Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2663-2021
© Author(s) 2021. 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-25-2663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 May 2021
Research article |
| 20 May 2021
| 20 May 2021
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Dongryeol Ryu
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
J. Angus Webb
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Anna Lintern
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Department of Civil Engineering, Monash University, VIC 3800, Australia
Danlu Guo
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
David Waters
Queensland Department of Resources, Toowoomba, QLD 4350, Australia
Andrew W. Western
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
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Shabnam Saffarpour, Andrew W. Western, Russell Adams, and Jeffrey J. McDonnell
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Z. K. Tesemma, Y. Wei, M. C. Peel, and A. W. Western
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Z. Lu, Y. Wei, H. Xiao, S. Zou, J. Xie, J. Ren, and A. Western
Hydrol. Earth Syst. Sci., 19, 2261–2273, https://doi.org/10.5194/hess-19-2261-2015, https://doi.org/10.5194/hess-19-2261-2015, 2015
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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.
J. F. Costelloe, T. J. Peterson, K. Halbert, A. W. Western, and J. J. McDonnell
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Groundwater surface mapping is used as an independent data set to better estimate groundwater discharge to streamflow. The groundwater surfaces indicated when other techniques likely overestimated the groundwater discharge component of baseflow. Groundwater surfaces also identified areas where regional groundwater could not be contributing to tributary streamflow. This method adds significant value to water resource management where sufficient groundwater monitoring data are available.
C.-H. Su and D. Ryu
Hydrol. Earth Syst. Sci., 19, 17–31, https://doi.org/10.5194/hess-19-17-2015, https://doi.org/10.5194/hess-19-17-2015, 2015
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Global environmental monitoring requires geophysical measurements from a variety of sources and sensors to close the information gap. This paper proposes a novel approach for analysing temporal scale-by-scale differences (biases and errors) between geophysical estimates from disparate sources. This allows assessment of different bias correction schemes, and forms the basis for a multi-scale bias correction scheme and data-adaptive, non-linear de-noising.
Z. K. Tesemma, Y. Wei, M. C. Peel, and A. W. Western
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-10515-2014, https://doi.org/10.5194/hessd-11-10515-2014, 2014
Revised manuscript not accepted
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Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
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Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
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Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
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Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
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., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with 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 using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
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Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based 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 to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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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 such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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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 requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 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.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
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Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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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
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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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.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
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Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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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.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
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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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
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.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript accepted for HESS
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping better prepare for and respond to floods.
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.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
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Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
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.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
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.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
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This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
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.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2024-993, https://doi.org/10.5194/egusphere-2024-993, 2024
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This study uses deep learning to predict spatially contiguous water runoff in Switzerland from 1962–2023. It outperforms traditional models, requiring less data and computational power. Key findings include increased dry years and summer water scarcity. This method offers significant advancements in water monitoring.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
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This study presents the first distributed hydrological simulation which confirms the claims raised by historians that the Eastward Diversion Project of the Tone River in Japan was conducted four centuries ago to increase low flows and subsequent travelling possibilities surrounding the Capitol Edo (Tokyo) using inland navigation. We reconstructed six historical river maps and indirectly validated the historical simulations with reachable ancient river ports via increased low-flow water levels.
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.
Cited articles
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., and Kløve, B.:
A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model,
J. Hydrol.,
524, 733–752, 2015.
Abbott, M., Bathurst, J., Cunge, J., O'connell, P., and Rasmussen, J.:
An introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 2: Structure of a physically-based, distributed modelling system,
J. Hydrol.,
87, 61–77, 1986.
Arnold, J. G. and Fohrer, N.:
SWAT2000: current capabilities and research opportunities in applied watershed modelling,
Hydrol. Process.,
19, 563–572, 2005.
Atkinson, A. B.:
The box-cox transformation: Review and extensions,
Stat. Sci., 36, 239–255, 2021.
Atkinson, R., Power, R., Lemon, D., O'Hagan, R., Dovey, D., and Kinny, D.: The Australian Hydrological Geospatial Fabric–Development Methodology and Conceptual Architecture, Citeseer, 2008.
Barnard, R., Leadley, P. W., and Hungate, B. A.:
Global change, nitrification, and denitrification: a review,
Global Biogeochem. Cy.,
19, GB1007, https://doi.org/10.1029/2004GB002282, 2005.
Bartley, R., Speirs, W. J., Ellis, T. W., and Waters, D. K.:
A review of sediment and nutrient concentration data from Australia for use in catchment water quality models,
Mar. Pollut. Bull.,
65, 101–116, 2012.
Bartley, R., Thompson, C., Croke, J., Pietsch, T., Baker, B., Hughes, K., and Kinsey-Henderson, A.:
Insights into the history and timing of post-European land use disturbance on sedimentation rates in catchments draining to the Great Barrier Reef,
Mar. Pollut. Bull.,
131, 530–546, 2018.
