• Ahmed, K. F., , Wang G. , , Silander J. , , Wilson A. , , Allen J. , , Horton R. , , and Anyah R. , 2013: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. Northeast. Global Planet. Change, 100, 320332, doi:10.1016/j.gloplacha.2012.11.003.

    • Search Google Scholar
    • Export Citation
  • Bjerklie, D., , Trombley T. , , and Viger R. , 2011: Simulations of historical and future trends in snowfall and groundwater recharge for basins draining to Long Island Sound. Earth Interact., 15, doi:10.1175/2011EI374.

    • Search Google Scholar
    • Export Citation
  • Bormann, H., 2011: Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Climatic Change, 104, 729–753, doi:10.1007/s10584-010-9869-7.

  • Caya, D., , and Laprise R. , 1999: A semi-implicit semi-Lagrangian regional climate model: The Canadian RCM. Mon. Wea. Rev., 127, 341362, doi:10.1175/1520-0493(1999)127<0341:ASISLR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dorigo, W. A., , Scipal K. , , Parinussa R. M. , , Liu Y. Y. , , Wagner W. , , de Jeu R. A. M. , , and Naeimi V. , 2010: Error characterization of global active and passive microwave soil moisture data sets. Hydrol. Earth Syst. Sci., 14, 26052616, doi:10.5194/hess-14-2605-2010.

    • Search Google Scholar
    • Export Citation
  • Eitzinger, J., , Marinkovic D. , , and Hösch J. , 2002: Sensitivity of different evapotranspiration calculation methods in different crop–weather models. Integrated Assessment and Decision Support: Proceedings of the First Biennial Meeting of the iEMSs, Vol. 2, A. E. Rizzoli and A. J. Jakeman, Eds., 395–400. [Available online at www.iemss.org/iemss2002/proceedings/pdf/volume%20due/161_eitzinger.pdf.]

  • Fisher, J., , Tu K. , , and Baldocchi D. , 2008: Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ., 112, 909919, doi:10.1016/j.rse.2007.06.025.

    • Search Google Scholar
    • Export Citation
  • Fisher, J., and et al. , 2009: The land–atmosphere water flux in the tropics. Global Change Biol., 15, 26942714, doi:10.1111/j.1365-2486.2008.01813.x.

    • Search Google Scholar
    • Export Citation
  • Ford, T., , and Quiring S. , 2013: Influence of MODIS-derived dynamic vegetation on VIC-simulated soil moisture in Oklahoma. J. Hydrometeor., 14, 19101921, doi:10.1175/JHM-D-13-037.1.

    • Search Google Scholar
    • Export Citation
  • Immerzeel, W. W., , and Bold P. , 2008: Calibration of a distributed hydrological model based on satellite evapotranspiration. J. Hydrol., 349, 411424, doi:10.1016/j.jhydrol.2007.11.017.

    • Search Google Scholar
    • Export Citation
  • Kite, G. W., , and Droogers P. , 2000: Comparing evapotranspiration estimates from satellites, hydrological models, and field data. J. Hydrol., 229, 318, doi:10.1016/S0022-1694(99)00195-X.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , Lettenmaier D. P. , , Wood E. F. , , and Burges S. J. , 1994: A simple hydrologically based model of land surface water and energy fluxes for GSMs. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Liu, Y. Y., , Dorigo W. A. , , Parinussa R. M. , , de Jeu R. A. M. , , Wagner W. , , McCabe M. F. , , Evans J. P. , , and van Dijk A. I. J. M. , 2012: Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ., 123, 280297, doi:10.1016/j.rse.2012.03.014.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , Sun G. , , McNulty S. G. , , and Amatya D. M. , 2005: A comparison of six potential evapotranspiration methods for regional use in the southeastern United States. J. Amer. Water Resour. Assoc., 41, 621633, doi:10.1111/j.1752-1688.2005.tb03759.x.

    • Search Google Scholar
    • Export Citation
  • Marshall, E., , and Randhir T. , 2008: Effect of climate change on watershed system: A regional analysis. Climatic Change, 89, 263280, doi:10.1007/s10584-007-9389-2.

    • Search Google Scholar
    • Export Citation
  • McKenney, M. S., , and Rosenberg N. J. , 1993: Sensitivity of some potential evapotranspiration estimation methods to climate change. Agric. For. Meteor., 64, 81110, doi:10.1016/0168-1923(93)90095-Y.

