Articles | Volume 22, issue 12
https://doi.org/10.5194/hess-22-6257-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-6257-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Dec 2018
Research article |
| 06 Dec 2018
| 06 Dec 2018
Evaluating post-processing approaches for monthly and seasonal streamflow forecasts
Fitsum Woldemeskel
CORRESPONDING AUTHOR
Bureau of Meteorology, VIC, Melbourne, Australia
David McInerney
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
Julien Lerat
Bureau
of Meteorology, ACT, Canberra, Australia
Mark Thyer
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
Dmitri Kavetski
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
School
of Engineering, University of Newcastle, Callaghan, NSW, Australia
Daehyok Shin
Bureau of Meteorology, VIC, Melbourne, Australia
Narendra Tuteja
Bureau
of Meteorology, ACT, Canberra, Australia
George Kuczera
School
of Engineering, University of Newcastle, Callaghan, NSW, Australia
Related authors
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
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We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
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
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 27, 873–893, https://doi.org/10.5194/hess-27-873-2023, https://doi.org/10.5194/hess-27-873-2023, 2023
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Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
Short summary
Short summary
We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
Short summary
Short summary
Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
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
Short summary
Short summary
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
Siyuan Tian, Luigi J. Renzullo, Robert C. Pipunic, Julien Lerat, Wendy Sharples, and Chantal Donnelly
Hydrol. Earth Syst. Sci., 25, 4567–4584, https://doi.org/10.5194/hess-25-4567-2021, https://doi.org/10.5194/hess-25-4567-2021, 2021
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Accurate daily continental water balance predictions are valuable in monitoring and forecasting water availability and land surface conditions. A simple and robust method was developed for an operational water balance model to constrain model predictions temporally and spatially with satellite soil moisture observations. The improved soil water storage prediction can provide constraints in model forecasts that persist for several weeks.
Bree Bennett, Mark Thyer, Michael Leonard, Martin Lambert, and Bryson Bates
Hydrol. Earth Syst. Sci., 23, 4783–4801, https://doi.org/10.5194/hess-23-4783-2019, https://doi.org/10.5194/hess-23-4783-2019, 2019
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A new stochastic rainfall model evaluation framework is introduced, with three key features: (1) streamflow-based, to directly evaluate modelled streamflow performance, (2) virtual, to avoid confounding errors in hydrological models or data, and (3) targeted, to isolate errors according to specific sites/months. The framework identified the importance of rainfall in the
wetting-upmonths for providing reliable predictions of streamflow over the entire year despite their low flow volumes.
Lanying Zhang, George Kuczera, Anthony S. Kiem, and Garry Willgoose
Hydrol. Earth Syst. Sci., 22, 6399–6414, https://doi.org/10.5194/hess-22-6399-2018, https://doi.org/10.5194/hess-22-6399-2018, 2018
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Analyses of run lengths of Pacific decadal variability (PDV) suggest that there is no significant difference between run lengths in positive and negative phases of PDV and that it is more likely than not that the PDV run length has been non-stationary in the past millennium. This raises concerns about whether variability seen in the instrumental record (the last ~100 years), or even in the shorter 300–400 year paleoclimate reconstructions, is representative of the full range of variability.
Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, https://doi.org/10.5194/hess-22-871-2018, 2018
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This work developed models to predict how much water will be available in the next month to maximise environmental and social outcomes in southern Australia. Initialising the models with observed streamflow data, instead of warmed up by rainfall data, improved the results, even at a monthly lead time, making sure only data representative of the forecast period to develop the models were also important. If this step was ignored, and instead all data were used, poor predictions could be produced.
A. F. M. Kamal Chowdhury, Natalie Lockart, Garry Willgoose, George Kuczera, Anthony S. Kiem, and Nadeeka Parana Manage
Hydrol. Earth Syst. Sci., 21, 6541–6558, https://doi.org/10.5194/hess-21-6541-2017, https://doi.org/10.5194/hess-21-6541-2017, 2017
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Stochastic rainfall models are required to be be able to assess the reliability of dams used for urban water supply. Traditional Markov chain stochastic models do well at reproducing the mean and variance of rainfall at daily to weekly resolution but fail to simultaneously reproduce the variability of monthly to decadal rainfall. This paper presents four new extensions to Markov chain models that address this decadal deficiency and compares their performance for two field sites.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
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Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
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Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
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Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
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Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
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Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
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What controls the tail behaviour of flood series: rainfall or runoff generation?
Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-54, https://doi.org/10.5194/hess-2024-54, 2024
Revised manuscript accepted for HESS
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This paper developed hybrid distributed hydrological models by employing a distributed model as the backbone, and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves understanding about the hydrological sensitivities to climate change in large alpine basins.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Franziska Maria Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
EGUsphere, https://doi.org/10.5194/egusphere-2024-864, https://doi.org/10.5194/egusphere-2024-864, 2024
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We compare the catchment forcing data provided in large-sample datasets, namely the Caravan dataset and three of the original CAMELS datasets (US, BR, GB). We show that the differences affect hydrological model performance and that the data quality in the Caravan dataset is lower than the one in the CAMELS datasets, both for precipitation and potential evapotranspiration. We want to raise awareness of the lower data quality in Caravan and we suggest possible improvements for the Caravan dataset.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-46, https://doi.org/10.5194/hess-2024-46, 2024
Revised manuscript accepted for HESS
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system, and establish the application of the model for simulating flow and transport in karst systems.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-81, https://doi.org/10.5194/hess-2024-81, 2024
Revised manuscript accepted for HESS
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We used hydrological models, field measurements and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics, and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
EGUsphere, https://doi.org/10.5194/egusphere-2024-629, https://doi.org/10.5194/egusphere-2024-629, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Nienke Tessa Tempel, Laurene Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
EGUsphere, https://doi.org/10.5194/egusphere-2024-115, https://doi.org/10.5194/egusphere-2024-115, 2024
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This study explores the impact of climatic variability on root zone water storage capacities thus on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-275, https://doi.org/10.5194/hess-2023-275, 2024
Revised manuscript accepted for HESS
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Recently, a special type of neural network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models require the use of large-sample hydrology datasets.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-264, https://doi.org/10.5194/hess-2023-264, 2024
Revised manuscript accepted for HESS
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Large-scale hydrologic a needed tool to explore complex watershed processes and how they may evolve under a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration with a set of experiments in the Upper Colorado River Basin.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Cited articles
Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.:
Reliable long-range ensemble streamflow forecasts: Combining calibrated
climate forecasts with a conceptual runoff model and a staged error model,
Water Resour. Res., 52, 8238–8259, https://doi.org/10.1002/2016WR019193, 2016.
Bennett, J. C., Wang, Q. J., Robertson, D. E., Schepen, A., Li, M., and Michael, K.:
Assessment of an ensemble seasonal streamflow forecasting system for Australia, Hydrol.
Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, 2017.
Bogner, K. and Kalas, M.: Error-correction methods and evaluation of an
ensemble based hydrological forecasting system for the Upper Danube
catchment, Atmos. Sci. Lett., 9, 95–102, https://doi.org/10.1002/asl.180, 2008.
Bourdin, D. R., Nipen, T. N., and Stull, R. B.: Reliable probabilistic
forecasts from an ensemble reservoir inflow forecasting system, Water
Resour. Res., 50, 3108–3130, https://doi.org/10.1002/2014WR015462, 2014.
Box, G. E. P. and Cox, D. R.: An analysis of transformations, J. R. Stat.
Soc. Ser. B, 211–252, https://doi.org/10.2307/2287791, 1964.
Brown, J. D., Wu, L., He, M., Regonda, S., Lee, H., and Seo, D. J.:
Verification of temperature, precipitation, and streamflow forecasts from
the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental
design and forcing verification, J. Hydrol., 519, 2869–2889,
https://doi.org/10.1016/j.jhydrol.2014.05.028, 2014.
Carpenter, T. M. and Georgakakos, K. P.: Assessment of Folsom lake response
to historical and potential future climate scenarios: 1. Forecasting, J.
Hydrol., 249, 148–175, https://doi.org/10.1016/S0022-1694(01)00417-6, 2001.
Carrillo, G., Troch, P. A., Sivapalan, M., Wagener, T., Harman, C.,
and Sawicz, K.: Catchment classification: hydrological analysis of catchment
behavior through process-based modeling along a climate gradient, Hydrol.
Earth Syst. Sci., 15, 3411–3430, https://doi.org/10.5194/hess-15-3411-2011, 2011.
