Articles | Volume 21, issue 7
https://doi.org/10.5194/hess-21-3827-2017
© Author(s) 2017. 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-21-3827-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Jul 2017
Research article |
| 27 Jul 2017
A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction
Department of Civil Engineering, Monash University, Clayton, Victoria, Australia
Jeffrey P. Walker
Department of Civil Engineering, Monash University, Clayton, Victoria, Australia
David E. Robertson
CSIRO, Land and Water, Clayton, Victoria, Australia
Valentijn R. N. Pauwels
Department of Civil Engineering, Monash University, Clayton, Victoria, Australia
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Ashley J. Wright, David E. Robertson, Jeffrey P. Walker, and Valentijn R. N. Pauwels
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-450, https://doi.org/10.5194/hess-2019-450, 2019
Revised manuscript not accepted
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This paper details the development of a methodology to optimize the weighting of rainfall gauges for hydrologic simulation. In particular, catchments with a low gauge density and/or proportion of observations available are not well suited to this methodology. Application of this methodology with models that are consistent with a conceptual understanding of the rainfall-runoff process yield improvements of 7.1 % in evaluation periods.
Ashkan Shokri, James C. Bennett, David E. Robertson, Jean-Michel Perraud, Andrew J. Frost, and Eric A. Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-805, https://doi.org/10.5194/egusphere-2025-805, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Predicting river flow accurately is crucial for managing water resources, especially in a changing climate. This study used deep learning to improve streamflow predictions across Australia. By either enhancing existing models or working independently with climate data, the deep learning approaches provided more reliable results than traditional methods. These findings can help water managers better plan for floods, droughts, and long-term water availability.
Arash Aghakhani, David E. Robertson, and Valentijn R. N. Pauwels
EGUsphere, https://doi.org/10.5194/egusphere-2025-553, https://doi.org/10.5194/egusphere-2025-553, 2025
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Australia's shifting climate, with recurring droughts and wet periods, makes streamflow prediction challenging. This study combines GR4J model with machine learning to improve daily streamflow forecasts in Western Victoria. By identifying key factors affecting river flow, it offers valuable insights for water management. The findings show that machine learning can reveal limitations in traditional models, leading to more accurate predictions in drought-prone regions.
Hapu Arachchige Prasantha Hapuarachchi, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, Xiaoyong Sophie Zhang, David Ewen Robertson, James Clement Bennett, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 26, 4801–4821, https://doi.org/10.5194/hess-26-4801-2022, https://doi.org/10.5194/hess-26-4801-2022, 2022
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Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
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Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
Hydrol. Earth Syst. Sci., 25, 2261–2277, https://doi.org/10.5194/hess-25-2261-2021, https://doi.org/10.5194/hess-25-2261-2021, 2021
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Estimates of recharge to groundwater are often driven by biophysical processes occurring in the soil column and, particularly in remote areas, are also always affected by uncertainty. Using data assimilation techniques to merge remotely sensed observations with outputs of numerical models is one way to reduce this uncertainty. Here, we show the benefits of using such a technique with satellite evapotranspiration rates and coupled hydrogeological models applied to a semi-arid site in Australia.
Adrien Guyot, Jayaram Pudashine, Alain Protat, Remko Uijlenhoet, Valentijn R. N. Pauwels, Alan Seed, and Jeffrey P. Walker
Hydrol. Earth Syst. Sci., 23, 4737–4761, https://doi.org/10.5194/hess-23-4737-2019, https://doi.org/10.5194/hess-23-4737-2019, 2019
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We characterised for the first time the rainfall microphysics for Southern Hemisphere temperate latitudes. Co-located instruments were deployed to provide information on the sampling effect and spatio-temporal variabilities at micro scales. Substantial differences were found across the instruments, increasing with increasing values of the rain rate. Specific relations for reflectivity–rainfall are presented together with related uncertainties for drizzle and stratiform and convective rainfall.