    • Search Google Scholar
    • Export Citation
  • Mearns, L., and et al. , 2012: The North American Regional Climate Change Assessment Program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 13371362, doi:10.1175/BAMS-D-11-00223.1.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., , Heinsch F. , , Zhao M. , , and Running S. , 2007: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ., 111, 519536, doi:10.1016/j.rse.2007.04.015.

    • Search Google Scholar
    • Export Citation
  • Naeimi, V., , Scipal K. , , Bartalis Z. , , Hasenauer S. , , and Wagner W. , 2009: An improved soil moisture retrieval algorithm for ERS and MetOp scatterometer observations. IEEE Trans. Geosci. Remote Sens., 47, 19992013, doi:10.1109/TGRS.2008.2011617.

    • Search Google Scholar
    • Export Citation
  • NWS, 2013: Connecticut River at Thompsonville hydrograph. Advanced Hydrologic Prediction Service, NOAA/NWS. Accessed 12 September 2013. [Available online at http://water.weather.gov/ahps2/hydrograph.php?wfo=box&gage=tmvc3&hydro_type=2.]

  • Ottlé, C., , and Vidal-Madjar D. , 1994: Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region. J. Hydrol., 158, 241264, doi:10.1016/0022-1694(94)90056-6.

    • Search Google Scholar
    • Export Citation
  • Pal, J. S., and et al. , 2007: Regional climate modeling for the developing world: The ICTP RegCM3 and RegCNET. Bull. Amer. Meteor. Soc., 88, 13951409, doi:10.1175/BAMS-88-9-1395.

    • Search Google Scholar
    • Export Citation
  • Parr, D. T., , and Wang G. L. , 2014: Hydrological changes in the U.S. Northeast using the Connecticut River basin as a case study: Part 1. Modeling and analysis of the past. Global Planet. Change, 122, 208222, doi:10.1016/j.gloplacha.2014.08.009.

    • Search Google Scholar
    • Export Citation
  • Renard, B., , Kavetski D. , , Kuczera G. , , Thyer M. , , and Franks S. , 2010: Understanding predictive uncertainty in hydrologic modeling. The challenge of identifying input and structural errors. Water Resour. Res., 46, W05521, doi:10.1029/2009WR008328.

    • Search Google Scholar
    • Export Citation
  • 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 Resour. Res., 36, 3653–3662, doi:10.1029/2000WR900130.

  • Sheffield, J., , and Wood E. , 2007: Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrological cycle. J. Geophys. Res., 112, D17115, doi:10.1029/2006JD008288.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., , Livneh B. , , and Wood E. F. , 2007: Representation of terrestrial hydrology and large-scale drought of the continental United States from the North American Regional Reanalysis. J. Hydrometeor., 13, 856–876, doi:10.1175/JHM-D-11-065.1.

  • Tang, Q., , Vivoni E. R. , , Muñoz-Arriola F. , , and Lettenmaier D. P. , 2012: Predictability of evapotranspiration patterns using remotely sensed vegetation dynamics during the North American monsoon. J. Hydrometeor., 13, 103121, doi:10.1175/JHM-D-11-032.1.

    • Search Google Scholar
    • Export Citation
  • Trambauer, P., , Dutra E. , , Maskey S. , , Werner M. , , Pappernberger F. , , van Beek L. P. H. , , and Uhlenbrook S. , 2014: Comparison of different evaporation estimates over the African continent. Hydrol. Earth Syst. Sci., 18, 193212, doi:10.5194/hess-18-193-2014.

    • Search Google Scholar
    • Export Citation
  • USGS, 2011: HYDRO1k documentation. Accessed 22 June 2015. [Available online at http://webgis.wr.usgs.gov/globalgis/metadata_qr/metadata/hydro1k.htm.]

  • USGS, 2012: USGS water data for Connecticut. Accessed 30 January 2012. [Available online at http://waterdata.usgs.gov/ct/nwis.]

  • Vano, J., , Das T. , , and Lettenmaier D. , 2012: Hydrologic sensitivities of Colorado River runoff to changes in precipitation and temperature. J. Hydrometeor., 13, 932949, doi:10.1175/JHM-D-11-069.1.

