Development and application of assimilation techniques of hydrometeorological remotely-sensed data in meteorological and land-surface models
Περίληψη
The dissertation focuses on the study of the continental water cycle and combines advanced atmospheric and land surface modelling systems with remotely-sensed data to thoroughly investigate the interactions between water cycle processes taking place underground, at the land surface and in the lower atmospheric boundary layer. The first part of the dissertation includes the study of the propagation of error of remotely-sensed precipitation data to the soil moisture fields simulated by an advanced land surface model (LSM). The study facilitates the in-depth investigation and comparison of two major error sources in the simulation of land surface state properties at the regional scale, namely the error induced by rainfall forcing and the error induced by model internal parameterizations. Through this error sensitivity analysis, two other major contributions are achieved; namely, the comparison of the performance of three extensive sources of remotely-sensed rainfall data (NEXRAD, TRMM and CMORPH) as well as the evaluation of the performance of the advanced LSM (Community Land Model, version 3.5). The second part of the dissertation investigates the interaction between water reservoirs over land (rivers, lakes etc.) and the underlying groundwater at the regional/seasonal scale. The study improves the CLM3.5 parameterization of the interactions between surface- and groundwater (TOPMODEL approach) by considering the discharge of land surface streams into groundwater that was not accounted before. The results show significant improvement in the simulations of soil moisture and other land surface properties in the areas including or surrounding major river networks. Both studies identify similar climatological effects of the prescribed analyses. Specifically, the CLM error statistics and the new river-groundwater interaction parameterization highly depend on the wetness conditions that prevail during the simulation period (improved results on wet areas or seasons). The third part of the dissertation discusses the development and application of a new assimilation technique through the ingestion of remotely-sensed precipitation data in the land surface schemes of advanced mesoscale models. The technique significantly improves the models’ quantitative precipitation forecasting capability in cases of extreme thunderstorms, thus offering a valuable tool to meteorologists worldwide. The assimilation technique is tested for two different advanced weather forecasting systems (i.e., the POSEIDON and the WRF systems) in two different continental regimes (i.e., continental USA and Europe, respectively) with two different sources of remotely-sensed rainfall for the LSM data ingestion (i.e., NEXRAD and CMORPH estimates, respectively). Moreover, an extensive feedback investigation is performed, advancing our understanding of the complex land-atmosphere interaction processes
Η διατριβή επικεντρώνεται στη μελέτη του ηπειρωτικού υδάτινου κύκλου και συνδυάζει προηγμένα εδαφικά και ατμοσφαιρικά μοντέλα με δεδομένα τηλεπισκόπισης για τη διερεύνηση των αλληλεπιδράσεων μεταξύ των διεργασιών του υδάτινου κύκλου που πραγματοποιούνται κάτω από την επιφάνεια της γης, στην επιφάνεια της αλλά και στο ατμοσφαιρικό οριακό στρώμα. Το πρώτο μέρος της διατριβής περιλαμβάνει τη μελέτη της διάδοσης του σφάλματος που χαρακτηρίζει τα δεδομένα βροχόπτωσης από τηλεπισκόπιση στα πεδία εδαφικής υγρασίας που προσομοιώνονται από ένα προηγμένο εδαφικό μοντέλο. Η μελέτη διευκολύνει την εις βάθος διερεύνηση και σύγκριση δύο σημαντικών πηγών σφάλματος στη προσομοίωση των ιδιοτήτων της επιφανειακής εδαφικής κατάστασης σε περιοχική κλίμακα, δηλαδή το σφάλμα που οφείλεται στις εκτιμήσεις βροχόπτωσης και το σφάλμα που προκαλείται από τις εσωτερικές παραμετροποιήσεις του μοντέλου. Μέσω αυτής της ανάλυσης ευαισθησίας, επιτυγχάνεται επίσης η σύγκριση των επιδόσεων τριών σημαντικών συνόλων δεδομένων βροχόπτωσης από τηλεπισκόπιση (NEXRAD, TRMM και CMORPH) καθώς και η αξιολόγηση των επιδόσεων του προηγμένου εδαφικού μοντέλου (CLM3.5). Το δεύτερο μέρος της διατριβής διερευνά την αλληλεπίδραση μεταξύ των επιφανειακών υδάτων (ποτάμια, λίμνες κ.λπ.) και των υποκείμενων υπόγειων υδάτων, επίσης σε περιοχική και εποχική κλίμακα. Η μελέτη βελτιώνει την παραμετροποίηση του CLM3.5 ως προς τις αλληλεπιδράσεις μεταξύ επιφανειακών και υπόγειων υδάτων (προσέγγιση TOPMODEL), λαμβάνοντας υπόψη τη διήθηση των επιφανειακών υδάτων στα υπόγεια ύδατα που δεν υπήρχε μέχρι τώρα. Τα αποτελέσματα δείχνουν σημαντική βελτίωση στις προσομοιώσεις των πεδίων εδαφικής υγρασίας και άλλων επιφανειακών εδαφικών παραμέτρων στις περιοχές που περιλαμβάνουν μεγάλους ποταμούς ή γύρω από αυτούς. Και οι δύο προαναφερόμενες αναλύσεις παρουσιάζουν παρεμφερή κλιματολογικά χαρακτηριστικά. Συγκεκριμένα, τόσο οι στατιστικές των σφαλμάτων στο CLM όσο και η νέα παραμετροποίηση αλληλεπίδρασης μεταξύ ποταμών και υπόγειων υδάτων εξαρτώνται σε μεγάλο βαθμό από τις συνθήκες υγρασίας που επικρατούν κατά την περίοδο των προσομοιώσεων (καλύτερα αποτελέσματα στις περιπτώσεις υγρών περιοχών και περιόδων). Το τρίτο μέρος της διατριβής πραγματεύεται την ανάπτυξη και εφαρμογή μιας νέας τεχνικής αφομοίωσης δεδομένων βροχόπτωσης από τηλεπισκόπιση στα εδαφικά σχήματα προηγμένων μετεωρολογικών μοντέλων. Η τεχνική βελτιώνει σημαντικά την ικανότητα πρόγνωσης βροχοπτώσεων σε περιπτώσεις έντονων καταιγιδοφόρων συστημάτων, προσφέροντας έτσι ένα πολύτιμο εργαλείο στους μετεωρολόγους παγκοσμίως. Η τεχνική αφομοίωσης αξιολογείται για δύο διαφορετικά προηγμένα συστήματα πρόγνωσης καιρού (συστήματα POSEIDON και WRF) σε δύο διαφορετικές ηπειρωτικές περιοχές (ΗΠΑ και Ευρώπη, αντίστοιχα) με χρήση δύο διαφορετικών πηγών δεδομένων βροχόπτωσης για την εισαγωγή στο εδαφικό σχήμα (NEXRAD και CMORPH, αντίστοιχα). Επιπλέον, πραγματοποιείται ενδελεχής μελέτη για την καλύτερη κατανόηση των σύνθετων διεργασιών αλληλεπίδρασης και ανατροφοδοτήσεων μεταξύ εδάφους και ατμόσφαιρας.
