Special Issue "Hydrologic, Hydraulic and Geomorphic Modeling for Small and Ungauged Basins"

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

Deadline for manuscript submissions: closed (20 March 2020).

Special Issue Editors

Dr. Andrea Petroselli
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Guest Editor
Department of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, Viterbo, Italy
Interests: rainfall-runoff modeling; flood prone area estimation; surface hydrology; GIS Terrain Analysis for hydrogeomorphic applications; hydrological processes monitoring and modelling
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Dr. Fernando Nardi
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Guest Editor
Water Resources Research and Documentation Centre (WARREDOC), University for Foreigners of Perugia, Perugia, Italy
Interests: hydrology; hydraulic modelling; 2D flood modelling; GIS; geospatial data; Digital Terrain Models; geomorphology; geomorphic modelling; flood hazard and risk mapping and management; citizen science and big data for earth and water science
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Prof. Salvatore Grimaldi
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Guest Editor
Department of Innovation in Biology, Agri-food and Forest systems (DIBAF), Tuscia University, Viterbo, Italy
Interests: hydrological observations; tracers for surface hydrology; river velocity estimation; surface travel time estimation; large scale particle image velocimetry; image analysis for hydrological applications; rainfall measurements; time series analysis; long memory models; linear parametric models; multivariate distributions; copula function; hydrological modelling in ungauged basins; rainfall runoff models; GIS terrain analysis; DEM analysis; geomorphological unit hydrograph; flood mapping; design hydrograph
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Modelling approaches are a pivotal component of hydrologic, hydraulic and geomorphic studies devoted to water resources and risk management. Numerical models become crucial when dealing with ungauged basins that are lacking data and observations on physical processes and features, and that represent a significant portion of the world’s fluvial systems, especially in secondary river networks and in developing countries. Analysts are still challenged by the adoption of simplified parsimonious models, characterized by a limited number of input parameters or by the quest for calibration and validation data, which are often also missing. In these circumstances, advanced and sophisticated hydrological and hydraulic models cannot be calibrated with observations, and in many applications, the user can count only on few data sources such as topography, land cover information and low-resolution rainfall data. Lately, new opportunities have arisen from new sources of remotely sensed information that is also linked to informal unstructured data (e.g., social networks), citizen science approaches, and low-cost sensors, among others. Nevertheless, ungauged basins indeed represent a challenging condition for modellers, leading to an uncertainty in the predictions that is difficult to quantify.

We welcome the submission of original and innovative research papers focusing on modelling aspects of hydrological, hydraulic and geomorphic processes addressing water resources management issues in ungauged basins, with the aim of using the available information and reducing the uncertainty in the estimations as much as possible. We expect that this Special Issue will reduce the uncertainty in the determination of design variables linked to water cycle processes and features considered in different hydrologic and environmental processes occurring in ungauged basins.

Assoc. Prof. Andrea Petroselli
Assoc. Prof. Fernando Nardi
Prof. Salvatore Grimaldi
Guest Editors

Manuscript Submission Information

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Keywords

  • Ungauged basins
  • Hydrological modeling
  • Hydraulic modelling
  • Geomorphic modelling
  • Uncertainty
  • Flood prone area mapping
  • Rainfall-runoff modelling

Published Papers (7 papers)

