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Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

McMillan, Hilary, Krueger, Tobias, Freer, Jim·2012·Hydrological Processes

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Abstract

This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties. Copyright © 2012 John Wiley & Sons, Ltd.

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Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Computer Models of Watershed Hydrology
Singh, V. P. (1995) · Medical Entomology and Zoology · Cited by 3,273
A manifesto for the equifinality thesis
Beven, K. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.07.007 · Cited by 2,581
Mean seasonal and spatial variability in gauge‐corrected, global precipitation
Legates, D. R., & Willmott, C. J. (1990) · International Journal of Climatology · doi:10.1002/joc.3370100202 · Cited by 1,468
Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology
Kirchner, J. W. (2006) · Water Resources Research · doi:10.1029/2005wr004362 · Cited by 1,321
Uncertainty in the environmental modelling process – A framework and guidance
Refsgaard, J. C., van der Sluijs, J. P., Højberg, A. L., & Vanrolleghem, P. A. (2007) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2007.02.004 · Cited by 1,197
A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters
Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2003) · Water Resources Research · doi:10.1029/2002wr001642 · Cited by 1,196
Partial Area Contributions to Storm Runoff in a Small New England Watershed
Dunne, T., & Black, R. D. (1970) · Water Resources Research · doi:10.1029/wr006i005p01296 · Cited by 1,152
Water quality modeling: A review of the analysis of uncertainty
Beck, M. B. (1987) · Water Resources Research · doi:10.1029/wr023i008p01393 · Cited by 954
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation
Vrugt, J. A., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., & Robinson, B. A. (2008) · Water Resources Research · doi:10.1029/2007wr006720 · Cited by 891
Fitting and interpretation of sediment rating curves
Asselman, N. E. M. (2000) · Journal of Hydrology · doi:10.1016/s0022-1694(00)00253-5 · Cited by 735
High‐resolution fields of global runoff combining observed river discharge and simulated water balances
Fekete, B. M., Vörösmarty, C. J., & Grabs, W. (2002) · Global Biogeochemical Cycles · doi:10.1029/1999gb001254 · Cited by 697
Uncertainty in river discharge observations: a quantitative analysis
Di Baldassarre, G., & Montanari, A. (2009) · Hydrology and Earth System Sciences · doi:10.5194/hess-13-913-2009 · Cited by 656
Reconciling theory with observations: elements of a diagnostic approach to model evaluation
Gupta, H. V., Wagener, T., & Liu, Y. (2008) · Hydrological Processes · doi:10.1002/hyp.6989 · Cited by 644
Info-Gap Decision Theory: Decisions Under Severe Uncertainty
Ben‐Haim, Y. (2006) · Cited by 626
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Seibert, J., & McDonnell, J. J. (2002) · Water Resources Research · doi:10.1029/2001wr000978 · Cited by 615
An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction
Ajami, N. K., Duan, Q., & Sorooshian, S. (2007) · Water Resources Research · doi:10.1029/2005wr004745 · Cited by 597 · cited 1×
“, DREAM (Vrugt et al., 2008; Schoups and Vrugt, 2010), IBUNE (Ajami et al., 2007) and extended GLUE (Beven, 2006; Beven et al., 2006; Pappenberger et al., 2006; Liu et al., 2009; Krueger et al., 2010a; in press; Quinton et al., 2011 ).”
Background
Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., & Verstraten, J. M. (2005) · Water Resources Research · doi:10.1029/2004wr003059 · Cited by 582
Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory
Kavetski, D., Kuczera, G., & Franks, S. W. (2006) · Water Resources Research · doi:10.1029/2005wr004368 · Cited by 580
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors
Schoups, G., & Vrugt, J. A. (2010) · Water Resources Research · doi:10.1029/2009wr008933 · Cited by 565
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Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins
Yadav, M., Wagener, T., & Gupta, H. (2007) · Advances in Water Resources · doi:10.1016/j.advwatres.2007.01.005 · Cited by 540
CUMULATIVE UNCERTAINTY IN MEASURED STREAMFLOW AND WATER QUALITY DATA FOR SMALL WATERSHEDS
Harmel, R. D., Cooper, R. J., Slade, R. M., Haney, R. L., & Arnold, J. G. (2006) · Transactions of the ASABE · doi:10.13031/2013.20488 · Cited by 478
An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations
Horowitz, A. J. (2003) · Hydrological Processes · doi:10.1002/hyp.1299 · Cited by 459
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?