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Albergel, C., C. Rüdiger, T. Pellarin, J.-C. Calvet, N. Fritz, F. Froissard, D. Suquia, A. Petitpa, B. Piguet, and E. Martin, 2008: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrol. Earth Syst. Sci., 12, 1323-1337.
Alley, W. M., Healy, R. W., LaBaugh, J. W., and Reilly, T. E., 2002: Flow and storage in groundwater systems. Science, v. 296, p. 1985-1990.
Anagnostou, E. N., V. Maggioni, E. I. Nikolopoulos, T. Meskele, F. Hossain and A. Papadopoulos, 2010: Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates. IEEE Trans. Geosci. Remote Sens., 48, 1667-1683.
Anyah, R. O., C. P. Weaver, G. Miguez-Macho, Y. Fan, and A. Robock, 2008: Incorporating water table dynamics in climate modeling: 3. Simulated groundwater
influence on coupled land-atmosphere variability. J. Geophys. Res., 113, D07103, doi:10.1029/ 2007JD009087.
Barnston, A. G., 1992: Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score. Wea. Forecasting, 7, 699-709.
Beljaars, A. C. M., P. Viterbo, M. J. Miller, and A. K. Betts, 1996: The anomalous rainfall over the United States during July 1993: Sensitivity to land surface parameterization and soil- moisture anomalies. Mon. Wea. Rev., 124, 362–383.
Betts, A. K., 1986: A new convective adjustment scheme. Part E. Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 677–691.
Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and Arctic air mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693–709.
Betts, A. K., 2004: Understanding hydrometeorology using global models. Bull. Amer.Meteor. Soc., 85, 1673-1688.
Beven, K. J., and M. J. Kirkby, 1979: A physically-based variable contributing area model of basin Hydrology. Hydrol. Sci. Bull., 24, 43–69.
Bierkens, M. P. and B. J. J. M. van den Hurk, 2007: Groundwater convergence as a possible mechanism for multi-year persistence in rainfall. Geophys. Res. Lett., 34, doi:10.1029/2006GL028396.
Blankenship C. B., J. L. Case, B. T. Zavodsky, and W. L. Crosson, 2016: Assimilation of SMOS Retrievals in the Land Information System. IEEE Trans. Geosci. Remote Sens., 54, 6320-6332.
Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. Zeng, Y. Dai, R. E. Dickinson, and Z.-L. Yang, 2002: The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J. Climate, 15, 3123-3149.
Branstetter, M., 2001: Development of a Parallel River Transport Algorithm and Applications to Climate Studies. PhD thesis, University of Texas at Austin. J. Famiglietti, supervisor.
Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson and M. D. Eilts, 1995: The Oklahoma Mesonet: A Technical Overview. J. Atmos. Ocean. Technol., 12, 5-19.
Buckingham, E., 1914: On physically similar systems; illustrations of the use of dimensional equations. Phys. Rev., 4, 345–376. doi:10.1103/PhysRev.4.345.
Budyko, M. I., 1974: Climate and Life. Academic Press, 508 pp.
Chen, F., K. Mitchell, J. Schaake, Y. Xue, H.‐L. Pan, V. Koren, Q. Y. Duan, M. Ek, and A. Betts, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251–7277.
Chen, F. Z., Z. I. Janjic, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterization in the new land-surface scheme of the NCEP mesoscale Eta model. Boundary Layer Meteorol., 48.
Chen, J., and P. Kumar, 2001: Topographic influence on the seasonal and interannual variation of water and energy balance of basins in North America. J. Climate, 14(9),1989–2014.
Cherkauer K. A., and D. P. Lettenmaier, 1999: Hydrologic effects of frozen soils in the upper Mississippi River basin. J. Geophys. Res. Atmos., 104 (D16), 19599-19610.
Ciach, G. J., W. F. Krajewski, and G. Villarini, 2007: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeor., 8, 1325-1347.
