Articles | Volume 22, issue 5
Research article 22 May 2018
Research article | 22 May 2018
Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
Jason M. Hunter et al.
No articles found.
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,Short summary
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.
Danlu Guo, Seth Westra, and Holger R. Maier
Hydrol. Earth Syst. Sci., 21, 2107–2126,Short summary
This study assessed the impact of baseline climate conditions on the sensitivity of potential evapotranspiration (PET) to a large range of plausible changes in temperature, relative humidity, solar radiation and wind speed at 30 Australian locations. Around 2-fold greater PET changes were observed at cool and humid locations compared to others, indicating potential for elevated water loss in the future. These impacts can be useful to inform the selection of PET models under a changing climate.
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Modelling approachesAssessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UKFrom skill to value: isolating the influence of end user behavior on seasonal forecast assessmentThe value of citizen science for flood risk reduction: cost–benefit analysis of a citizen observatory in the Brenta-Bacchiglione catchmentRisk assessment in water resources planning under climate change at the Júcar River basinInterplay of changing irrigation technologies and water reuse: example from the upper Snake River basin, Idaho, USABenchmarking an operational hydrological model for providing seasonal forecasts in SwedenA novel causal structure-based framework for comparing basin-wide water-energy-food-ecology nexuses applied to the data-limited Amu Darya and Syr Darya river basinsMulti-level storylines for participatory sociohydrological modelling – involving marginalized communities in Tz'olöj Ya', Mayan GuatemalaImpact of the quality of hydrological forecasts on the managementand revenue of hydroelectric reservoirs – a conceptual approachThe benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisionsEvapotranspiration partition using the multiple energy balance version of the ISBA-A-gs land surface model over two irrigated crops in a semi-arid Mediterranean region (Marrakech, Morocco)Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimationIrrigation return flow causing a nitrate hotspot and denitrification imprints in groundwater at Tinwald, New ZealandProjection of irrigation water demand based on the simulation of synthetic crop coefficients and climate changeMulti-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithmA novel data-driven analytical framework on hierarchical water allocation integrated with blue and virtual water transfersA novel regional irrigation water productivity model coupling irrigation- and drainage-driven soil hydrology and salinity dynamics and shallow groundwater movement in arid regions in ChinaAn evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS dataOn the assimilation of environmental tracer observations for model-based decision supportInferred inflow forecast horizons guiding reservoir release decisions across the United StatesAssessment of potential implications of agricultural irrigation policy on surface water scarcity in BrazilAbility of a soil–vegetation–atmosphere transfer model and a two-source energy balance model to predict evapotranspiration for several crops and climate conditionsAssessing water security in the São Paulo metropolitan region under projected climate changeWHAT-IF: an open-source decision support tool for water infrastructure investment planning within the water–energy–food–climate nexusRepresentation and improved parameterization of reservoir operation in hydrological and land-surface modelsWater restrictions under climate change: a Rhône–Mediterranean perspective combining bottom-up and top-down approachesQuantifying thermal refugia connectivity by combining temperature modeling, distributed temperature sensing, and thermal infrared imagingReconstructed natural runoff helps to quantify the relationship between upstream water use and downstream water scarcity in China's river basinsCan global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model?Characterizing the potential for drought action from combined hydrological and societal perspectivesIncorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river systemAssessment of food trade impacts on water, food, and land security in the MENA regionAssessing the effect of flood restoration on surface–subsurface interactions in Rohrschollen Island (Upper Rhine river – France) using integrated hydrological modeling and thermal infrared imagingImplications of water management representations for watershed hydrologic modeling in the Yakima River basinClimate change vs. socio-economic development: understanding the future South Asian water gapDo users benefit from additional information in support of operational drought management decisions in the Ebro basin?Global phosphorus recovery from wastewater for agricultural reuseEvaporation suppression and energy balance of water reservoirs covered with self-assembling floating elementsDeveloping a decision support tool for assessing land use change and BMPs in ungauged watersheds based on decision rules provided by SWAT simulationModeling the glacial lake outburst flood process chain in the Nepal Himalaya: reassessing Imja Tsho's hazardSeasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methodsGrey water footprint reduction in irrigated crop production: effect of nitrogen application rate, nitrogen form, tillage practice and irrigation strategyA spatially detailed blue water footprint of the United States economyThe development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish riversBasin-scale impacts of hydropower development on the Mompós Depression wetlands, ColombiaAutomatic design of basin-specific drought indexes for highly regulated water systemsAssessment of actual evapotranspiration over a semiarid heterogeneous land surface by means of coupled low-resolution remote sensing data with an energy balance model: comparison to extra-large aperture scintillometer measurementsReconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patternsAssessing impacts of dike construction on the flood dynamics of the Mekong Delta
Andres Peñuela, Christopher Hutton, and Francesca Pianosi
Hydrol. Earth Syst. Sci., 24, 6059–6073,Short summary
In this paper we evaluate the potential use of seasonal weather forecasts to improve reservoir operation in a UK water supply system. We found that the use of seasonal forecasts can improve the efficiency of reservoir operation but only if the forecast uncertainty is explicitly considered. We also found the degree of efficiency improvement is strongly affected by the decision maker priorities and the hydrological conditions.
Matteo Giuliani, Louise Crochemore, Ilias Pechlivanidis, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 24, 5891–5902,Short summary
This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to end users in the Lake Como basin (Italy), which allows the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both the forecast system setup and end user behavioral factors, showing that the estimated forecast value is potentially undermined by different levels of end user risk aversion.
Michele Ferri, Uta Wehn, Linda See, Martina Monego, and Steffen Fritz
Hydrol. Earth Syst. Sci., 24, 5781–5798,Short summary
As part of the flood risk management strategy of the Brenta-Bacchiglione catchment (Italy), a citizen observatory for flood risk management is currently being implemented. A cost–benefit analysis of the citizen observatory was undertaken to demonstrate the value of this approach in monetary terms. Results show a reduction in avoided damage of 45 % compared to a scenario without implementation of the citizen observatory. The idea is to promote this methodology for future flood risk management.
Sara Suárez-Almiñana, Abel Solera, Jaime Madrigal, Joaquín Andreu, and Javier Paredes-Arquiola
Hydrol. Earth Syst. Sci., 24, 5297–5315,Short summary
This work responds to the need for an effective methodology that integrates climate change projections into water planning and management to guide complex basin decision-making. This general approach is based on a model chain for management and drought risk assessments and applied to the Júcar River basin (Spain), showing a worrying deterioration of the basin's future water resources availability and drought indicators, despite a considerable uncertainty of results from the mid-century onwards.
