1,721,037 research outputs found
Water Management in mountain basins (including environmental flow)
No full textSimposio tenuto in occasione della General Assembly dell'European Geosciences Union
Predizione a scala regionale di indici di magra in bacini montani non strumentati e possibili implicazioni nella stima del Deflusso Minimo Vitale.
No full textIl lavoro descrive un nuovo metodo per la predizione, a scala regionale, di indici idrologici di magra in bacini non strumentati. Il metodo è stato testato in un areale di circa 8000 km2 nell'Appennino settentrionale. Esso si basa su analisi statistiche (Weight of Evidence, Logistic Regression, Neural Networks) applicate a dati spazialmente distribuiti, che permettono di stimare valori di Base Flow Index per le formazioni geologiche costituenti un bacino (parametro direttamente correlato ai coefficienti di immagazzinamento) utilizzando come evidenze di supporto la distribuzione ed i dati di portata di sorgenti perenni presenti nel territorio. Utilizzando valori osservati di BFI e di alcuni indici di magra calcolati in 26 stazioni idrometriche strumentate (ottenuti a partire da serie idrologiche quinquennali di dati giornalieri di portata) è stato possibile validare i risultati della predizione spaziale di BFI ed utilizzare il metodo per stimare, con buona approssimazione, i valori degli indici di magra in ogni bacino, ovvero in qualsiasi punto posto su un’asta torrentizia dell’area di studio
Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence
Full text linkStatistical dependency measures such as Kendall’s Tau or Spearman’s Rho are frequently used to analyse the coherence between time series in environmental data analyses. Autocorrelation of the data can, however, result in spurious cross correlations if not accounted for. Here, we present the asymptotic distribution of the estimators of Spearman’s Rho and Kendall’s Tau, which can be used for statistical hypothesis testing of cross-correlations between autocorrelated observations. The results are derived using U-statistics under the assumption of absolutely regular (or β-mixing) processes. These comprise many short-range dependent processes, such as ARMA-, GARCH- and some copula-based models relevant in the environmental sciences. We show that while the assumption of absolute regularity is required, the specific type of model does not have to be specified for the hypothesis test. Simulations show the improved performance of the modified hypothesis test for some common stochastic models and small to moderate sample sizes under autocorrelation. The methodology is applied to observed climatological time series of flood discharges and temperatures in Europe. While the standard test results in spurious correlations between floods and temperatures, this is not the case for the proposed test, which is more consistent with the literature on flood regime changes in Europe
Flood trends in Europe: Are changes in small and big floods different?
Full text linkRecent studies have revealed evidence of trends in the median or mean flood discharge in Europe over the last 5 decades, with clear and coherent regional patterns. The aim of this study is to assess whether trends in flood discharges also occurred for larger return periods, accounting for the effect of catchment scale. We analyse 2370 flood discharge records, selected from a newly available pan-European flood database, with record length of at least 40 years over the period 1960-2010 and with contributing catchment area ranging from 5 to 100 000 km2. To estimate regional flood trends, we use a non-stationary regional flood frequency approach consisting of a regional Gumbel distribution, whose median and growth factor can vary in time with different strengths for different catchment sizes. A Bayesian Markov chain Monte Carlo (MCMC) approach is used for parameter estimation. We quantify regional trends (and the related sample uncertainties), for floods of selected return periods and for selected catchment areas, across Europe and for three regions where coherent flood trends have been identified in previous studies. Results show that in northwestern Europe the trends in flood magnitude are generally positive. In small catchments (up to 100 km2), the 100-year flood increases more than the median flood, while the opposite is observed in medium and large catchments, where even some negative trends appear, especially in northwestern France. In southern Europe flood trends are generally negative. The 100-year flood decreases less than the median flood, and, in the small catchments, the median flood decreases less compared to the large catchments. In eastern Europe the regional trends are negative and do not depend on the return period, but catchment area plays a substantial role: the larger the catchment, the more negative the trend
Analysis of the relationships between hydrogeological characteristics of mountain basins and low flow discharge: Regional-scale prediction of hydrological indexes in ungauged basins of the northern apennines
No full textIn the last decade the north of Italy suffered a marked hydrologic deficit as a consequence of decreasing mean annual precipitation and increasing demand from industry and agriculture. Rivers that outflow from the northern Apennines display a highly variable discharge rate, mainly because of inherited hydro-geological factors such as the extensive presence of low permeability sedimentary rocks with low storage coefficient in the mountain catchments. The growing interest for a proper management of water courses, that has led to directives regulating the amount of minimum water that must permanently flow downstream from points of water diversions and yield, makes the prediction of hydrological indexes in ungauged basins located in the mountain areas of relevant practical importance, since it can support a sustainable planning of surface water management along the entire water course. The research has being aimed at developing a spatial analysis tool for regional-scale prediction of hydro-geological indexes in ungauged basins, that still represent the majority of cases in the upper catchment areas of the northern Apennines. This has been dealt with by linking statistical indexes of discharge calculated for gauged basins (Q95, Q355), obtained with five years of continuous daily data, to the results of spatial analysis methods (such as Weight of Evidence, Logistic Regression, Neural Networks), that allow the storage coefficient of different bedrock types to be relatively ranked using the spatial distribution of permanent groundwater springs as main supporting evidence. The paper summarises the main results obtained that were validated in three basis within the whole study area
