Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 12 6 2008 10.5194/hess-12-1403-2008 http://www.hydrol-earth-syst-sci.net/12/1403/2008/ http://www.hydrol-earth-syst-sci.net/12/1403/2008/hess-12-1403-2008.html http://www.hydrol-earth-syst-sci.net/12/1403/2008/hess-12-1403-2008.pdf 1403 1413 2008-12-18 Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins? H. C. Winsemius h.c.winsemius@tudelft.nl H. H. G. Savenije W. G. M. Bastiaanssen Department of Water Management, Delft, University of Technology, Delft, The Netherlands WaterWatch, Wageningen, The Netherlands In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are constrained by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. The approach has been applied to the ungauged Luangwa river basin (150 000 (km)²) in Zambia. The information contained in these evaporation estimates imposes compliance of the model with the largest outgoing water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. The model results in turn provide a better understanding of the information density of remotely sensed evaporation. Model parameters to which evaporation is sensitive, have been spatially distributed on the basis of dominant land cover characteristics. Consequently, their values were conditioned by means of Monte-Carlo sampling and evaluation on satellite evaporation estimates. The results show that behavioural parameter sets for model units with similar land cover are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos) show optimal parameter ranges that reflect vegetation with a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation) which is easily moisture stressed. The forested areas and highlands show parameter ranges that indicate a much deeper root zone which is more drought resistent. Clustering was consequently used to formulate fuzzy membership functions that can be used to constrain parameter realizations in further calibration. Unrealistic parameter ranges, found for instance in the high unsaturated soil zone values in the highlands may indicate either overestimation of satellite-based evaporation or model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct parameter clustering was found for forested model units. We hypothesize that this is due to the presence of two dominant forest types that differ substantially in their moisture regime. This could indicate that the spatial discretization used in this study is oversimplified. Allen, R G., Pereira, L S., Raes, D., and Smith, M.: FAO Irrigation and Drainage Paper No. 56 – Crop Evapotranspiration, Tech. rep., FAO, 1998. Allen, R G., Tasumi, M., and Trezza, R.: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model, J. Irrig. Drain. Eng., 133, 380–394, \doi10.1061/(ASCE)0733-9437(2007)133:4(380), 2007. Bastiaanssen, W. G M., Hoekman, H H., and Roebeling, R A.: A methodology for assessment of surface resistance and soil water storage variability at mesoscale based on remote sensing measurements, in: IAHS Special Publication, 2, p 65, IAHS Press, Wallingford, USA, 1994. Bastiaanssen, W. G M., Menenti, M., Feddes, R A., and Holtslag, A. A M.: A remote sensing surface energy balance algorithm for land (SEBAL). Part 2. Validation, J. Hydrol., 212/213, 213–229, 1998. Bastiaanssen, W M., Noordman, E. J M., Pelgrum, H., Davids, G., Thoreson, B P., and Allen, R G.: SEBAL model with remotely sensed data to improve water-resources management under actual field conditions, J. J. Irrig. Drain. E-Asce, 131, 85–93, 2005. Bastiaanssen, W. M G., Ahmed, M D., and Chemin, Y.: Satellite surveillance of evaporative depletion across the Indus Basin, Water Resour. Res., 38, 1273–1282, 2002. Batjes, N H.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (version 1.1)., Tech. rep., ISRIC – World Soil Information, Wageningen, The Netherlands, 2006. Beven, K J. and Binley, A M.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Proc., 6, 279–298, 1992. Beven, K J. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, 2001. Brutsaert, W.: Evaporation into the Atmosphere, Reidel, Dordrecht, The Netherlands, 1982. Campo, L., Caparrini, F., and Castelli, F.: Use of multi-platform, multi-temporal remote-sensing data for calibration of a distributed hydrological model: an application in the Arno basin, Italy, Hydrol. Process., 20, 2693–2712, 2006. Chidumayo, E N.: Effects of climate on the growth of exotic and indigenous trees in central Zambia, J. Biogeogr., 32, 111–120, 2005. Farah, H O.: Estimation of regional evaporation under different weather conditions from satellite and meteorological data; A case study in the Naivasha Basin, Kenya, Ph.D. Thesis, Wageningen University, The Netherlands, 2001. Fenicia, F., McDonnell, J J., and Savenije, H. H G.: Learning from model improvement: On the contribution of complementary data to process understanding, Water Resour. Res., 44, W06419, \doi10.1029/2007WR006386, 2008. Franks, S W. and Beven, K J.: Estimation of evapotranspiration at the landscape scale: a fuzzy disaggregation approach, Water Resour. Res., 33, 2929–2938, 1997. Franks, S W., Gineste, P., Beven, K J., and Merot, P.: On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process, Water