Bayarri, M. J., Berger, J. O., Forte, A., and García-Donato, G.:
Criteria for Bayesian model choice with application to variable selection,
Ann Statist.,
40, 1550–1577, 2012.
Beiter, D., Weiler, M., and Blume, T.: Characterising hillslope–stream connectivity with a joint event analysis of stream and groundwater levels, Hydrol. Earth Syst. Sci., 24, 5713–5744, https://doi.org/10.5194/hess-24-5713-2020, 2020.
Bieger, K., Hörmann, G., and Fohrer, N.:
Simulation of streamflow and sediment with the soil and water assessment tool in a data scarce catchment in the three Gorges region, China,
J. Environ. Qual.,
43, 37–45, 2014.
Box, G. E. and Cox, D. R.:
An analysis of transformations,
J. Roy. Stat. Soc. B Met.,
26, 211–243, 1964.
Brevik, E. C., Fenton, T. E., and Lazari, A.:
Soil electrical conductivity as a function of soil water content and implications for soil mapping,
Precis. Agric.,
7, 393–404, 2006.
Brodie, J. E., Kroon, F., Schaffelke, B., Wolanski, E., Lewis, S., Devlin, M., Bohnet, I., Bainbridge, Z., Waterhouse, J., and Davis, A.:
Terrestrial pollutant runoff to the Great Barrier Reef: an update of issues, priorities and management responses,
Mar. Pollut. Bull.,
65, 81–100, 2012.
Castillo, I., Schmidt-Hieber, J., and Van der Vaart, A.:
Bayesian linear regression with sparse priors,
Ann Statist.,
43, 1986–2018, 2015.
Chang, F.-J., Tsai, Y.-H., Chen, P.-A., Coynel, A., and Vachaud, G.:
Modeling water quality in an urban river using hydrological factors–Data driven approaches,
J. Environ. Manage.,
151, 87–96, 2015.
Chen, L., Sun, C., Wang, G., Xie, H., and Shen, Z.:
Event-based nonpoint source pollution prediction in a scarce data catchment,
J. Hydrol.,
552, 13–27, 2017.
Clow, D. W. and Sueker, J. K.:
Relations between basin characteristics and stream water chemistry in alpine/subalpine basins in Rocky Mountain National Park, Colorado,
Water Resour. Res.,
36, 49–61, 2000.
Cooke, S. E., Ahmed, S. M., and MacAlpine, N.:
Introductory guide to surface water quality monitoring in agriculture,
Conservation and Development Branch, Alberta Agriculture, Food and Rural Development, Edmonton, 2000.
Correll, D.: Buffer zones and water quality protection: general principles, Buffer zones: Their processes and potential in water protection, September 1996, Harpenden, England, 7–20, 1996.
Cuomo, A. and Guida, D.:
Using hydro-chemograph analyses to reveal runoff generation processes in a Mediterranean catchment,
Hydrol. Process.,
30, 4462–4476, 2016.
Daoud, J. I.:
Multicollinearity and regression analysis,
J. Phys.-Conf. Ser., 949, 012003, https://doi.org/10.1088/1742-6596/949/1/012009, 2017.
Davis, A. M., Pearson, R. G., Brodie, J. E., and Butler, B.:
Review and conceptual models of agricultural impacts and water quality in waterways of the Great Barrier Reef catchment area,
Mar. Freshwater Res.,
68, 1–19, 2017.
Day, K. A. and McKeon, G. M.:
An index of summer rainfall for Queensland's grazing lands,
J. Appl. Meteorol. Clim.,
57, 1623–1641, 2018.
De Keersmaecker, W., Lhermitte, S., Tits, L., Honnay, O., Somers, B., and Coppin, P.:
A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover,
Global Ecol. Biogeogr.,
24, 539–548, 2015.
de Mello, K., Valente, R. A., Randhir, T. O., dos Santos, A. C. A., and Vettorazzi, C. A.:
Effects of land use and land cover on water quality of low-order streams in Southeastern Brazil: Watershed versus riparian zone,
Catena,
167, 130–138, 2018.
Deletic, A. B. and Maksimovic, C.:
Evaluation of water quality factors in storm runoff from paved areas,
J. Environ. Eng.,
124, 869–879, 1998.
Didan, K.:
MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V006, NASA EOSDIS LP DAAC, https://doi.org/10.5067/MODIS/MOD13A2.006, 2015.
DNRME:
available at: https://water-monitoring.information.qld.gov.au/ (last access: 1 September 2016),
Department of Natural Resources, Mines and Energy of Queensland, Brisbane, 2016.