Charles, A., Miles, E., Griesser, A., de Wit, R., Shelton, K., Cottrill, A.,
Spillman, C., Hendon, H., McIntosh, P., Nakaegawa, T., Atalifo, T., Prakash,
B., Seuseu, S., Nihmei, S., Church, J., Jones, D., and Kuleshov, Y.:
Dynamical Seasonal Prediction of Climate Extremes in the Pacific, in 20th
International Congress on Modelling and Simulation (Modsim2013), 2841–2847, 2013.
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.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47,
W09301, https://doi.org/10.1029/2010WR009827, 2011.
Cloke, H., Pappenberger, F., Thielen, J., and Thiemig, V.: Operational
European Flood Forecasting, in Environmental Modelling, John
Wiley & Sons, Ltd., 415–434, 2013.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation
forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst.
Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
Dawid, A. P.: Present Position and Potential Developments: Some Personal
Views: Statistical theory: the prequential approach (with discussion), J. R.
Stat. Soc. Ser. A, 147, 278–292, https://doi.org/10.2307/2981683, 1984.
DeChant, C. M. and Moradkhani, H.: Improving the characterization of initial
condition for ensemble streamflow prediction using data assimilation, Hydrol.
Earth Syst. Sci., 15, 3399–3410, https://doi.org/10.5194/hess-15-3399-2011, 2011.
Demargne, J., Wu, L., Regonda, S. K., Brown, J. D., Lee, H., He, M., Seo, D.
J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y.: The
science of NOAA's operational hydrologic ensemble forecast service, B.
Am. Meteorol. Soc., 95, 79–98, https://doi.org/10.1175/BAMS-D-12-00081.1, 2014.
Evin, G., Thyer, M., Kavetski, D., McInerney, D., and Kuczera, G.: Comparison
of joint versus postprocessor approaches for hydrological uncertainty
estimation accounting for error autocorrelation and heteroscedasticity,
Water Resour. Res., 50, 2350–2375, https://doi.org/10.1002/2013WR014185, 2014.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible
approach for conceptual hydrological modeling: 1. Motivation and theoretical
development, Water Resour. Res., 47, 1–13, https://doi.org/10.1029/2010WR010174,
2011.
Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C.,
and Kavetski, D.: State updating and calibration period selection to improve
dynamic monthly streamflow forecasts for an environmental flow management
application, Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, 2018.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.:
Improving uncertainty estimation in urban hydrological modeling by statistically
describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated
Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum
CRPS Estimation, Mon. Weather Rev., 133, 1098–1118,
https://doi.org/10.1175/MWR2904.1, 2005.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. R. Stat. Soc. Ser. B, 69, 243–268,
https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007.
Hashino, T., Bradley, A. A., and Schwartz, S. S.: Evaluation of bias-correction
methods for ensemble streamflow volume forecasts, Hydrol. Earth Syst. Sci., 11,
939–950, https://doi.org/10.5194/hess-11-939-2007, 2007.
Hazelton, M. L.: Methods of Moments Estimation BT – International
Encyclopedia of Statistical Science, edited by: Lovric, M., Springer Berlin
Heidelberg, Berlin, Heidelberg, 816–817, 2011.
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000.
Hudson, D., Marshall, A. G., Yin, Y., Alves, O., and Hendon, H. H.: Improving
Intraseasonal Prediction with a New Ensemble Generation Strategy, Mon.
Weather Rev., 141, 4429–4449, https://doi.org/10.1175/MWR-D-13-00059.1, 2013.
Humphrey, G. B., Gibbs, M. S., Dandy, G. C., and Maier, H. R.: A hybrid
approach to monthly streamflow forecasting: Integrating hydrological model
outputs into a Bayesian artificial neural network, J. Hydrol., 540,
623–640, https://doi.org/10.1016/j.jhydrol.2016.06.026, 2016.
Jeffrey, S. J., Carter, J. O., Moodie, K. B., and Beswick, A. R.: Using
spatial interpolation to construct a comprehensive archive of Australian
climate data, Environ. Model. Softw., 16, 309–330,
https://doi.org/10.1016/S1364-8152(01)00008-1, 2001.
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, 2006.