Resour. Res., 34, 787–797, 1998. Freer, J., Beven, K J., and Ambroise, B.: Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach, Water Resour. Res., 32, 2161–2173, 1996. Frost, P.: The Miombo in Transition: Woodlands and Welfare in Africa, chap. The ecology of miombo woodlands, 11–58, Center for International Forestry Research, 1996. Fuller, D O.: Canopy phenology of some mopane and miombo woodlands in eastern Zambia, Global Ecol. Biogeogr., 8, 199–209, 1999. Gragne, A S., Uhlenbrook, S., Mohamed, Y., and Kebede, S.: Catchment modeling and model transferability in upper Blue Nile basin, Lake Tana, Ethiopia, Hydrol. Earth Syst. Sci. Discuss., 5, 811–842, 2008. Herman, A., Kumar, V B., Arkin, P A., and Kousky, J V.: Objectively determined 10-day African rainfall estimates created for Famine Early Warning Systems, Int. J. Remote Sensing, 18, 2147–2159, 1997. Huffman, G J., Adler, R F., Bolvin, D T., Gu, G., Nelkin, E J., Bowman, K P., Hong, Y., Stocker, E F., and Wolff, D B.: The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear,combined-sensor precipitation estimates at fine scales, J. Hydrometeor., 8, 38–55, 2007. Immerzeel, W W. and Droogers, P.: Calibration of a distributed hydrological model based on satellite evapotranspiration, J. Hydrol., 349, 411–424, 2008. Immerzeel, W W., Gaur, A., and Zwart, S J.: Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment, Agric. Water Manag., 95, 11–24, 2008. Jarvis, P G.: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Phil. Trans. R. Soc. Lond., 273, 593–610, 1976. Johrar, R K.: Estimation of effective soil hydraulic parameters for water management studies in semi-arid zones, PhD Thesis, Wageningen University, The Netherlands, 2002. Kavetski, D., Kuczera, G., and Franks, S W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, \doi10.1029/2005WR004368, 2006. Kuczera, G.: Improved parameter inference in catchment models, 2, Combining different kinds of hydrologic data and testing their compatibility, Water Resour. Res., 19, 1163–1172, 1983. Lewis, D M.: Observations of tree growth, woodland structure and elephant damage on Colophospermum mopane in Luangwa valley, Zambia, Afr. J. Ecol., 29, 207–221, 1991. Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, 1997. Liu, Y. and Gupta, H V.: Uncertainty in hydrologic modeling: Toward and integrated data assimilation framework, Wat. Resour. Res., 43, W07401, \doi10.1029/2006WR005756, 2007. LSA SAF: Validation report, Tech. Rep. SAF/LAND/IM/VR/1.6, LSA SAF, Lisboa, Portugal, 2007. Mohamed, Y A., Bastiaanssen, W. G M., and Savenije, H. H G.: Spatial variability of evaporation and moisture storage in the swamps of the upper Nile studied by remote sensing techniques, J. Hydrol., 289, 145–164, 2004. Monteith, J L.: Evaporation and surface temperature, Q. J. R. Meteorol. Soc., 107, 1–27, 1981. New, M., Lister, D., Hulme, M., and Makin, I.: A high-resolution data set of surface climate over global land areas, Climate research, 21, 1–25, 2002. Penman, H L.: Natural evaporation from open water, bare soil and grass, Proc. Roy. Soc. London, A193, 120–145, 1948. Reynolds, R W.: A real-time global sea surface temperature analysis, J. Climate, 1, 75–86, 1988. Savenije, H. H G.: Equifinality, a blessing in disguise?, Hydrol. Process., 15, 2835–2838, 2001. Schuurmans, J M., Troch, P A., Veldhuizen, A A., Bastiaanssen, W. G M., and Bierkens, M. F P.: Assimilation of remotely sensed latent heat flux in a distributed hydrological model, Adv. Water Resour., 26, 151–159, \doi10.1016/S0309-1708(02)00089-1, 2003. Seibert, J. and McDonnell, J J.: On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration, Wat. Resour. Res., 38, 1241, \doi10.1029/2001WR000978, 2002. Sivapalan, M.: Prediction in Ungauged Basins: a grand challenge for theoretical hydrology, Hydrol. Process., 17, 3163–3170, 2003. Son, K. and Sivapalan, M.: Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data, Water Resources Research, 43, W01415, \doi10.1029/2006WR005032, 2007. Uhlenbrook, S., Seibert, J., Leibundgut, C., and Rodhe, A.: Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure, Hydrol. Sci., 44, 779–797, 1999. Vaché, K B. and McDonnell, J J.: A process-based rejectionist framework for evaluating catchment runoff model structure, Wat. Resour. Res., 42, W02409, \doi10.1029/2005WR004247, 2006. Voogt, M.: METEOLOOK, a physically based regional distribution model for measured meteorological variables, M.Sc. Thesis, University of Technology, Delft, The Netherlands, 2006. Vose, R S., Schmoyer, R L., Steurer, P M., Peterson, T C., Heim, R., Karl, T R., and Eischeid, J.: The Global Historical Climatology Network: long-term monthly temperature, precipitation, sea level pressure, and station pressure data, ORNL/CDIAC-53, NDP-041, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, USA, 1992. Wagener, T. and Gupta, H V.: Model identification for hydrological forecasting under uncertainty, Stoch. Environ. Res. Risk Assess., 19, 378–387, 2005. Young, P C.: Model validation, chap. Data-based mechanistic modelling and validation of rainfall-flow processes, 117–161, Wiley, Chichester, UK, 2001.