Articles | Volume 17, issue 9
https://doi.org/10.5194/hess-17-3455-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-3455-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates
J. H. Spaaks
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands
Netherlands eScience Center, Amsterdam, the Netherlands
W. Bouten
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands
Related subject area
Subject: Hillslope hydrology | Techniques and Approaches: Theory development
Young and new water fractions in soil and hillslope waters
Energy efficiency in transient surface runoff and sediment fluxes on hillslopes – a concept to quantify the effectiveness of extreme events
Morphological controls on surface runoff: an interpretation of steady-state energy patterns, maximum power states and dissipation regimes within a thermodynamic framework
Soil moisture: variable in space but redundant in time
A history of the concept of time of concentration
Are dissolved organic carbon concentrations in riparian groundwater linked to hydrological pathways in the boreal forest?
The influence of diurnal snowmelt and transpiration on hillslope throughflow and stream response
Slope–velocity equilibrium and evolution of surface roughness on a stony hillslope
Assessment of land use impact on hydraulic threshold conditions for gully head cut initiation
Technical note: Inference in hydrology from entropy balance considerations
Ecohydrological effects of stream–aquifer water interaction: a case study of the Heihe River basin, northwestern China
Hillslope-scale experiment demonstrates the role of convergence during two-step saturation
Impacts of climate variability on wetland salinization in the North American prairies
Runoff formation from experimental plot, field, to small catchment scales in agricultural North Huaihe River Plain, China
Addressing secondary school students' everyday ideas about freshwater springs in order to develop an instructional tool to promote conceptual reconstruction
Hydrological heterogeneity in Mediterranean reclaimed slopes: runoff and sediment yield at the patch and slope scales along a gradient of overland flow
Effect of hydraulic parameters on sediment transport capacity in overland flow over erodible beds
Large-scale runoff generation – parsimonious parameterisation using high-resolution topography
Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery
Seasonal evaluation of the land surface scheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian region in Hungary
Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions
Modelling field scale water partitioning using on-site observations in sub-Saharan rainfed agriculture
Evaluation of alternative formulae for calculation of surface temperature in snowmelt models using frequency analysis of temperature observations
Growth of a high-elevation large inland lake, associated with climate change and permafrost degradation in Tibet
Selection of an appropriately simple storm runoff model
Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception
Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation
A contribution to understanding the turbidity behaviour in an Amazon floodplain
Global spatial optimization with hydrological systems simulation: application to land-use allocation and peak runoff minimization
Implementing small scale processes at the soil-plant interface – the role of root architectures for calculating root water uptake profiles
Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity
Modelling the inorganic nitrogen behaviour in a small Mediterranean forested catchment, Fuirosos (Catalonia)
Soil bioengineering for risk mitigation and environmental restoration in a humid tropical area
Climate and terrain factors explaining streamflow response and recession in Australian catchments
Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site
Characteristics of 2-D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS
The contribution of groundwater discharge to the overall water budget of two typical Boreal lakes in Alberta/Canada estimated from a radon mass balance
Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau
Calibration analysis for water storage variability of the global hydrological model WGHM
Earth's Critical Zone and hydropedology: concepts, characteristics, and advances
Reducing scale dependence in TOPMODEL using a dimensionless topographic index
Spatial variation in soil active-layer geochemistry across hydrologic margins in polar desert ecosystems
Nitrogen retention in natural Mediterranean wetland-streams affected by agricultural runoff
Recent trends in groundwater levels in a highly seasonal hydrological system: the Ganges-Brahmaputra-Meghna Delta
Water availability, demand and reliability of in situ water harvesting in smallholder rain-fed agriculture in the Thukela River Basin, South Africa
Variability of the groundwater sulfate concentration in fractured rock slopes: a tool to identify active unstable areas
Copula based multisite model for daily precipitation simulation
Solid phase evolution in the Biosphere 2 hillslope experiment as predicted by modeling of hydrologic and geochemical fluxes
Deriving a global river network map and its sub-grid topographic characteristics from a fine-resolution flow direction map
Surface water acidification and critical loads: exploring the F-factor
Marius G. Floriancic, Scott T. Allen, and James W. Kirchner
EGUsphere, https://doi.org/10.5194/egusphere-2024-437, https://doi.org/10.5194/egusphere-2024-437, 2024
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We use a 3-year timeseries of tracer data in streamflow and soils to illustrate how water moves through the subsurface to become streamflow. Less than 50% of soil water consists of rainfall from the last 3 weeks. Most annual streamflow is older than 3 months, waters in deep subsurface layers are even older, thus deep layers are not the only source of streamflow. After wet periods more rainfall was found in the subsurface and the stream, suggesting that water moves quicker through wet landscapes.