Vrugt, J. A., ter Braak, C. J. F., Gupta, H. V., & Robinson, B. A. (2009) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-008-0274-y · Cited by 455
Bayesian analysis of input uncertainty in hydrological modeling: 2. Application
Kavetski, D., Kuczera, G., & Franks, S. W. (2006) · Water Resources Research · doi:10.1029/2005wr004376 · Cited by 442
Large‐scale particle image velocimetry for measurements in riverine environments
Muste, M., Fujita, I., & Hauet, A. (2008) · Water Resources Research · doi:10.1029/2008wr006950 · Cited by 425
Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology
Mantovan, P., & Todini, E. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.04.046 · Cited by 402
Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis
Thyer, M., Renard, B., Kavetski, D., Kuczera, G., Franks, S. W., & Srikanthan, S. (2009) · Water Resources Research · doi:10.1029/2008wr006825 · Cited by 398
Conceptualization and scale in hydrology
Klemeš, V. (1983) · Journal of Hydrology · doi:10.1016/0022-1694(83)90208-1 · Cited by 398
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method
Stedinger, J. R., Vogel, R. M., Lee, S. U., & Batchelder, R. (2008) · Water Resources Research · doi:10.1029/2008wr006822 · Cited by 386
Uncertainties in Precipitation and Their Impacts on Runoff Estimates
Fekete, B. M., Vörösmarty, C. J., Roads, J. O., & Willmott, C. J. (2004) · Journal of Climate · doi:10.1175/1520-0442(2004)017<0294:uipati>2.0.co;2 · Cited by 381
A formal framework for scenario development in support of environmental decision-making
Mahmoud, M., Liu, Y., Hartmann, H., Stewart, S., Wagener, T., Semmens, D., Stewart, R., Gupta, H., Dominguez, D., Dominguez, F., et al. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2008.11.010 · Cited by 378
Ensemble predictions of runoff in ungauged catchments
McIntyre, N., Lee, H., Wheater, H., Young, A., & Wagener, T. (2005) · Water Resources Research · doi:10.1029/2005wr004289 · Cited by 371
Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty
Wagener, T., & Wheater, H. S. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.07.015 · Cited by 368
A dynamic TOPMODEL
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Self-citation
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Gippel, C. J. (1995) · Hydrological Processes · doi:10.1002/hyp.3360090108 · Cited by 358
The Remote Sensing of Clouds and Precipitation from Space: A Review
Stephens, G. L., & Kummerow, C. D. (2007) · Journal of the Atmospheric Sciences · doi:10.1175/2006jas2375.1 · Cited by 335
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging
Vrugt, J. A., & Robinson, B. A. (2007) · Water Resources Research · doi:10.1029/2005wr004838 · Cited by 334
Local Random Errors in Tipping-Bucket Rain Gauge Measurements
Ciach, G. J. (2003) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/1520-0426(2003)20<752:lreitb>2.0.co;2 · Cited by 334
Evaluating the potential for measuring river discharge from space
Bjerklie, D. M., Lawrence Dingman, S., Vorosmarty, C. J., Bolster, C. H., & Congalton, R. G. (2003) · Journal of Hydrology · doi:10.1016/s0022-1694(03)00129-x · Cited by 333
Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density
Johnes, P. J. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.07.006 · Cited by 324
Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall‐runoff models: Influence of calibration data variability and length on model credibility
Sorooshian, S., Gupta, V. K., & Fulton, J. L. (1983) · Water Resources Research · doi:10.1029/wr019i001p00251 · Cited by 291
Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models
Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., & Lavabre, J. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00437-1 · Cited by 287
Estimating discharge in rivers using remotely sensed hydraulic information
Bjerklie, D. M., Moller, D., Smith, L. C., & Dingman, S. L. (2005) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.11.022 · Cited by 283
Uncertainties Associated with Digital Elevation Models for Hydrologic Applications: A Review
Wechsler, S. P. (2007) · Hydrology and Earth System Sciences · doi:10.5194/hess-11-1481-2007 · Cited by 280
Phosphorus Transport Pathways to Streams in Tile‐Drained Agricultural Watersheds
Gentry, L. E., David, M. B., Royer, T. V., Mitchell, C. A., & Starks, K. M. (2007) · Journal of Environmental Quality · doi:10.2134/jeq2006.0098 · Cited by 276
Accuracy of precipitation measurements for hydrologic modeling
Larson, L. W., & Peck, E. L. (1974) · Water Resources Research · doi:10.1029/wr010i004p00857 · Cited by 272
Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling
Daren Harmel, R., & Smith, P. K. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.01.043 · Cited by 265
So just why would a modeller choose to be incoherent?