Shan Zuidema, Danielle Grogan, Alexander Prusevich, Richard Lammers, Sarah Gilmore, and Paula Williams
Hydrol. Earth Syst. Sci., 24, 5231–5249,Short summary
In our case study we find that increasing the efficiency of irrigation technology will have unintended consequences like reducing water available for aquifer replenishment or for other irrigators. The amount of water needed to stabilize regional aquifers exceeds the amount that could be saved by improving irrigation efficiency. Since users depend upon local groundwater storage, which is more sensitive to management decisions than river flow, comanagement of surface and groundwater is critical.
Marc Girons Lopez, Louise Crochemore, and Ilias G. Pechlivanidis
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
The Swedish hydrological warning service is extending its use of seasonal forecasts, which requires an analysis of the available methods. We evaluate the simple ESP method and find out how and why forecasts vary in time and space. We find that forecasts are useful up to 3 months into the future, especially during winter and in northern Sweden. They tend to be good in slowly-reacting catchments and bad in flashy and highly-regulated ones. We finally link them with areas of similar behaviour.
Haiyang Shi, Geping Luo, Hongwei Zheng, Chunbo Chen, Jie Bai, Tie Liu, Shuang Liu, Jie Xue, Peng Cai, Huili He, Friday Uchenna Ochege, Tim van de Voorde, and Philippe de Maeyer
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
Some river basins are considered to be very similar because they have the similar background such as transboundary, facing threats of human activities. But we still lack understanding of the differences under their general similarities. Therefore, we proposed a framework based on Bayesian network to group watersheds based on similarity levels and compare the causal and systematic differences within the group. We applied it to the Amu and Syr Darya river basin and discussed its universality.
Jessica A. Bou Nassar, Julien J. Malard, Jan F. Adamowski, Marco Ramírez Ramírez, Wietske Medema, and Héctor Tuy
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
Our research suggests a method that facilitates the inclusion of marginalized stakeholders in model-building activities to address problems in water resources. Our case-study showed that knowledge produced by typically excluded stakeholders had significant and unique contributions to the outcome of the process. Moreover, our method facilitated the identification of relationships between societal, economic, and hydrological factors, and fostered collaborations across different communities.
Manon Cassagnole, Maria-Helena Ramos, Ioanna Zalachori, Guillaume Thirel, Rémy Garçon, Joël Gailhard, and Thomas Ouillon
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESS
Alexander Kaune, Faysal Chowdhury, Micha Werner, and James Bennett
Hydrol. Earth Syst. Sci., 24, 3851–3870,Short summary
This paper was developed from PhD research focused on assessing the value of using hydrological datasets in water resource management. Previous studies have assessed how well data can help in predicting river flows, but there is a lack of knowledge of how well data can help in water allocation decisions. In our research, it was found that using seasonal streamflow forecasts improves the available water estimates, resulting in better water allocation decisions in a highly regulated basin.
Ghizlane Aouade, Lionel Jarlan, Jamal Ezzahar, Salah Er-Raki, Adrien Napoly, Abdelfattah Benkaddour, Said Khabba, Gilles Boulet, Sébastien Garrigues, Abdelghani Chehbouni, and Aaron Boone
Hydrol. Earth Syst. Sci., 24, 3789–3814,Short summary
Our objective is to question the representation of the energy budget in surface–vegetation–atmosphere transfer models for the prediction of the convective fluxes in crops with complex structures (row) and under transient hydric regimes due to irrigation. The main result is that a coupled multiple energy balance approach is necessary to properly predict surface exchanges for these complex crops. It also points out the need for other similar studies on various crops with different sparsity levels.
Mohammad Taghi Sattari, Halit Apaydin, Shahab Shamshirband, and Amir Mosavi
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial intelligence by minimum meteorological parameters in the Corum region which is an agricultural center of Turkey. GPR and SVR kernel-based, BFGS-ANN and LSTM models were used to estimate ET0 amounts in 10 different combinations. The results show that all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others.
Michael Kilgour Stewart and Philippa Lauren Aitchison-Earl
Hydrol. Earth Syst. Sci., 24, 3583–3601,Short summary
This paper is important for water resource management, being concerned with irrigation return flow causing
hotspotsin nitrate concentrations in groundwater and
denitrification imprintswhere nitrate concentrations are reduced by denitrification although the dissolved oxygen concentration is not low. The work is highly significant for modelling of nitrate transport through soil–groundwater systems, for understanding denitrification processes, and for managing fertilizer application to land.
Michel Le Page, Younes Fakir, Lionel Jarlan, Aaron Boone, Brahim Berjamy, Saïd Khabba, and Mehrez Zribi
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort summary
In the context of major changes, the Southern Mediterranean area faces serious challenges with low and continuously decreasing water resources mainly attributed to agricultural use. A method for projecting irrigation water demand under both anthropogenic and climatic changes is proposed. Time series of satellite imagery are used to determine a set of semi-empirical equations that can be easily adapted to different future scenarios.
Céline Monteil, Fabrice Zaoui, Nicolas Le Moine, and Frédéric Hendrickx
Hydrol. Earth Syst. Sci., 24, 3189–3209,Short summary
Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or a field measurement. Based on our experience in hydrological modelling, we propose combining two algorithms to obtain a fast and accurate way of calibrating complex models (many parameters and many objectives). We built an R package, caRamel, so that this multi-objective calibration algorithm can be easily implemented.
Liming Yao, Zhongwen Xu, Huijuan Wu, and Xudong Chen
Hydrol. Earth Syst. Sci., 24, 2769–2789,Short summary
Results show that coalitional strategy of blue and virtual water transfers can substantially save water and improve utilization efficiency without harming sectors' benefits and increasing ecological stresses. Under various polices, we use data-driven analysis to simulate hydrological and economic parameters, such as available water, crop import price, and water market price. Different water allocation and transfer results are obtained by adjusting hydrological and economic parameters.