Knoche, M., Fischer, C., Pohl, E., Krause, P., and Merz, R.: Combined
uncertainty of hydrological model complexity and satellite-based forcing data
evaluated in two data-scarce semi-arid catchments in Ethiopia, J. Hydrol.,
519, 2049–2066, https://doi.org/10.1016/j.jhydrol.2014.10.003, 2014.
Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian
total error analysis of conceptual rainfall-runoff models: Characterising
model error using storm-dependent parameters, J. Hydrol., 331,
161–177, https://doi.org/10.1016/j.jhydrol.2006.05.010, 2006.
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological
variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007.
Laugesen, R., Tuteja, N. K., Shin, D., Chia, T., and Khan, U.: Seasonal
Streamflow Forecasting with a workflow-based dynamic hydrologic modelling
approach, in: MODSIM 2011 – 19th International Congress on Modelling and
Simulation – Sustaining Our Future: Understanding and Living with
Uncertainty, 2352–2358, available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-84858823270&partnerID=tZOtx3y1 (last access: 7 November 2018),
2011.
Lerat, J., Pickett-Heaps, C., Shin, D., Zhou, S., Feikema, P., Khan, U.,
Laugesen, R., Tuteja, N., Kuczera, G., Thyer, M., and Kavetski, D.: Dynamic
streamflow forecasts within an uncertainty framework for 100 catchments in
Australia, in In: 36th Hydrology and Water Resources Symposium: The art and
science of water, 1396–1403, Barton, ACT: Engineers Australia, 2015.
Li, M., Wang, Q. J., Bennett, J. C., and Robertson, D. E.: Error reduction and
representation in stages (ERRIS) in hydrological modelling for ensemble streamflow
forecasting, Hydrol. Earth Syst. Sci., 20, 3561–3579, https://doi.org/10.5194/hess-20-3561-2016, 2016.
Lü, H., Crow, W. T., Zhu, Y., Ouyang, F., and Su, J.: Improving
streamflow prediction using remotely-sensed soil moisture and snow depth,
Remote Sens., 8, 503, https://doi.org/10.3390/rs8060503, 2016.
Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved post-processing
of hydrologic forecast ensembles, Hydrol. Proc., 28, 104–122,
https://doi.org/10.1002/hyp.9562, 2014.
Matte, S., Boucher, M.-A., Boucher, V., and Fortier Filion, T.-C.: Moving beyond the
cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse
decision maker, Hydrol. Earth Syst. Sci., 21, 2967–2986, https://doi.org/10.5194/hess-21-2967-2017, 2017.
McInerney, D., Thyer, M., Kavetski, D., Lerat, J., and Kuczera, G.: Improving
probabilistic prediction of daily streamflow by identifying Pareto optimal
approaches for modeling heteroscedastic residual errors, Water Resour. Res.,
53, 2199–2239, https://doi.org/10.1002/2016WR019168, 2017.
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D.,
and Arnold, J. R.: An intercomparison of approaches for improving operational
seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935,
https://doi.org/10.5194/hess-21-3915-2017, 2017.
Middleton, N., Programme, U. N. E., and Thomas, D. S. G.: World Atlas of
Desertification, Arnold, 1997.
Morss, R. E., Lazo, J. K., and Demuth, J. L.: Examining the use of weather
forecasts in decision scenarios: Results from a us survey with implications
for uncertainty communication, Meteorol. Appl., 17, 149–162,
https://doi.org/10.1002/met.196, 2010.
Murphy, A. H. and Ehrendorfer, M.: On the relationship between the accuracy
and value of forecasts in the cost–loss ratio situation, Weather Forecast.,
2, 243–251, https://doi.org/10.1175/1520-0434(1987)002<0243:OTRBTA>2.0.CO;2, 1987.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a
parsimonious model for streamflow simulation, J. Hydrol., 279,
275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Pokhrel, P., Robertson, D. E., and Wang, Q. J.: A Bayesian joint probability post-processor
for reducing errors and quantifying uncertainty in monthly streamflow predictions,
Hydrol. Earth Syst. Sci., 17, 795–804, https://doi.org/10.5194/hess-17-795-2013, 2013.
