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22 pages, 13198 KiB

Open AccessArticle

Proxy Data of Surface Water Floods in Rural Areas: Application to the Evaluation of the IRIP Intense Runoff Mapping Method Based on Satellite Remote Sensing and Rainfall Radar

by Arnaud Cerbelaud, Pascal Breil, Gwendoline Blanchet, Laure Roupioz and Xavier Briottet

Water 2022, 14(3), 393; https://doi.org/10.3390/w14030393 - 27 Jan 2022

Cited by 3 | Viewed by 3587

Abstract

Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to [...] Read more.

Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall. Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Here, the IRIP geomatics mapping model, or “Indicator of Intense Pluvial Runoff”, is faced with rainfall radar measurements and damage maps derived from satellite imagery and supervised classification algorithms. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2000 km² of rural areas during two flash-flood events. The results of this study show that the greater the IRIP susceptibility scores, the more SWFs are detected by the remote sensing-based detection algorithm. Proportions of damaged plots become even larger when considering areas which experienced heavier precipitations. A negative relationship between the mean IRIP accumulation scores and the intensity of rainfall is found among damaged plots, confirming that SWFs preferably occur over potentially riskier areas where rainfall is lower. Land use and soil hydraulic conductivity are identified as the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. Multivariate logistic regression is also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity for explaining SWF occurrence. This work overall confirms the relevance of IRIP methodology while suggesting improvements to its core framework to implement better prevention strategies against SWF-related hazards. Full article

(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)

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19 pages, 3062 KiB

Open AccessArticle

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

by Peibing Song, Weifeng Liu, Jiahui Sun, Chao Wang, Lingzhong Kong, Zhenxue Nong, Xiaohui Lei and Hao Wang

Water 2020, 12(8), 2086; https://doi.org/10.3390/w12082086 - 23 Jul 2020

Cited by 23 | Viewed by 2408

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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14 pages, 2427 KiB

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

by Anastasios Perdios and Andreas Langousis

Water 2020, 12(2), 610; https://doi.org/10.3390/w12020610 - 24 Feb 2020

Cited by 8 | Viewed by 3743

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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18 pages, 3988 KiB

Open AccessFeature PaperArticle

Linking Climate, Basin Morphology and Vegetation Characteristics to Fu’s Parameter in Data Poor Conditions

by Dario Ruggiu and Francesco Viola

Water 2019, 11(11), 2333; https://doi.org/10.3390/w11112333 - 7 Nov 2019

Cited by 2 | Viewed by 2910

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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20 pages, 7246 KiB

Open AccessArticle

Investigating Parameter Transferability across Models and Events for a Semiarid Mediterranean Catchment

by Enrica Perra, Monica Piras, Roberto Deidda, Giuseppe Mascaro and Claudio Paniconi

Water 2019, 11(11), 2261; https://doi.org/10.3390/w11112261 - 28 Oct 2019

Cited by 2 | Viewed by 3180

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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43 pages, 6456 KiB

Open AccessArticle

Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms

by Georgia Papacharalampous, Hristos Tyralis, Andreas Langousis, Amithirigala W. Jayawardena, Bellie Sivakumar, Nikos Mamassis, Alberto Montanari and Demetris Koutsoyiannis

Water 2019, 11(10), 2126; https://doi.org/10.3390/w11102126 - 14 Oct 2019

Cited by 51 | Viewed by 11135

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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18 pages, 1998 KiB

Open AccessArticle

Runoff Prediction Method Based on Adaptive Elman Neural Network

by Chenming Li, Lei Zhu, Zhiyao He, Hongmin Gao, Yao Yang, Dan Yao and Xiaoyu Qu

Water 2019, 11(6), 1113; https://doi.org/10.3390/w11061113 - 28 May 2019

Cited by 32 | Viewed by 3749

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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37 pages, 4634 KiB

Open AccessReview

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

by Hristos Tyralis, Georgia Papacharalampous and Andreas Langousis

Water 2019, 11(5), 910; https://doi.org/10.3390/w11050910 - 30 Apr 2019

Cited by 409 | Viewed by 27185

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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