Predicting Monthly Spring Discharges Using a Simple Statistical Model

Diodato, Nazzareno; Guerriero, Luigi; Fiorillo, Francesco; Esposito, Libera; Revellino, Paola; Grelle, G.; Guadagno, Francesco M.

doi:10.1007/s11269-014-0527-0

Cited by 36 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The groundwater hydrodynamic processes in aquifers are nonlinear. A former rainfall event displays less impact (though not negligible) than a recent one on the aquifer hydrodynamic fluctuations (Canuti et al, 1985;Crozier, 1986;Diodato et al, 2014). The daily precipitation/recharge time series cannot therefore be used without appropriate corrections.…”

Section: Antecedent Cumulative Summentioning

confidence: 99%

An efficient workflow to accurately compute groundwater recharge for the study of rainfall-triggered deep-seated landslides, application to the Séchilienne unstable slope (western Alps)

Vallet

Bertrand

Fabbri

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Pore water pressure build-up by recharge of underground hydrosystems is one of the main triggering factors of deep-seated landslides. In most deep-seated landslides, pore water pressure data are not available since piezometers, if any, have a very short lifespan because of slope movements. As a consequence, indirect parameters, such as the calculated recharge, are the only data which enable understanding landslide hydrodynamic behaviour. However, in landslide studies, methods and recharge-area parameters used to determine the groundwater recharge are rarely detailed. In this study, the groundwater recharge is estimated with a soil-water balance based on characterisation of evapotranspiration and parameters characterising the recharge area (soil available water capacity, runoff and vegetation coefficient). A workflow to compute daily groundwater recharge is developed. This workflow requires the records of precipitation, air temperature, relative humidity, solar radiation and wind speed within or close to the landslide area. The determination of the parameters of the recharge area is based on a spatial analysis requiring field observations and spatial data sets (digital elevation models, aerial photographs and geological maps). This study demonstrates that the performance of the correlation with landslide displacement velocity data is significantly improved using the recharge estimated with the proposed workflow. The coefficient of determination obtained with the recharge estimated with the proposed workflow is 78 % higher on average than that obtained with precipitation, and is 38 % higher on average than that obtained with recharge computed with a commonly used simplification in landslide studies (recharge = precipitation minus non-calibrated evapotranspiration method).

show abstract

Section: Antecedent Cumulative Summentioning

confidence: 99%

An efficient workflow to accurately compute groundwater recharge for the study of rainfall-triggered deep-seated landslides, application to the Séchilienne unstable slope (western Alps)

Vallet

Bertrand

Fabbri

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The additional training and validation area is the Fussen-Lech basin, which has been described in detail by Diodato et al (2014). This basin is in the mountainous range of the upper Danube basin and the annual average precipitation is 1525 mm (data from Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region, HISTALP, http://www.zamg.ac.at/histalp; Efthymiadis et al, 2006).…”

Section: Environmental Settingmentioning

confidence: 99%

“…Examples of erosion modeling at longer time scales were presented by Macklin and Lewin (2003), Zolitschka (2003), Dotterweich (2008), Notebaert et al (2009), andWard et al (2009). In these cases, efforts were directed toward reducing the complexity of models to a few key inputs in order to capture interannual and multidecadal variability (after Diodato et al, 2014). Many of these models have not been able to reflect in much detail the potential effect of changes in the hydroclimatological force driving erosion and sediment yield.…”

Section: Introductionmentioning

confidence: 99%

Estimating long-term sediment export using a seasonal rainfall-dependent hydrological model in the Glonn River basin, Germany

Diodato

Vente

Bellocchi³

et al. 2015

Geomorphology

Self Cite

View full text Add to dashboard Cite

“…Statistical models have been widely applied for explaining earth surface and hydrologic processes (e.g. Diodato et al, 2014;Diodato et al, 2015;Guerriero et al, 2015) and have the potential to support natural hazard evaluation (e.g. Di Martire, De Rosa, Pesce, Santangelo, & Calcaterra, 2012;Grelle et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Flood hazard of major river segments, Benevento Province, Southern Italy

Guerriero

Focareta²,

Fusco

et al. 2018

Journal of Maps

Self Cite

View full text Add to dashboard Cite

On 15 October 2015, a storm-induced flood hit the central sector of Benevento Province (southern Italy) causing two deaths and severe damage to infrastructure, buildings and local agriculture. This area has a long history of similar events and since 1924 its major river segments have been monitored with several hydrometric stations. We used data from two of these stations and a LiDAR derived high-resolution topography to develop a flood hazard map. For map computation, we first derived a flood inundation map from topography. Subsequently we estimated the probability of exceedance of each specific fluvial stage from the combination of a Generalized Extreme Value and a Gamma fits of available hydrometric data. As boundary condition, we considered a reference scenario corresponding to an estimated 500 year flood. The hazard maps provide an overview of the flood hazard in the central sector of Benevento Province and floodplains zonation in flood perspective. ARTICLE HISTORY

show abstract

Predicting Monthly Spring Discharges Using a Simple Statistical Model

Cited by 36 publications

References 23 publications

An efficient workflow to accurately compute groundwater recharge for the study of rainfall-triggered deep-seated landslides, application to the Séchilienne unstable slope (western Alps)

An efficient workflow to accurately compute groundwater recharge for the study of rainfall-triggered deep-seated landslides, application to the Séchilienne unstable slope (western Alps)

Estimating long-term sediment export using a seasonal rainfall-dependent hydrological model in the Glonn River basin, Germany

Flood hazard of major river segments, Benevento Province, Southern Italy

Contact Info

Product

Resources

About