Clark, C. A., and R. W. Arritt, 1995: Numerical simulations of the effect of soil moisture and vegetation cover on the development of deep convection. J. Appl. Meteorol., 34, 2029–2045.
Cohen, D., M. A. Person, R. Daannen, S. Locke, D. Dahlstromn, V. Zabielski, T. C. Winter, D. O. Rosenburry, H. Wright, E. Ito, J. L. Nieber, and W. J. Gutowski Jr., 2006: Groundwater-supported evapotranspiration within glaciated watersheds and under conditions of climate change. J. Hydrol., 320, 484–500.
Cosgrove, B. A., D. Lohmann, K. E. Mitchell, P. R. Houser, E. F. Wood, J. C. Schaake, A. Robock, C. Marshall, J. Sheffield, Q. Duan, L. Luo, R. W. Higgins, R. T. Pinker, J. D. Tarpley, and J. Meng, 2003a: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108(D22), 8842, doi:10.1029/2002JD003118.
Cosgrove, B. A., D. Lohmann, K. E. Mitchell, P. R. Houser, E. F. Wood, J. C. Schaake, A. Robock, J. Sheffield, Q. Duan, L. Luo, R. W. Higgins, R. T. Pinker, and J. D. Tarpley, 2003b: Land surface model spin-up behaviour in the North American Land Data Assimilation System (NLDAS), J. Geophys. Res., 108, D228845, doi:10.1029/2002JD003316.
Dahm, C. N., M. B. Baker, D. I. Moore, and J. R. Thibault, 2003: Coupled biogeochemical and hydrological responses of streams and rivers to drought. Freshwater Biology, 48, 1219–1231.
David, C. H., D. J. Gochis, D. R. Maidment, W. Yu, D. N. Yates, and Z. -L. Yang, 2009: Using NHDPlus as the Land Base for the Noah-distributed Model. Trans. GIS, 13(4), 363–377.
De Jeu, R., and W. Dorigo, 2016: On the importance of satellite observed soil moisture, Int. J. Appl. Earth Observ. Geoinf., 45 (Part B), 107–109.
Dorigo W., and R. De Jeu, 2016: Satellite soil moisture for advancing our understanding of earth system processes and climate change, Int. J. Appl. Earth Observ. Geoinf., 48, 1–4.
Drusch, M., and P. Viterbo, 2007: Assimilation of Screen-Level Variables in ECMWF’s Integrated Forecast System: A Study on the Impact on the Forecast Quality and Analyzed Soil Moisture. Mon. Wea. Rev., 135, 300–314.
Ducharne, A., R. D. Koster, M. J. Suarez, M. Stieglitz, and P. Kumar, 2000: A catchment- based approach to modeling land surface processes in a general circulation model: 2. Parameter estimation and model demonstration. J. Geophys. Res., 105(D20), 24,823– 24,838.
Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 47–64.
Entekhabi, D., 1995: Recent advances in land-atmosphere interaction research. U.S. Natl. Rep. Int. Union Geod. Geophys. 1991– 1994, Rev. Geophys., 33, 995– 1003.
Entekhabi, D., E. G. Njoku, P. Houser, M. Spencer, T. Doiron, Y. Kim, J. Smith, R. Girard, S. Belair, W. Crow, T. J. Jackson, Y. H. Kerr, J. S. Kimball, R. Koster, K. C. McDonald, P. E. O’Neill, T. Pultz, S. W. Running, J. Shi, E. Wood, and J. Van Zyl, 2004: The Hydrosphere State (Hydros) Satellite Mission: an earth system pathfinder for global mapping of soil moisture and land freeze/thaw. IEEE Trans. Geosci. Remote Sens., 42(10), 2184–2195.
Entekhabi, D., E. G. Njoku, P. E. O'Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, S. D. Goodman, T. J. Jackson, J. Johnson, J. Kimball, J. R. Piepmeier, R. D. Koster, N. Martin, K. C. McDonald, M. Moghaddam, S. Moran, R. Reichle, J. C. Shi, M. W. Spencer, S. W. Thurman, L. Tsang, and J. Van Zyl, 2010: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704-716.
Entekhabi, D., S. Yueh, P.E. O’Neill, K. Kellogg, A. Allen, R. Bindlish, M. Brown, S. Chan, A. Colliander, W. T. Crow, N. Das, G. De Lannoy, R. S. Dunbar, W. N. Edelstein, J. K. Entin, V. Escobar, S. D. Goodman, T. J. Jackson, B. Jai, J. Johnson, E. Kim, S. Kim, J. Kimball, R. D. Koster, A. Leon, K. C. McDonald, M. Moghaddam, P. Mohammed, S. Moran, E. G. Njoku, J. R. Piepmeier, R. Reichle, F. Rogez, J.-C. Shi, M. W. Spencer, S. W. Thurman, L. Tsang, J. Van Zyl, B. Weiss, and R. West 2014. SMAP handbook: Mapping soil moisture and freeze/thaw from space. Publ. JPL 400-1567. NASA, Jet Propulsion Lab., Pasadena, CA.
Famiglietti, J. S., and E. F. Wood, 1994: Multiscale modeling of spatially variable water and energy balance processes. Water Resour. Res., 30, 3061–3078.
Fan, Y., G. Miguez-Macho, C. P. Weaver, R. Walko, and A. Robock, 2007: Incorporating water table dynamics in climate modeling: 1. Water table observations and
equilibrium water table simulations. J. Geophys. Res., 112, D10125, doi:10.1029/2006JD008111.
Fang L., C.R. Hain, X. Zhan, and M.C. Anderson, 2016: An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model. Int. J. Appl. Earth Observ. Geoinf., 48, 34-47.
