1,721,108 research outputs found
Design flood estimation using model selection criteria
No full textThe design flood is defined as the discharge value corresponding to an assigned non-exceedance probability, which is usually expressed in terms of the return period. Estimation of the design flood is usually carried out by fitting observed data samples with a suitable probability distribution. The objective of this study is to evaluate if model selection criteria, which are seldom used in hydrological applications, can help identifying the best probability model for this purpose. The study analyzes the performance of three model selection criteria, namely, the Akaike information criterion, the Bayesian information criterion. and the Anderson-Darling criterion. The three methods are compared trough an extensive numerical analysis by using synthetic data samples. The study demonstrates that model selection criteria are a valuable tool for reducing the uncertainty of design flood estimatio
The interplay between reservoir storage and operating rules under evolving conditions
Full text linkReservoir storage helps manage hydrological variability, increasing predictability and productivity of water supply. However, there are inevitable tradeoffs, with control of high frequency variability coming at the expense of robustness to low frequency variability. Tightly controlling variability can reduce incentives to maintain adaptive capacity needed during events that exceed design thresholds. With multiple dimensions of change projected for many water supply systems globally, increased knowledge on the role of design and operational choices in balancing short-term control and long-term adaptability is needed. Here we investigated how the scale of reservoir storage (relative to demands and streamflow variability) and reservoir operating rules interact to mitigate shortage risk under changing supplies and/or demands. To address these questions, we examined three water supply systems that have faced changing conditions: the Colorado River in the Western United States, the Melbourne Water Supply System in Southeastern Australia, and the Western Cape Water Supply System in South Africa. Moreover, we parameterize a sociohydrological model of reservoir dynamics using time series from the three case studies above. We then used the model to explore the impacts of storage and operational rules. We found that larger storage volumes lead to a greater time before the shortage is observed, but that this time is not consistently used for adaptation. Additionally, our modeling results show that operating rules that trigger withdrawal decreases sooner tend to increase the probability of an adaptive response; the findings from this model are bolstered by the three case studies. While there are many factors influencing the response to water stress, our results demonstrate the importance of: i) evaluating design and operational choices in concert, and ii) examining the role of information salience in adapting water supply systems to changing conditions
Drought and Human Mobility in Africa
Full text linkHuman mobility from droughts is multifaceted and depends on environmental, political, social, demographic and economic factors. Although droughts cannot be considered as the single trigger, they significantly influence people's decision to move. Yet, the ways in which droughts influence patterns of human settlements have remained poorly understood. Here we explore the relationships between drought occurrences and changes in the spatial distribution of human settlements across 50 African countries for the period 1992–2013. For each country, we extract annual drought occurrences from two indicators, the international disaster database EM-DAT and the standardized precipitation evapotranspiration index (SPEI-12) records, and we evaluate human settlement patterns by considering urban population data and human distance to rivers, as derived from nighttime lights. We then compute human displacements as variations in human distribution between adjacent years, which are then associated with drought (or non-drought) years. Our results show that drought occurrences across Africa are often associated with (other things being equal) human mobility toward rivers or cities. In particular, we found that human settlements tend to get closer to water bodies or urban areas during drought conditions, as compared to non-drought periods, in 70%–81% of African countries. We interpret this tendency as a physical manifestation of drought adaptation, and discuss how this may result into increasing flood risk or overcrowding urban areas. As such, our results shed light on the interplay between human mobility and climate change, bolstering the analysis on the spatiotemporal dynamics of drought risks in a warming world
Criteri di selezione del modello probabilistico nell'analisi di frequenza degli estremi idrologici
No full text
Hydrological risk. Modeling flood memory and human proximity to rivers
Full text linkRecent literature in sociohydrology has shown the important role of flood memory in shaping hydrological risk. In this paper, we present a system dynamics model of human-flood interactions that simulates how the river proximity of human settlements is altered by changes in flood memory. We also compare our model outcomes with an unprecedented dataset consisting of historical and archeological observations of human settlements in the Czech Republic that have been affected by major flood events. This comparison allows us to evaluate the potentials and limitations of our sociohydrological model in capturing essential features of flood risk changes, including the process of resettling farther and closer to the river. Our results show that the accumulation (and decay) of collective memory has potential in explaining temporal changes of flood risk driven by the occurrence (or absence) of major events. As such, this study contributes to advancing knowledge about the complex dynamics of human-water systems, while providing useful insights in the field of flood risk reduction