Samuel Schroers, Ulrike Scherer, and Erwin Zehe
Hydrol. Earth Syst. Sci., 27, 2535–2557, https://doi.org/10.5194/hess-27-2535-2023, https://doi.org/10.5194/hess-27-2535-2023, 2023
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The hydrological cycle shapes our landscape. With an accelerating change of the world's climate and hydrological dynamics, concepts of evolution of natural systems become more important. In this study, we elaborated a thermodynamic framework for runoff and sediment transport and show from model results as well as from measurements during extreme events that the developed concept is useful for understanding the evolution of the system's mass, energy, and entropy fluxes.
Samuel Schroers, Olivier Eiff, Axel Kleidon, Ulrike Scherer, Jan Wienhöfer, and Erwin Zehe
Hydrol. Earth Syst. Sci., 26, 3125–3150, https://doi.org/10.5194/hess-26-3125-2022, https://doi.org/10.5194/hess-26-3125-2022, 2022
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In hydrology the formation of landform patterns is of special interest as changing forcings of the natural systems, such as climate or land use, will change these structures. In our study we developed a thermodynamic framework for surface runoff on hillslopes and highlight the differences of energy conversion patterns on two related spatial and temporal scales. The results indicate that surface runoff on hillslopes approaches a maximum power state.
Mirko Mälicke, Sibylle K. Hassler, Theresa Blume, Markus Weiler, and Erwin Zehe
Hydrol. Earth Syst. Sci., 24, 2633–2653, https://doi.org/10.5194/hess-24-2633-2020, https://doi.org/10.5194/hess-24-2633-2020, 2020
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We could show that distributed soil moisture time series bear a considerable amount of information about dynamic changes in soil moisture. We developed a new method to describe spatial patterns and analyze their persistency. By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty. This does help to understand when and why the soil is drying in an organized manner.
Keith J. Beven
Hydrol. Earth Syst. Sci., 24, 2655–2670, https://doi.org/10.5194/hess-24-2655-2020, https://doi.org/10.5194/hess-24-2655-2020, 2020
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The concept of time of concentration in the analysis of catchment responses dates back over 150 years. It is normally discussed in terms of the velocity of flow of a water particle from the furthest part of a catchment to the outlet. This is also the basis for the definition in the International Glossary of Hydrology, but this is in conflict with the way in which it is commonly used. This paper provides a clarification of the concept and its correct useage.
Stefan W. Ploum, Hjalmar Laudon, Andrés Peralta-Tapia, and Lenka Kuglerová
Hydrol. Earth Syst. Sci., 24, 1709–1720, https://doi.org/10.5194/hess-24-1709-2020, https://doi.org/10.5194/hess-24-1709-2020, 2020
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Near-stream areas, or riparian zones, are important for the health of streams and rivers. If these areas are disturbed by forestry or other anthropogenic activity, the water quality and all life in streams may be at risk. We examined which riparian areas are particularly sensitive. We found that only a few wet areas bring most of the rainwater from the landscape to the stream, and they have a unique water quality. In order to maintain healthy streams and rivers, these areas should be protected.
Brett Woelber, Marco P. Maneta, Joel Harper, Kelsey G. Jencso, W. Payton Gardner, Andrew C. Wilcox, and Ignacio López-Moreno
Hydrol. Earth Syst. Sci., 22, 4295–4310, https://doi.org/10.5194/hess-22-4295-2018, https://doi.org/10.5194/hess-22-4295-2018, 2018
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The hydrology of high-elevation headwaters in midlatitudes is typically dominated by snow processes, which are very sensitive to changes in energy inputs at the top of the snowpack. We present a data analyses that reveal how snowmelt and transpiration waves induced by the diurnal solar cycle generate water pressure fluctuations that propagate through the snowpack–hillslope–stream system. Changes in diurnal energy inputs alter these pressure cycles with potential ecohydrological consequences.