Doran, J., Mielke, L., and Stamatiadis, S.:
Microbial activity and N cycling as regulated by soil water-filled pore space, 11th International Conference, Int. Soil Tillage Res. Orgo (ISTRO), Edinburgh, Scotland, 49–54, 1988.
Fernandez, C., Ley, E., and Steel, M. F.:
Benchmark priors for Bayesian model averaging,
J. Econometrics,
100, 381–427, 2001.
Filoso, S., Vallino, J., Hopkinson, C., Rastetter, E., and Claessens, L.: Modeling nitrogen transport in the Ipswich River Basin, Massachusetts, using a hydrological simulation program in FORTRAN (HSPF) 1, J. Am. Water Resour. As., 40, 1365–1384, https://doi.org/10.1111/j.1752-1688.2004.tb01592.x, 2004.
Fox, J., Weisberg, S., Adler, D., Bates, D., Baud-Bovy, G., Ellison, S., Firth, D., Friendly, M., Gorjanc, G., and Graves, S.:
Package “car”,
R Foundation for Statistical Computing, Vienna, 2012.
Francesconi, W., Srinivasan, R., Pérez-Miñana, E., Willcock, S. P., and Quintero, M.:
Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review,
J. Hydrol.,
535, 625–636, 2016.
Freckleton, R. P.:
Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error,
Behav. Ecol. Sociobiol.,
65, 91–101, 2011.
Frost, A., Ramchurn, A., and Smith, A.:
The Bureau's Operational AWRA Landscape (AWRA-L) Model,
Bureau of Meteorology, Melbourne, 47, 2016.
Fu, B., Merritt, W. S., Croke, B. F., Weber, T., and Jakeman, A. J.:
A review of catchment-scale water quality and erosion models and a synthesis of future prospects,
Environ. Modell. Softw.,
114, 75–97, 2019.
Garzon-Garcia, A., Wallace, R., Huggins, R., Turner, R. D., Smith, R., Orr, D., Ferguson, B., Gardiner, R., Thomson, B., and Warne, M.:
Total suspended solids, nutrient and pesticide loads (2013–2014) for rivers that discharge to the Great Barrier Reef,
Department of Science, Information Technology and Innovation,
Brisbane, Australia, 2016.
Gelman, A., Stern, H. S., Carlin, J. B., Dunson, D. B., Vehtari, A., and Rubin, D. B.:
Bayesian data analysis,
Chapman and Hall/CRC, Florida, US, 2013.
Geng, X., Heiss, J. W., Michael, H. A., and Boufadel, M. C.:
Subsurface flow and moisture dynamics in response to swash motions: Effects of beach hydraulic conductivity and capillarity,
Water Resour. Res.,
53, 10317–10335, 2017.
George, E. I. and McCulloch, R. E.:
Variable selection via Gibbs sampling,
J. Am. Stat. Assoc.,
88, 881–889, 1993.
Geoscience Australia:
GEODATA 9 second DEM and D8: digital elevation model version 3 and flow direction grid 2008, Bioregion Assessment Source Dataset,
Geoscience Australia, Canberra, Australia, 2008.
Gilbert, M. and Brodie, J.:
Population and major land use in the Great Barrier Reef catchment area spatial and temporal trends,
Great Barrier Reef Marine Park Authority, Townsville, 2001.
Gorman, D., Russell, B. D., and Connell, S. D.:
Land-to-sea connectivity: linking human-derived terrestrial subsidies to subtidal habitat change on open rocky coasts,
Ecol. Appl.,
19, 1114–1126, 2009.
Granger, S., Bol, R., Anthony, S., Owens, P., White, S., and Haygarth, P.: Towards a holistic classification of diffuse agricultural water pollution from intensively managed grasslands on heavy soils, Adv. Agron., 105, 83–115, 2010.
Griffith, J. A.:
Geographic techniques and recent applications of remote sensing to landscape-water quality studies,
Water Air Soil Poll.,
138, 181–197, 2002.
Griffith, J. A., Martinko, E. A., Whistler, J. L., and Price, K. P.:
Interrelationships among landscapes, NDVI, and stream water quality in the US Central Plains,
Ecol. Appl.,
12, 1702–1718, 2002.
Guo, D., Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende-Michl, U., Leahy, P., Wilson, P., and Western, A.:
Key Factors Affecting Temporal Variability in Stream Water Quality,
Water Resour. Res.,
55, 112–129, 2019.
Guo, D., Lintern, A., Webb, J. A., Ryu, D., Bende-Michl, U., Liu, S., and Western, A. W.: A data-based predictive model for spatiotemporal variability in stream water quality, Hydrol. Earth Syst. Sci., 24, 827–847, https://doi.org/10.5194/hess-24-827-2020, 2020.
Guthke, A.:
Defensible model complexity: A call for data-based and goal-oriented model choice,
Groundwater,
55, 646–650, 2017.