The influence of non-stationarity in extreme hydrological events on flood frequency estimation
Full text link
HESS Opinions: The sword of Damocles of the impossible flood
Full text linkExtremely large floods that far exceed previously observed records are often considered virtually "impossible", yet they are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. Neglecting such floods may lead to emergency situations where society is unprepared and to disastrous consequences. Four reasons why extremely large floods are often considered next to impossible are explored here, including physical (e.g. climate change), psychological, socio-economic and combined reasons. It is argued that the risk associated with an "impossible"flood may often be larger than expected and that a bottom-up approach should be adopted that starts from the people affected and explores possibilities of risk management, giving high priority to social in addition to economic risks. Suggestions are given for managing this risk of a flood considered impossible by addressing the diverse causes of the presumed impossibility
Temporal Scaling of Streamflow Elasticity to Precipitation: A Global Analysis
Full text linkStreamflow elasticity to precipitation, defined as the percent change of streamflow resulting from a 1% change in precipitation, is sometimes used as an alternative to rainfall-runoff models in climate impact analyses. Elasticity is usually estimated from long streamflow and precipitation series aggregated at annual time steps while the climate impact analyses are usually geared toward changes at decadal scales. The purpose of this paper is therefore to understand how the elasticity depends on the aggregation time scale and the process controls of such a dependence. We analyze streamflow records of 7,053 catchments around the world over the period 1950–2016, and select 5,327 with reliable elasticity estimates for aggregation time ranging from 13 to 60 months. We find a significant scaling of streamflow elasticity to precipitation with aggregation time in 66% of the catchments which is much larger than expected by chance. Positive scaling occurs much more frequently than negative scaling. More arid/less rainy catchments, less forested catchments and catchments with a large base flow contribution to streamflow are more frequently characterized by a positive scaling. A random forest classification model identifies aridity index, latitude, mean annual precipitation, the potential evapotranspiration seasonality, the base flow index and the precipitation seasonality as relevant explanatory variables of the scaling. We interpret the sign of the scaling by non-linear runoff generation in arid regions, by the effect of climate modes and snow processes, and by the regulation capacity of vegetation to transpire more water if the past years were wet. It is suggested to use decadal elasticities instead of annual elasticities in climate impact analyses in order to account for their scaling behavior
Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria
No full textFlood changes may be attributed to drivers of change that belong to three main classes: atmospheric, catchment and river system drivers. In this work, we propose a data-based attribution approach for selecting which driver best relates to variations in time of the flood frequency curve. The flood peaks are assumed to follow a Gumbel distribution, whose location parameter changes in time as a function of the decadal variations of one of the following alternative covariates: annual and extreme precipitation for different durations, an agricultural land-use intensification index, and reservoir construction in the catchment, quantified by an index. The parameters of this attribution model are estimated by Bayesian inference. Prior information on one of these parameters, the elasticity of flood peaks to the respective driver, is taken from the existing literature to increase the robustness of the method to spurious correlations between flood and covariate time series. Therefore, the attribution model is informed in two ways: by the use of covariates, representing the drivers of change, and by the priors, representing the hydrological understanding of how these covariates influence floods. The Watanabe-Akaike information criterion is used to compare models involving alternative covariates. We apply the approach to 96 catchments in Upper Austria, where positive flood peak trends have been observed in the past 50 years. Results show that, in Upper Austria, one or seven day extreme precipitation is usually a better covariate for variations of the flood frequency curve than precipitation at longer time scales. Agricultural land-use intensification rarely is the best covariate, and the reservoir index never is, suggesting that catchment and river drivers are less important than atmospheric ones. Not all the positive flood trends correspond to a significant correlation between floods and the covariates, suggesting that other drivers or other flood-driver relations should be considered to attribute flood trends in Upper Austria
- …