Ashley J. Wright, David E. Robertson, Jeffrey P. Walker, and Valentijn R. N. Pauwels
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-450, https://doi.org/10.5194/hess-2019-450, 2019
Revised manuscript not accepted
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This paper details the development of a methodology to optimize the weighting of rainfall gauges for hydrologic simulation. In particular, catchments with a low gauge density and/or proportion of observations available are not well suited to this methodology. Application of this methodology with models that are consistent with a conceptual understanding of the rainfall-runoff process yield improvements of 7.1 % in evaluation periods.
Siyuan Tian, Luigi J. Renzullo, Albert I. J. M. van Dijk, Paul Tregoning, and Jeffrey P. Walker
Hydrol. Earth Syst. Sci., 23, 1067–1081, https://doi.org/10.5194/hess-23-1067-2019, https://doi.org/10.5194/hess-23-1067-2019, 2019
Stephen P. Charles, Quan J. Wang, Mobin-ud-Din Ahmad, Danial Hashmi, Andrew Schepen, Geoff Podger, and David E. Robertson
Hydrol. Earth Syst. Sci., 22, 3533–3549, https://doi.org/10.5194/hess-22-3533-2018, https://doi.org/10.5194/hess-22-3533-2018, 2018
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Predictions of irrigation-season water availability are important for water-limited Pakistan. We assess a Bayesian joint probability approach, using flow and climate indices as predictors, to produce streamflow forecasts for inflow to Pakistan's two largest dams. The approach produces skilful and reliable forecasts. As it is simple and quick to apply, it can be used to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's water management.
Andrew Schepen, Tongtiegang Zhao, Quan J. Wang, and David E. Robertson
Hydrol. Earth Syst. Sci., 22, 1615–1628, https://doi.org/10.5194/hess-22-1615-2018, https://doi.org/10.5194/hess-22-1615-2018, 2018
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Rainfall forecasts from dynamical global climate models (GCMs) require post-processing before use in hydrological models. Existing methods generally lack the sophistication to achieve calibrated forecasts of both daily amounts and seasonal accumulated totals. We develop a new statistical method to post-process Australian GCM rainfall forecasts for 12 perennial and ephemeral catchments. Our method produces reliable forecasts and outperforms the most commonly used statistical method.
James C. Bennett, Quan J. Wang, David E. Robertson, Andrew Schepen, Ming Li, and Kelvin Michael
Hydrol. Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, https://doi.org/10.5194/hess-21-6007-2017, 2017
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We assess a new streamflow forecasting system in Australia. The system is designed to meet the need of water agencies for 12-month forecasts. The forecasts perform well in a wide range of rivers. Forecasts for shorter periods (up to 6 months) are generally informative. Forecasts sometimes did not perform well in a few very dry rivers. We test several techniques for improving streamflow forecasts in drylands, with mixed success.
Sean W. D. Turner, James C. Bennett, David E. Robertson, and Stefano Galelli
Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, https://doi.org/10.5194/hess-21-4841-2017, 2017
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This study investigates the relationship between skill and value of ensemble seasonal streamflow forecasts. Using data from a modern forecasting system, we show that skilled forecasts are more likely to provide benefits for reservoirs operated to maintain a target water level rather than reservoirs operated to satisfy a target demand. We identify the primary causes for this behaviour and provide specific recommendations for assessing the value of forecasts for reservoirs with supply objectives.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
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The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Valentijn R. N. Pauwels and Edoardo Daly
Hydrol. Earth Syst. Sci., 20, 4689–4706, https://doi.org/10.5194/hess-20-4689-2016, https://doi.org/10.5194/hess-20-4689-2016, 2016
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We demonstrate that the classical approach to solve the surface energy balance equation in land surface models has its issues, and we propose an improved method.