Harris, G. P.:
Biogeochemistry of nitrogen and phosphorus in Australian catchments, rivers and estuaries: effects of land use and flow regulation and comparisons with global patterns,
Mar. Freshwater Res.,
52, 139–149, 2001.
Hem, J. D.:
Fluctuations in concentration of dissolved solids of some southwestern streams,
Eos T. Am. Geophys. Un.,
29, 80–84, 1948.
Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E.-J.:
A Conceptual Introduction to Bayesian Model Averaging,
Advances in Methods and Practices in Psychological Science,
3, 200–215, 2020.
Hirsch, R. M., Moyer, D. L., and Archfield, S. A.:
Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay river inputs,
J. Am. Water Resour. As.,
46, 857–880, 2010.
Hoeting, J. A., Raftery, A. E., and Madigan, D.:
Bayesian variable and transformation selection in linear regression,
J. Comput. Graph. Stat.,
11, 485–507, 2002.
Höge, M., Guthke, A., and Nowak, W.:
The hydrologist's guide to Bayesian model selection, averaging and combination,
J. Hydrol.,
572, 96–107, 2019.
Howarth, R. W., Sharpley, A., and Walker, D.:
Sources of nutrient pollution to coastal waters in the United States: Implications for achieving coastal water quality goals,
Estuaries,
25, 656–676, 2002.
Jarihani, B., Sidle, R., Bartley, R., Roth, C., and Wilkinson, S.:
Characterisation of hydrological response to rainfall at multi spatio-temporal scales in savannas of semi-arid Australia,
Water,
9, 540, https://doi.org/10.3390/w9070540, 2017.
Jayakrishnan, R., Srinivasan, R., Santhi, C., and Arnold, J.:
Advances in the application of the SWAT model for water resources management,
Hydrol. Process.,
19, 749–762, 2005.
Kasiviswanathan, K. and Sudheer, K.:
Quantification of the predictive uncertainty of artificial neural network based river flow forecast models,
Stoch. Env. Res. Risk A.,
27, 137–146, 2013.
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.
Khan, U., Cook, F. J., Laugesen, R., Hasan, M. M., Plastow, K., Amirthanathan, G. E., Bari, M. A., and Tuteja, N. K.:
Development of catchment water quality models within a realtime status and forecast system for the Great Barrier Reef,
Environ. Modell. Softw.,
132, 104790, https://doi.org/10.1016/j.envsoft.2020.104790, 2020.
Koci, J., Sidle, R. C., Jarihani, B., and Cashman, M. J.:
Linking hydrological connectivity to gully erosion in savanna rangelands tributary to the Great Barrier Reef using Structure-from-Motion photogrammetry,
Land Degrad. Dev.,
31, 20–36, 2019.
Koci, J., Sidle, R. C., Kinsey-Henderson, A. E., Bartley, R., Wilkinson, S. N., Hawdon, A. A., Jarihani, B., Roth, C. H., and Hogarth, L.:
Effect of reduced grazing pressure on sediment and nutrient yields in savanna rangeland streams draining to the Great Barrier Reef,
J. Hydrol.,
582, 124520, https://doi.org/10.1016/j.jhydrol.2019.124520, 2020.
Kohavi, R.:
A study of cross-validation and bootstrap for accuracy estimation and model selection,
International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Quebec Canada,
1137–1145, 1995.
Kratz, T., Webster, K., Bowser, C., Maguson, J., and Benson, B.:
The influence of landscape position on lakes in northern Wisconsin,
Freshwater Biol.,
37, 209–217, 1997.
Kruschke, J.:
Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan,
Academic Press, Cambridge, Massachusetts, United States, 2014.
Kuhnert, P., Wang, Y.-G., Henderson, B., Stewart, L., and Wilkinson, S.:
Statistical methods for the estimation of pollutant loads from monitoring data, Final Project Report, Report to the Marine and Tropical Sciences Research Facility,
Reef and Rainforest Research Centre Limited, Cairns, 2009.
Ladson, A. R., Brown, R., Neal, B., and Nathan, R.:
A standard approach to baseflow separation using the Lyne and Hollick filter,
Australasian Journal of Water Resources,
17, 25–34, 2013.
Lam, Q., Schmalz, B., and Fohrer, N.:
Modelling point and diffuse source pollution of nitrate in a rural lowland catchment using the SWAT model,
Agr. Water Manage.,
97, 317–325, 2010.
Lessels, J. and Bishop, T.:
A simulation based approach to quantify the difference between event-based and routine water quality monitoring schemes,
Journal of Hydrology: Regional Studies,
4, 439–451, 2015.