Prudhomme, C., Hannaford, J., Harrigan, S., Boorman, D., Knight, J., Bell,
V., Jackson, C., Svensson, C., Parry, S., Bachiller-Jareno, N., Davies, H.,
Davis, R., Mackay, J., McKenzie, A., Rudd, A., Smith, K., Bloomfield, J.,
Ward, R., and Jenkins, A.: Hydrological Outlook UK: an operational streamflow
and groundwater level forecasting system at monthly to seasonal time scales,
Hydrol. Sci. J., 62, 2753–2768, https://doi.org/10.1080/02626667.2017.1395032,
2017.
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., and Franks, S.
W.: Toward a reliable decomposition of predictive uncertainty in
hydrological modeling: Characterizing rainfall errors using conditional
simulation, Water Resour. Res., 47, W11516, https://doi.org/10.1029/2011WR010643,
2011.
Robertson, D. E. and Wang, Q. J.: Selecting predictors for seasonal
streamflow predictions using a Bayesian joint probability (BJP) modelling
approach, 18th World IMACS/MODSIM Congr. Cairns, Aust. 13–17 July 2009,
376–382, 2009.
Robertson, D. E. and Wang, Q. J.: A Bayesian Approach to Predictor Selection
for Seasonal Streamflow Forecasting, J. Hydrometeorol., 13, 155–171,
https://doi.org/10.1175/JHM-D-10-05009.1, 2011.
Robertson, D. E., Pokhrel, P., and Wang, Q. J.: Improving statistical forecasts of
seasonal streamflows using hydrological model output, Hydrol. Earth Syst. Sci.,
17, 579–593, https://doi.org/10.5194/hess-17-579-2013, 2013a.
Robertson, D. E., Shrestha, D. L., and Wang, Q. J.: Post-processing
rainfall forecasts from numerical weather prediction models for short-term
streamflow forecasting, Hydrol. Earth Syst. Sci., 17, 3587–3603,
https://doi.org/10.5194/hess-17-3587-2013, 2013b.
Sawicz, K. A., Kelleher, C., Wagener, T., Troch, P., Sivapalan, M., and Carrillo, G.:
Characterizing hydrologic change through catchment classification, Hydrol. Earth
Syst. Sci., 18, 273–285, https://doi.org/10.5194/hess-18-273-2014, 2014.
Schick, S., Rössler, O., and Weingartner, R.: Monthly streamflow forecasting at
varying spatial scales in the Rhine basin, Hydrol. Earth Syst. Sci., 22, 929–942,
https://doi.org/10.5194/hess-22-929-2018, 2018.
Senlin, Z., Feikema, P., Shin, D., Tuteja, N. K., MacDonald, A., Sunter, P.,
Kent, D., Le, B., Pipunic, R., Wilson, T., Pickett-Heaps, C., and Lerat, J.:
Operational efficiency measures of the national seasonal streamflow forecast
service in Australia, edited by: Syme, G., MacDonald, D. H., Fulton, B., and Piantadosi,
J., The Modelling and Simulation Society of Australia and New
Zealand Inc, Hobart, Australia., 2017.
Seo, D.-J., Herr, H. D., and Schaake, J. C.: A statistical post-processor for
accounting of hydrologic uncertainty in short-range ensemble streamflow prediction,
Hydrol. Earth Syst. Sci. Discuss., 3, 1987–2035, https://doi.org/10.5194/hessd-3-1987-2006, 2006.
Shapiro, S. S. and Wilk, M. B.: An Analysis of Variance Test for Normailty
(Complete Samples), Biometrika, 52, 591–611, https://doi.org/10.2307/1267427,
1965.
Smith, T., Marshall, L., and Sharma, A.: Modeling residual hydrologic errors
with Bayesian inference, J. Hydrol., 528, 29–37,
https://doi.org/10.1016/j.jhydrol.2015.05.051, 2015.
Tang, Q. and Lettenmaier, D. P.: Use of satellite snow-cover data for
streamflow prediction in the Feather River Basin, California, Int. J. Remote
Sens., 31, 3745–3762, https://doi.org/10.1080/01431161.2010.483493, 2010.
Taschetto, A. S. and England, M. H.: An analysis of late twentieth century
trends in Australian rainfall, Int. J. Climatol., 29, 791–807,
https://doi.org/10.1002/joc.1736, 2009.