Jingyuan Xue, Zailin Huo, Shuai Wang, Chaozi Wang, Ian White, Isaya Kisekka, Zhuping Sheng, Guanhua Huang, and Xu Xu
Hydrol. Earth Syst. Sci., 24, 2399–2418,Short summary
Due to increasing food demand and limited water resources, the quantification of the irrigation water productivity (IWP) is critical. Hydrological processes in irrigated areas differ in different watersheds owing to different irrigation–drainage activities, and this is more complex with shallow groundwater. Considering the complexity of the IWP, we developed a regional IWP model to simulate its spatial distribution; this informs irrigation managers on where they can improve IWP and save water.
Bouchra Ait Hssaine, Olivier Merlin, Jamal Ezzahar, Nitu Ojha, Salah Er-Raki, and Said Khabba
Hydrol. Earth Syst. Sci., 24, 1781–1803,
Matthew J. Knowling, Jeremy T. White, Catherine R. Moore, Pawel Rakowski, and Kevin Hayley
Hydrol. Earth Syst. Sci., 24, 1677–1689,Short summary
The incorporation of novel and diverse data sources into predictive models is expected to improve the reliability of model forecasts. This study critically and rigorously explores the extent to which this expectation holds given the imperfect nature of numerical models (and therefore their compromised ability to appropriately assimilate information-rich data). We show that environmental tracer observations may be of variable benefit in reducing forecast uncertainty and may induce forecast bias.
Sean W. D. Turner, Wenwei Xu, and Nathalie Voisin
Hydrol. Earth Syst. Sci., 24, 1275–1291,Short summary
To understand human vulnerability to flood and drought risk across large regions, researchers increasingly use large-scale hydrological models that convert climate to river flows. These models include the important effects of river regulation by dams but do not currently capture dam operators' use of flow forecasts to mitigate risk. This research addresses this problem by developing an approach to infer the forecast horizons contributing to the operations of a large sample of dams.
Sebastian Multsch, Maarten S. Krol, Markus Pahlow, André L. C. Assunção, Alberto G. O. P. Barretto, Quirijn de Jong van Lier, and Lutz Breuer
Hydrol. Earth Syst. Sci., 24, 307–324,Short summary
Expanding irrigation in agriculture is one of Brazil's strategies to increase production. In this study the amount of water required to grow the main crops has been calculated and compared to the water that is available in rivers at least 95 % of the time. Future decisions regarding expanding irrigated cropping areas must, while intensifying production practices, consider the likely regional effects on water scarcity levels, in order to reach sustainable agricultural production.
Guillaume Bigeard, Benoit Coudert, Jonas Chirouze, Salah Er-Raki, Gilles Boulet, Eric Ceschia, and Lionel Jarlan
Hydrol. Earth Syst. Sci., 23, 5033–5058,Short summary
The purpose of our work is to estimate landscape evapotranspiration (ET) fluxes over agricultural areas by relying on two surface modeling approaches with increasing complexity and input data needs. Both approaches, compared sequentially and over the entire crop cycle, showed quite similar performance except under developed vegetation and stressed conditions. This study helps lay the groundwork for exploring the complementarities between instantaneous and continuous ET mapping with TIR data.
Gabriela Chiquito Gesualdo, Paulo Tarso Oliveira, Dulce Buchala Bicca Rodrigues, and Hoshin Vijai Gupta
Hydrol. Earth Syst. Sci., 23, 4955–4968,Short summary
We investigate the influence of anticipated climate change on water security in the Jaguari Basin, which is the main source of freshwater for 9 million people in the São Paulo metropolitan region. Our findings indicate an expansion of the basin critical period, and identify October and November as the most vulnerable months. There is an urgent need to implement efficient mitigation and adaptation policies that recognize the annual pattern of variation between insecure and secure periods.
Raphaël Payet-Burin, Mikkel Kromann, Silvio Pereira-Cardenal, Kenneth Marc Strzepek, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 23, 4129–4152,Short summary
We present an open-source tool for water infrastructure investment planning considering interrelations between the water, food, and energy systems. We apply it to the Zambezi River basin to evaluate economic impacts of hydropower and irrigation development plans. We find trade-offs between the development plans and sensitivity to uncertainties (e.g. climate change, carbon taxes, capital costs of solar technologies, environmental policies) demonstrating the necessity for an integrated approach.
Fuad Yassin, Saman Razavi, Mohamed Elshamy, Bruce Davison, Gonzalo Sapriza-Azuri, and Howard Wheater
Hydrol. Earth Syst. Sci., 23, 3735–3764,
Eric Sauquet, Bastien Richard, Alexandre Devers, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 23, 3683–3710,Short summary
This study aims to identify catchments and the associated water uses vulnerable to climate change. Vulnerability is considered here to be the likelihood of water restrictions which are unacceptable for agricultural uses. This study provides the first regional analysis of the stated water restrictions, highlighting heterogeneous decision-making processes; data from a national system of compensation to farmers for uninsurable damages were used to characterize past failure events.
Jessica R. Dzara, Bethany T. Neilson, and Sarah E. Null
Hydrol. Earth Syst. Sci., 23, 2965–2982,Short summary
In Nevada's Walker River, stream temperatures nearly always exceed optimal temperature thresholds for adult trout. We used high-resolution measured data to verify simulated stream temperatures and estimate the spatial distribution of cold-water pockets for fish. Irrigation return canals, beaver dams, and groundwater seeps were river features with cold-water, and the average distance between pockets of cold-water in this river was 2.8 km.
Xinyao Zhou, Yonghui Yang, Zhuping Sheng, and Yongqiang Zhang
Hydrol. Earth Syst. Sci., 23, 2491–2505,Short summary
Quantifying the impact of upstream water use on downstream water scarcity is critical for water management. Comparing natural and observed runoff in China's 12 basins, this study found surface water use increased 1.6 times for the 1970s-2000s, driving most arid and semi-arid (ASA) basins into water scarcity status. The water stress decreased downstream in ASA basins due to reduced upstream inflow since the 2000s. Upstream water use caused over a 30 % increase in water scarcity in ASA basins.
Alexander Kaune, Micha Werner, Patricia López López, Erasmo Rodríguez, Poolad Karimi, and Charlotte de Fraiture
Hydrol. Earth Syst. Sci., 23, 2351–2368,Short summary
The value of using longer periods of record of river discharge information from global precipitation datasets is assessed for irrigation area planning. Results show that for all river discharge simulations the benefit of choosing the irrigated area based on the 30 years of simulated data is higher compared to using only 5 years of observed discharge data. Hence, irrigated areas can be better planned using 30 years of river discharge information from global precipitation datasets.
