Special Issue "Techniques for Mapping and Assessing Surface Runoff"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology and Hydrogeology".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Assoc. Prof. Francesco Viola
Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering, University of Cagliari, 09123 Cagliari, Italy
Interests: streamflow modelling; ephemeral catchments; uncertainty evaluation; prevision in ungauged basins; ecohydrology; climate change

Special Issue Information

Dear Colleagues,

Information about runoff is fundamental in water resources assessment and planning and for water quality analysis. As measurements are rare, especially in developing countries, modelling, statistical, or regionalization techniques are necessary to assess the spatial and temporal variability of runoff. This Special Issue welcomes contributions helping the scientific community and technicians to foster knowledge on runoff assessment at different spatial scales, from hillslope to catchment scales, explicitly considering the influence of climate and the peculiarities of arid or hyper-humid areas. Novel approaches are needed to predict runoff at any cross section of natural or urbanized rivers, from hourly, to daily, to annual time scales in order to support decision makers with reliable quantile predictions. Integrations with climate models are also envisable, to forecast runoff in real-time with civil protection aims, or have long-range previsions to support water resources management and dams operations.

Great attention must be paid to runoff estimation in climate change conditions, with a particular focus on countries where rainfall is supposed to decrease in the next century. At the same time, extreme rainfall alterations and their impact on runoff still require researchers’ attention.

Dr. Francesco Viola
Guest Editor

Manuscript Submission Information

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Keywords

  • Runoff modeling
  • Quantile estimation
  • Runoff probability distribution
  • Climate change
  • Extreme intensification.

Published Papers (7 papers)

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Research

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Open AccessArticle
Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin
Water 2020, 12(8), 2086; https://doi.org/10.3390/w12082086 - 23 Jul 2020
Abstract
Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear [...] Read more.
Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Open AccessArticle
Revisiting the Statistical Scaling of Annual Discharge Maxima at Daily Resolution with Respect to the Basin Size in the Light of Rainfall Climatology
Water 2020, 12(2), 610; https://doi.org/10.3390/w12020610 - 24 Feb 2020
Abstract
Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the [...] Read more.
Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, an important question arises on how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing this question, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. To do so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: (a) the local rainfall climatology is an important contributor to the observed statistics of peak annual discharges, and (b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that commonly met in actual rainfields, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light on the approximate type of scaling of annual discharge maxima with the basin size. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Open AccessFeature PaperArticle
Linking Climate, Basin Morphology and Vegetation Characteristics to Fu’s Parameter in Data Poor Conditions
Water 2019, 11(11), 2333; https://doi.org/10.3390/w11112333 - 07 Nov 2019
Cited by 1
Abstract
The prediction of long term water balance components is not a trivial issue, even when empirical Budyko’s type approaches are used, because parameter estimation is often hampered by missing or poor hydrological data. In order to overcome this issue, we provided regression equations [...] Read more.
The prediction of long term water balance components is not a trivial issue, even when empirical Budyko’s type approaches are used, because parameter estimation is often hampered by missing or poor hydrological data. In order to overcome this issue, we provided regression equations that link climate, morphological, and vegetation parameters to Fu’s parameter. Climate is here defined as a specific seasonal pattern of potential evapotranspiration and rain: five climatic scenarios have been considered to mimic different conditions worldwide. A weather generator has been used to create stochastic time series for the related climatic scenario, which in turn has been used as an input to a conceptual hydrological model to obtain long-term water balance components with low computational effort, while preserving fundamental process descriptions. The morphology and vegetation’s role in determining water partitioning process has been epitomized in four parameters of the conceptual model. Numerical simulations explored a large set of basins in the five climates. Results show that climate superimposes partitioning rules for a given basin; morphological and vegetation watershed properties, as conceptualized by model parameters, determine the Fu’s parameter within a given climate. A sensitive analysis confirmed that vegetation has the most influencing role in determining water partitioning rules, followed by soil permeability. Finally, linear regressions relating basin characteristics to Fu’s parameter have been obtained in the five climates and tested in a basin for each case, obtaining encouraging results. The small amount of data required and the very low computational effort of the method make this approach ideal for practitioners and hydrologists involved in annual runoff assessment. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Open AccessArticle
Investigating Parameter Transferability across Models and Events for a Semiarid Mediterranean Catchment
Water 2019, 11(11), 2261; https://doi.org/10.3390/w11112261 - 28 Oct 2019
Cited by 2
Abstract
Physically based distributed hydrologic models (DHMs) simulate watershed processes by applying physical equations with a variety of simplifying assumptions and discretization approaches. These equations depend on parameters that, in most cases, can be measured and, theoretically, transferred across different types of DHMs. The [...] Read more.
Physically based distributed hydrologic models (DHMs) simulate watershed processes by applying physical equations with a variety of simplifying assumptions and discretization approaches. These equations depend on parameters that, in most cases, can be measured and, theoretically, transferred across different types of DHMs. The aim of this study is to test the potential of parameter transferability in a real catchment for two contrasting periods among three DHMs of varying complexity. The case study chosen is a small Mediterranean catchment where the TIN-based Real-time Integrated Basin Simulator (tRIBS) model was previously calibrated and tested. The same datasets and parameters are used here to apply two other DHMs—the TOPographic Kinematic Approximation and Integration model (TOPKAPI) and CATchment HYdrology (CATHY) models. Model performance was measured against observed discharge at the basin outlet for a one-year period (1930) corresponding to average wetness conditions for the region, and for a much drier two-year period (1931–1932). The three DHMs performed comparably for the 1930 period but showed more significant differences (the CATHY model in particular for the dry period. In order to improve the performance of CATHY for this latter period, an hypothesis of soil crusting was introduced, assigning a lower saturated hydraulic conductivity to the top soil layer. It is concluded that, while the physical basis for the three models allowed transfer of parameters in a broad sense, transferability can break down when simulation conditions are greatly altered. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Open AccessArticle
Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms
Water 2019, 11(10), 2126; https://doi.org/10.3390/w11102126 - 14 Oct 2019
Cited by 8
Abstract
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 [...] Read more.
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Open AccessArticle
Runoff Prediction Method Based on Adaptive Elman Neural Network
Water 2019, 11(6), 1113; https://doi.org/10.3390/w11061113 - 28 May 2019
Cited by 5
Abstract
The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an [...] Read more.
The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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Review

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Open AccessReview
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
Water 2019, 11(5), 910; https://doi.org/10.3390/w11050910 - 30 Apr 2019
Cited by 51
Abstract
Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be [...] Read more.
Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
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