| Title | Predicting Shallow Water Table Depth at Regional Scale: Optimizing Monitoring Network in Space and Time |
| Abstract | Shallow water table levels can be predicted using several approaches, either based on climatic records, on field evidences based on soil morphology, or on the outputs of physically based models. In this study, data from a monitoring network in a relevant agricultural area of Northern Italy (ca. 12,000 Km2) were used to develop a data driven model for predicting water table depth in space and time from meteorological data and long-term water table characteristics and to optimize sampling density in space and time. Evolutionary Polynomial Regressions (EPR) were used to calibrate a predictive tool based on climatic data and on the records from 48 selected sites (N = 5,611). The model was validated against the water table depths observed in 15 independent sites (N = 1,739), resulting in a mean absolute error of 30.8 cm (R 2 = 0.61). The model was applied to the whole study area, using the geostatistical estimates of the average water table depth as input, to provide spatio-temporal maps of the water table depth. The impact of the degradation of data input in the temporal and spatial domain was then assessed following two approaches. In the first case, three different EPR models were calibrated based on 25 %, 50 % and 75 % of the available data, and the error indexes compared. In the second case, an increasing number of monitoring sites were removed from the initial data set, and the associated increased kriging standard deviation was assessed. Reducing the average sampling frequency from 1.5 per month to 1 every 40 days did not impact significantly on the prediction capability of the proposed model. Reducing the sampling frequency to 1 every 4 months resulted in a loss of accuracy <3 %, while removing more than half locations from the network, resulted in a global loss of information <15 %. |
| Source | Water resources management (Dordr., Online) |
| Keywords | Water table . Evolutionary Polynomial Regressions . Simulated Annealing . Temporal degradation . Bootstrapping |
| Journal | Water resources management (Dordr., Online) |
| Editor | Kluwer, Dordrecht, Paesi Bassi |
| Year | 2013 |
| Type | Articolo in rivista |
| DOI | 10.1007/s11269-013-0461-6 |
| Authors | Emanuele Barca, Maria Costanza Calzolari, Giuseppe Passarella, Fabrizio Ungaro |
| Text | 270470 2013 10.1007/s11269 013 0461 6 Scopus 2 s2.0 84890198282 ISI Web of Science WOS 000328338300011 Water table . Evolutionary Polynomial Regressions . Simulated Annealing . Temporal degradation . Bootstrapping Predicting Shallow Water Table Depth at Regional Scale Optimizing Monitoring Network in Space and Time Emanuele Barca, Maria Costanza Calzolari, Giuseppe Passarella, Fabrizio Ungaro CNR IRSA Istituto di Ricerca sulle Acque, U.O.S. di Bari, Via De Blasio 5, 70123 Bari, Italy CNR IBIMET Istituto di Biometeorologia, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy CNR IRSA Istituto di Ricerca sulle Acque, U.O.S. di Bari, Via De Blasio 5, 70123 Bari, Italy CNR IBIMET Istituto di Biometeorologia, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy Shallow water table levels can be predicted using several approaches, either based on climatic records, on field evidences based on soil morphology, or on the outputs of physically based models. In this study, data from a monitoring network in a relevant agricultural area of Northern Italy ca. 12,000 Km2 were used to develop a data driven model for predicting water table depth in space and time from meteorological data and long term water table characteristics and to optimize sampling density in space and time. Evolutionary Polynomial Regressions EPR were used to calibrate a predictive tool based on climatic data and on the records from 48 selected sites N = 5,611 . The model was validated against the water table depths observed in 15 independent sites N = 1,739 , resulting in a mean absolute error of 30.8 cm R 2 = 0.61 . The model was applied to the whole study area, using the geostatistical estimates of the average water table depth as input, to provide spatio temporal maps of the water table depth. The impact of the degradation of data input in the temporal and spatial domain was then assessed following two approaches. In the first case, three different EPR models were calibrated based on 25 %, 50 % and 75 % of the available data, and the error indexes compared. In the second case, an increasing number of monitoring sites were removed from the initial data set, and the associated increased kriging standard deviation was assessed. Reducing the average sampling frequency from 1.5 per month to 1 every 40 days did not impact significantly on the prediction capability of the proposed model. Reducing the sampling frequency to 1 every 4 months resulted in a loss of accuracy <3 %, while removing more than half locations from the network, resulted in a global loss of information <15 %. Published version http //link.springer.com/article/10.1007%2Fs11269 013 0461 6 Predicting Shallow Water Table Depth at Regional Scale Optimizing Monitoring Network in Space and Time art_10_1007_s11269_013_0461_6.pdf Articolo in rivista Kluwer 1573 1650 Water resources management Dordr., Online Water resources management Dordr., Online Water resour. manag. Dordr., Online Water resources management. Dordr., Online giuseppe.passarella PASSARELLA GIUSEPPE fabrizio.ungaro UNGARO FABRIZIO emanuele.barca BARCA EMANUELE mariacostanza.calzolari CALZOLARI MARIA COSTANZA TA.P04.005.008 Integrazione di metodologie per il monitoraggio e la modellizzazione per la gestione delle risorse idriche TA.P05.004.006 Idropedologia un ponte fra discipline, scale e dati |