Tobias Pilz, José Miguel Delgado, Sebastian Voss, Klaus Vormoor, Till Francke, Alexandre Cunha Costa, Eduardo Martins, and Axel Bronstert
Hydrol. Earth Syst. Sci., 23, 1951–1971,Short summary
This work investigates different model types for drought prediction in a dryland region. Consequently, the performances of seasonal reservoir volume forecasts derived by a process-based and a statistical hydrological model were evaluated. The process-based approach obtained lower accuracy while resolution and reliability of drought prediction were comparable. Initialisation of the process-based model is worthwhile for more in-depth analyses, provided adequate rainfall forecasts are available.
Erin Towler, Heather Lazrus, and Debasish PaiMazumder
Hydrol. Earth Syst. Sci., 23, 1469–1482,Short summary
Drought is a function of both natural and human influences, but fully characterizing the interactions between human and natural influences on drought remains challenging. To better characterize the drought feedback loop, this study combines hydrological and societal perspectives to characterize the potential for drought action. We discuss how the results can be used to reduce potential disagreement among stakeholders and promote sustainable water management.
Daeha Kim, Jong Ahn Chun, and Si Jung Choi
Hydrol. Earth Syst. Sci., 23, 1145–1162,Short summary
In this study, we proposed an approach for gauging the risks of non-successful water supply and environmental reliabilities varying across a large river basin. The proposed method enables the measurement of system robustness to climate change with consideration of conflicting stakeholder interests. We simply converted the expected system performance under climate stresses into binary outcomes and applied them to the logistic regressions. A case study for a South Korean river basin is provided.
Sang-Hyun Lee, Rabi H. Mohtar, and Seung-Hwan Yoo
Hydrol. Earth Syst. Sci., 23, 557–572,Short summary
In this study, we quantified the holistic impacts of food trade on food security and water–land savings and revealed that the MENA region saved significant amounts of national water and land based on the import of barley, maize, rice, and wheat within the period from 2000 to 2012. In addition, the MENA region focused more on increasing the volume of virtual water imported during the period 2006–2012, yet little attention was paid to the expansion of connections with country exporters.
Benjamin Jeannot, Sylvain Weill, David Eschbach, Laurent Schmitt, and Frederick Delay
Hydrol. Earth Syst. Sci., 23, 239–254,Short summary
A hydrological model is used in combination with thermal measurements to investigate the effect of restoration actions in an artificial island of the Upper Rhine river. The injection of water in a newly built channel is efficient as it enhances overall hydrologic dynamics of the system with possible benefits for water quality and biodiversity. The combined use of the model and thermal measurements is also proven to be a relevant tool to study the effect of restoration on hydrological systems.
Jiali Qiu, Qichun Yang, Xuesong Zhang, Maoyi Huang, Jennifer C. Adam, and Keyvan Malek
Hydrol. Earth Syst. Sci., 23, 35–49,Short summary
Complex water management activities challenge hydrologic modeling. We evaluated how different representations of reservoir operation and agricultural irrigation affect streamflow simulations in the Yakima River basin. Results highlight the importance of the inclusion of reliable reservoir and irrigation information in watershed models for improving watershed hydrology modeling. Models used here are public and hold the promise to benefit water assessment and management in other basins.
René Reijer Wijngaard, Hester Biemans, Arthur Friedrich Lutz, Arun Bhakta Shrestha, Philippus Wester, and Walter Willem Immerzeel
Hydrol. Earth Syst. Sci., 22, 6297–6321,Short summary
This study assesses the combined impacts of climate change and socio-economic developments on the future water gap for the Indus, Ganges, and Brahmaputra river basins until the end of the 21st century. The results show that despite projected increases in surface water availability, the strong socio-economic development and associated increase in water demand will likely lead to an increase in the water gap, indicating that socio-economic changes will be the key driver in the evolving water gap.
Clara Linés, Ana Iglesias, Luis Garrote, Vicente Sotés, and Micha Werner
Hydrol. Earth Syst. Sci., 22, 5901–5917,Short summary
In this paper we follow a user-based approach to examine operational drought management decisions and how the role of information on them can be assessed. The approach combines a stakeholder consultation and a decision model representing the interrelated decisions of the irrigation association and farmers. The decision model was extended to include information on snow cover from satellite. This contributed to better decisions in the simulation and ultimately higher benefits for the farmers.
Dirk-Jan D. Kok, Saket Pande, Jules B. van Lier, Angela R. C. Ortigara, Hubert Savenije, and Stefan Uhlenbrook
Hydrol. Earth Syst. Sci., 22, 5781–5799,Short summary
Phosphorus (P) is important to global food security. Thus it is concerning that natural P reserves are predicted to deplete within the century. Here we explore the potential of P recovery from wastewater (WW) at global scale. We identify high production and demand sites to determine optimal market prices and trade flows. We show that 20 % of the agricultural demand can be met, yet only 4 % can be met economically. Nonetheless, this recovery stimulates circular economic development in WW treatment.
Milad Aminzadeh, Peter Lehmann, and Dani Or
Hydrol. Earth Syst. Sci., 22, 4015–4032,Short summary
Significant evaporative losses from local water reservoirs in arid regions exacerbate water shortages during dry spells. We propose a systematic approach for modeling energy balance and fluxes from covered water bodies using self-assembling floating elements, considering cover properties and local conditions. The study will provide a scientific and generalized basis for designing and implementing this important water conservation strategy to assist with its adaptation in various arid regions.
Junyu Qi, Sheng Li, Charles P.-A. Bourque, Zisheng Xing, and Fan-Rui Meng
Hydrol. Earth Syst. Sci., 22, 3789–3806,Short summary
The paper proposed an approach to develop a decision support tool to evaluate impacts of land use change and best management practices (BMPs) on water quantity and quality for large ungauged watersheds. It was developed based on statistical equations derived from Soil and Water Assessment Tool (SWAT) simulations in a small experimental watershed. The decision support tool reproduced annual stream discharge and sediment and nutrient loadings for another watershed fairly well.
Jonathan M. Lala, David R. Rounce, and Daene C. McKinney
Hydrol. Earth Syst. Sci., 22, 3721–3737,Short summary
Many glacial lakes in the Himalayas are held in place by natural sediment dams, which are prone to collapse, causing a glacial lake outburst flood (GLOF). This study models a GLOF as a process chain, in which an avalanche enters the lake, creates a large wave that erodes the sediment dam, and produces a flood downstream. Results indicate that Imja Tsho presents little hazard for the next 30 years, but the model is replicable and should be used at other lakes that may present greater hazard.