Jason Beringer, Lindsay B. Hutley, Ian McHugh, Stefan K. Arndt, David Campbell, Helen A. Cleugh, James Cleverly, Víctor Resco de Dios, Derek Eamus, Bradley Evans, Cacilia Ewenz, Peter Grace, Anne Griebel, Vanessa Haverd, Nina Hinko-Najera, Alfredo Huete, Peter Isaac, Kasturi Kanniah, Ray Leuning, Michael J. Liddell, Craig Macfarlane, Wayne Meyer, Caitlin Moore, Elise Pendall, Alison Phillips, Rebecca L. Phillips, Suzanne M. Prober, Natalia Restrepo-Coupe, Susanna Rutledge, Ivan Schroder, Richard Silberstein, Patricia Southall, Mei Sun Yee, Nigel J. Tapper, Eva van Gorsel, Camilla Vote, Jeff Walker, and Tim Wardlaw
Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, https://doi.org/10.5194/bg-13-5895-2016, 2016
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OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national facility to monitor and assess trends, and improve predictions, of Australia’s terrestrial biosphere and climate. We describe the evolution, design, and status as well as an overview of data processing. We suggest that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing Australian ecosystems.
Ming Li, Q. J. Wang, James C. Bennett, and David E. Robertson
Hydrol. Earth Syst. Sci., 20, 3561–3579, https://doi.org/10.5194/hess-20-3561-2016, https://doi.org/10.5194/hess-20-3561-2016, 2016
C. Alvarez-Garreton, D. Ryu, A. W. Western, C.-H. Su, W. T. Crow, D. E. Robertson, and C. Leahy
Hydrol. Earth Syst. Sci., 19, 1659–1676, https://doi.org/10.5194/hess-19-1659-2015, https://doi.org/10.5194/hess-19-1659-2015, 2015
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We assimilate satellite soil moisture into a rainfall-runoff model for improving flood prediction within a data-scarce region. We argue that the spatially distributed satellite data can alleviate the model prediction limitations. We show that satellite soil moisture DA reduces the uncertainty of the streamflow ensembles. We propose new techniques for the DA scheme, including seasonal error characterisation, bias correction of the satellite retrievals, and model error representation.
M. Li, Q. J. Wang, J. C. Bennett, and D. E. Robertson
Hydrol. Earth Syst. Sci., 19, 1–15, https://doi.org/10.5194/hess-19-1-2015, https://doi.org/10.5194/hess-19-1-2015, 2015
M. Dessie, N. E. C. Verhoest, V. R. N. Pauwels, T. Admasu, J. Poesen, E. Adgo, J. Deckers, and J. Nyssen
Hydrol. Earth Syst. Sci., 18, 5149–5167, https://doi.org/10.5194/hess-18-5149-2014, https://doi.org/10.5194/hess-18-5149-2014, 2014
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In this study, topography is considered as a proxy for the variability of most of the catchment characteristics. The model study suggests that classifying the catchments into different runoff production areas based on topography and including the impermeable rocky areas separately in the modeling process mimics the rainfall–runoff process in the Upper Blue Nile basin well and yields a useful result for operational management of water resources in this data-scarce region.