Mark A. Nearing, Viktor O. Polyakov, Mary H. Nichols, Mariano Hernandez, Li Li, Ying Zhao, and Gerardo Armendariz
Hydrol. Earth Syst. Sci., 21, 3221–3229, https://doi.org/10.5194/hess-21-3221-2017, https://doi.org/10.5194/hess-21-3221-2017, 2017
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This study presents novel scientific understanding about the way that hillslope surfaces form when exposed to rainfall erosion, and the way those surfaces interact with and influence runoff velocities during rain events. The data show that hillslope surfaces form such that flow velocities are independent of slope gradient and dependent on flow rates alone. This result represents a shift in thinking about surface water runoff.
Aliakbar Nazari Samani, Qiuwen Chen, Shahram Khalighi, Robert James Wasson, and Mohammad Reza Rahdari
Hydrol. Earth Syst. Sci., 20, 3005–3012, https://doi.org/10.5194/hess-20-3005-2016, https://doi.org/10.5194/hess-20-3005-2016, 2016
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We hypothesized that land use had important effects on hydraulic threshold conditions for gully head cut initiation. We investigated the effects using an experimental plot. The results indicated that the use of a threshold value of τcr = 35 dyne cm−2 and ωu = 0.4 Cm S−1 in physically based soil erosion models is susceptible to high uncertainty when assessing gully erosion.
Stefan J. Kollet
Hydrol. Earth Syst. Sci., 20, 2801–2809, https://doi.org/10.5194/hess-20-2801-2016, https://doi.org/10.5194/hess-20-2801-2016, 2016
Yujin Zeng, Zhenghui Xie, Yan Yu, Shuang Liu, Linying Wang, Binghao Jia, Peihua Qin, and Yaning Chen
Hydrol. Earth Syst. Sci., 20, 2333–2352, https://doi.org/10.5194/hess-20-2333-2016, https://doi.org/10.5194/hess-20-2333-2016, 2016
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In arid areas, stream–aquifer water exchange essentially sustains the growth and subsistence of riparian ecosystem. To quantify this effect for intensity and range, a stream–riverbank scheme was incorporated into a state-of-the-art land model, and some runs were set up over Heihe River basin, northwestern China. The results show that the hydrology circle is significantly changed, and the ecological system is benefitted greatly by the river water lateral transfer within a 1 km range to the stream.
A. I. Gevaert, A. J. Teuling, R. Uijlenhoet, S. B. DeLong, T. E. Huxman, L. A. Pangle, D. D. Breshears, J. Chorover, J. D. Pelletier, S. R. Saleska, X. Zeng, and P. A. Troch
Hydrol. Earth Syst. Sci., 18, 3681–3692, https://doi.org/10.5194/hess-18-3681-2014, https://doi.org/10.5194/hess-18-3681-2014, 2014
U. Nachshon, A. Ireson, G. van der Kamp, S. R. Davies, and H. S. Wheater
Hydrol. Earth Syst. Sci., 18, 1251–1263, https://doi.org/10.5194/hess-18-1251-2014, https://doi.org/10.5194/hess-18-1251-2014, 2014
S. Han, D. Xu, and S. Wang
Hydrol. Earth Syst. Sci., 16, 3115–3125, https://doi.org/10.5194/hess-16-3115-2012, https://doi.org/10.5194/hess-16-3115-2012, 2012
S. Reinfried, S. Tempelmann, and U. Aeschbacher
Hydrol. Earth Syst. Sci., 16, 1365–1377, https://doi.org/10.5194/hess-16-1365-2012, https://doi.org/10.5194/hess-16-1365-2012, 2012
L. Merino-Martín, M. Moreno-de las Heras, S. Pérez-Domingo, T. Espigares, and J. M. Nicolau