Beven, K. J., Smith, P. J., & Freer, J. E. (2008) · Journal of Hydrology · doi:10.1016/j.jhydrol.2008.02.007 · Cited by 261
Self-citation
Assessing the Hydrological Significance of the World's Mountains
Viviroli, D., Weingartner, R., & Messerli, B. (2003) · Mountain Research and Development · doi:10.1659/0276-4741(2003)023[0032:athsot]2.0.co;2 · Cited by 256
Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation
Steiner, M., Smith, J. A., Burges, S. J., Alonso, C. V., & Darden, R. W. (1999) · Water Resources Research · doi:10.1029/1999wr900142 · Cited by 253
Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models
McMillan, H., Jackson, B., Clark, M., Kavetski, D., & Woods, R. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.01.026 · Cited by 253
Self-citation
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Fenicia, F., McDonnell, J. J., & Savenije, H. H. G. (2008) · Water Resources Research · doi:10.1029/2007wr006386 · Cited by 249
Measurement and analysis of small-scale convective storm rainfall variability
Goodrich, D. C., Faurès, J.-M., Woolhiser, D. A., Lane, L. J., & Sorooshian, S. (1995) · Journal of Hydrology · doi:10.1016/0022-1694(95)02703-r · Cited by 249
Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions
McMillan, H., Freer, J., Pappenberger, F., Krueger, T., & Clark, M. (2010) · Hydrological Processes · doi:10.1002/hyp.7587 · Cited by 245
Self-citation
Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., & Franks, S. W. (2011) · Water Resources Research · doi:10.1029/2011wr010643 · Cited by 239
What do we mean by ‘uncertainty’? The need for a consistent wording about uncertainty assessment in hydrology
Montanari, A. (2007) · Hydrological Processes · doi:10.1002/hyp.6623 · Cited by 239
An analysis of small-scale rainfall variability in different climatic regimes
KRAJEWSKI, W. F., CIACH, G. J., & HABIB, E. (2003) · Hydrological Sciences Journal · doi:10.1623/hysj.48.2.151.44694 · Cited by 238
Fluvial process and morphology of the Brahmaputra River in Assam, India
Sarma, J. N. (2005) · Geomorphology · doi:10.1016/j.geomorph.2005.02.007 · Cited by 233
Estimating the suspended sediment loads of rivers in the LOIS study area using infrequent samples
Phillips, J. M., Webb, B., Walling, D. E., & Leeks, G. J. L. (1999) · Hydrological Processes · Cited by 231
Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall‐runoff simulations
Montanari, A. (2005) · Water Resources Research · doi:10.1029/2004wr003826 · Cited by 231
Confronting input uncertainty in environmental modelling
Kavetski, D., Franks, S. W., & Kuczera, G. (2003) · Water Science and Application · doi:10.1029/ws006p0049 · Cited by 224
Separating natural and epistemic uncertainty in flood frequency analysis
Merz, B., & Thieken, A. H. (2005) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.11.015 · Cited by 221
On red herrings and real herrings: disinformation and information in hydrological inference
Beven, K., & Westerberg, I. (2011) · Hydrological Processes · doi:10.1002/hyp.7963 · Cited by 221
Evaluation of River Load Estimation Methods for Total Phosphorus
Dolan, D. M., Yui, A. K., & Geist, R. D. (1981) · Journal of Great Lakes Research · doi:10.1016/s0380-1330(81)72047-1 · Cited by 219
Measurement uncertainty
Meyer, V. R. (2007) · Journal of Chromatography A · doi:10.1016/j.chroma.2007.02.082 · Cited by 210
Quantifying uncertainty in estimation of hydrologic metrics for ecohydrological studies
Kennard, M. J., Mackay, S., Pusey, B. J., Olden, J. D., & Marsh, N. (2009) · River Research and Applications · doi:10.1002/rra.1249 · Cited by 204
Challenges in obtaining reliable measurements of point rainfall
Sieck, L. C., Burges, S. J., & Steiner, M. (2007) · Water Resources Research · doi:10.1029/2005wr004519 · Cited by 196
CHOICE OF SAMPLING STRATEGY AND ESTIMATION METHOD FOR CALCULATING NITROGEN AND PHOSPHORUS TRANSPORT IN SMALL LOWLAND STREAMS
KRONVANG, B., & BRUHN, A. J. (1996) · Hydrological Processes · doi:10.1002/(sici)1099-1085(199611)10:11<1483::aid-hyp386>3.0.co;2-y · Cited by 195
Integrating tracer experiments with modeling to assess runoff processes and water transit times