Fels, S. B., and M. D. Schwarzkopf, 1975: The simplified exchange approximation: A new method for radiative transfer calculations. J. Atmos. Sci., 32, 1475–1488.
Ferrier, B. S., Y. Lin, T. Black, E. Rogers, and G. DiMego, 2002: Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta model. Preprints, 15th Conference on Numerical Weather Prediction, San Antonio, TX, Amer. Meteorol. Soc., 280-283.
Fischer, E.M., S.I. Seneviratne, P.L. Vidale, D. Lüthi, and C. Schär, 2007: Soil Moisture– Atmosphere Interactions during the 2003 European Summer Heat Wave. J. Climate, 20, 5081–5099.
Fulton, R., J. Breidenbach, D-J Seo, and D. Miller, 1998: The WSR-88D Rainfall Algorithm. Wea. Forecasting, 13, 377-395.
Gedney, N., and P. M. Cox, 2003: The sensitivity of global climate model simulations to the representation of soil moisture heterogeneity. J. Hydrometeor., 4, 1265–1275.
Gehne, M., T.M. Hamill, G.N. Kiladis, and K.E. Trenberth, 2016: Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales. J. Climate, 29, 7773–7795, https://doi.org/10.1175/JCLI-D-15-0618.1.
Gochis D. J., and F. Chen, 2003: Hydrological Enhancements to the Community Noah Land Surface Model. Web document, http://www.ucar.edu/library/collections/
technotes/technotes.jsp
Gottschalck, J., J. Meng, M. Rodell, and P. Houser, 2005: Analysis of multiple precipitation products and preliminary assessment of their impact on Global Land
Data Assimilation System land surface states. J. Hydrometeor., 6, 573–598.
Gutowski, W. J., Jr., 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, 4283, doi:10.1029/2001JD000392.
Heidke, P., 1926: Berechnung des erfolges und der gute der windstarkevorhersagen im sturmwarnungsdienst. Georg. Ann., 8, 310-349.
Hossain, F., and E. N. Anagnostou, 2004: Assessment of current passive microwave and infrared based satellite rainfall remote sensing for flood prediction, J. Geophys. Res., 109, D07102, doi:10.1029/2003JD003986.
Hossain, F., and E. N. Anagnostou, 2005a: Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture. Adv. Water Resour., 28, 1336-1350.
Hossain, F., and E. N. Anagnostou, 2005b: Using a multi-dimensional satellite rainfall error model to characterize uncertainty in soil moisture fields simulated by an offline land surface model. Geophys. Res. Lett., 32, L15402, doi:10.1029/2005GL023122.
Hossain, F., and E. N. Anagnostou, 2006: A two-dimensional satellite rainfall error model, IEEE Trans. Geosci. Remote Sens, 44, 1511-1522.
Hossain, F., and G.J. Huffman, 2008: Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales. J. Hydrometeor., 9, 563–575.
Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker, and D. B. Wolff, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55.
Jacob R., J. Larson, and E. Ong, 2005: MxN Communication and Parallel Interpolation in CCSM3 Using the Model Coupling Toolkit. Int. J. High Perf. Comp. Appl., 19(3), 293-307.
Janjic, Z. I., 1984: Non-linear advection schemes and energy cascade on semi-staggered grids. Mon. Wea. Rev., 112, 1234–1245.
Janjic, Z. I., 1994: The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945.
Janjic, Z. I., 1996a: The Mellor-Yamada level 2.5 scheme in the NCEP Eta Model. 11th Conference on Numerical Weather Prediction, Norfolk, VA, Amer. Meteorol. Soc., 333-334.
Janjic, Z. I., 1996b: The Surface Layer in the NCEP Eta Model. 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteorol. Soc., 354–355.
Janjic, Z. I., 2000: Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models”. J. Atmos. Sci., 57, p. 3686.
Janjic, Z. I., 2002: Nonsingular Implementation of the Mellor–Yamada Level 2.5 Scheme in the NCEP Meso model, NCEP Office Note, No. 437, 61 pp.
Janjic, Z.I., 2003: A nonhydrostatic model based on a new approach. Meteorol. Atmos. Phys., 82, 271-285.
Jiang X., G. Y. Niu, and Z. L. Yang, 2009: Impacts of vegetation and groundwater dynamics on warm season precipitation over the Central United States. J. of Geophys. Res. Atmos., 114, D06109.
Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503.
Ju W. M., J. M. Chen, T. A. Black, A. G. Barr, H. McCaughey, and N. T. Roulet, 2006: Hydrological effects on carbon cycles of Canada's forests and wetlands. Tellus B, 58(1) 16-30.
Kain, J. S., and J. M. Fritsch, 1992: The role of the convective ‘‘trigger function’’ in numerical forecasts of mesoscale convective systems. Meteor. Atmos. Phys., 49, 93– 106.
Kallos, G., S. Nickelic, A. Papadopoulos, D. Jovic, O. Kakaliagou, N. Misirlis, L. Boukas, N. Mimikou, G. Sakellaridis, J. Papageorgiou, E. Anadranistakis, and M. Manousakis, 1997: The regional weather forecasting system SKIRON: An overview. Proc. Symp. on Regional Weather Prediction on Parallel Computer Environments, Athens, Greece, University of Athens, 109–122.
Kerr Y. H., P. Waldteufel, J. P. Wigneron, J. M. Martinuzzi, J. Font, and M. Berger, 2001: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) Mission. IEEE Trans. Geosci. Remote Sens., 39(8), 1729–1735.