Hydrol. Earth Syst. Sci., 16, 1305–1320, https://doi.org/10.5194/hess-16-1305-2012, https://doi.org/10.5194/hess-16-1305-2012, 2012
M. Ali, G. Sterk, M. Seeger, M. Boersema, and P. Peters
Hydrol. Earth Syst. Sci., 16, 591–601, https://doi.org/10.5194/hess-16-591-2012, https://doi.org/10.5194/hess-16-591-2012, 2012
L. Gong, S. Halldin, and C.-Y. Xu
Hydrol. Earth Syst. Sci., 15, 2481–2494, https://doi.org/10.5194/hess-15-2481-2011, https://doi.org/10.5194/hess-15-2481-2011, 2011
W. Ma, Y. Ma, Z. Hu, Z. Su, J. Wang, and H. Ishikawa
Hydrol. Earth Syst. Sci., 15, 1403–1413, https://doi.org/10.5194/hess-15-1403-2011, https://doi.org/10.5194/hess-15-1403-2011, 2011
E. L. Wipfler, K. Metselaar, J. C. van Dam, R. A. Feddes, E. van Meijgaard, L. H. van Ulft, B. van den Hurk, S. J. Zwart, and W. G. M. Bastiaanssen
Hydrol. Earth Syst. Sci., 15, 1257–1271, https://doi.org/10.5194/hess-15-1257-2011, https://doi.org/10.5194/hess-15-1257-2011, 2011
W. Korres, C. N. Koyama, P. Fiener, and K. Schneider
Hydrol. Earth Syst. Sci., 14, 751–764, https://doi.org/10.5194/hess-14-751-2010, https://doi.org/10.5194/hess-14-751-2010, 2010
H. Makurira, H. H. G. Savenije, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 14, 627–638, https://doi.org/10.5194/hess-14-627-2010, https://doi.org/10.5194/hess-14-627-2010, 2010
C. H. Luce and D. G. Tarboton
Hydrol. Earth Syst. Sci., 14, 535–543, https://doi.org/10.5194/hess-14-535-2010, https://doi.org/10.5194/hess-14-535-2010, 2010
J. Liu, S. Kang, T. Gong, and A. Lu
Hydrol. Earth Syst. Sci., 14, 481–489, https://doi.org/10.5194/hess-14-481-2010, https://doi.org/10.5194/hess-14-481-2010, 2010
A. I. J. M. van Dijk
Hydrol. Earth Syst. Sci., 14, 447–458, https://doi.org/10.5194/hess-14-447-2010, https://doi.org/10.5194/hess-14-447-2010, 2010
H. H. Bulcock and G. P. W. Jewitt
Hydrol. Earth Syst. Sci., 14, 383–392, https://doi.org/10.5194/hess-14-383-2010, https://doi.org/10.5194/hess-14-383-2010, 2010
L. S. Kuchment, P. Romanov, A. N. Gelfan, and V. N. Demidov
Hydrol. Earth Syst. Sci., 14, 339–350, https://doi.org/10.5194/hess-14-339-2010, https://doi.org/10.5194/hess-14-339-2010, 2010
E. Alcântara, E. Novo, J. Stech, J. Lorenzzetti, C. Barbosa, A. Assireu, and A. Souza
Hydrol. Earth Syst. Sci., 14, 351–364, https://doi.org/10.5194/hess-14-351-2010, https://doi.org/10.5194/hess-14-351-2010, 2010
I.-Y. Yeo and J.-M. Guldmann
Hydrol. Earth Syst. Sci., 14, 325–338, https://doi.org/10.5194/hess-14-325-2010, https://doi.org/10.5194/hess-14-325-2010, 2010
C. L. Schneider, S. Attinger, J.-O. Delfs, and A. Hildebrandt
Hydrol. Earth Syst. Sci., 14, 279–289, https://doi.org/10.5194/hess-14-279-2010, https://doi.org/10.5194/hess-14-279-2010, 2010
G. Baroni, A. Facchi, C. Gandolfi, B. Ortuani, D. Horeschi, and J. C. van Dam
Hydrol. Earth Syst. Sci., 14, 251–270, https://doi.org/10.5194/hess-14-251-2010, https://doi.org/10.5194/hess-14-251-2010, 2010
C. Medici, S. Bernal, A. Butturini, F. Sabater, M. Martin, A. J. Wade, and F. Frances
Hydrol. Earth Syst. Sci., 14, 223–237, https://doi.org/10.5194/hess-14-223-2010, https://doi.org/10.5194/hess-14-223-2010, 2010
A. Petrone and F. Preti