McGuire, K. J., Weiler, M., & McDonnell, J. J. (2007) · Advances in Water Resources · doi:10.1016/j.advwatres.2006.07.004 · Cited by 193
Understanding uncertainty in distributed flash flood forecasting for semiarid regions
Yatheendradas, S., Wagener, T., Gupta, H. V., Unkrich, C. L., Goodrich, D. C., Schaffner, M., & Stewart, A. M. (2008) · Water Resources Research · doi:10.1029/2007wr005940 · Cited by 189
Probabilistic Quantitative Precipitation Estimation in Complex Terrain
Clark, M. P., & Slater, A. G. (2006) · Journal of Hydrometeorology · doi:10.1175/jhm474.1 · Cited by 185
Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures
Freer, J. E., McMillan, H., McDonnell, J. J., & Beven, K. J. (2004) · Journal of Hydrology · doi:10.1016/j.jhydrol.2003.12.037 · Cited by 182
Self-citation
Using satellite altimetry data to augment flow estimation techniques on the Mekong River
Birkinshaw, S., O’Donnell, G., Moore, P., Kilsby, C. G., Fowler, H. J., & Berry, P. A. M. (2010) · Hydrological Processes · doi:10.1002/hyp.7811 · Cited by 178
Stage‐discharge uncertainty derived with a non‐stationary rating curve in the Choluteca River, Honduras
Westerberg, I. K., Guerrero, J.-L., Seibert, J., Beven, K. J., & Halldin, S. (2010) · Hydrological Processes · doi:10.1002/hyp.7848 · Cited by 173
Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation
Widén-Nilsson, E., Halldin, S., & Xu, C. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.04.002 · Cited by 172
Towards a limits of acceptability approach to the calibration of hydrological models: Extending observation error
Liu, Y., Freer, J., Beven, K., & Matgen, P. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.01.016 · Cited by 162
Self-citation
Adjustment of daily precipitation data at 10 climate stations in Alaska: Application of World Meteorological Organization intercomparison results
Yang, D., Goodison, B. E., Ishida, S., & Benson, C. S. (1998) · Water Resources Research · doi:10.1029/97wr02681 · Cited by 162
Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time‐dependent parameters
Reichert, P., & Mieleitner, J. (2009) · Water Resources Research · doi:10.1029/2009wr007814 · Cited by 161
Adjustment of tipping-bucket precipitation gauge measurements
Sevruk, B. (1996) · Atmospheric Research · doi:10.1016/0169-8095(95)00066-6 · Cited by 160
Knowledge, uncertainty and physical geography: towards the development of methodologies for questioning belief
Brown, J. D. (2004) · Transactions of the Institute of British Geographers · doi:10.1111/j.0020-2754.2004.00342.x · Cited by 147
On the colour and spin of epistemic error (and what we might do about it)
Beven, K. J., Smith, P. J., & Wood, A. W. (2011) · Hydrology and earth system sciences · doi:10.5194/hess-15-3123-2011 · Cited by 147
Determination of carbon, phosphorus, nitrogen and silicon species in waters
Robards, K., McKelvie, I. D., Benson, R. L., Worsfold, P. J., Blundell, N. J., & Casey, H. (1994) · Analytica Chimica Acta · doi:10.1016/0003-2670(93)e0542-f · Cited by 145
A dynamic rating curve approach to indirect discharge measurement
Dottori, F., Martina, M. L. V., & Todini, E. (2009) · Hydrology and earth system sciences · doi:10.5194/hess-13-847-2009 · Cited by 144
Hydrological field data from a modeller's perspective: Part 1. Diagnostic tests for model structure
McMillan, H., Clark, M., Bowden, W. B., Duncan, M. J., & Woods, R. A. (2010) · Hydrological Processes · doi:10.1002/hyp.7841 · Cited by 141
Self-citation
The relationship between data and the precision of parameter estimates of hydrologic models
Gupta, V. K., & Sorooshian, S. (1985) · Journal of Hydrology · doi:10.1016/0022-1694(85)90167-2 · Cited by 133
Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate, upland catchments
Birkel, C., Tetzlaff, D., Dunn, S., & Soulsby, C. (2009) · Hydrological Processes · doi:10.1002/hyp.7478 · Cited by 129
Hydrological field data from a modeller's perspective: Part 2: process‐based evaluation of model hypotheses
Clark, M., McMillan, H., Collins, D. B. G., Kavetski, D., & Woods, R. A. (2010) · Hydrological Processes · doi:10.1002/hyp.7902 · Cited by 125
Self-citation
How much rain does a rain gage gage?