Kingston D. G., D. M. Hannah, D. M. Lawler, and G. R. McGregor, 2009: Climate-river flow relationships across montane and lowland environments in northern Europe. Hydrol. Processes, 23 (7), 985-996.
Kirschbaum, D., G. Huffman, G. Skofronick-Jackson, S. Braun, E. Stocker, K. Garrett, E. Jones, R. Adler, H. Wu, A. McNally, and B. Zaitchik, 2017: NASA’s Remotely- sensed Precipitation: A Reservoir for Applications Users. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-15-00296.1.
Kollet, S. J., and R.M. Maxwell, 2006: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model.Adv. Water Resour., 29, 945–958.
Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment- based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105(D20), 24,809–24,822.
Koster, R. D., and M. J. Suarez, 2001: Soil moisture memory in climate models, J. Hydrometeor., 6, 558– 570.
Koster, R. D., M. J. Suarez, P. Liu, U. Jambor, M. Kistler, A. Berg, R. Reichle, M. Rodell, and J. Famiglietti, 2004a: Realistic initialization of land surface states: impacts on subseasonal forecast skill, J. Hydrometeor., 5, 1049-1063.
Koster, R. D., P. A. Dirmeyer, Z. Guo, G. Bonan, E. Chan, P. Cox, C. T. Gordon, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu, C.-H. Lu, S. Malyshev, B. McAvaney, K. Mitchel, D. Mocko, T. Oki, K. Oleson, A. Pitman, Y. C. Sud, C. M. Taylor, D. Verseghy, R. Vasic, Y. Xue, and T. Yamada, 2004b: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138-1140.
Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood, and J. Sheffield, 2006: Land Information System - An interoperable framework for high resolution land surface modeling, Environ. Model. Softw., 21, 1402-1415.
Kumar, S. V., R. H. Reichle, R. D. Koster, W. T. Crow, and C. D. Peters-Lidard, 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. of Hydrometeor., 10, 1534-1547.
Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the earth’s atmosphere. J. Atmos. Sci., 31, 118–133.
Larson J., R. Jacob, and E. Ong, 2005: The Model Coupling Toolkit: A New Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models. Int. J. High Perf. Comp. Appl., 19(3), 277-292.
Lawrence, P. J., and T. N. Case, 2007: Representing a new MODIS Consistent Land Surface in the Community Land Model (CLM 3.0). J. Geophys. Res., 112, G01023,
doi: 10.1029/2006JG000168.
Leese, J., T. Jackson, A. Pitman, and P. Dirmeyer, 2001: meeting summary – GEWEX/BAHC international workshop on soil moisture monitoring, analysis, and
prediction for hydrometeorological and hydroclimatological applications. Bull. Amer. Meteor. Soc., 82, 1423–1430.
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, 8613, doi:10.1029/ 2002JD003090.
Lin, L.-F., A. M. Ebtehaj, J. Wang, and R. L. Bras, 2017: Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model. Water Resour. Res., 53, 1309–1335, doi:10.1002/2015WR017548.
Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Preprints. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2.
Liu, Q., R.H. Reichle, R. Bindlish, M.H. Cosh, W.T. Crow, R. de Jeu, G.J. De Lannoy, G.J. Huffman, and T.J. Jackson, 2011a: The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System. J. Hydrometeor., 12, 750–765, https://doi.org/10.1175/JHM-D- 10-05000.1
Liu Y.Y., R.M. Parinussa, W.A. Dorigo, R.A.M. De Jeu, W. Wagner, A.I.J.M. Van Dijk, M.F. McCabe, and J.P. Evans, 2011b: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci., 15, 425-436.
Liu Y.Y., W.A. Dorigo, R.M. Parinussa, R.A.M. De Jeu, W. Wagner, M.F. McCabe, J.P. Evans, and A.I.J.M. Van Dijk, 2012: Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ., 123, 280-297.
Maggioni, V., R. H. Reichle, and E. N. Anagnostou, 2011: The effect of satellite rainfall error modeling on soil moisture prediction uncertainty. J. Hydrometeor., 12, 413–428.
Maggioni, V., R.H. Reichle, and E.N. Anagnostou, 2012: The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data
Assimilation System. J. Hydrometeor., 13, 1107–1118, https://doi.org/10.1175/JHM- D-11-0115.1
Maggioni V. and P. R. Houser, 2017: Soil Moisture Data Assimilation. In: Park S., Xu L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. III, Springer, Cham
Mahfouf, J.-F., 1991: Analysis of soil moisture from near-surface parameters. A feasibility study. J. Appl. Meteor., 30, 1534–1547.
Maxwell, R. M., and N. L. Miller, 2005: Development of a coupled land surface and groundwater model. J. Hydrometeor., 6, 233– 247.
McNally, A., S. Shukla, K.R. Arsenault, S. Wang, C.D. Peters-Lidard, and J.P Verdin, 2016: Evaluating ESA CCI soil moisture in East Africa. Int. J. Appl. Earth Obs. Geoinf., doi:10.1016/j.jag.2016.01.001.
Mecklenburg, S., M. Drusch, Y. H. Kerr, M. Martin-Neira, D. Steven, G. Buenadicha, N. Reul, E. Daganzo, R. Oliva, and R. Crapolicchio, 2012: ESA’s Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations. IEEE Trans. Geosci. Rem. Sens., 50, no. 5, part 1, 1354–1366.
Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851-875.