Hydrol. Earth Syst. Sci., 14, 239–250, https://doi.org/10.5194/hess-14-239-2010, https://doi.org/10.5194/hess-14-239-2010, 2010
A. I. J. M. van Dijk
Hydrol. Earth Syst. Sci., 14, 159–169, https://doi.org/10.5194/hess-14-159-2010, https://doi.org/10.5194/hess-14-159-2010, 2010
C. Gruhier, P. de Rosnay, S. Hasenauer, T. Holmes, R. de Jeu, Y. Kerr, E. Mougin, E. Njoku, F. Timouk, W. Wagner, and M. Zribi
Hydrol. Earth Syst. Sci., 14, 141–156, https://doi.org/10.5194/hess-14-141-2010, https://doi.org/10.5194/hess-14-141-2010, 2010
M. Barnolas, T. Rigo, and M. C. Llasat
Hydrol. Earth Syst. Sci., 14, 129–139, https://doi.org/10.5194/hess-14-129-2010, https://doi.org/10.5194/hess-14-129-2010, 2010
A. Schmidt, J. J. Gibson, I. R. Santos, M. Schubert, K. Tattrie, and H. Weiss
Hydrol. Earth Syst. Sci., 14, 79–89, https://doi.org/10.5194/hess-14-79-2010, https://doi.org/10.5194/hess-14-79-2010, 2010
R. Liu, J. Wen, X. Wang, L. Wang, H. Tian, T. T. Zhang, X. K. Shi, J. H. Zhang, and SH. N. Lv
Hydrol. Earth Syst. Sci., 14, 47–58, https://doi.org/10.5194/hess-14-47-2010, https://doi.org/10.5194/hess-14-47-2010, 2010
S. Werth and A. Güntner
Hydrol. Earth Syst. Sci., 14, 59–78, https://doi.org/10.5194/hess-14-59-2010, https://doi.org/10.5194/hess-14-59-2010, 2010
H. Lin
Hydrol. Earth Syst. Sci., 14, 25–45, https://doi.org/10.5194/hess-14-25-2010, https://doi.org/10.5194/hess-14-25-2010, 2010
A. Ducharne
Hydrol. Earth Syst. Sci., 13, 2399–2412, https://doi.org/10.5194/hess-13-2399-2009, https://doi.org/10.5194/hess-13-2399-2009, 2009
J. E. Barrett, M. N. Gooseff, and C. Takacs-Vesbach
Hydrol. Earth Syst. Sci., 13, 2349–2358, https://doi.org/10.5194/hess-13-2349-2009, https://doi.org/10.5194/hess-13-2349-2009, 2009
V. García-García, R. Gómez, M. R. Vidal-Abarca, and M. L. Suárez
Hydrol. Earth Syst. Sci., 13, 2359–2371, https://doi.org/10.5194/hess-13-2359-2009, https://doi.org/10.5194/hess-13-2359-2009, 2009
M. Shamsudduha, R. E. Chandler, R. G. Taylor, and K. M. Ahmed
Hydrol. Earth Syst. Sci., 13, 2373–2385, https://doi.org/10.5194/hess-13-2373-2009, https://doi.org/10.5194/hess-13-2373-2009, 2009
J. C. M. Andersson, A. J. B. Zehnder, G. P. W. Jewitt, and H. Yang
Hydrol. Earth Syst. Sci., 13, 2329–2347, https://doi.org/10.5194/hess-13-2329-2009, https://doi.org/10.5194/hess-13-2329-2009, 2009
S. Binet, L. Spadini, C. Bertrand, Y. Guglielmi, J. Mudry, and C. Scavia
Hydrol. Earth Syst. Sci., 13, 2315–2327, https://doi.org/10.5194/hess-13-2315-2009, https://doi.org/10.5194/hess-13-2315-2009, 2009
A. Bárdossy and G. G. S. Pegram
Hydrol. Earth Syst. Sci., 13, 2299–2314, https://doi.org/10.5194/hess-13-2299-2009, https://doi.org/10.5194/hess-13-2299-2009, 2009
K. Dontsova, C. I. Steefel, S. Desilets, A. Thompson, and J. Chorover
Hydrol. Earth Syst. Sci., 13, 2273–2286, https://doi.org/10.5194/hess-13-2273-2009, https://doi.org/10.5194/hess-13-2273-2009, 2009
D. Yamazaki, T. Oki, and S. Kanae
Hydrol. Earth Syst. Sci., 13, 2241–2251, https://doi.org/10.5194/hess-13-2241-2009, https://doi.org/10.5194/hess-13-2241-2009, 2009
L. Rapp and K. Bishop
Hydrol. Earth Syst. Sci., 13, 2191–2201, https://doi.org/10.5194/hess-13-2191-2009, https://doi.org/10.5194/hess-13-2191-2009, 2009
Cited articles
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007.
Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., and Shuttleworth, W. J.: Parameter sensitivity analysis for different complexity land surface models using multicriteria methods, J. Geophys. Res., 111, D20101, https://doi.org/10.1029/2005JD006377, 2006.
Beck, M. B.: Water quality modeling: a review of the analysis of uncertainty, Water Resour. Res., 23, 1393–1442, https://doi.org/10.1029/WR023i008p01393, 1987.
Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling – a review, Hydrol. Process., 9, 251–290, 1995.
Box, G. E. P. and Tiao, G. C.: Bayesian Inference in Statistical Analysis, Addison-Wesley-Longman, Reading, Massachusetts, 1973.
Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663–3674, 2000.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: value of semidistributed modeling, Water Resour. Res., 37, 2749–2759, https://doi.org/10.1029/2000WR000207, 2001.
Clark, M. P. and Vrugt, J. A.: Unraveling uncertainties in hydrologic model calibration: addressing the problem of compensatory parameters, Geophys. Res. Lett., 33, L06406, https://doi.org/10.1029/2005GL025604, 2006.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): a modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Doherty, J. and Welter, D.: A short exploration of structural noise, Water Resour. Res., 46, W05525, https://doi.org/10.1029/2009WR008377, 2010.
Duan, Q., Gupta, V. K., and Sorooshian, S.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, 1992.
Dunn, S. M.: Imposing constraints on parameter values of a conceptual hydrological model using baseflow response, Hydrol. Earth Syst. Sci., 3, 271–284, https://doi.org/10.5194/hess-3-271-1999, 1999.
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010.
Eigbe, U., Beck, M. B., Wheater, H. S., and Hirano, F.: Kalman filtering in groundwater flow modelling: problems and prospects, Stoch. Hydrol. Hydraul., 12, 15–32, 1https://doi.org/0.1007/s004770050007, 1998.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, 1994.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
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, https://doi.org/10.1029/97WR03041, 1998.
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, available at: http://www.jstor.org/stable/pdfplus/2246093.pdf, Stat. Sci., 7, 457–472, 1992.
Georgakakos, K. P., Seo, D., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222–241, https://doi.org/10.1016/j.jhydrol.2004.03.037, 2004.
Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, Massachusetts, 1989.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–763, 1998.
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, https://doi.org/10.1002/hyp.6989, 2008.
Gupta, V. K. and Sorooshian, S.: The relationship between data and the precision of parameter estimates of hydrologic models, J. Hydrol., 81, 57–77, 1985.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian model averaging: a tutorial, Stat. Sci., 14, 382–401, 1999.
Hopp, L. and McDonnell, J. J.: Connectivity at the hillslope scale: Identifying interactions between storm size, bedrock permeability, slope angle and soil depth, J. Hydrol., 376, 378–391, 2009.
Jazwinski, A. H.: Stochastic Processes and Filtering Theory, Academic Press, New York, 1970.
Kalman, R. E.: A new approach to linear filtering and prediction problems, available at: http://www.cs.unc.edu/ welch/kalman/media/pdf/Kalman1960.pdf, T. ASME J. Basic Eng., 82, 35–45, 1960.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005WR004368, 2006a.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, https://doi.org/10.1029/2005WR004376, 2006b.
Kirchner, J. W.: Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Kleme\us, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13–24, 1986.
Kuczera, G. and Mroczkowski, M.: Assessment of hydrologic parameter uncertainty and the worth of multiresponse data, Water Resour. Res., 34, 1481–1489, https://doi.org/10.1029/98WR00496, 1998.
Kuczera, G. and Parent, E.: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69–85, 1998.
Lin, Z. and Beck, M. B.: On the identification of model structure in hydrological and environmental systems, Water Resour. Res., 43, W02402, https://doi.org/10.1029/2005WR004796, 2007.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087–1091, 1953.
Mroczkowski, M., Raper, G. P., and Kuczera, G.: The quest for more powerful validation of conceptual catchment models, Water Resour. Res., 33, 2325–2335, 1997.
Neuman, S. P.: Maximum likelihood Bayesian averaging of uncertain model predictions, Stoch. Env. Res. Risk A., 17, 291–305, https://doi.org/10.1007/s00477-003-0151-7, 2003.