J. C. Bennett, Q. J. Wang, P. Pokhrel, and D. E. Robertson
Nat. Hazards Earth Syst. Sci., 14, 219–233, https://doi.org/10.5194/nhess-14-219-2014, https://doi.org/10.5194/nhess-14-219-2014, 2014
B. Samain and V. R. N. Pauwels
Hydrol. Earth Syst. Sci., 17, 4525–4540, https://doi.org/10.5194/hess-17-4525-2013, https://doi.org/10.5194/hess-17-4525-2013, 2013
D. E. Robertson, D. L. Shrestha, and Q. J. Wang
Hydrol. Earth Syst. Sci., 17, 3587–3603, https://doi.org/10.5194/hess-17-3587-2013, https://doi.org/10.5194/hess-17-3587-2013, 2013
V. R. N. Pauwels, G. J. M. De Lannoy, H.-J. Hendricks Franssen, and H. Vereecken
Hydrol. Earth Syst. Sci., 17, 3499–3521, https://doi.org/10.5194/hess-17-3499-2013, https://doi.org/10.5194/hess-17-3499-2013, 2013
N. De Vleeschouwer and V. R. N. Pauwels
Hydrol. Earth Syst. Sci., 17, 2001–2016, https://doi.org/10.5194/hess-17-2001-2013, https://doi.org/10.5194/hess-17-2001-2013, 2013
D. L. Shrestha, D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi
Hydrol. Earth Syst. Sci., 17, 1913–1931, https://doi.org/10.5194/hess-17-1913-2013, https://doi.org/10.5194/hess-17-1913-2013, 2013
P. Pokhrel, D. E. Robertson, and Q. J. Wang
Hydrol. Earth Syst. Sci., 17, 795–804, https://doi.org/10.5194/hess-17-795-2013, https://doi.org/10.5194/hess-17-795-2013, 2013
D. E. Robertson, P. Pokhrel, and Q. J. Wang
Hydrol. Earth Syst. Sci., 17, 579–593, https://doi.org/10.5194/hess-17-579-2013, https://doi.org/10.5194/hess-17-579-2013, 2013
L. Loosvelt, H. Vernieuwe, V. R. N. Pauwels, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 461–478, https://doi.org/10.5194/hess-17-461-2013, https://doi.org/10.5194/hess-17-461-2013, 2013
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Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
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I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
Veysel Yildiz, Robert Milton, Solomon Brown, and Charles Rougé
Hydrol. Earth Syst. Sci., 27, 2499–2507, https://doi.org/10.5194/hess-27-2499-2023, https://doi.org/10.5194/hess-27-2499-2023, 2023
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The proposed approach is based on the parameterisation of flow duration curves (FDCs) to generate hypothetical streamflow futures. (1) We sample a broad range of future climates with modified values of three key streamflow statistics. (2) We generate an FDC for each hydro-climate future. (3) The resulting ensemble is ready to support robustness assessments in a changing climate. Our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, https://doi.org/10.5194/hess-26-4345-2022, 2022
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Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
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How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
Onno Bokhove, Tiffany Hicks, Wout Zweers, and Thomas Kent
Hydrol. Earth Syst. Sci., 24, 2483–2503, https://doi.org/10.5194/hess-24-2483-2020, https://doi.org/10.5194/hess-24-2483-2020, 2020
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Wetropolis is a
table-topdemonstration model with extreme rainfall and flooding, including random rainfall, river flow, flood plains, an upland reservoir, a porous moor, and a city which can flood. It lets the viewer experience extreme rainfall and flood events in a physical model on reduced spatial and temporal scales with an event return period of 6.06 min rather than, say, 200 years. We disseminate its mathematical design and how it has been shown most prominently to over 500 flood victims.
Gianfranco Urciuoli, Luca Comegna, Marianna Pirone, and Luciano Picarelli
Hydrol. Earth Syst. Sci., 24, 1669–1676, https://doi.org/10.5194/hess-24-1669-2020, https://doi.org/10.5194/hess-24-1669-2020, 2020
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The aim of this paper is to demonstrate, through a numerical approach, that the presence of soil layers of higher permeability, a not unlikely condition in some deep landslides in clay, may be exploited to improve the efficiency of systems of drainage trenches for slope stabilization. The problem has been examined for the case that a unique pervious layer, parallel to the ground surface, is present at an elevation higher than the bottom of the trenches.
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470, https://doi.org/10.5194/hess-23-4453-2019, https://doi.org/10.5194/hess-23-4453-2019, 2019
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We develop a new indicator of reservoir effects, called the rainfall–reservoir composite index (RRCI). RRCI, coupled with the effects of static reservoir capacity and scheduling-related multivariate rainfall, has a better performance than the previous indicator in terms of explaining the variation in the downstream floods affected by reservoir operation. A covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
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Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Bart van Osnabrugge, Remko Uijlenhoet, and Albrecht Weerts
Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, https://doi.org/10.5194/hess-23-1453-2019, 2019
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A correct estimate of the amount of future precipitation is the most important factor in making a good streamflow forecast, but evaporation is also an important component that determines the discharge of a river. However, in this study for the Rhine River we found that evaporation forecasts only give an almost negligible improvement compared to methods that use statistical information on climatology for a 10-day streamflow forecast. This is important to guide research on low flow forecasts.