Mesinger, F., Z. I. Janjic, S. Nickovic, D. Gavrilov, and D. G. Deaven, 1988: The step- mountain coordinate: Model description and performance for cases of Alpine lee cyclogenesis and for a case of an Appalachian redevelopment. Mon. Wea. Rev., 116, 1493–1518.
Miguez-Macho, G., Y. Fan, C. P. Weaver, R. Walko, and A. Robock, 2007: Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation. J. of Geophys. Res., 112, D13108, doi:10.1029/2006JD008112.
Mitchell, K. E., D. Lohmann, P. R. Houser, E. F. Wood, J. C. Schaake, A. Robock, B. A. Cosgrove, J. Sheffield, Q. Duan, L. Luo, R. W. Higgins, R. T. Pinker, J. D. Tarpley, D. P. Lettenmaier, C. H. Marshall, J. K. Entin, M. Pan, W. Shi, V. Koren, J. Meng, B. H. Ramsay, and A. A. Bailey, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90,doi:10.1029/2003JD003823.
Niu, G. Y., and Z. L. Yang, 2003: The versatile integrator of surface atmospheric processes—Part 2: Evaluation of three topography-based runoff schemes. Global Planet. Change, 38(1–2), 191–208.
Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, and L. E. Gulden, 2005: A simple TOPMODEL- based runoff parameterization (SIMTOP) for use in global climate models, J. Geophys. Res., 110, D21106, doi:10.1029/2005JD006111.
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, D07103, doi:10.1029/ 2006JD007522.
O’Callaghan, J. F., and D. M. Mark, 1984: The extraction of drainage networks from digital elevation data, Comp. Vision Graph. Image Process., 28(3), 323-344.
Oleson, K. W., Y. Dai, G. Bonan, M. Bosilovich, R. Dickinson, P. Dirmeyer, F. Hoffman, P. Houser, S. Levis, G.-Y. Niu, P. Thornton, M. Vertenstein, Z.-L. Yang, and X. Xeng, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-4611STR, 173 pp.
Oleson, K. W., G.-Y. Niu, Z.-L. Yang, D. M. Lawrence, P. E. Thornton, P. J. Lawrence, R. Stöckli, R. E. Dickinson, G. B. Bonan, S. Levis, A. Dai, and T. Qian, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.
Osman Y. R., and M. P. Bruen, 2002: Modelling stream–aquifer seepage in an alluvial aquifer: an improved loosing-stream package for MODFLOW. J. Hydrol., 264, 69– 86.
Papadopoulos, A., G. Kallos, S. Nickovic, D. Jovic, M. Dacic, and P. Katsafados, 1997:Sensitivity studies of the surface and radiation parameterization schemes of the SKIRON system. Proc. Int. Symp. on Regional Weather Prediction on Parallel Computer Environments, 15-17 October 1997, Athens, Greece, 155-164.
Papadopoulos, A., G. Kallos, P. Katsafados, and S. Nickovic, 2002: The Poseidon weather forecasting system: An overview. GAOS, 8, 219-237.
Papadopoulos, A., E. Serpetzoglou, and E. N. Anagnostou, 2008: Improving NWP through radar rainfall-driven land surface parameters: A case study on convective precipitation forecasting. Adv. Water Resour., 31, Special Issue on Hydrologic Remote Sensing, 1456-1469.
Papadopoulos, A., E. Serpetzoglou, and E. N. Anagnostou, 2009: Evaluating the impact of lightning data assimilation on mesoscale model simulations of a flash flood inducing storm. Atm. Res., 94 (4), 715–725.
Ramos da Silva, R., and R. Avissar, 2006: The Hydrometeorology of a Deforested Region of the Amazon Basin. J. Hydrometeor., 7, 1028–1042.
Reichle, R.H., R.D. Koster, J. Dong, and A.A. Berg, 2004: Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation. J. Hydrometeor., 5, 430–442.
Reichle, R. H., R. D. Koster, P. Liu, S. P. P. Mahanama, E. G. Njoku, and M. Owe, 2007: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033.
Reichle, R. H., W. T. Crow, R. D. Koster, H. Sharif, and S. P. P. Mahanama, 2008: Contribution of soil moisture retrievals to land data assimilation products. Geophys. Res. Lett., 35, dx.doi.org/10.1029/2007GL031986.
Reichle, R. H., G. J. M. De Lannoy, B. A. Forman, C. S. Draper, and Q. Liu, 2013: Connecting Satellite Observations with Water Cycle Variables through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS. Surv. Geophys., 35, 577– 606, dx.doi.org/10.1007/s10712-013-9220-8.
Robock, A., K.Y. Vinnikov, G. Srinivasan, J.K. Entin, S.E. Hollinger, N.A. Speranskaya, S. Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteor. Soc., 81, 1281–1299.
Robock, A., L. Luo, E. F. Wood, F. Wen, K. E. Mitchell, P. R. Houser, J. C. Schaake, D. Lohmann, B. Cosgrove, J. Sheffield, Q. Duan, R. W. Higgins, R. T. Pinker, J. D. Tarpley, J. B. Basara, and K. C. Crawford, 2003: Evaluation of the North American Land Data Assimilation System over the Southern Great Plains during the warm season, J. Geophys. Res., 108, 8846, doi:10.1029/2002JD003245, 2003.
Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, D. Lohmann, and D. Toll, 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381–394.
Rodell, M., P. R. Houser, A. A. Berg, and J. S. Famiglietti, 2005: Evaluation of ten methods for initializing a land surface model, J. Hydrometeor., 6, 146-155.
Rushton K., 2007: Representation in regional models of saturated river–aquifer interaction for gaining/losing rivers. J. Hydrol., 334, 262–281.