Timbal, B. and McAvaney, B. J.: An Analogue based method to downscale
surface air temperature: Application for Australia, Clim. Dynam, 17,
947–963, https://doi.org/10.1007/s003820100156, 2001.
Turner, S. W. D., Bennett, J. C., Robertson, D. E., and Galelli, S.: Complex
relationship between seasonal streamflow forecast skill and value in reservoir
operations, Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, 2017.
Tuteja, N. K., Shin, D., Laugesen, R., Khan, U., Shao, Q., Wang, E., Li, M.,
Zheng, H., Kuczera, G., Kavetski, D., Evin, G., Thyer, M., MacDonald, A.,
Chia, T., and Le, B.: Experimental evaluation of the dynamic seasonal
streamflow forecasting approach, Melbourne, 2011.
Tuteja, N. K., Zhou, S., Lerat, J., Wang, Q. J., Shin, D., and Robertson, D.
E.: Overview of Communication Strategies for Uncertainty in Hydrological
Forecasting in Australia, in: Handbook of Hydrometeorological Ensemble
Forecasting, edited by: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H.
L., and Schaake, J. C., Springer Berlin Heidelberg, Berlin,
Heidelberg, 1–19, 2016.
Tyralla, C. and Schumann, A. H.: Incorporating structural uncertainty of
hydrological models in likelihood functions via an ensemble range approach,
Hydrol. Sci. J., 61, 1679–1690, https://doi.org/10.1080/02626667.2016.1164314,
2016.
Wandishin, M. S. and Brooks, H. E.: On the relationship between Clayton's
skill score and expected value for forecasts of binary events, Meteorol.
Appl., 9, 455–459, https://doi.org/10.1017/S1350482702004085, 2002.
Wang, Q. J. and Robertson, D. E.: Multisite probabilistic forecasting of
seasonal flows for streams with zero value occurrences, Water Resour. Res.,
47, https://doi.org/10.1029/2010WR009333, 2011.
Wang, Q. J., Robertson, D. E., and Chiew, F. H. S.: A Bayesian joint
probability modeling approach for seasonal forecasting of streamflows at
multiple sites, Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008WR007355,
2009.
Wang, Q. J., Shrestha, D. L., Robertson, D. E., and Pokhrel, P.: A log-sinh
transformation for data normalization and variance stabilization, Water
Resour. Res., 48, W05514, https://doi.org/10.1029/2011WR010973, 2012.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Amsterdam, Academic Press, 2011.
Wood, A. W. and Schaake, J. C.: Correcting Errors in Streamflow Forecast
Ensemble Mean and Spread, J. Hydrometeorol., 9, 132–148,
https://doi.org/10.1175/2007JHM862.1, 2008.
Ye, W., Bates, B. C., Viney, N. R., Sivapalan, M., and Jakeman, A. J.:
Performance of conceptual rainfall-runoff models in low-yielding ephemeral
catchments, Water Resour. Res., 33, 153–166, https://doi.org/10.1029/96WR02840,
1997.
Yin, Y., Alves, O., Oke, P. R., Yin, Y., Alves, O., and Oke, P. R.: An
ensemble ocean data assimilation system for seasonal prediction, Mon.
Weather Rev., 139, 786–808, https://doi.org/10.1175/2010MWR3419.1, 2011.
Zhang, Q., Xu, C.-Y., and Zhang, Z.: Observed changes of drought/wetness
episodes in the Pearl River basin, China, using the standardized
precipitation index and aridity index, Theor. Appl. Climatol., 98,
89–99, https://doi.org/10.1007/s00704-008-0095-4, 2009.
Zhao, T., Schepen, A., and Wang, Q. J.: Ensemble forecasting of sub-seasonal
to seasonal streamflow by a Bayesian joint probability modelling approach,
J. Hydrol., 541, 839–849,
https://doi.org/10.1016/j.jhydrol.2016.07.040, 2016.
Short summary
This paper evaluates several schemes for post-processing monthly and seasonal streamflow forecasts using the Australian Bureau of Meteorology's streamflow forecasting system. Through evaluation across 300 catchments, the best-performing scheme has been identified, which is found to substantially improve important aspects of the forecast quality. This finding is significant because the improved forecasts help water managers and users of the service to make better-informed decisions.
This paper evaluates several schemes for post-processing monthly and seasonal streamflow...