Nierop, K. G. J., Jansen, B., Vrugt, J. A., and Verstraten, J. M.: Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants, Chemosphere, 49, 1191–1200, 2002.
Popper, K.: The Logic of Scientific Discovery, Routledge, first published as Logik der Forschung, 1935 by Verlag von Julius Springer, Vienna, Austria, 2009.
Raftery, A. E., Balabdaoui, F., Gneiting, T., and Polakowski, M.: Using Bayesian Model averaging to calibrate forecast ensembles, Tech. rep., Department of Statistics, University of Washington, Seattle, Washington, 2003.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174, 2005.
Reusser, D. E. and Zehe, E.: Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity, Water Resour. Res., 47, W07550, https://doi.org/10.1029/2010WR009946, 2011.
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, 1931.
Schoups, G. and Vrugt, J. A.: A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors, Water Resour. Res., 46, W10531, https://doi.org/10.1029/2009WR008933, 2010.
Seibert, J.: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm, Hydrol. Earth Syst. Sci., 4, 215–224, https://doi.org/10.5194/hess-4-215-2000, 2000.
Sieber, A. and Uhlenbrook, S.: Sensitivity analyses of a distributed catchment model to verify the model structure, J. Hydrol., 310, 216–235, 2005.
\uSimůnek, J.: SWMS_3D – numerical model of three-dimensional flow and solute transport in a variably saturated porous medium, software, avaiable at: http://www.pc-progress.com/Downloads/Programs_UCR/SWMS_3D.zip (last access: 2 February 2013), 1994.
\uSimůnek, J., Huang, K., and van Genuchten, M. T.: The SWMS_3D Code for Simulating Water Flow and Solute Transport in Three-Dimensional Variably-Saturated Media, US Salinity Laboratory, Agricultural Research Service, Research Report No. 139, US Department of Agriculture, Riverside, California, 1995.
Sorooshian, S., Gupta, V. K., and Fulton, J. L.: Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: influence of calibration data variability and length on model credibility, Water Resour. Res., 19, 251–259, https://doi.org/10.1029/WR019i001p00251, 1983.
Spear, R. C. and Hornberger, G. M.: Eutrophication in peel inlet – II. Identification of critical uncertainties via generalized sensitivity analysis, Water Res., 14, 43–49, https://doi.org/10.1016/0043-1354(80)90040-8, 1980.
Tang, Y., Reed, P., and Wagener, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?, Hydrol. Earth Syst. Sci., 10, 289–307, https://doi.org/10.5194/hess-10-289-2006, 2006.
van Genuchten, M. T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980.
von Bertalanffy, L.: The theory of open systems in physics and biology, Science, 111, 23–29, 1950.
Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multi-objective optimization of hydrologic models, Water Resour. Res., 39, 1214, https://doi.org/10.1029/2002WR001746, 2003a.
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, https://doi.org/10.1029/2002WR001642, 2003b.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Vrugt, J. A., van Belle, J., and Bouten, W.: Pareto front analysis of flight time and energy use in long-distance bird migration, J. Avian Biol., 38, 432–442, https://doi.org/10.1111/j.0908-8857.2007.03909.x, 2007.
Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13–26, https://doi.org/10.5194/hess-5-13-2001, 2001.
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.: Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis, Hydrol. Process., 17, 455–476, https://doi.org/10.1002/hyp.1135, 2003.
Western, A. W. and Blöschl, G.: On the spatial scaling of soil moisture, J. Hydrol., 217, 203–224, 1999.
Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrological models, J. Hydrol., 204, 83–97, 1998.
Young, P.: A general theory of modeling for badly defined dynamic systems, in: Modeling, Identification and Control in Environmental Systems – Proceedings of the IFIP Working Conference on Modeling and Simulation of Land, Air, and Water Resources Systems, edited by: Vansteenkiste, G. C., 103–135, North-Holland Pub. Co., Amsterdam, 1978.
Young, P.: Uncertainty and forecasting of water quality, in: The Validity and Credibility of Models for Badly Defined Systems, Springer Verlag, 69–98, 1983.
Young, P.: The identification and estimation of nonlinear stochastic systems, in: Nonlinear Dynamics and Statistics, Birkhäuser, Boston, 127–166, 2001.