A Markov chain Monte Carlo version of the genetic algorithm differential evolution: Easy Bayesian computing for real parameter spaces.” Stat. Comput. 16 (3): 239–249. https://doi.org/10.1007/s11222-006-8769-1.
Todini, E. 2007. “Hydrological catchment modelling: Past, present and future.” Hydrol. Earth Syst. Sci. 11 (1): 468–482. https://doi.org/10.5194/hess-11-468-2007.
Tramblay, Y., C. Bouvier, C. Martin, J. F. Didon-Lescot, D. Todorovik, and J. M. Domergue. 2010. “Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling.” J. Hydrol. 387 (3–4): 176–187. https://doi.org/10.1016/j.jhydrol.2010.04.006.
Uber, M., J. Vandervaere, I. Zin, I. Braud, M. Heistermann, and C. Legoût. 2018. “How does initial soil moisture influence the hydrological response? A case study from southern France.” Hydrol. Earth Syst. Sci. 22 (12): 6127–6146. https://doi.org/10.5194/hess-22-6127-2018.
Vinnarasi, R., and C. T. Dhanya. 2016. “Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall.” J. Geophys. Res. 121 (5): 2146–2160. https://doi.org/10.1002/2015JD024310.
Wagener, T., D. P. Boyle, M. J. Lees, H. S. Wheater, H. V. Gupta, and S. Sorooshian. 2001. “A framework for development and application of hydrological models.” Hydrol. Earth Syst. Sci. 5 (1): 13–26. https://doi.org/10.5194/hess-5-13-2001.
Wahr, J., S. Swenson, V. Zlotnicki, and I. Velicogna. 2004. “Time-variable gravity from GRACE: First results.” Geophys. Res. Lett. 31 (11): L11501. https://doi.org/10.1029/2004GL019779.
Wang, D. 2012. “Evaluating interannual water storage changes at watersheds in Illinois based on long-term soil moisture and groundwater level data.” Water Resour. Res. 48 (3): W03502. https://doi.org/10.1029/2011WR010759.
Wang, G., Y. Kim, and D. Wang. 2007. “Quantifying the strength of soil moisture–precipitation coupling and its sensitivity to changes in surface water budget.” J. Hydrometeorol. 8 (3): 551–570. https://doi.org/10.1175/JHM573.1.
Werth, S., and A. Güntner. 2010. “Calibration analysis for water storage variability of the global hydrological model WGHM.” Hydrol. Earth Syst. Sci. 14 (1): 59–78. https://doi.org/10.5194/hess-14-59-2010.
Werth, S., A. Güntner, S. Petrovic, and R. Schmidt. 2009. “Integration of GRACE mass variations into a global hydrological model.” Earth Planet. Sci. Lett. 277 (1–2): 166–173. https://doi.org/10.1016/j.epsl.2008.10.021.
Wood, E. F., et al. 2011. “Hyperresolution global land surface modelling: Meeting a grand challenge for monitoring Earth’s terrestrial water.” Water Resour. Res. 47 (5): W05301. https://doi.org/10.1029/2010WR010090.
Xie, H., L. Longuevergne, C. Ringler, and B. R. Scanlon. 2012. “Calibration and evaluation of a semi-distributed watershed model of sub-Saharan Africa using GRACE data.” Hydrol. Earth Syst. Sci. 16 (9): 3083–3099. https://doi.org/10.5194/hess-16-3083-2012.
Yassin, F., S. Razavi, H. Wheater, G. Sapriza-Azuri, B. Davison, and A. Pietroniro. 2017. “Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach.” Hydrol. Processes 31 (19): 3320–3333. https://doi.org/10.1002/hyp.11267.
Yeh, P. J. F., and E. A. B. Eltahir. 2005. “Representation of water table dynamics in a land surface scheme. Part I: Model development.” J. Clim. 18 (12): 1861–1880. https://doi.org/10.1175/JCLI3330.1.
Yilmaz, K., J. Vrugt, H. Gupta, and S. Sorooshian. 2010. “Chapter 3: Model calibration in watershed hydrology.” In Advances in data-based approaches for hydrologic modeling and forecasting, edited by B. Sivakumar and R. Berndtsson, 53–105. Hackensack, NJ: World Scientific.
Zaitchik, B. F., and M. Rodell. 2009. “Forward-looking assimilation of MODIS-derived snow-covered area into a land surface model.” J. Hydrometeorol. 10 (1): 130–148. https://doi.org/10.1175/2008JHM1042.1.
Zaitchik, B. F., M. Rodell, and R. H. Reichle. 2008. “Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River Basin.” J. Hydrometeorol. 9 (3): 535–548. https://doi.org/10.1175/2007JHM951.1.
Zehe, E., and G. Blöschl. 2004. “Predictability of hydrologic response at the plot and catchment scales: Role of initial conditions.” Water Resour. Res. 40 (10): W10202. https://doi.org/10.1029/2003WR002869.
Zeinivand, H., and F. De Smedt. 2009. “Hydrological modelling of snow accumulation and melting on river basin scale.” Water Resour. Manage. 23 (11): 2271–2287. https://doi.org/10.1007/s11269-008-9381-2.
Zou, L., C. Zhan, J. Xia, T. Wang, and C. J. Gippeld. 2017. “Implementation of evapotranspiration data assimilation with catchment scale distributed hydrological model via an ensemble Kalman filter.” J. Hydrol. 549 (Jun): 685–702. https://doi.org/10.1016/j.jhydrol.2017.04.036.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 26 • Issue 4 • April 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 12, 2020
Accepted: Nov 30, 2020
Published online: Jan 31, 2021
Published in print: Apr 1, 2021
Discussion open until: Jun 30, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.