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Research

Open AccessArticle
Flood Risk Evaluation in Ungauged Coastal Areas: The Case Study of Ippocampo (Southern Italy)
Water 2020, 12(5), 1466; https://doi.org/10.3390/w12051466 - 21 May 2020
Abstract
The growing concentration of population and the related increase in human activities in coastal areas require numerical simulations to analyze the effects of flooding events that might occur in susceptible coastal areas in order to determine effective coastal management practices and safety measures [...] Read more.
The growing concentration of population and the related increase in human activities in coastal areas require numerical simulations to analyze the effects of flooding events that might occur in susceptible coastal areas in order to determine effective coastal management practices and safety measures to safeguard the inhabited coastal areas. The reliability of the analysis is dependent on the correct evaluation of key inputs such as return period of flooding events, vulnerability of exposed assets, and other risk factors (e.g., spatial distribution of elements at risk, their economic value, etc.). This paper defines a methodology to assess the effects of flooding events associated with basin run-off and storm surge in coastal areas. The assessment aims at quantifying in economic terms (e.g., loss of assets) the risk of coastal areas subject to flooding events. The methodology proposed in this paper was implemented to determine the areas subject to inundation on a coastal area in Southern Italy prone to hydrogeological instability and coastal inundation. A two-dimensional hydraulic model was adopted to simulate storm surges generated by severe sea storms coupled with intense rainfalls in order to determine the areas subject to inundation in the low-land area along the Adriatic coast object of this study. In conclusion, the economic risk corresponding to four different flooding scenarios was assessed by correlating the exceedance probability of each flooding scenario with the potential economic losses that might be realized in the inundated areas. The results of the assessment can inform decision-makers responsible for the deployment of risk mitigation measures. Full article
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Open AccessArticle
Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin
Water 2020, 12(5), 1237; https://doi.org/10.3390/w12051237 - 26 Apr 2020
Abstract
Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative [...] Read more.
Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative contributions of changing environment to the observed hydrological trends and response on the runoff generation process in its watershed. On the basis of observed hydrological and meteorological data from 1965–2010, the Mann-Kendall trend test and climate elasticity method were used to distinguish the effects of climate change and human activities on runoff in the Gushanchuan basin. The results indicate that the runoff in the Gushanchuan Basin has experienced significant declines as large as 77% from 1965 to 2010, and a mutation point occurred around 1997; the contribution rate of climate change to runoff change is 12.9–15.1%, and the contribution rate of human activities to runoff change is 84.9–87.1%. Then we divided long-term data sequence into two stages around the mutation point, and analyzed runoff generation mechanisms based on land use and cover changes (LUCC). We found that the floods in the Gushanchuan Basin were still dominated by Excess-infiltration runoff, but the proportion in 1965–1997 and 1998–2010 decreased gradually (68.46% and 45.83% in turn). The proportion of Excess-storage runoff and Mixed runoff has increased, which means that the runoff is made up of more runoff components. The variation law of the LUCC indicates that the forest area increased by 49.61%, the confluence time increased by 50.42%, and the water storage capacity of the watershed increased by 30.35%. Full article
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Open AccessArticle
New Empirical Model Using Landscape Hydric Potential Method to Estimate Median Peak Discharges in Mountain Ungauged Catchments
Water 2020, 12(4), 983; https://doi.org/10.3390/w12040983 - 30 Mar 2020
Abstract
Designing hydraulic structures, such as culverts, bridges, weirs, and check dams, while planning new flood inundation areas, needs correct assessment of design discharges. In gauged catchments with long time series of discharges, statistical methods are commonly used based on fixed theoretical distributions and [...] Read more.
Designing hydraulic structures, such as culverts, bridges, weirs, and check dams, while planning new flood inundation areas, needs correct assessment of design discharges. In gauged catchments with long time series of discharges, statistical methods are commonly used based on fixed theoretical distributions and on empirical distributions. However, in ungauged catchments, this approach is not possible. In addition to more advanced methods, which are used today, e.g., rainfall–runoff models, much more simple approaches are still needed based on regionalization and empirical models. Thus, the objective of this work is to develop a new empirical model for calculating peak discharge expressed as the median of annual peak discharge (QMED). The innovative aspect of this paper is the use of a new parameter, named landscape hydric potential (LHP), as a descriptor of water storage in catchments. LHP has a crucial role as the descriptor of water storage in catchment and, thus, it has an influence on forming discharges. The work was done in the Upper Vistula basin in the Polish Carpathians. This study was carried out in mountain catchments located in the Upper Vistula basin, in the south part of Poland in in the Polish Carpathians. Results show that the proposed model could provide appropriate calculations in changing climate conditions, as well as when land use is changed. The proposed model is simple and effective; for calculating QMED, it needs only two parameters: catchment area and LHP. Since the model has a significant and high correlation coefficient, it could be used for assessing of QMED in ungauged mountain catchments. The determined form of the empirical equation finds application in the entire Upper Vistula basin, for catchments with a surface area from 24 km2 up to 660 km2. Full article
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Open AccessArticle
Influence of Changes of Catchment Permeability and Frequency of Rainfall on Critical Storm Duration in an Urbanized Catchment—A Case Study, Cracow, Poland
Water 2019, 11(12), 2557; https://doi.org/10.3390/w11122557 - 03 Dec 2019
Cited by 2
Abstract
The increase of impermeable areas in a catchment is known to elevate flood risk. To adequately understand and plan for these risks, changes in the basin water cycle must be quantified as imperviousness increases, requiring the use of hydrological modeling to obtain design [...] Read more.
The increase of impermeable areas in a catchment is known to elevate flood risk. To adequately understand and plan for these risks, changes in the basin water cycle must be quantified as imperviousness increases, requiring the use of hydrological modeling to obtain design runoff volumes and peak flow rates. A key stage of modeling is adopting the structure of the model and estimating its parameters. Due to the fact that most impervious basins are uncontrolled, hydrological models that do not require parameter calibration are advantageous. At the same time, it should be remembered that these models are sensitive to the values of assumed parameters. The purpose of this work is to determine the effect of catchment impermeability on the flow variability in the Sudół Dominikański stream in Cracow, Poland, and assess the influence of the frequency of rainfall on values of time of concentration (here it is meant as critical storm duration). The major finding in this work is that the critical storm duration for all different scenarios of catchment imperviousness depends on the rainfall exceedance probability. In the case of rainfall probability lower than 5.0%, the critical storm duration was equal to 2 h, for higher probabilities (p ≥ 50%) it was equal to 24 h. Simulations showed that the increase of impermeable areas caused peak time abbreviation. In the case of rainfall with exceedance probability p = 1.0% and critical storm duration Dkr = 2 h, the peak time decreased about 12.5% and for impermeable areas increased from 22.01 to 44.95%. Full article
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Open AccessArticle
Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios
Water 2019, 11(6), 1231; https://doi.org/10.3390/w11061231 - 13 Jun 2019
Cited by 10
Abstract
Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to [...] Read more.
Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome. Full article
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Open AccessArticle
Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods
Water 2019, 11(6), 1226; https://doi.org/10.3390/w11061226 - 12 Jun 2019
Cited by 1
Abstract
The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on [...] Read more.
The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered. Full article
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Open AccessArticle
Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors
Water 2019, 11(6), 1130; https://doi.org/10.3390/w11061130 - 29 May 2019
Cited by 9
Abstract
The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm [...] Read more.
The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons. Full article
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