Rutas hidrológicas : recordando a un colega por los senderos de la hidrología
R ECORDANDO A UN COLEGA POR LOS SENDEROS DE LA HIDROLOGÍA R UTAS H IDROLÓGICAS 16 attributes and meteorology for large sample studies – Chile dataset. Hydrology and Earth System Sciences 22 (11): 5817– 5846 DOI: 10.5194/hess-22-5817-2018 Andreadis KM, Storck P, Lettenmaier DP. 2009. Modeling snow accumulation and ablation processes in forested environments. Water Resources Research 45: W05429 DOI: 10.1029/2008WR007042 Andréassian V, Hall A, Chahinian N, Schaake J. 2006. Why should hydrologists work on a large number of basin data sets? In Large Sample Basin Experiments for Hydrological Model Parameterization. Results of the Model Parameter Experiment - MOPEX. IAHS Publ. 3071–5. Bahr B, Meier F, Peckham SD. 1997. The physical basis of glacier volume-area scaling. Journal of Geophysical Research 102 (B9): 20355–20362 DOI: doi:10.1029/97JB01696 Beck HE, van Dijk AIJM, de Roo A, Miralles DG, McVicar TR, Schellekens J, Bruijnzeel LA. 2016. Global-scale regionalization of hydrologic model parameters. Water Resources Research 52 (5): 3599–3622 DOI: 10.1002/2015WR018247 Berghuijs WR, Sivapalan M, Woods R a, Savenije HHG. 2014. Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales. Water Resources Research 50 (7): 5638–5661 DOI: 10.1002/2014WR015692 Beven K. 1997. TOPMODEL: a critique. Hydrological processes 11 (December 1996): 1069–1085 Beven K. 2000. Uniqueness of place and process representations in hydrological modelling. Hydrology and Earth System Sciences 4 (2): 203–213 Beven KJ, Cloke HL. 2012. Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water” by Eric F. Wood et al. Water Resources Research 48: W01801 DOI: 10.1029/2011WR010982 Bozkurt D, Rojas M, Boisier JP, Valdivieso J. 2018. Projected hydroclimate changes over Andean basins in central Chile from downscaled CMIP5 models under the low and high emission scenarios. Climatic Change 150 (3–4): 131–147 DOI: 10.1007/s10584-018-2246-7 Burnash R, Ferral R, McGuire R. 1973. A generalized streamflow simulation system - Conceptual modeling for digital computers. Sacramento, California. C3S. 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS) Available at: https://cds.climate.copernicus.eu/cdsapp#!/home [Accessed January 20, 2018] Cheeseman P, Stutz J. 1996. Bayesian Cassification (AutoClass): Theory and Results. In Advances in Knowledge Discovery and Data Mining. Chen F, Mitchell K, Schaake J, Xue Y, Pan H, Koren V, Duan QY, Ek M, Betts A, Climatology LS, et al. 1996. Modeling of land surface evaporation by four schemes and comparison with FIFE observations. 101 (1): 7251–7268 Cherkauer KA, Bowling LC, Lettenmaier DP. 2003. Variable infiltration capacity cold land process model updates. Global and Planetary Change 38 (1–2): 151–159 DOI: 10.1016/S0921- 8181(03)00025-0 Clark MP, Bierkens MFP, Samaniego L, Woods RA, Uijlenhoet R, Bennett KE, Pauwels VRN, Cai X, Wood AW, Peters-Lidard CD. 2017. The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism. Hydrology and Earth System Sciences 21 (7): 3427– 3440 DOI: 10.5194/hess-21-3427-2017 Clark MP, McMillan HK, Collins DBG, Kavetski D, Woods RA. 2011. Hydrological field data from a modeller’s perspective: Part 2: process-based evaluation of model hypotheses. Hydrological Processes 25 (4): 523–543 DOI: 10.1002/hyp.7902 Clark MP, Nijssen B, Lundquist JD, Kavetski D, Rupp DE, Woods RA, Freer JE, Gutmann ED, Wood AW, Brekke LD, et al. 2015. A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research DOI: 10.1002/2015WR017198 Clark MP, Rupp DE, Woods R a., Zheng X, Ibbitt RP, Slater AG, Schmidt J, Uddstrom MJ. 2008a. Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources 31 (10): 1309–1324 DOI: 10.1016/j.advwatres.2008.06.005 Clark MP, Schaefli B, Schymanski SJ, Samaniego L, Luce CH, Jackson BM, Freer JE, Arnold JR, Moore RD, Istanbulluoglu E, et al. 2016. Improving the theoretical underpinnings of process-based hydrologic models. Water Resources Research 52 (3): 2350–2365 DOI: 10.1002/2015WR017910 Clark MP, Slater AG, Rupp DE, Woods RA, Vrugt JA, Gupta H V., Wagener T, Hay LE. 2008b. Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resources Research 44: W00B02 DOI: 10.1029/2007WR006735 Condom T, Escobar M, Purkey D, Pouget JC, Suarez W, Ramos C, Apaestegui J, Tacsi A, Gomez J. 2012. Simulating the implications of glaciers’ retreat for water management: A case study in the Rio Santa basin, Peru. Water International 37 (4): 442–459 DOI: 10.1080/02508060.2012.706773 Coron L, Andréassian V, Perrin C, Bourqui M, Hendrickx F. 2014. On the lack of robustness of hydrologic models regarding water balance simulation: A diagnostic approach applied to three models of increasing complexity on 20 mountainous catchments. Hydrology and Earth System Sciences 18 (2): 727–746 DOI: 10.5194/hess-18-727-2014 Coxon G, Freer J, Lane R, Dunne T, Knoben WJM, Howden NJK, Quinn N, Wagener T, Woods R. 2019. DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geoscientific Model Development 12 (6): 2285– 2306 DOI: 10.5194/gmd-12-2285-2019 Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, et al. 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137 (656): 553–597 DOI: 10.1002/qj.828 Deems JS, Painter TH, Finnegan DC. 2013. Lidar measurement of snow depth: A review. Journal of Glaciology 59 (215): 467– 479 DOI: 10.3189/2013JoG12J154 Dembélé M, Hrachowitz M, Savenije HHG, Mariéthoz G, Schaefli B. 2020. Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets. Water Resources Research 56 (1): 1–26 DOI: 10.1029/2019WR026085 DGA. 1983a. Balance hidrológico nacional regiones V, VI, VII y Metropolitana. Santiago de Chile. DGA. 1983b. Balance Hídrico Nacional. Regiones VIII, IX y X. Santiago de Chile. DGA. 1984. Balance Hidrológico Nacional XI Región. Santiago de Chile. DGA. 1985. Balance hidrológico nacional Cuenca del Rio Itata. Santiago de Chile. DGA. 1987. Balance hídrico de Chile DGA. 1995. Manual de cálculo de crecidas y caudales mínimos en cuencas sin información fluviométrica. Santiago de Chile.
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