Stephen P. Charles, Quan J. Wang, Mobin-ud-Din Ahmad, Danial Hashmi, Andrew Schepen, Geoff Podger, and David E. Robertson
Hydrol. Earth Syst. Sci., 22, 3533–3549,Short summary
Predictions of irrigation-season water availability are important for water-limited Pakistan. We assess a Bayesian joint probability approach, using flow and climate indices as predictors, to produce streamflow forecasts for inflow to Pakistan's two largest dams. The approach produces skilful and reliable forecasts. As it is simple and quick to apply, it can be used to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's water management.
Abebe D. Chukalla, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 22, 3245–3259,Short summary
This paper provides the first detailed and comprehensive study regarding the potential for reducing the grey WF of crop production by changing management practice such as the nitrogen application rate, nitrogen form (inorganic N or manure N), tillage practice and irrigation strategy. The paper shows that although water pollution (grey WF) can be reduced dramatically, this comes together with a great yield reduction.
Richard R. Rushforth and Benjamin L. Ruddell
Hydrol. Earth Syst. Sci., 22, 3007–3032,Short summary
The National Water Economy Database is a new data resource to better understand the human economy's water use impact on the hydrosphere. NWED quantifies and maps a spatially detailed and economically complete blue water footprint for the United States, utilizing several datasets: US Geological Survey, the US Department of Agriculture, the US Energy Information Administration, the US Department of Transportation, the US Department of Energy, and the US Bureau of Labor Statistics.
Kean Foster, Cintia Bertacchi Uvo, and Jonas Olsson
Hydrol. Earth Syst. Sci., 22, 2953–2970,Short summary
Hydropower makes up nearly half of Sweden's electrical energy production. Careful reservoir management is required for optimal production throughout the year and accurate seasonal forecasts are essential for this. In this work we develop a seasonal forecast prototype and evaluate its ability to predict spring flood volumes, a critical variable, in northern Sweden. We show that the prototype is better than the operational system on average 65 % of the time and reduces the volume error by ~ 6 %.
Héctor Angarita, Albertus J. Wickel, Jack Sieber, John Chavarro, Javier A. Maldonado-Ocampo, Guido A. Herrera-R., Juliana Delgado, and David Purkey
Hydrol. Earth Syst. Sci., 22, 2839–2865,Short summary
The Magdalena River basin has great hydropower potential. A number of large dams are proposed in the upstream reaches of the two largest rivers that converge in the lowland floodplains. While these dams are expected to more than double national electricity production, the implications for the wetlands and the people that depend on them are highly uncertain. Our assessment of these implications provides insights to guide basin-level infrastructure development and ecosystem conservation projects.
Marta Zaniolo, Matteo Giuliani, Andrea Francesco Castelletti, and Manuel Pulido-Velazquez
Hydrol. Earth Syst. Sci., 22, 2409–2424,Short summary
Drought indexes are an effective tool to support drought management in water systems, but their definition must be tailored to the features of the considered basin. In highly regulated contexts, non-generalizable ad hoc methods are usually employed to design these indexes. This paper contributes the novel Framework for Index-based Drought Analysis (FRIDA) that supports the automatic construction of basin-customized drought indexes representing a surrogate of the basin drought conditions.
Sameh Saadi, Gilles Boulet, Malik Bahir, Aurore Brut, Émilie Delogu, Pascal Fanise, Bernard Mougenot, Vincent Simonneaux, and Zohra Lili Chabaane
Hydrol. Earth Syst. Sci., 22, 2187–2209,Short summary
This study evaluated the performances of an energy balance model (SPARSE model) forced by MODIS remote sensing products in an operational context to estimate instantaneous and daily evapotranspiration. The validation protocol was based on an unprecedented dataset with an extra-large aperture scintillometer. Indeed, to our knowledge, this is the ﬁrst work based on XLAS measurements acquired over the course of more than 2 years.
Zhongwei Huang, Mohamad Hejazi, Xinya Li, Qiuhong Tang, Chris Vernon, Guoyong Leng, Yaling Liu, Petra Döll, Stephanie Eisner, Dieter Gerten, Naota Hanasaki, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 22, 2117–2133,Short summary
This study generate a historical global monthly gridded water withdrawal data (0.5 × 0.5 degrees) for the period 1971–2010, distinguishing six water use sectors (irrigation, domestic, electricity generation, livestock, mining, and manufacturing). This dataset is the first reconstructed global water withdrawal data product at sub-annual and gridded resolution that is derived from different models and data sources, and was generated by spatially and temporally downscaling country-scale estimates.
Dung Duc Tran, Gerardo van Halsema, Petra J. G. J. Hellegers, Long Phi Hoang, Tho Quang Tran, Matti Kummu, and Fulco Ludwig
Hydrol. Earth Syst. Sci., 22, 1875–1896,Short summary
We modeled hydrological changes under impacts of large-scale dike constructions for intensive rice production in the floodplain of the Vietnamese Mekong Delta. Four scenarios show a significant increase in peak water levels in the upstream rivers, but very few water level changes are found downstream. Water balance calculations show where the floodwater goes under four dike construction scenarios. Its impacts on the tidal areas need to be clarified in the future with a 3-D hydraulic model.
Adamowski, J. and Chan, H. F.: A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol, 407, 28–40, https://doi.org/10.1016/j.jhydrol.2011.06.013, 2011.
Alvarez-Garreton, C., Ryu, D., Western, A. W., Su, C.-H., Crow, W. T., Robertson, D. E., and Leahy, C.: Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes, Hydrol. Earth Syst. Sci., 19, 1659–1676, https://doi.org/10.5194/hess-19-1659-2015, 2015.
Banerjee, P., Singh, V. S., Chatttopadhyay, K., Chandra, P. C., and Singh, B.: Artificial neural network model as a potential alternative for groundwater salinity forecasting, J. Hydrol., 398, 212–220, https://doi.org/10.1016/j.jhydrol.2010.12.016, 2011.
Barnett, S.: Gurra Gurra Wetland Complex – Groundwater Data Review, Dept. of Water, Land and Biodiversity Conservation, 4, 2007.