Schaake, J. C., Q. Duan, V. Koren, K. E. Mitchell, P. R. Houser, E. F. Wood, A. Robock, D. P. Lettenmaier, D. Lohmann, B. Cosgrove, J. Sheffield, L. Luo, R. W. Higgins, R. T. Pinker, and J. D. Tarpley, 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS), J. Geophys. Res., 109, D01S90, doi:10.1029/2002JD003309.
Schär, C., D. Luthi, and U. Beyerle, 1999: The soil-precipitation feedback: A process study with a regional climate model. J. of Climate, 12, 722-741.
Schmidhalter, U., H. S. Salem, and J. J. Oertli, 1994: Measuring and modeling root water uptake based on 36 chloride discrimination in a silt loam soil affected by groundwater. Soil Science, 158, 97–105.
Schwarzkopf, M. D., and S. B. Fels, 1991: The simplified exchange method revisited: An accurate, rapid method for computation of infrared cooling rates and fluxes. J. Geophys. Res., 96, 9075–9096.
Scott, R. L., T. E. Huxman, D. G. Williams, and D. C. Goodrich, 2006: Ecohydrological impacts of woody-plant encroachment: Seasonal patterns of water and carbon dioxide exchange within a semiarid riparian environment. Glob. Change Biol., 12, 311–324.
Serpetzoglou E., E. N. Anagnostou, A. Papadopoulos, E. I. Nikolopoulos, and V. Maggioni, 2010: Error Propagation of Remote Sensing Rainfall Estimates in Soil
Moisture Prediction from a Land Surface Model. J. Hydrometeor, 11, 705–720, doi: 10.1175/2009JHM1166.1.
Serpetzoglou, E., T. S. Karacostas, I. Pytharoulis, P. Zanis, A. Papadopoulos, and E. N. Anagnostou: Sensitivity analysis on the ingestion of remotely sensed precipitation data into the land surface scheme of a mesoscale model. Nat. Hazards Earth Syst. Sci., to be submitted.
Seuffert, G., P. Gross, C. Simmer, and E. F. Wood, 2002: The influence of hydrologic modeling on the predicted local weather: two-way coupling of a mesoscale weather prediction model and a land surface hydrologic model. J. Hydrometeor., 3, 505–523.
Shafer, M. A., C.A. Fiebrich, D. S. Arndt, S. E. Fredrickson, and T. W. Hughes, 2000: Quality assurance procedures in the Oklahoma Mesonet. J. Atm. Ocean. Tech., 17, 474-494.
Skofronick-Jackson, G., W. A. Petersen, W. Berg, C. Kidd, E. F. Stocker, D. B. Kirschbaum, R. Kakar, S. A. Braun, G. J. Huffman, T. Iguchi, P. E. Kirstetter, C. Kummerow, R. Meneghini, R. Oki, W. S. Olson, Y. N. Takayabu, K. Furukawa, and T. Wilheit, 2017: The Global Precipitation Measurement (GPM) Mission for Science and Society. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-15-00306.1.
Smith, E. A., G. Asrar, Y. Furuhama, A. Ginati, A. Mugnai, K. Nakamura, R. F. Adler, M.-D. Chou, M. Desbois, J. F. Durning, J. K. Entin, F. Einaudi, R. R. Ferraro, R. Guzzi, P. R. Houser, P. H. Hwang, T. Iguchi, P. Joe, R. Kakar, J. A. Kaye, M. Kojima, C. Kummerow, K.-S. Kuo, D. P. Lettenmaier, V. Levizzani, N. Lu, A. V. Mehta, C. Morales, P. Morel, T. Nakazawa, S. P. Neeck, K. Okamoto, R. Oki, G. Raju, J. M. Shepherd, J. Simpson, B.-J. Sohn, E. F. Stocker, W.-K. Tao, J. Testud, G. J. Tripoli, E. F. Wood, S. Yang, and W. Zhang, 2007: International Global Precipitation Measurement (GPM) program and mission: An overview. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Springer, 611–654.
Snyder, K. A., and D. G. Williams, 2000: Water sources used by riparian trees varies among stream types on the San Pedro River, Arizona. Agric. Forest Meteorol., 105, 227–240.
Sophocleous, M. A., 2002: Interactions between groundwater and surface water: the state of the science. Hydrogeology J., 10 (1), pp. 52–67.
Sorooshian, S., K.L. Hsu, X. Gao, H.V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall.
Bull. Amer. Meteor. Soc., 81, 2035–2046.
Steiner A. L., J. S. Pal, S. A. Rauscher, J. L. Bell, N. S. Diffenbaugh, A. Boone, L. C. Sloan, and F. Giorgi., 2009: Land surface coupling in regional climate simulations of the West African monsoon. Climate Dynamics, 33(6), 869-892.
Steinwand, A. L., R. F. Harrington, and D. Or, 2006: Water balance for Great Basin phreatophytes derived from eddy covariance, soil water, and water table measurements. J. Hydrol., 329, 595–605.
Stieglitz, M., D. Rind, J. Famiglietti, and C. Rosenzweig, 1997: An efficient approach to modeling the topographic control of surface hydrology for regional and global climate modeling. J. Climate, 10, 118–137.
Stöckli, R., D. M. Lawrence, G.-Y. Niu, K. W. Oleson, P. E. Thornton, Z.-L. Yang, G. B. Bonan, A. S. Denning, and S. W. Running, 2008: Use of FLUXNET in the Community Land Model development, J. Geophys. Res., 113, G01025, doi:10.1029/2007JG000562.
