Special Issue "Advances in Hydrologic Forecasts and Water Resources Management "

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 March 2020).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Fi-John Chang
E-Mail Website1 Website2
Guest Editor
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
Interests: artificial intelligence; artificial neural network; hydrology; water resources management; ecohydrology; real-time flood forecasting; system analysis; multiobjective reservoir operation; water–food–energy nexus
Special Issues and Collections in MDPI journals
Prof. Dr. Shenglian Guo
E-Mail Website
Guest Editor
State Key Laboratory of Water Resources and Hdropower Engineering Science, Wuhan University, China
Interests: design flood estimation; hydrological simulation; ensemble flood forecasting; mega reservoir operation; adaptive water resources management; water-society system nexus

Special Issue Information

Dear Colleagues,

In the face of climate change and population growth in many parts of the world, we need appropriate tools that can assist in dealing with the difficulties introduced by the increasing complexity of water problems. This Special Issue will feature the latest advances and developments in operational hydrologic forecasts and water resources management. The focus is centered on artificial intelligence (AI) techniques in data-mining for operational hydrologic forecasting and evolutionary algorithms (EAs) for reservoir operation. The main themes of this Special Issue include but are not limited to the following: 

  • AI techniques for multiobjective reservoir operation;
  • Machine learning approaches for operational hydrologic forecasting;
  • Data assimilation for real-time hydrologic forecasting;
  • Uncertainty assessment on hydrological forecasts;
  • Advances in flood forecasting and flood risk assessment;
  • Drought forecasting and warning;
  • Integrated water resources management.

This Special Issue aims at gathering the latest developments in sustainable water resources management as well as operational hydrologic forecasts at different spatiotemporal scales and contexts. The integration of natural sciences with economic and social sciences is also very much appreciated.

Prof. Fi-John Chang
Prof. Shenglian Guo
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • Machine learning
  • Water resources management
  • Multiobjective reservoir operation
  • Hydrologic forecasting
  • Uncertainty
  • Risk

Published Papers (15 papers)

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Editorial

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Editorial
Advances in Hydrologic Forecasts and Water Resources Management
Water 2020, 12(6), 1819; https://doi.org/10.3390/w12061819 - 24 Jun 2020
Cited by 7 | Viewed by 1091
Abstract
The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources management and the mitigation of natural disasters. [...] Read more.
The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modelling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has the great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modelling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; and (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue can not only advance water sciences but can also support policy makers toward more sustainable and effective water resources management. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )

Research

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Article
Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things
Water 2020, 12(6), 1578; https://doi.org/10.3390/w12061578 - 31 May 2020
Cited by 1 | Viewed by 920
Abstract
Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) [...] Read more.
Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest
Water 2020, 12(5), 1433; https://doi.org/10.3390/w12051433 - 18 May 2020
Cited by 1 | Viewed by 845
Abstract
Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this [...] Read more.
Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective
Water 2020, 12(5), 1393; https://doi.org/10.3390/w12051393 - 14 May 2020
Cited by 2 | Viewed by 966
Abstract
The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, [...] Read more.
The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Application of Empirical Mode Decomposition Method to Synthesize Flow Data: A Case Study of Hushan Reservoir in Taiwan
Water 2020, 12(4), 927; https://doi.org/10.3390/w12040927 - 25 Mar 2020
Cited by 3 | Viewed by 895
Abstract
Although empirical mode decomposition (EMD) was developed to analyze nonlinear and non-stationary data in the beginning, the purpose of this study is to propose a new method—based on EMD—to synthesize and generate data which be interfered with the non-stationary problems. While using EMD [...] Read more.
Although empirical mode decomposition (EMD) was developed to analyze nonlinear and non-stationary data in the beginning, the purpose of this study is to propose a new method—based on EMD—to synthesize and generate data which be interfered with the non-stationary problems. While using EMD to decompose flow record, the intrinsic mode functions and residue of a given record can be re-arranged and re-combined to generate synthetic time series with the same period. Next, the new synthetic and historical flow data will be used to simulate the water supply system of Hushan reservoir, and explore the difference between the newly synthetic and historical flow data for each goal in the water supply system of Hushan reservoir. Compared the historical flow with the synthetic data generated by EMD, the synthetic data is similar to the historical flow distribution overall. The flow during dry season changes in significantly (±0.78 m3/s); however, the flow distribution during wet season varies significantly (±0.63 m3/s). There are two analytic scenarios for demand. For Scenario I, without supporting industrial demand, the simulation results of the generation data of Method I and II show that both are more severe than the current condition, the shortage index of each method is between 0.67–1.96 but are acceptable. For Scenario II, no matter in which way the synthesis flow is simulated, supporting industrial demand will seriously affect the equity of domestic demand, the shortage index of each method is between 1.203 and 2.12. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health
Water 2020, 12(3), 915; https://doi.org/10.3390/w12030915 - 24 Mar 2020
Cited by 2 | Viewed by 1089
Abstract
Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. [...] Read more.
Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Multi-Dimensional Interval Number Decision Model Based on Mahalanobis-Taguchi System with Grey Entropy Method and Its Application in Reservoir Operation Scheme Selection
Water 2020, 12(3), 685; https://doi.org/10.3390/w12030685 - 03 Mar 2020
Cited by 2 | Viewed by 816
Abstract
In decision-making with interval numbers, there are problems such as how to reduce the loss of decision information to improve decision accuracy and the difficulty of using interval numbers for sorting. On the basis of fully considering the subjective and objective weights of [...] Read more.
In decision-making with interval numbers, there are problems such as how to reduce the loss of decision information to improve decision accuracy and the difficulty of using interval numbers for sorting. On the basis of fully considering the subjective and objective weights of indexes, the grey entropy method (GEM) is improved by taking advantage of the Mahalanobis-Taguchi System (MTS) in which the orthogonal design has few tests but much obtained information, and the Mahalanobis distance can reflect the correlation between indexes. Then, the signal-to-noise ratio is integrated with the improved degree of balance and approach, and a multi-dimensional interval number decision model based on MTS and GEM is put forth. This model is applied to selecting the optimal scheme of controlling the Pankou reservoir’s water level in flood season. Compared with the decision results of other methods, the optimal scheme selected by the proposed model can achieve greater benefits within an acceptable risk range and thus better coordinate the balance between risk and benefit, which verifies the feasibility and validity of the model. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network
Water 2020, 12(2), 578; https://doi.org/10.3390/w12020578 - 20 Feb 2020
Cited by 7 | Viewed by 1142
Abstract
It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a [...] Read more.
It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Uncertainty Analysis of Spatiotemporal Models with Point Estimate Methods (PEMs)—The Case of the ANUGA Hydrodynamic Model
Water 2020, 12(1), 229; https://doi.org/10.3390/w12010229 - 14 Jan 2020
Cited by 4 | Viewed by 675
Abstract
Practitioners often neglect the uncertainty inherent to models and their inputs. Point Estimate Methods (PEMs) offer an alternative to the common, but computationally demanding, method for assessing model uncertainty, Monte Carlo (MC) simulation. PEMs rerun the model with representative values of the probability [...] Read more.