Beecham, R., Arranz, P., Boddy, J., Burrell, M., Gilmore, R., Javam, A., Martin, J., O'Neill, R., and Salbe, I.: Implementing daily salinity models in the NSW Murray Darling Basin tributaries, in: Modsim 2003, International Congress on Modelling and Simulation, Vol 1–4: Vol 1: Natural Systems, Pt 1; Vol 2: Natural Systems, Pt 2; Vol 3: Socio-Economic Systems; Vol 4: General Systems, 362–367, 2003.
Bowden, G. J., Maier, H. R., and Dandy, G. C.: Optimal division of data for neural network models in water resources applications, Water Resour. Res., 38, 1010, https://doi.org/10.1029/2001wr000266, 2002.
Bowden, G. J., Maier, H. R., and Dandy, G. C.: Input determination for neural network models in water resources applications. Part 1. Background and methodology, J. Hydrol., 301, 75–92, https://doi.org/10.1016/j.jhydrol.2004.06.021, 2005a.
Bowden, G. J., Maier, H. R., and Dandy, G. C.: Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river, J. Hydrol., 301, 93–107, https://doi.org/10.1016/j.jhydrol.2004.06.020, 2005b.
Bowden, G. J., Maier, H. R., and Dandy, G. C.: Real-time deployment of artificial neural network forecasting models: Understanding the range of applicability, Water Resour. Res., 48, W10549, https://doi.org/10.1029/2012WR011984, 2012.
Chang, F.-J. and Chang, Y.-T.: Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Adv. Water Resour., 29, 1–10, https://doi.org/10.1016/j.advwatres.2005.04.015, 2006.
Chang, F.-J. and Tsai, M.-J.: A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques, J. Hydrol., 535, 228–236, https://doi.org/10.1016/j.jhydrol.2016.01.056, 2016.
Chang, F.-J., Tsai, W.-P., Chen, H.-K., Yam, R. S.-W., and Herricks, E. E.: A self-organizing radial basis network for estimating riverine fish diversity, J. Hydrol., 476, 280–289, https://doi.org/10.1016/j.jhydrol.2012.10.038, 2013.
Chang, F.-J., Chen, P.-A., Chang, L.-C., and Tsai, Y.-H.: Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques, Sci. Total Environ., 562, 256–269, https://doi.org/10.1016/j.scitotenv.2016.03.219, 2016.
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.
Commonwealth of Australia: Water Act (An act to make provision for the management of the water resources of the Murray-Darling Basin, and to make provision for other matters of national interest in relation to water and water information, and for related purposes), Commonwealth Consolidated Acts, Sects. 28–32, 2007.
Corzo, G. and Solomatine, D.: Baseflow separation techniques for modular artificial neural network modelling in flow forecasting, Hydrol. Sci. J., 52, 491–507, https://doi.org/10.1623/hysj.52.3.491, 2007.
Corzo, G. A., Solomatine, D. P., Hidayat, de Wit, M., Werner, M., Uhlenbrook, S., and Price, R. K.: Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin, Hydrol. Earth Syst. Sci., 13, 1619–1634, https://doi.org/10.5194/hess-13-1619-2009, 2009.
Dessie, M., Verhoest, N. E. C., Pauwels, V. R. N., Admasu, T., Poesen, J., Adgo, E., Deckers, J., and Nyssen, J.: Analyzing runoff processes through conceptual hydrological modeling in the Upper Blue Nile Basin, Ethiopia, Hydrol. Earth Syst. Sci., 18, 5149–5167, https://doi.org/10.5194/hess-18-5149-2014, 2014.
Duan, W. L., He, B., Takara, K., Luo, P. P., Nover, D., and Hu, M. C.: Modeling suspended sediment sources and transport in the Ishikari River basin, Japan, using SPARROW, Hydrol. Earth Syst. Sci., 19, 1293–1306, https://doi.org/10.5194/hess-19-1293-2015, 2015.
Duku, C., Rathjens, H., Zwart, S. J., and Hein, L.: Towards ecosystem accounting: a comprehensive approach to modelling multiple hydrological ecosystem services, Hydrol. Earth Syst. Sci., 19, 4377–4396, https://doi.org/10.5194/hess-19-4377-2015, 2015.
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, W11510, https://doi.org/10.1029/2010wr010174, 2011.
Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., and Gibbs, M. S.: An evaluation framework for input variable selection algorithms for environmental data-driven models, Environ. Modell. Softw., 62, 33–51, https://doi.org/10.1016/j.envsoft.2014.08.015, 2014.
Gallice, A., Schaefli, B., Lehning, M., Parlange, M. B., and Huwald, H.: Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model, Hydrol. Earth Syst. Sci., 19, 3727–3753, https://doi.org/10.5194/hess-19-3727-2015, 2015.
Gibbs, M. S., Maier, H. R., and Dandy, G. C.: A generic framework for regression regionalization in ungauged catchments, Environ. Modell. Softw., 27–28, 1–14, https://doi.org/10.1016/j.envsoft.2011.10.006, 2012.
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.
Goss, K. F.: Environmental flows, river salinity and biodiversity conservation: managing trade-offs in the Murray & Darling basin, Aust. J. Bot., 51, 619–625, https://doi.org/10.1071/BT03003, 2003.
Government of South Australia, Department of Environment, Water and Natural Resources (DEWNR): WaterConnect groundwater data, available at: www.waterconnect.sa.gov.au (last access: 9 November 2017), 2015.
Gragne, A. S., Sharma, A., Mehrotra, R., and Alfredsen, K.: Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework, Hydrol. Earth Syst. Sci., 19, 3695–3714, https://doi.org/10.5194/hess-19-3695-2015, 2015.
Grayson, R. B. and Blöschl, G.: Spatial patterns in catchment hydrology: Observations and modelling, Cambridge University Press, UK, https://doi.org/10.1002/esp.378, 2000.
Guo, D., Westra, S., and Maier, H. R.: An R package for modelling actual, potential and reference evapotranspiration, Environ. Modell. Softw., 78, 216–224, https://doi.org/10.1016/j.envsoft.2015.12.019, 2016.
Habib, E., Nuttle, W. K., Rivera-Monroy, V. H., Gautam, S., Wang, J., Meselhe, E., and Twilley, R. R.: Assessing effects of data limitations on salinity forecasting in Barataria basin, Louisiana, with a Bayesian analysis, J. Coastal Res., 23, 749–763, https://doi.org/10.2112/06-0723.1, 2007.