Letcher, R. A., Jakeman, A. J., Calfas, M., Linforth, S., Baginska, B., and Lawrence, I.:
A comparison of catchment water quality models and direct estimation techniques,
Environ. Modell. Softw.,
17, 77–85, 2002.
Liang, F., Paulo, R., Molina, G., Clyde, M. A., and Berger, J. O.:
Mixtures of g priors for Bayesian variable selection,
J. Am. Stat. Assoc.,
103, 410–423, 2008.
Lintern, A., Webb, J., Ryu, D., Liu, S., Bende-Michl, U., Waters, D., Leahy, P., Wilson, P., and Western, A.:
Key factors influencing differences in stream water quality across space,
WIRES Water,
5, e1260, https://doi.org/10.1002/wat2.1260, 2018a.
Lintern, A., Webb, J., Ryu, D., Liu, S., Waters, D., Leahy, P., Bende-Michl, U., and Western, A.:
What are the key catchment characteristics affecting spatial differences in riverine water quality?,
Water Resour. Res.,
54, 7252–7272, 2018b.
Lintern, A., McPhillips, L., Winfrey, B., Duncan, J., and Grady, C.:
Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds,
Environ. Sci. Technol.,
54, 9159–9174, 2020.
Liu, S., Ryu, D., Webb, J., Lintern, A., Waters, D., Guo, D., and Western, A.:
Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis,
Mar. Pollut. Bull.,
137, 137–151, 2018.
Liu, Y., Wang, C., He, N., Wen, X., Gao, Y., Li, S., Niu, S., Butterbach-Bahl, K., Luo, Y., and Yu, G.:
A global synthesis of the rate and temperature sensitivity of soil nitrogen mineralization: latitudinal patterns and mechanisms,
Glob. Change Biol.,
23, 455–464, 2017.
Lloyd, C., Freer, J., Johnes, P., and Collins, A.:
Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments,
Sci. Total Environ.,
543, 388–404, 2016.
Ly, K., Metternicht, G., and Marshall, L.:
Transboundary river catchment areas of developing countries: Potential and limitations of watershed models for the simulation of sediment and nutrient loads. A review,
Journal of Hydrology: Regional Studies,
24, 100605, https://doi.org/10.1016/j.ejrh.2019.100605, 2019.
Mainali, J., Chang, H., and Chun, Y.:
A review of spatial statistical approaches to modeling water quality,
Progress in Physical Geography: Earth and Environment,
43, 801–826, https://doi.org/10.1177/0309133319852003, 2019.
Mantovan, P. and Todini, E.:
Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology,
J. Hydrol.,
330, 368–381, 2006.
Masocha, M., Murwira, A., Magadza, C. H., Hirji, R., and Dube, T.:
Remote sensing of surface water quality in relation to catchment condition in Zimbabwe,
Phys. Chem. Earth Pt. A/B/C,
100, 13–18, 2017.
McCloskey, G., Waters, D., Baheerathan, R., Darr, S., Dougall, C., Ellis, R., Fentie, B., and Hateley, L.: Modelling reductions of pollutant loads due to improved management practices in the great barrier reef catchments: updated methodology and results-technical report for reef report card 2015, Queensland Department of Natural Resources and Mines, Brisbane, Queensland, 2017.
McCloskey, G., Baheerathan, R., Dougall, C., Ellis, R., Bennett, F., Waters, D., Darr, S., Fentie, B., Hateley, L., and Askildsen, M.:
Modelled estimates of fine sediment and particulate nutrients delivered from the Great Barrier Reef catchments,
Mar. Pollut. Bull.,
165, 112163, https://doi.org/10.1016/j.marpolbul.2021.112163, 2021.
McGrane, S. J.:
Impacts of urbanisation on hydrological and water quality dynamics, and urban water management: a review,
Hydrolog. Sci. J.,
61, 2295–2311, 2016.
McKergow, L. A., Weaver, D. M., Prosser, I. P., Grayson, R. B., and Reed, A. E.:
Before and after riparian management: sediment and nutrient exports from a small agricultural catchment, Western Australia,
J. Hydrol.,
270, 253–272, 2003.
McKergow, L. A., Prosser, I. P., Hughes, A. O., and Brodie, J.:
Regional scale nutrient modelling: exports to the Great Barrier Reef world heritage area,
Mar. Pollut. Bull.,
51, 186–199, 2005a.
McKergow, L. A., Prosser, I. P., Hughes, A. O., and Brodie, J.:
Sources of sediment to the Great Barrier Reef world heritage area,
Mar. Pollut. Bull.,
51, 200–211, 2005b.
Melland, A., Mellander, P.-E., Murphy, P., Wall, D., Mechan, S., Shine, O., Shortle, G., and Jordan, P.:
Stream water quality in intensive cereal cropping catchments with regulated nutrient management,
Environ. Sci. Policy,
24, 58–70, 2012.