    • Search Google Scholar
    • Export Citation
  • Vinukollu, R., , Wood E. , , Ferguson C. , , and Fisher J. , 2011: Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches. Remote Sens. Environ., 115, 801823, doi:10.1016/j.rse.2010.11.006.

    • Search Google Scholar
    • Export Citation
  • Wattenbach, M., , Franz D. , , Liang W. , , Schmidt M. , , Seitz F. , , and Güntner A. , 2012: Integration of MODIS LAI products into the hydrological model WGHM indicate the sensitivity of total water storage simulations to vegetation cover dynamics. Geophysical Research Abstracts, Vol. 14, Abstract EGU2012-10116. [Available online at http://meetingorganizer.copernicus.org/EGU2012/EGU2012-10116.pdf.]

  • Weiß, M., , and Menzel L. , 2008: A global comparison of four potential evapotranspiration equations and their relevance to stream flow modelling in semi-arid environments. Adv. Geosci., 18, 1523, doi:10.5194/adgeo-18-15-2008.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., , Maurer E. P. , , Kumar A. , , and Lettenmaier D. P. , 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., , Leung L. R. , , Sridhar V. , , and Lettenmaier D. P. , 2004: Hydrological implications of dynamical and statistical approaches to downscaling climate model surface temperature and precipitation fields. Climatic Change, 62, 189216, doi:10.1023/B:CLIM.0000013685.99609.9e.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., , Ek M. , , and Wei H. , 2012a: Comparative analysis of relationships between NLDAS-2 forcings and model outputs. Hydrol. Processes, 26, 467474, doi:10.1002/hyp.8240.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012b: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048.

  • Xia, Y., and et al. , 2012c: Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res., 117, D03110, doi:10.1029/2011JD016051.

  • Xia, Y., , Hobbins M. , , Mu Q. , , and Ek M. , 2015: Evaluation of NLDAS-2 evapotranspiration against tower flux site observations. Hydrol. Processes, 29, 1757–1771, doi:10.1002/hyp.10299.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y. Q., , Viney N. R. , , Chiew F. H. S. , , van Dijk A. I. J. M. , , and Liu Y. Y. , 2011: Improving hydrological and vegetation modelling using regional calibration schemes together with remote sensing data. MODSIM2011: 19th International Congress on Modelling and Simulation, F. Chan, D. Marinova, and R. S. Anderssen, Eds., Modelling and Simulation Society of Australia and New Zealand, 3448–3454. [Available online at http://mssanz.org.au/modsim2011/I4/zhang.pdf.]

  • Zhou, Y., , Zhang Y. , , Vaze J. , , Lane P. , , and Xu S. , 2013: Improving runoff estimates using remote sensing vegetation data for bushfire impacted catchments. Agric. For. Meteor., 182-183, 332341, doi:10.1016/j.agrformet.2013.04.018.

    • Search Google Scholar
    • Export Citation
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Integrating Remote Sensing Data on Evapotranspiration and Leaf Area Index with Hydrological Modeling: Impacts on Model Performance and Future Predictions

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  • 1 Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut
  • | 2 USGS New England Water Science Center, East Hartford, Connecticut
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Abstract

Using the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. The methodology of incorporating ET data into VIC as a bias-correction tool also influences the modeled future hydrological trends. Compared to the default VIC, VICET portrays a future characterized by greater drought risk and a stronger decreasing trend of minimum river flows. Integrating remote sensing data with hydrological modeling helps characterize the range of model-related uncertainties and more accurately reconstruct historic river flow estimates, leading to a better understanding and prediction of hydrological response to future climate changes.

Corresponding author address: Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269. E-mail: gwang@engr.uconn.edu

Abstract

Using the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. The methodology of incorporating ET data into VIC as a bias-correction tool also influences the modeled future hydrological trends. Compared to the default VIC, VICET portrays a future characterized by greater drought risk and a stronger decreasing trend of minimum river flows. Integrating remote sensing data with hydrological modeling helps characterize the range of model-related uncertainties and more accurately reconstruct historic river flow estimates, leading to a better understanding and prediction of hydrological response to future climate changes.

Corresponding author address: Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269. E-mail: gwang@engr.uconn.edu
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