Cédric Rebolho, Vazken Andréassian, and Nicolas Le Moine
Hydrol. Earth Syst. Sci., 22, 5967–5985, https://doi.org/10.5194/hess-22-5967-2018, https://doi.org/10.5194/hess-22-5967-2018, 2018
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Inundation models are useful for hazard management and prevention. They are traditionally based on hydraulic partial differential equations (with satisfying results but large data and computational requirements). This study presents a simplified approach combining reach-scale geometric properties with steady uniform flow equations. The model shows promising results overall, although difficulties persist in the most complex urbanised reaches.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
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We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Guillaume Le Bihan, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons
Hydrol. Earth Syst. Sci., 21, 5911–5928, https://doi.org/10.5194/hess-21-5911-2017, https://doi.org/10.5194/hess-21-5911-2017, 2017
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This paper illustrates how an integrated flash flood monitoring (or forecasting) system may be designed to directly provide information on possibly flooded areas and associated impacts on a very detailed river network and over large territories. The approach is extensively tested in the regions of Alès and Draguignan, located in south-eastern France. Validation results are presented in terms of accuracy of the estimated flood extents and related impacts (based on insurance claim data).
Ricardo Zubieta, Augusto Getirana, Jhan Carlo Espinoza, Waldo Lavado-Casimiro, and Luis Aragon
Hydrol. Earth Syst. Sci., 21, 3543–3555, https://doi.org/10.5194/hess-21-3543-2017, https://doi.org/10.5194/hess-21-3543-2017, 2017
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This paper indicates that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, in comparison to observed rainfall (by 11.1 % and 15.7 %, respectively). Statistical analysis indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets for estimating observed streamflows in Andean–Amazonian regions (Ucayali Basin, southern regions of the Amazon Basin of Peru and Ecuador).
Anna Botto, Daniele Ganora, Pierluigi Claps, and Francesco Laio
Hydrol. Earth Syst. Sci., 21, 3353–3358, https://doi.org/10.5194/hess-21-3353-2017, https://doi.org/10.5194/hess-21-3353-2017, 2017
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The paper provides an easy-to-use implementation of the UNCODE framework, which allows one to estimate the design flood value by directly accounting for sample uncertainty. Other than a design tool, this methodology is also a practical way to quantify the value of data in the design process.
Amin Elshorbagy, Raja Bharath, Anchit Lakhanpal, Serena Ceola, Alberto Montanari, and Karl-Erich Lindenschmidt
Hydrol. Earth Syst. Sci., 21, 2219–2232, https://doi.org/10.5194/hess-21-2219-2017, https://doi.org/10.5194/hess-21-2219-2017, 2017
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Flood mapping is one of Canada's major national interests. This work presents a simple and effective method for large-scale flood hazard and risk mapping, applied in this study to Canada. Readily available data, such as remote sensing night-light data, topography, and stream network were used to create the maps.
D. C. Verdon-Kidd and A. S. Kiem
Hydrol. Earth Syst. Sci., 19, 4735–4746, https://doi.org/10.5194/hess-19-4735-2015, https://doi.org/10.5194/hess-19-4735-2015, 2015
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Rainfall intensity-frequency-duration (IFD) relationships are required for the design and planning of water supply and management systems around the world. Currently IFD information is based on the "stationary climate assumption". However, this paper provides evidence of regime shifts in annual maxima rainfall time series using 96 daily rainfall stations and 66 sub-daily rainfall stations across Australia. Importantly, current IFD relationships may under- or overestimate the design rainfall.