Neff, E. L. (1977) · Journal of Hydrology · doi:10.1016/0022-1694(77)90001-4 · Cited by 119
Calibration of hydrological model GR2M using Bayesian uncertainty analysis
Huard, D., & Mailhot, A. (2008) · Water Resources Research · doi:10.1029/2007wr005949 · Cited by 118
Monitoring and preliminary interpretation of in-river turbidity and remote sensed imagery for suspended sediment transport studies in the Humber catchment
Wass, P. D., Marks, S., Finch, J. W., Leeks, G. J. L., & Ingram, J. K. G. (1997) · The Science of The Total Environment · doi:10.1016/s0048-9697(96)05370-3 · Cited by 118
Suspended sediment fluxes in the Humber catchment, UK
Wass, P. D., & Leeks, G. J. L. (1999) · Hydrological Processes · Cited by 117
Uncertainties in selected river water quality data
Rode, M., & Suhr, U. (2007) · Hydrology and earth system sciences · doi:10.5194/hess-11-863-2007 · Cited by 115
Application of the log-normal and normal distributions to stormwater quality parameters
Buren, M. A. V., Watt, W. E., & Maršálek, J. (1997) · Water Research · doi:10.1016/s0043-1354(96)00246-1 · Cited by 114
Ensemble evaluation of hydrological model hypotheses
Krueger, T., Freer, J. E., Quinton, J. N., Macleod, C. J. A., Bilotta, G. S., Brazier, R. E., Butler, P., & Haygarth, P. M. (2010) · Water Resources Research · doi:10.1029/2009wr007845 · Cited by 111
Self-citation
The influence of contrasting suspended particulate matter transport regimes on the bias and precision of flux estimates
Moatar, F., Person, G., Meybeck, M., Coynel, A., Etcheber, H., & Crouzet, P. (2006) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2006.07.029 · Cited by 110
Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modeling applications
Harmel, R. D., Smith, D. R., King, K. W., & Slade, R. M. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2008.12.006 · Cited by 107
Dominant processes concept in hydrology: moving forward
Sivakumar, B. (2004) · Hydrological Processes · doi:10.1002/hyp.5606 · Cited by 106
Stream discharge using mobile large‐scale particle image velocimetry: A proof of concept
Kim, Y., Muste, M., Hauet, A., et al. (2008) · Water Resources Research · doi:10.1029/2006wr005441 · Cited by 105
Factors influencing the residence time of catchment waters: A virtual experiment approach
Dunn, S., McDonnell, J. J., & Vaché, K. B. (2007) · Water Resources Research · doi:10.1029/2006wr005393 · Cited by 102
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Bjerklie, D. M. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.04.011 · Cited by 101
A comparison of surface‐grab and cross sectionally integrated stream‐water‐quality sampling methods
Martin, G. R., Smoot, J. L., & White, K. D. (1992) · Water Environment Research · doi:10.2175/wer.64.7.4 · Cited by 100
Comment on “Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology” by Pietro Mantovan and Ezio Todini
Beven, K. J., Smith, P. J., & Freer, J. E. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.02.023 · Cited by 100
Self-citation
Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges
Villarini, G., & Krajewski, W. F. (2008) · Advances in Water Resources · doi:10.1016/j.advwatres.2008.04.007 · Cited by 100
Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis
Bulygina, N., McIntyre, N., & Wheater, H. S. (2009) · Hydrology and earth system sciences · doi:10.5194/hess-13-893-2009 · Cited by 100
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Quality control of a global hourly rainfall dataset
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See all 522

Authors: McMillan, H., Krueger, T., & Freer, J.
Venue: Hydrological Processes
Publisher: Wiley
Published: 2012-06-11
Volume: 26
Issue: 26
Pages: 4078-4111
ISSN: 0885-6087 (ISSN-L)
1099-1085
DOI: 10.1002/hyp.9384
OpenAlex: W1514248671
Cited by: 522
References: 149
FWCI: 21.81
Type: journal article
Language: en
Open access: closed

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Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

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