Practitioners often neglect the uncertainty inherent to models and their inputs. Point Estimate Methods (PEMs) offer an alternative to the common, but computationally demanding, method for assessing model uncertainty, Monte Carlo (MC) simulation. PEMs rerun the model with representative values of the probability distribution of the uncertain variable. The results can estimate the statistical moments of the output distribution. Hong’s method is the specific PEM implemented here for a case study that simulates water runoff using the ANUGA model for an area in Glasgow, UK. Elevation is the source of uncertainty. Three realizations of the Sequential Gaussian Simulation, which produces the random error fields that can be used as inputs for any spatial model, are scaled according to representative values of the distribution and their weights. The output from a MC simulation is used for validation. A comparison of the first two statistical moments indicates that Hong’s method tends to underestimate the first moment and overestimate the second moment. Model efficiency performance measures validate the usefulness of Hong’s method for the approximation of the first two moments, despite the method suffering from outliers. Estimation was less accurate for higher moments but the moment estimates were sufficient to use the Grams-Charlier Expansion to fit a distribution to them. Regarding probabilistic flood-inundation maps, Hong’s method shows very similar probabilities in the same areas as the MC simulation. However, the former requires just three 11-minute simulation runs, rather than the 500 required for the MC simulation. Hong’s method therefore appears attractive for approximating the uncertainty of spatiotemporal models. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Small and Medium-Scale River Flood Controls in Highly Urbanized Areas: A Whole Region Perspective
Water 2020, 12(1), 182; https://doi.org/10.3390/w12010182 - 09 Jan 2020
Cited by 6 | Viewed by 902
Abstract
While rapid urbanization promotes social and economic development, it poses a serious threat to the health of rivers, especially the small and medium-scale rivers. Flood control for small and medium-scale rivers in highly urbanized areas is particularly important. The purpose of this study [...] Read more.
While rapid urbanization promotes social and economic development, it poses a serious threat to the health of rivers, especially the small and medium-scale rivers. Flood control for small and medium-scale rivers in highly urbanized areas is particularly important. The purpose of this study is to explore the most effective flood control strategy for small and medium-scale rivers in highly urbanized areas. MIKE 11 and MIKE 21 were coupled with MIKE FLOOD model to simulate flooding with the flood control standard, after which the best flooding control scheme was determined from a whole region perspective (both the mainstream and tributary conditions were considered). The SheGong River basin located near the Guangzhou Baiyun international airport in Guangzhou city over south China was selected for the case study. The results showed that the flooding area in the basin of interest accounts for 42% of the total, with maximum inundation depth up to 0.93 m under the 20-year return period of the designed flood. The flood-prone areas are the midstream and downstream where urbanization is high; however the downstream of the adjacent TieShan River is still able to bear more flooding. Therefore, the probable cost-effective flood control scheme is to construct two new tributaries transferring floodwater in the mid- and downstream of the SheGong River into the downstream of the TieShan River. This infers that flood control for small and medium-scale rivers in highly urbanized areas should not simply consider tributary flood regimes but, rather, involve both tributary and mainstream flood characters from a whole region perspective. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River
Water 2019, 11(12), 2539; https://doi.org/10.3390/w11122539 - 01 Dec 2019
Cited by 3 | Viewed by 1051
Abstract
Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These [...] Read more.
Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation–optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR; (2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin
Water 2019, 11(11), 2417; https://doi.org/10.3390/w11112417 - 18 Nov 2019
Cited by 4 | Viewed by 1046
Abstract
The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a [...] Read more.
The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation
Water 2019, 11(10), 2040; https://doi.org/10.3390/w11102040 - 29 Sep 2019
Cited by 7 | Viewed by 1000
Abstract
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search [...] Read more.
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Article
Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity
Water 2019, 11(10), 2029; https://doi.org/10.3390/w11102029 - 28 Sep 2019
Cited by 3 | Viewed by 1079
Abstract
Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal [...] Read more.
Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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Case Report
Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China
Water 2019, 11(9), 1939; https://doi.org/10.3390/w11091939 - 18 Sep 2019
Cited by 1 | Viewed by 1081
Abstract
Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a [...] Read more.
Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a general strategy to effectively tackle the dangerous situation created by a giant dammed lake with 770 million cubic meters of water volume and formulate an emergency disposal solution for the 25 million cubic meters of debris, composed of engineering measures of floodgate excavation and non-engineering measures of reservoirs/hydropower stations operation. Such a disposal solution can not only reduce a large-scale flood (10,000-year return period, 0.01%) into a small-scale flood (10-year return period, 10%) but minimize the flood risk as well, guaranteeing no death raised by the giant landslide. Full article
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
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