P. A. Marker, N. Foged, X. He, A. V. Christiansen, J. C. Refsgaard, E. Auken, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, https://doi.org/10.5194/hess-19-3875-2015, 2015
H. Vernieuwe, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, https://doi.org/10.5194/hess-19-2685-2015, 2015
H. Vuollekoski, M. Vogt, V. A. Sinclair, J. Duplissy, H. Järvinen, E.-M. Kyrö, R. Makkonen, T. Petäjä, N. L. Prisle, P. Räisänen, M. Sipilä, J. Ylhäisi, and M. Kulmala
Hydrol. Earth Syst. Sci., 19, 601–613, https://doi.org/10.5194/hess-19-601-2015, https://doi.org/10.5194/hess-19-601-2015, 2015
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The global potential for collecting usable water from dew on an
artificial collector sheet was investigated by utilising 34 years of
meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were
mostly below 0.1mm.
D. E. Mora, L. Campozano, F. Cisneros, G. Wyseure, and P. Willems
Hydrol. Earth Syst. Sci., 18, 631–648, https://doi.org/10.5194/hess-18-631-2014, https://doi.org/10.5194/hess-18-631-2014, 2014
M. T. Pham, W. J. Vanhaute, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 5167–5183, https://doi.org/10.5194/hess-17-5167-2013, https://doi.org/10.5194/hess-17-5167-2013, 2013
M. Schwarz, F. Giadrossich, and D. Cohen
Hydrol. Earth Syst. Sci., 17, 4367–4377, https://doi.org/10.5194/hess-17-4367-2013, https://doi.org/10.5194/hess-17-4367-2013, 2013
G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, J. L. Salinas, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, https://doi.org/10.5194/hess-17-3295-2013, 2013
L. Brocca, S. Liersch, F. Melone, T. Moramarco, and M. Volk
Hydrol. Earth Syst. Sci., 17, 3159–3169, https://doi.org/10.5194/hess-17-3159-2013, https://doi.org/10.5194/hess-17-3159-2013, 2013
T. A. McMahon, M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar
Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
E. Habib, Y. Ma, D. Williams, H. O. Sharif, and F. Hossain
Hydrol. Earth Syst. Sci., 16, 3767–3781, https://doi.org/10.5194/hess-16-3767-2012, https://doi.org/10.5194/hess-16-3767-2012, 2012
A. Pathirana, B. Gersonius, and M. Radhakrishnan
Hydrol. Earth Syst. Sci., 16, 2499–2509, https://doi.org/10.5194/hess-16-2499-2012, https://doi.org/10.5194/hess-16-2499-2012, 2012
K. X. Soulis and J. D. Valiantzas
Hydrol. Earth Syst. Sci., 16, 1001–1015, https://doi.org/10.5194/hess-16-1001-2012, https://doi.org/10.5194/hess-16-1001-2012, 2012
G. Corato, T. Moramarco, and T. Tucciarelli
Hydrol. Earth Syst. Sci., 15, 2979–2994, https://doi.org/10.5194/hess-15-2979-2011, https://doi.org/10.5194/hess-15-2979-2011, 2011
A. Elshorbagy, G. Corzo, S. Srinivasulu, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, https://doi.org/10.5194/hess-14-1943-2010, 2010
A. D. Koussis
Hydrol. Earth Syst. Sci., 14, 1093–1097, https://doi.org/10.5194/hess-14-1093-2010, https://doi.org/10.5194/hess-14-1093-2010, 2010
J. A. Velázquez, T. Petit, A. Lavoie, M.-A. Boucher, R. Turcotte, V. Fortin, and F. Anctil
Hydrol. Earth Syst. Sci., 13, 2221–2231, https://doi.org/10.5194/hess-13-2221-2009, https://doi.org/10.5194/hess-13-2221-2009, 2009
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Short summary
The accurate reduction of hydrologic model input data is an impediment towards understanding input uncertainty and model structural errors. This paper compares the ability of two transforms to reduce rainfall input data. The resultant transforms are compressed to varying extents and reconstructed before being evaluated with standard simulation performance summary metrics and descriptive statistics. It is concluded the discrete wavelet transform is most capable of preserving rainfall time series.
The accurate reduction of hydrologic model input data is an impediment towards understanding...