Merritt, W. S., Letcher, R. A., and Jakeman, A. J.:
A review of erosion and sediment transport models,
Environ. Modell. Softw.,
18, 761–799, 2003.
Meyer, D. L., Townsend, E. C., and Thayer, G. W.:
Stabilization and erosion control value of oyster cultch for intertidal marsh,
Restor. Ecol.,
5, 93–99, 1997.
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.:
Hydrologic and water quality models: Performance measures and evaluation criteria,
T. ASABE,
58, 1763–1785, 2015.
Muro, J., Strauch, A., Heinemann, S., Steinbach, S., Thonfeld, F., Waske, B., and Diekkrüger, B.:
Land surface temperature trends as indicator of land use changes in wetlands,
Int. J. Appl. Earth Obs.,
70, 62–71, 2018.
Musolff, A., Schmidt, C., Selle, B., and Fleckenstein, J. H.:
Catchment controls on solute export,
Adv. Water Resour.,
86, 133–146, 2015.
Nakagawa, S. and Freckleton, R. P.:
Model averaging, missing data and multiple imputation: a case study for behavioural ecology,
Behav. Ecol. Sociobiol.,
65, 103–116, 2011.
Nash, J. E. and Sutcliffe, J. V.:
River flow forecasting through conceptual models part I—A discussion of principles,
J. Hydrol.,
10, 282–290, 1970.
Neil, D. T., Orpin, A. R., Ridd, P. V., and Yu, B.:
Sediment yield and impacts from river catchments to the Great Barrier Reef lagoon: a review,
Mar. Freshwater Res.,
53, 733–752, 2002.
O'Hara, R. B. and Sillanpää, M. J.:
A review of Bayesian variable selection methods: what, how and which,
Bayesian Anal.,
4, 85–117, 2009.
Orr, D., Turner, R. D. R., Huggins, R., Vardy, S., and J., W. M. S.: Wet Tropics water quality statistics for high and base flow conditions, Great Barrier Reef Catchment Loads Monitoring Program, Department of Science, Information
Technology, Innovation and the Arts, Brisbane, 2014.
Paliwal, R., Sharma, P., and Kansal, A.:
Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS,
J. Environ. Manage.,
83, 131–144, 2007.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen–Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Pérez-Gutiérrez, J. D., Paz, J. O., and Tagert, M. L. M.:
Seasonal water quality changes in on-farm water storage systems in a south-central US agricultural watershed,
Agr. Water Manage.,
187, 131–139, 2017.
Pilgrim, D. H. and Canterford, R. P.:
Australian rainfall and runoff,
Institution of Engineers, Australia, 1987.
Plummer, M.:
JAGS: Just another Gibbs sampler, version 3.4. 0,
available at: http://mcmc-jags.sourceforge.net (last access: 1 June 2016), 2013a.
Plummer, M.: rjags: Bayesian graphical models using MCMC, R package version, 3, available at: http://CRAN.R-project.org/package=rjags (last access: 1 June 2016), 2013b.
Posch, K., Arbeiter, M., and Pilz, J.:
A novel Bayesian approach for variable selection in linear regression models,
Comput. Stat. Data An.,
144, 106881, https://doi.org/10.1016/j.csda.2019.106881, 2020.
Qi, J., Li, S., Bourque, C. P.-A., Xing, Z., and Meng, F.-R.: Developing a decision support tool for assessing land use change and BMPs in ungauged watersheds based on decision rules provided by SWAT simulation, Hydrol. Earth Syst. Sci., 22, 3789–3806, https://doi.org/10.5194/hess-22-3789-2018, 2018.
Queensland Government: Reef 2050 Water Quality Improvement Plan – Management practices,Department of Environment and Science, Brisbane, 2017.
R Core Team:
R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org/ (last access: 1 June 2016), 2013.
Raftery, A. E., Madigan, D., and Hoeting, J. A.:
Bayesian model averaging for linear regression models,
J. Am. Stat. Assoc.,
92, 179–191, 1997.
Raupach, M., Briggs, P., Haverd, V., King, E., Paget, M., and Trudinger, C.:
Australian water availability project (AWAP): CSIRO marine and atmospheric research component: final report for phase 3,
Centre for Australian weather and climate research (bureau of meteorology and CSIRO), Melbourne, 67, 2009.
Ren, W., Zhong, Y., Meligrana, J., Anderson, B., Watt, W. E., Chen, J., and Leung, H.-L.:
Urbanization, land use, and water quality in Shanghai: 1947–1996,
Environ. Int.,
29, 649–659, 2003.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.:
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors,
Water Resour. Res.,
46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Richards, R. P. and Baker, D. B.:
Trends in nutrient and suspended sediment concentrations in Lake Erie tributaries, 1975–1990,
J. Great Lakes Res.,
19, 200–211, 1993.