A hydraulic study on the applicability of flood rating curves
Full text linkSeveral hydrological studies have shown that river discharge records are affected by significantuncertainty. This uncertainty is expected to be very high for river flow data referred to floodevents, when the stage–discharge rating curve is extrapolated far beyond the measurementrange. This study examines the standard methodologies for the construction and extrapolation ofrating curves to extreme flow depths and shows the need of proper approaches to reduce theuncertainty of flood discharge data. To this end, a comprehensive analysis is performed on a16 km reach of the River Po (Italy) where five hydraulic models (HEC-RAS) were built. The resultsof five topographical surveys conducted during the last 50 years are used as geometric input. Theapplication demonstrates that hydraulically built stage–discharge curves for the five cases differonly for ordinary flows, so that a common rating curve for flood discharges can be derived. Thisresult confirms the validity of statistical approaches to the estimation of the so-called ‘flood ratingcurve’, a unique stage–discharge curve based on data of contemporaneous annual maxima ofstage and discharge values, which appears insensitive to marginal changes in river geome
Exploring the role of risk perception in influencing flood losses over time
Full text linkWhat implications do societies’ risk perceptions have for flood losses? This study uses a stylized, socio-hydrological model to simulate the mutual feedbacks between human societies and flood events. It integrates hydrological modelling with cultural theory and proposes four ideal types of society that reflect existing dominant risk perception and management: risk neglecting, risk monitoring, risk downplaying and risk controlling societies. We explore the consequent trajectories of flood risk generated by the interactions between floods and people for these ideal types of society over time. The results suggest that flood losses are substantially reduced when awareness-raising attitudes are promoted through inclusive, participatory approaches in the community. In contrast, societies that rely on top-down hierarchies and structural measures to protect settlements on floodplains may still suffer significant losses during extreme events. This study illustrates how predictions formed through social science theories can be applied and tested in hydrological modelling
Model selection techniques for the frequency analysis of hydrological extremes
No full textThe frequency analysis of hydrological extremes requires fitting a probability distribution to the observed data to suitably represent the frequency of occurrence of rare events. The choice of the model to be used for statistical inference is often based on subjective criteria, or it is considered a matter of probabilistic hypotheses testing. In contrast, specific tools for model selection, like the well-known Akaike information criterion (AIC) and the Bayesian information criterion (BIC), are seldom used in hydrological applications. The objective of this study is to verify whether the AIC and BIC work correctly when they are applied to identifying the probability distribution of hydrological extremes, i.e., when the available samples are small and the parent distribution is highly asymmetric. An additional model selection criterion, based on the Anderson-Darling goodness-of-fit test statistic, is here proposed, and the performances of the three methods are compared through an extensive numerical analysis. The capability of the three criteria to recognize the correct parent distribution from the available data samples varies from case to case, and it is rather good in some cases (in particular when the parent is a two-parameter distribution) and unsatisfactory in others. An application to flood peak time series from 1000 catchments located in the United Kingdom provides some further information on the qualities and drawbacks of the considered criteria. From the numerical simulations and data-based analyses it can be concluded that the three model selection techniques considered here produce results of comparable quality
How much observed river flow data are uncertain? A theoretical analysis and an attempt to asses the effect on parameterization and performance evaluation of rainfall-runoff models
No full textRainfall-runoff models are usually optimized and tested on the basis of (so called)
"observed" river flow data. However, strictly speaking river flows are never observed.
It is well known that what is observed is usually the river stage, that is subsequently
converted in a river flow value by means of a rating curve. Therefore, the "observed"
river flow is affected by uncertainty, that can be induced by many different causes. As
a matter of fact, the river stage measure is affected by errors, as well as the estimated
rating curve. For instance there are approximations in the gauging instruments, as well
as in the extrapolation of the rating curve outside the range of the observations that
were used for its estimation. This study is aimed at analyzing the uncertainty that
may affect "observed" river flow data. An attempt is made to quantify the different
sources of errors, and to propagate them through the river flow estimation procedure,
therefore retrieving an estimation of the total uncertainty in the observed variables. A
simulation study is also performed by using synthetic data affected by known sources
of uncertainty, in order to assess the potential effect of erroneous observations on
rainfall-runoff model parameterization. The effect of errors in the observed variables
on total uncertainty in the simulation of river flow data will be also investigated
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