Hamilton, S. H., ElSawah, S., Guillaume, J. H. A., Jakeman, A. J., and Pierce, S. A.: Integrated assessment and modelling: Overview of salient dimensions, Environ. Modell. Softw., 64, 215–229, https://doi.org/10.1016/j.envsoft.2014.12.005, 2015.
Harrington, N., Van den Akker, J., and Brown, K.: Padthaway Salt Accession Study Volume Three: Conceptual Models, Government of South Australia, Dept. of Water, Land and Biodiversity Conservation, Adelaide, 11, 15–16, 38, 112, 2006.
Hart, B. T., Bailey, P., Edwards, R., Hortle, K., James, K., McMahon, A., Meredith, C., and Swadling, K.: A Review of the Salt Sensitivity of the Australian Fresh-Water Biota, Hydrobiologia, 210, 105–144, https://doi.org/10.1007/bf00014327, 1991.
Hsu, K. L., Gupta, H. V., Gao, X. G., Sorooshian, S., and Imam, B.: Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38, 1302, https://doi.org/10.1029/2001wr000795, 2002.
Huang, W. R. and Foo, S.: Neural network modeling of salinity variation in Apalachicola River, Water Res., 36, 356–362, https://doi.org/10.1016/s0043-1354(01)00195-6, 2002.
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.
Humphrey, G. B., Maier, H. R., Wu, W., Mount, N. J., Dandy, G. C., Abrahart, R. J., and Dawson, C. W.: Improved validation framework and R-package for artificial neural network models, Environ. Modell. Softw., 92, 82–106, https://doi.org/10.1016/j.envsoft.2017.01.023, 2017.
Jain, A. and Kumar, S.: Dissection of trained neural network hydrological models for knowledge extraction, Water Resour. Res., 45, W07420, https://doi.org/10.1029/2008WR007194, 2009.
Jain, A., Sudheer, K. P., and Srinivasulu, S.: Identification of physical processes inherent in artificial neural network rainfall runoff models, Hydrol. Process., 18, 571–581, https://doi.org/10.1002/hyp.5502, 2004.
Jakeman, A. J., Letcher, R. A., and Norton, J. P.: Ten iterative steps in development and evaluation of environmental models, Environ. Modell. Softw., 21, 602–614, https://doi.org/10.1016/j.envsoft.2006.01.004, 2006.
Kasiviswanathan, K. S., He, J., Sudheer, K. P., and Tay, J.-H.: Potential application of wavelet neural network ensemble to forecast streamflow for flood management, J. Hydrol., 536, 161–173, https://doi.org/10.1016/j.jhydrol.2016.02.044, 2016.
Kavetski, D. and Fenicia, F.: Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights, Water Resour. Res., 47, W11511, https://doi.org/10.1029/2011wr010748, 2011.
Kelly, R. A., Jakeman, A. J., Barreteau, O., Borsuk, M. E., ElSawah, S., Hamilton, S. H., Henriksen, H. J., Kuikka, S., Maier, H. R., Rizzoli, A. E., van Delden, H., and Voinov, A. A.: Selecting among five common modelling approaches for integrated environmental assessment and management, Environ. Modell. Softw., 47, 159–181, https://doi.org/10.1016/j.envsoft.2013.05.005, 2013.
Kingston, G. B., Lambert, M. F., and Maier, H. R.: Bayesian training of artificial neural networks used for water resources modeling, Water Resour. Res., 41, W12409, https://doi.org/10.1029/2005wr004152, 2005.
Kisi, O. and Demir, V.: Evapotranspiration estimation using six different multi-layer perceptron algorithms, Irrigation & Drainage Systems Engineering, 5, 164, https://doi.org/10.4172/2168-9768.1000164, 2016.
Kisi, O. and Parmar, K. S.: Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution, J. Hydrol., 534, 104–112, https://doi.org/10.1016/j.jhydrol.2015.12.014, 2016.
Kornelsen, K. and Coulibaly, P.: Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset, J. Hydrol. Eng., 19, 26–43, https://doi.org/10.1061/(asce)he.1943-5584.0000767, 2014.
Li, L., Maier, H. R., Partington, D., Lambert, M. F., and Simmons, C. T.: Performance assessment and improvement of recursive digital baseflow filters for catchments with different physical characteristics and hydrological inputs, Environ. Modell. Softw., 54, 39–52, https://doi.org/10.1016/j.envsoft.2013.12.011, 2014.
Liu, W. C., Chen, W. B., Cheng, R. T., Hsu, M. H., and Kuo, A. Y.: Modeling the influence of river discharge on salt intrusion and residual circulation in Danshuei River estuary, Taiwan, Cont. Shelf Res., 27, 900–921, https://doi.org/10.1016/j.csr.2006.12.005, 2007.
Maier, H. R. and Dandy, G. C.: The use of artificial neural networks for the prediction of water quality parameters, Water Resour. Res., 32, 1013–1022, 1996.
Maier, H. R., Jain, A., Dandy, G. C., and Sudheer, K. P.: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Modell. Softw., 25, 891–909, https://doi.org/10.1016/j.envsoft.2010.02.003, 2010.
Markstrom, S. L., Hay, L. E., and Clark, M. P.: Towards simplification of hydrologic modeling: identification of dominant processes, Hydrol. Earth Syst. Sci., 20, 4655–4671, https://doi.org/10.5194/hess-20-4655-2016, 2016.
MDBC: Setting up of MSM-BIGMOD modelling Suite for the River Murray System, Technical, Murray-Darling Basin Commission, 2002.
Mohanty, S., Jha, M. K., Raul, S. K., Panda, R. K., and Sudheer, K. P.: Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites, Water Resour. Manag., 29, 5521–5532, https://doi.org/10.1007/s11269-015-1132-6, 2015.
Mount, N. J., Abrahart, R. J., Dawson, C. W., and Ab Ghani, N.: The need for operational reasoning in data-driven rating curve prediction of suspended sediment, Hydrol. Process., 26, 3982–4000, https://doi.org/10.1002/hyp.8439, 2012.
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F. J., and Abrahart, R. J.: Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan, Hydrolog. Sci. J., 61, 1182–1208, https://doi.org/10.1080/02626667.2016.1159683, 2016.
Moxey, A.: Agriculture and Water Quality: Monetary Costs and Benefits across OECD Countries, 11–12, 2012.