Rockey, J. and Temple, J.:
Growth econometrics for agnostics and true believers,
Eur. Econ. Rev.,
81, 86–102, 2016.
Rode, M., Arhonditsis, G., Balin, D., Kebede, T., Krysanova, V., Van Griensven, A., and Van der Zee, S. E.:
New challenges in integrated water quality modelling,
Hydrol. Process.,
24, 3447–3461, 2010.
Schwarz, G., Hoos, A., Alexander, R., and Smith, R.:
The SPARROW surface water-quality model: theory, application and user documentation,
US geological survey techniques and methods report, 6,
USGS, Reston, Virginia, https://doi.org/10.3133/tm6B3, 2006.
Sherriff, S. C., Rowan, J. S., Melland, A. R., Jordan, P., Fenton, O., and Ó hUallacháin, D.: Investigating suspended sediment dynamics in contrasting agricultural catchments using ex situ turbidity-based suspended sediment monitoring, Hydrol. Earth Syst. Sci., 19, 3349–3363, https://doi.org/10.5194/hess-19-3349-2015, 2015.
Sherriff, S. C., Rowan, J. S., Fenton, O., Jordan, P., Melland, A. R., Mellander, P.-E., and Huallachain, D. O.:
Storm event suspended sediment-discharge hysteresis and controls in agricultural watersheds: implications for watershed scale sediment management,
Environ. Sci. Technol.,
50, 1769–1778, 2016.
Shi, P., Zhang, Y., Li, Z., Li, P., and Xu, G.:
Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales,
Catena,
151, 182–190, 2017.
Singh, A., Jakubowski, A. R., Chidister, I., and Townsend, P. A.:
A MODIS approach to predicting stream water quality in Wisconsin,
Remote Sens. Environ.,
128, 74–86, 2013.
Singh, P., Bhunya, P., Mishra, S., and Chaube, U.:
A sediment graph model based on SCS-CN method,
J. Hydrol.,
349, 244–255, 2008.
Skoulikidis, N. T., Amaxidis, Y., Bertahas, I., Laschou, S., and Gritzalis, K.:
Analysis of factors driving stream water composition and synthesis of management tools—a case study on small/medium Greek catchments,
Sci. Total Environ.,
362, 205–241, 2006.
Snyder, C. S.:
Enhanced nitrogen fertiliser technologies support the '4R'concept to optimise crop production and minimise environmental losses,
Soil Res.,
55, 463–472, 2017.
Srivastav, R., Sudheer, K., and Chaubey, I.:
A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models,
Water Resour. Res.,
43, W10407, https://doi.org/10.1029/2006WR005352, 2007.
Steel, M. F.:
Model averaging and its use in economics,
arXiv [preprint] arXiv:1709.08221, 2019.
Tabacchi, E., Lambs, L., Guilloy, H., Planty-Tabacchi, A. M., Muller, E., and Decamps, H.:
Impacts of riparian vegetation on hydrological processes,
Hydrol. Process.,
14, 2959–2976, 2000.
Tang, W. and Carey, S. K.:
HydRun: A MATLAB toolbox for rainfall–runoff analysis,
Hydrol. Process.,
31, 2670–2682, 2017.
Thompson, S., Basu, N., Lascurain, J., Aubeneau, A., and Rao, P.:
Relative dominance of hydrologic versus biogeochemical factors on solute export across impact gradients,
Water Resour. Res.,
47, W00J05, https://doi.org/10.1029/2010WR009605, 2011.
Turner, R., Huggins, R., Wallace, R., Smith, R., Vardy, S., and Warne, M. S. J.:
Sediment, Nutrient and Pesticide Loads: Great Barrier Reef Catchment Loads Monitoring 2009–2010,
Department of Science, Information Technology, Innovation and the Arts, Brisbane, 53, 2012.
Tweed, S. O., Leblanc, M., Webb, J. A., and Lubczynski, M. W.:
Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia,
Hydrogeol. J.,
15, 75–96, 2007.
Ursino, N., Silvestri, S., and Marani, M.:
Subsurface flow and vegetation patterns in tidal environments,
Water Resour. Res.,
40, W05115, https://doi.org/10.1029/2003WR002702, 2004.
Ustaoğlu, F., Tepe, Y., and Taş, B.:
Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index,
Ecol. Indic., 1
13, 105815, https://doi.org/10.1016/j.ecolind.2019.105815, 2020.
Verheyen, D., Van Gaelen, N., Ronchi, B., Batelaan, O., Struyf, E., Govers, G., Merckx, R., and Diels, J.:
Dissolved phosphorus transport from soil to surface water in catchments with different land use,
Ambio,
44, 228–240, 2015.