Teuling, A. J., R. Uijlenhoet, F. Hupet, E. E. van Loon, and P. A. Troch, 2006: Estimating spatial mean root-zone soil moisture from point-scale observations. Hydrol. Earth Syst. Sci., 10, 755-767.
Tian, Y., C. D. Peters-Lidard, B. J. Choudhury, and M. Garcia, 2007: Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications. J. Hydrometeor., 8, 1165–1183.
Tian, X., Z. Xie, and A. Dai, 2008: A land surface soil moisture data assimilation system based on the dual-UKF method and the Community Land Model, J. Geophys. Res., 113, D14127, doi:10.1029/2007JD009650.
Viterbo, P., 1995: Initial values of soil water and the quality of summer forecasts. ECMWF Newslett., 69, 2–8.
Walker, J.P., P. Houser, and R. Reichle, 2003: New technologies require advances in hydrologic data assimilation, EOS, 84, 545.
Walker, J. P., and P. Houser, 2004: Requirements of a global near-surface soil moisture satellite mission: accuracy, repeat time, and spatial resolution. Adv. Water Resour., 27, 785-801.
Walko, R. L., L. E. Band, J. Baron, T. G. F. Kittel, R. Lammers, T. J. Lee, D. Ojima, R. A. Pielke Sr., C. Taylor, C. Tague, C. J. Tremback, and P. L. Vidale, 2000: Coupled atmosphere-biophysics-hydrology models for environmental modeling. J. Appl. Meteorol., 39, 931–944.
Wan, Z. M., 2008: New refinements and validation of the MODIS land-surface temperature/emissivity products. Rem. Sens. Environ., 112, 59–74.
Wang D., G. Wang, and E. N. Anagnostou, 2005: Use of Satellite-Based Precipitation Observation in Improving the Parameterization of Canopy Hydrological Processes in Land Surface Models. J. Hydrometeor., 6, 745–763.
Wang D., E. N. Anagnostou, and G. Wang, 2006: The effect of sub-grid rainfall variability on the water balance and flux exchange processes resolved at climate scale: the European region contrasted to Central Africa and Amazon rainforests. Adv. Geosci., 7, 269–274.
Wang D., G. Wang, and E. N. Anagnostou, 2009a: Impact of sub-grid variability of precipitation and canopy water storage on hydrological processes in a coupled land– atmosphere model. Climate Dynamics, Vol. 32, 649-662, DOI: 10.1007/s00382-008- 0435-1.
Wang, L., T. Koike, K. Yang, P. J.-F. Yeh, 2009b: Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin. J. Hydrol., doi:10.1016/j.jhydrol.2009.08.005.
Weaver C. P., 2006: Coupling between large-scale atmospheric processes and mesoscale land-atmosphere interactions in the US Southern Great Plains during summer. Part I: Case studies. J. Hydrometeor., 5(6), 1223-1246.
Wedgbrow, C. S., R. L. Wilby, H. R. Fox, and G. O'Hare, 2002: Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales. Int. J. Climatol., 22 (2), 219-236.
Wilks, D.S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.
Xie, Z. H., and X. Yuan, 2010: Prediction of water table under stream-aquifer interactions over an arid region. Hydrol. Process., 24 (2), 160-169.
Yang, Z. L., and G. Y. Niu, 2003: The versatile integrator of surface and atmosphere processes—Part 1: model description, Global Plan. Change, 38(1–2), 175–189.
Yeh, P. J.-F., and E. A. B. Eltahir, 2005a: Representation of water table dynamics in a land surface scheme. Part I: Model development. J. Climate, 18, 1861–1880.
Yeh, P. J.-F., and E. A. B. Eltahir, 2005b: Representation of water table dynamics in a land surface scheme. Part II: Subgrid variability. J. Climate, 18, 1881–1901.
Yeh, P. J. F., and J. S. Famiglietti, 2009: Regional Groundwater Evapotranspiration in Illinois. J. Hydrol., 10(2), 464-478.
York, J. P., M. Person, W. J. Gutowski, and T. C. Winter, 2002: Putting aquifers into atmospheric simulation models: An example from the Mill Creek Watershed,
northeastern Kansas. Adv. Water Resour., 25, 221–238.
Yuan X., Z. H. Xie, J. Zheng, X. J. Tian, and Z. L. Yang, 2008: Effects of water table dynamics on regional climate: A case study over east Asian monsoon area. J. Geophys. Res. Atmos., 113(d21), D21112.
Zampieri, M., F. D'Andrea, R. Vautard, Ph. Ciais, N. de Noblet-Ducoudré, and P. Yiou, 2009: Hot European Summers and the role of soil moisture in the propagation of Mediterranean drought. J. Climate, 22, 4747–4758.
Zampieri, Μ., E. Serpetzoglou, E. N. Anagnostou, E. I. Nikolopoulos, and A. Papadopoulos, 2012: Improving the representation of river–groundwater interactions
in land surface modeling at the regional scale: Observational evidence and parameterization applied in the Community Land Model. J. Hydrol., 420–421, 72-86.
Zhang W. H., and D. R. Montgomery, 1994: Digital elevation model grid size, landscape representation, and hydrologic simulation. Water Resour. Res., 30(4), 1019-1028.
Zhang, H., and C. S. Frederiksen, 2003: Local and non-local impacts of soil moisture initialization on AGCM seasonal forecasts: A model sensitivity study. J. Climate, 16, 2117– 2137.
Zhao, Q., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 1931–1953.
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