Post-Evaluation of Flood Hazards Induced by Former Artificial Interventions along a Coastal Mediterranean Settlement

Hydrological studies are useful in designing, planning, and managing water resources, infrastructure, and ecosystems. Probability distribution models are applied in extreme flood analysis, drought investigations, reservoir volumes studies, and time-series modelling, among other various hydrological studies. However, the selection of the most suitable probability distribution and associated parameter estimation procedure, as a fundamental step in flood frequency analysis, has remained the most difficult task for many researchers and water practitioners. This paper explains the current approaches that are used to identify the probability distribution functions that are best suited for the estimation of maximum, minimum, and mean streamflows. Then, it compares the performance of six probability distributions, and illustrates four fitting tests, evaluation procedures, and selection procedures through using a river basin as a case study. An assemblage of the latest computer statistical packages in an integrated development environment for the R programming language was applied. Maximum likelihood estimation (MLE), goodness-of-fit (GoF) tests-based analysis, and information criteria-based selection procedures were used to identify the most suitable distribution models. The results showed that the gamma (Pearson type 3) and lognormal distribution models were the best-fit functions for maximum streamflows, since they had the lowest Akaike Information Criterion values of 1083 and 1081, and Bayesian Information Criterion (BIC) values corresponding to 1087 and 1086, respectively. The Weibull, GEV, and Gumbel functions were the best-fit functions for the annual minimum flows of the Tana River, while the lognormal and GEV distribution functions the best-fit functions for the annual mean flows of the Tana River. The choices of the selected distribution functions may be used for forecasting hydrologic events and detecting the inherent stochastic characteristics of the hydrologic variables for predictions in the Tana River Basin. This paper also provides a significant contribution to the current understanding of predicting extreme hydrological events for various purposes. It indicates a direction for hydro-meteorological scientists within the current debate surrounding whether to use historical data and trend estimation techniques for predicting future events with issues of non-stationarity and underlying stochastic processes.

Section: Maximum Likelihood Estimation Theorymentioning

confidence: 99%

“…Rivers and floodplains are important components of any country's economic, social, and cultural development. However, extreme streamflows and flood changes in magnitude and frequency may have disastrous effects on humans, riparian communities, and the environment in river basins [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Identification of the Most Suitable Probability Distribution Models for Maximum, Minimum, and Mean Streamflow

Langat

Kumar

Koech

2019

“…The study area has experienced decades of intensive land use changes in the past, such as urbanization, deforestation and wildfires, with have highlighted the accelerated rates of urban sprawl, which are associated with the significant and rapid socio-economic growth noted in the area. The diachronically formulated artificial stream-channel modifications and registered urban expansion typically lead to a significant increase in flood hazard downstream [49]. Land use and land cover patterns for four periods between 1968 to 2007 were mapped using aerial photographs, vertical frame photographs and a Google image in ERDAS Imagine Essentials 9.3 from Leica Geosystems spatial analyst software [12] and it was revealed that, between 1968 and 2007, the urban area has increased more than fivefold, from 3.71% to 19.03% of the study area.…”

Section: Study Area Datasets and Past Urban Sprawl Descriptionmentioning

confidence: 99%

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield using Artificial Neural Networks

Myronidis

Ιωάννου²

2018

Self Cite

Urban expansion substantially alters the impervious areas in a catchment, which in turn affects surface runoff and sediment yield in the downstream areas. In this study, the Land Transformation Model (LTM) was used to forecast the urban land expansion in a catchment, whilst future land use maps were employed according to the Soil Conservation Service Curve Number method (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) model, so as to examine the urbanization effects on runoff and sediment yield production respectively. Compared to pristine conditions, urban land is anticipated to increase from 6% in 1979 to 31% by 2027. The latter expansion pointed to an increase of peak discharge by 2.2-2.6 times and of flood volume by 1.6-2.1 times, with the sediment yield ranging between 0.47 to 1.05 t/ha for the upcoming 2027 period. Furthermore, the urban sprawl effects on all the latter variables were more profound during short duration storm events. Forecasting urban expansion through integrated artificial neural networks (ANN) and geographic information system (GIS) techniques, in order to calculate the associated design storm hydrograph and sediment yield, is of great importance, in order to properly plan and design hydraulic works that can sustain future urban development.have been negatively impacted by the degree of urbanization [5] and the hydrologic cycle and in stream process have been altered [6]. These changes could seriously impact the mountain rivers sediment production and transport [7] with significant changes in the bed morphology [8] and floods with a high content of transported sediments and debris floods can occur [9]. Moreover, the temporal and spatial scales of these phenomena may vary greatly during intense precipitation events with respect to normal conditions [10].In recent decades, a water budget or hydrologic model have been commonly utilized [11,12] to evaluate the effects of the recorded urban expansion on the hydrology of catchments, so as to model the effects of different land use change scenarios on runoff and sediment yield [13,14]. However, since land use planners and policy makers are more and more interested in predicting urban expansion and its effect on natural resources, much of the research in recent years has focused on the coupling of hydrologic models with sophisticated spatial land use change (LUC) models, that are able to project LUC changes in the near future. LUC models can be used as tools for studying the human effect on the Earth's surface and to forecast land change into the future [15].LUC models use a variety of techniques, including linear extrapolation, suitability mapping, genetic algorithms, neural networks, scenario analysis, expert opinions, public participation and agent-based modelling, in order to produce a prediction map for the near future [16]. However, there are three main stream approaches for LUC models: models which rely primarily on economic models and data [17][18][19], models that rely equally on multiple approaches and data to determine ca...

“…Credible peak flows magnitude estimates with 25, 50, and 100 years return periods denote essential pieces of information for the culvert design in forest roads [1], with respect to flood risk hazard assessment studies [2], concrete gravity dams design [3], and many other hydraulic works [4].…”

Section: Introductionmentioning

confidence: 99%

Generating Regional Models for Estimating the Peak Flows and Environmental Flows Magnitude for the Bulgarian-Greek Rhodope Mountain Range Torrential Watersheds

Myronidis

Ivanova

2020

Self Cite

The flood magnitudes with 25, 50, and 100 years return periods and the environmental flows (Qenv) are of outmost importance in the context of hydraulic and hydrologic design. In this study, 25 watershed characteristics were linked with the aforementioned recurrence intervals, peak discharge values, as well as Qenv for 15 pristine torrential watersheds with more than 10 years of streamflow records in the Rhodopi mountain range with a view to generating regional relationships for the assessment of discharge annual peaks and environmental flows regarding the ungauged torrential watersheds in the region. The Log-Pearson Type III probability distribution was fitted in the discharge annual peaks time series, so as to predict Q25, Q50, and Q100, whereas the Tennant method was utilised so as to estimate the environmental flows magnitude. Similarly, the Kolmogorov–Smirnov and the Anderson–Darling tests were performed to verify the distribution fitting. The Principal Components Analysis method reduced the explanatory variables number to 14, whilst the stepwise multiple regression analysis indicated that the exponential model is suitable for predicting the Q25, the power model best forecasted the Q50 and Q100, whereas the linear model is appropriate for Qenv prognosis. In addition, the reliability of the obtained regression models was evaluated by employing the R2, the Nash–Sutcliffe efficiency, and the Index of Agreement Statistical Criteria, which were found to range from 0.91–0.96, 0.88–0.95 and 0.97–0.99, respectively, thereby denoting very strong and accurate forecasts by the generated equations. Thus, the developed equations could successfully predict the peak discharge values and environmental flows within the region’s ungauged watersheds with the drainage size not exceeding 330 km2.