Vymazal, J.:
Removal of nutrients in various types of constructed wetlands,
Sci. Total Environ.,
380, 48–65, 2007.
Wade, A. J., Durand, P., Beaujouan, V., Wessel, W. W., Raat, K. J., Whitehead, P. G., Butterfield, D., Rankinen, K., and Lepisto, A.: A nitrogen model for European catchments: INCA, new model structure and equations, Hydrol. Earth Syst. Sci., 6, 559–582, https://doi.org/10.5194/hess-6-559-2002, 2002.
Wakelin, S. A., Nelson, P. N., Armour, J. D., Rasiah, V., and Colloff, M. J.:
Bacterial community structure and denitrifier (nir-gene) abundance in soil water and groundwater beneath agricultural land in tropical North Queensland, Australia,
Soil Res.,
49, 65–76, 2011.
Walker, J. A.:
Model-averaged regression coefficients have a straightforward interpretation using causal conditioning,
bioRxiv, https://doi.org/10.1101/133785, 133785, 2019.
Walling, D.:
Dissolved loads and their measurement, Erosion and Sediment Yield: Some Methods of Measurement and Modelling,
Geo Books, Regency House Norwich (England), 111–177, 1984.
Wan, Y., Qian, Y., Migliaccio, K. W., Li, Y., and Conrad, C.:
Linking spatial variations in water quality with water and land management using multivariate techniques,
J. Environ. Qual.,
43, 599–610, 2014.
Wang, A., Yang, D., and Tang, L.:
Spatiotemporal variation in nitrogen loads and their impacts on river water quality in the upper Yangtze River basin,
J. Hydrol.,
590, 125487, https://doi.org/10.1016/j.jhydrol.2020.125487, 2020.
Wang, Q., Schepen, A., and Robertson, D. E.:
Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging,
J. Climate,
25, 5524–5537, 2012.
Waters, D. and Packett, R.:
Sediment and nutrient generation rates for Queensland rural catchments-an event monitoring program to improve water quality modelling,
Proceedings of the 5th Australian Stream
Management Conference, Australian rivers: making a difference, edited by: Wilson, A. L., Dehaan, R. L., Watts, R. J., Page, K. J., Bowmer, K. H., and Curtis, A., Charles Sturt University, Thurgoona, New South Wales, 425–430, 2007.
Webb, A. and King, E. L.:
A Bayesian hierarchical trend analysis finds strong evidence for large-scale temporal declines in stream ecological condition around Melbourne, Australia,
Ecography,
32, 215–225, 2009.
Weier, K., Doran, J., Power, J., and Walters, D.:
Denitrification and the dinitrogen/nitrous oxide ratio as affected by soil water, available carbon, and nitrate,
Soil Sci. Soc. Am. J.,
57, 66–72, 1993.
Wellen, C., Kamran-Disfani, A.-R., and Arhonditsis, G. B.:
Evaluation of the current state of distributed watershed nutrient water quality modeling,
Environ. Sci. Technol.,
49, 3278–3290, 2015.
White, R., Edis, R., Bramley, R., and Wood, A.:
Environmentally sound phosphorus management for sugarcane soils: final report on SRDC Project no CSS3S,
Sugar Research and Development Corporation, Brisbane, Australia, 1998.
Wintle, B. A., McCarthy, M. A., Volinsky, C. T., and Kavanagh, R. P.:
The use of Bayesian model averaging to better represent uncertainty in ecological models,
Conserv. Biol.,
17, 1579–1590, 2003.
Zhang, J., Zhang, Y., Song, J., and Cheng, L.:
Evaluating relative merits of four baseflow separation methods in Eastern Australia,
J. Hydrol.,
549, 252–263, 2017.
Zhang, Y., Guo, F., Meng, W., and Wang, X.-Q.:
Water quality assessment and source identification of Daliao river basin using multivariate statistical methods,
Environ. Monit Assess.,
152, 105, https://doi.org/10.1007/s10661-008-0300-z, 2009.
Zhu, G., Wang, S., Wang, C., Zhou, L., Zhao, S., Li, Y., Li, F., Jetten, M. S., Lu, Y., and Schwark, L.:
Resuscitation of anammox bacteria after > 10,000 years of dormancy,
ISME J., 13, 1098–1109, https://doi.org/10.1038/s41396-018-0316-5, 2019.
Zhu, Q., Castellano, M. J., and Yang, G.:
Coupling soil water processes and nitrogen cycle across spatial scales: Potentials, bottlenecks and solutions,
Earth-Sci. Rev., 187, 248–258,
https://doi.org/10.1016/j.earscirev.2018.10.005, 2018.
Short summary
Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation in stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful for identifying important factors determining temporal changes in water quality and, in turn, providing critical information for developing management strategies.
Riverine water quality can change markedly at one particular location. This study developed...
