Generating synthetic time series of springs discharge in relation to standardized precipitation indices. Case study in Central Italy

Romano, Emanuele; Bon, Andrea Del; Petrangeli, Anna Bruna; Preziosi, Elisabetta

doi:10.1016/j.jhydrol.2013.10.020

Cited by 26 publications

(19 citation statements)

References 34 publications

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“…Secondly, a slight increase begins from time order 5 that smoothly concludes in time order 13. This continuous and differentiated behavior (from 1 up to 13) may indicate a temporal dependence, persistent in the short and medium term, which is not detected by a correlogram (see Figure 4b, independent behavior in the intervals [3,4] and [10][11][12]). However, in a correlogam, there is also a lag (Time order) range where dependence raises between lags 5 and 9, as shown in Figure 4b, that noticeably coincides with a slight raise in dependence analysis though the BN approach (Figure 7b).…”

Section: Porma Rivermentioning

confidence: 88%

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

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Hydrological series are largely characterized by a strong random component in their behavior. More noticeable changes in the behavior patterns of rainfall/runoff temporal series are recently being observed. These modifications are not a trivial issue, especially in regards to peculiarities, non-linearities, diffused influences or higher time orders of dependence. This study mainly aimed to analyze the temporal dependence of an annual runoff series dynamically. This approach comprises a coupling between classic techniques (Autoregressive Moving Average Model, ARMA) and novel ones, based on Artificial Intelligent for hydrological research (Bayesian Networks, BNs). An ARMA model was built to provide reliable data to populate BNs. Then, causal reasoning, through Bayes's theorem, allows the identification of the logic structure of temporal dependences within time series. Furthermore, the resultant conditional probability permits the quantification of the relative percentage of annual runoff change, and provides the right time order of dependence. This research introduces an original methodology able to build a logic structure for a stochastic analysis of temporal behavior. This approach also aimed to provide a powerful and graphic modeling method for improving the understanding of the dynamic runoff series temporal behavior. This was successfully demonstrated in two unregulated river basin stretches, belonging to the Duero river basin which is the largest basin in Spain.

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Section: Porma Rivermentioning

confidence: 88%

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

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“…SPI-Q regression model: SPI-Q [27,28] is a multi-linear regression model simulating the monthly inflow to surface reservoir through a calibrated linear combination of Standardized Precipitation Indices (SPI, [29]) computed for different cumulative time scales of the precipitation data gathered in the area. 2.…”

Section: Impact Modelmentioning

confidence: 99%

Climate Change Adaptation in a Mediterranean Semi-Arid Catchment: Testing Managed Aquifer Recharge and Increased Surface Reservoir Capacity

Guyennon

Salerno

Portoghese

et al. 2017

Water

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Among different uses of freshwater, irrigation is the most impacting groundwater resource, leading to water table depletion and possible seawater intrusion. The unbalance between the availability of water resources and demand is currently exacerbated and could become worse in the near future in accordance with climate change observations and scenarios provided by Intergovernmental Panel on Climate Change (IPCC). In this context, Increasing Maximum Capacity of the surface reservoir (IMC) and Managed Aquifer Recharge (MAR) are adaptation measures that have the potential to enhance water supply systems resiliency. In this paper, a multiple-users and multiple-resources-Water Supply System (WSS) model is implemented to evaluate the effectiveness of these two adaptation strategies in a context of overexploited groundwater under the RCP 4.5 and the RCP 8.5 IPCC scenarios. The presented a case study that is located in the Puglia, a semi-arid region of South Italy characterized by a conspicuous water demand for irrigation. We observed that, although no significant long-term trend affects the proposed precipitation scenarios, the expected temperature increase highly impacts the WSS resources due to the associated increase of water demand for irrigation purposes. Under the RCP 4.5 the MAR scenario results are more effective than the IMC during long term wet periods (typically 5 years) and successfully compensates the impact on the groundwater resources. Differently, under RCP 8.5, due to more persistent dry periods, both adaptation scenarios fail and groundwater resource become exposed to massive sea water intrusion during the second half of the century. We conclude that the MAR scenario is a suitable adaptation strategy to face the expected future changes in climate, although mitigation actions to reduce green-house gases are strongly required.

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“…When spring flow measurements are not possible to carry out and there is not an appropriate knowledge about long time discharge series, spring discharge estimation is recommended. Many approaches have been proposed to analyze the relations between the rainfall time series over the recharge area and the spring outflow [5]. These models, for example, are based on continuous and discrete wavelet analysis [6,7], cross-correlation analysis [8,9,10], or machine learning models [11].…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, the model was applied to the Capodacqua di Spigno Spring, calibrating it on the available flow data, related to the years 1973-1977, as part of a scientific technical collaboration between the water agency Acqualatina S.p.A. and Sapienza University of Rome. The proposed model is compared to an existing and consolidated forecasting method that uses the SPI index for the estimation of the minimum annual spring discharge, a method already widely used in Italy and in many other countries [5,8]. The SPI analysis is often used internationally to study and characterize the hydrological drought [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Karst Spring Discharge Evaluation Using a Rainfall-Input Model: The Case Study of Capodacqua di Spigno Spring (Central Italy)

Sappa¹,

Fm²,

Iacurto³

et al. 2019

Preprint

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The increasing occurrence of widespread drought phenomena is a global environmental emergency, especially for the effects of ongoing climate change on groundwater availability. Dry years and extreme temperatures are common drivers of current climate impacts all over the world, including, for example, freshwater supply for drinking and agriculture purposes, ecosystems, forestry, health, etc.. In this frame, to ensure temporal water availability in water-stressed areas, a sustainable groundwater management is an increasing challenge. Most of groundwater in the South-East Latium Region, Central Italy, as in the whole Apennine Mountains chain, is stored in karst aquifers. In this area important water resources are present, but even here in the last decades they are affected by groundwater depletion as a consequence of occurring drought events, the upward trend in the globally average temperature and the increasing of anthropogenic activities. Due to the lack of flow rates data of springs in many areas of Italy, the spring response modeling could be a useful tool for supporting a proper water resource management. Several research studies proposed methods based on relationships between spring discharges and rainfall data. The goal of this paper is to propose a model, based on rainfall-discharges cross correlations, in order to assess the spring flow rate patterns of Capodacqua di Spigno Spring, which is the main one in the study area. The results obtained using the developed model has been compared to an existing method that uses the SPI index for the estimation of the minimum annual spring discharge.

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Generating synthetic time series of springs discharge in relation to standardized precipitation indices. Case study in Central Italy

Cited by 26 publications

References 34 publications

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Climate Change Adaptation in a Mediterranean Semi-Arid Catchment: Testing Managed Aquifer Recharge and Increased Surface Reservoir Capacity

Karst Spring Discharge Evaluation Using a Rainfall-Input Model: The Case Study of Capodacqua di Spigno Spring (Central Italy)

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