Prototype of a drought monitoring and forecasting system for the Tuscany region

Magno, Ramona; Angeli, Luca; Chiesi, Marta; Pasqui, Massimiliano

doi:10.5194/asr-11-7-2014

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…The large-scale teleconnection patterns have been shown to be an important driving factor of drought occurrence in different regions. As such, climate indices may be used as potential predictors for precipitation (or meteorological drought) prediction in regions with strong teleconnections (Bonaccorso et al, 2015;Magno et al, 2014;Morid et al, 2007;Özger et al, 2012;Santos et al, 2014). The large-scale teleconnection patterns have also been shown to affect hydrological conditions (through the effect on precipitation, temperature, or other meteorological variables) (Chiew & Mcmahon, 2002;Wanders & Wada, 2015) and have been used for predicting hydrological drought and agricultural drought (or streamflow and soil moisture) in different regions (Kingston et al, 2013;Kuss & Gurdak, 2014;Nicolai-Shaw et al, 2016;Ryu et al, 2010;Trambauer et al, 2013Trambauer et al, , 2015.…”

Section: Typical Predictorsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

361

232

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Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

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Section: Typical Predictorsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

361

232

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“…Accordingly, global circulation atmospheric and oceanic indices can be used as predictors to build up seasonal forecasting statistical models (Kim et al 2007, Kim & Kim 2010, Magno et al 2014.…”

Section: Empirical Forecasting Model (E)mentioning

confidence: 99%

Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin

Ferrise¹,

Toscano²,

Pasqui³

et al. 2015

Clim. Res.

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Uncertainty in weather conditions for the forthcoming growing season influences farmers' decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of < 20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions.

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“…The forecast's window (25°-65°N; 20°W-45°E) has a spatial resolution of 0.5°. This seasonal forecast empirical system adopts a physically based statistical approach which uses a multivariate regression (MR) model to estimate future anomalies [16].…”

Section: Forecasting Systemmentioning

confidence: 99%

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

et al. 2018

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Abstract:A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

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Prototype of a drought monitoring and forecasting system for the Tuscany region

Cited by 10 publications

References 14 publications

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

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