Noori, N. and Kalin, L.: Coupling SWAT and ANN models for enhanced daily streamflow prediction, J. Hydrol., 533, 141–151, https://doi.org/10.1016/j.jhydrol.2015.11.050, 2016.
Parasuraman, K., Elshorbagy, A., and Carey, S. K.: Modelling the dynamics of the evapotranspiration process using genetic programming, Hydrol. Sci. J., 52, 563–578, https://doi.org/10.1623/hysj.52.3.563, 2007.
Pulido-Velazquez, M., Peña-Haro, S., García-Prats, A., Mocholi-Almudever, A. F., Henriquez-Dole, L., Macian-Sorribes, H., and Lopez-Nicolas, A.: Integrated assessment of the impact of climate and land use changes on groundwater quantity and quality in the Mancha Oriental system (Spain), Hydrol. Earth Syst. Sci., 19, 1677–1693, https://doi.org/10.5194/hess-19-1677-2015, 2015.
Quiroga, V. M., Popescu, I., Solomatine, D. P., and Bociort, L.: Cloud and cluster computing in uncertainty analysis of integrated flood models, J. Hydroinform., 15, 55–70, https://doi.org/10.2166/hydro.2012.017, 2013.
Rath, J. S., Hutton, P. H., Chen, L., and Roy, S. B.: A hybrid empirical-Bayesian artificial neural network model of salinity in the San Francisco Bay-Delta estuary, Environ. Modell. Softw., 93, 193–208, https://doi.org/10.1016/j.envsoft.2017.03.022, 2017.
Rengasamy, P.: World salinization with emphasis on Australia, J. Exp. Bot., 57, 1017–1023, https://doi.org/10.1093/jxb/erj108, 2006.
Robertson, W. M. and Sharp, J. M.: Estimates of recharge in two arid basin aquifers: a model of spatially variable net infiltration and its implications (Red Light Draw and Eagle Flats, Texas, USA), Hydrogeol. J., 21, 1853–1864, https://doi.org/10.1007/s10040-013-1018-8, 2013.
Seibert, J.: On the need for benchmarks in hydrological modelling, Hydrol. Process., 15, 1063–1064, https://doi.org/10.1002/hyp.446, 2001.
Shamseldin, A. Y., Nasr, A. E., and O'Connor, K. M.: Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting, Hydrol. Earth Syst. Sci., 6, 671–684, https://doi.org/10.5194/hess-6-671-2002, 2002.
Shiri, J., Shamshirband, S., Kisi, O., Karimi, S., Bateni, S. M., Nezhad, S. H. H., and Hashemi, A.: Prediction of water-level in the Urima Lake using the extreme learning machine approach, Water Resour. Res., 30, 5217–5229, https://doi.org/10.1007/s11269-016-1480-x, 2016.
Suen, J. P. and Lai, H. N.: A salinity projection model for determining impacts of climate change on river ecosystems in Taiwan, J. Hydrol., 493, 124–131, https://doi.org/10.1016/j.jhydrol.2013.04.020, 2013.
Tefler, A., Burnell, R., and Charles, A.: Salt interception schemes and instream processes, in Mallee Salinity Workshop, Mallee Catchment Management Authority, 30 May 2012.
Tsai, M.-J., Abrahart, R. J., Mount, N. J., and Chang, F.-J.: Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan, Hydrol. Process., 28, 1055–1070, https://doi.org/10.1002/hyp.9559, 2014.
Wang, W., Van Gelder, P., Vrijling, J. K., and Ma, J.: Forecasting daily streamflow using hybrid ANN models, J. Hydrol., 324, 383–399, https://doi.org/10.1016/j.jhydrol.2005.09.032, 2006.
Welsh, D. W., Vaze, J., Dutta, D., Rassam, D., Rahman, J. M., Jolly, I. D., Wallbrink, P., Podger, G. M., Bethune, M., Hardy, M. J., Teng, J., and Lerat, J.: An integrated modelling framework for regulated river systems, Env. Modell. Softw., 39, 81–102, https://doi.org/10.1016/j.envsoft.2012.02.022, 2013.
Williams, W. D.: Anthropogenic salinisation of inland waters, Hydrobiologia, 466, 329–337, https://doi.org/10.1023/a:1014598509028, 2001.
Woods, J.: Modelling salt dynamics on the River Murray floodplain in South Australia: Conceptual model, data review and salinity risk approaches, Goyder Institute for Water Research Technical Report Series No. 15/9, 2015.
Wu, W., Dandy, G. C., and Maier, H. R.: Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling, Environ. Modell. Softw., 54, 108–127, https://doi.org/10.1016/j.envsoft.2013.12.016, 2014.
Yaseen, Z. M., Jaafar, O., Deo, R. C., Kisi, O., Adamowski, J., Quilty, J., and El-Shafie, A.: Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq, J. Hydrol., 542, 603–614, https://doi.org/10.1016/j.jhydrol.2016.09.035, 2016.
Zhang, H. B., Singh, V. P., Bin Wang, B., and Yu, Y. H.: CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system, J. Hydrol., 540, 246–256, https://doi.org/10.1016/j.jhydrol.2016.06.029, 2016.
Zhang, Q. and Stanley, S. J.: Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling, Water Res., 31, 2340–2350, https://doi.org/10.1016/s0043-1354(97)00072-9, 1997.
Zheng, F., Maier, H. R., Wu, W., Dandy, G. C., Gupta, H. V., and Zhang, T.: On lack of robustness in hydrological model development due to absence of guidelines for selecting calibration and evaluation data: Demonstration for data driven models, Water Resour. Res., 54, 1013–1030, https://doi.org/10.1002/2017WR021470, 2018.
Zounemat-Kermani, M., Kişi, Ö., Adamowski, J., and Ramezani-Charmahineh, A.: Evaluation of data driven models for river suspended sediment concentration modeling, J. Hydrol., 535, 457–472, https://doi.org/10.1016/j.jhydrol.2016.02.012, 2016.
This research proposes a generalised hybrid model development framework and applies it to a case study of salinity prediction in a reach of the Murray River. The hybrid model combines five sub-models which describe one process of salt entry each and are developed based on the amount of system knowledge and data that are available to support each individual process. The model demonstrates increased performance over two benchmark models and has implications for future model development processes.
This research proposes a generalised hybrid model development framework and applies it to a case...