A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales

Thanh, Nguyen Tien

doi:10.3390/cli6040079

Cited by 4 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in some regions, it may be impossible to gain detailed meteorological information, or some data may not be available as multi-annual records. Therefore, due to widespread access to data sources, SPI is commonly used in drought monitoring in different types of climate [24][25][26][27]. The primary purpose of this paper, carried out as part of the research strategy regarding the ongoing changes in the macroclimate of the Kujawsko-Pomorskie region, was to assess the need for irrigation in sugar beet crops based on meteorological conditions in the reference period of 1981-2010.…”

Section: Of 14mentioning

confidence: 99%

Assessment of Irrigation Needs in Sugar Beet (Beta vulgaris L.) in Temperate Climate of Kujawsko-Pomorskie Region (Poland)

Kuśmierek‐Tomaszewska

Żarski

Dudek

2019

Agronomy

View full text Add to dashboard Cite

The primary purpose of this work was to assess the need for irrigation in sugar beet cultivated in the temperate climate of the Kujawsko-Pomorskie region of Poland based on meteorological data from the period 1981–2010. The work was also aimed at determining the tendency of changes in the frequency and intensity of droughts during the period of high water needs for sugar beets (spanning July–August) and confirming the hypothesis that agricultural drought may be identified based on the indicator of meteorological drought—Standardized Precipitation Index (SPI). The occurrence of meteorological droughts amounted to 26.7–40.0%, depending on location. No significant trend of increasing dryness was found; however, quite the opposite, an upward tendency was identified, which indicates an improvement of precipitation conditions over time. It was found that sugar beet production in a temperate climate is carried out in the conditions of precipitation deficits, which amount to an average of 32–49 mm and a maximum of 112–173 mm in July–August, but the deficits showed neither significant nor targeted changes with time. A strong, significant relationship between meteorological (SPI) and agricultural (Pdef) drought indicators allows for a determination of sugar beet irrigation needs solely based on information on normalized precipitation values (SPI).

show abstract

Section: Of 14mentioning

confidence: 99%

Assessment of Irrigation Needs in Sugar Beet (Beta vulgaris L.) in Temperate Climate of Kujawsko-Pomorskie Region (Poland)

Kuśmierek‐Tomaszewska

Żarski

Dudek

2019

Agronomy

View full text Add to dashboard Cite

show abstract

“…Furthermore, ref. [16] used SPI to study meteorological droughts for different climate models at different time scales. SPI for drought characterization using multi-satellite precipitation data from the Tropical Precipitation Measurement Mission were computed by [17].…”

Section: Introductionmentioning

confidence: 99%

Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models

et al. 2022

View full text Add to dashboard Cite

In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.

show abstract

“…The main advantages of a statistical downscaling approach are low computational requirements, whereas dynamic downscaling is appreciated by researchers for its superiority of embracing more systematic characteristics in relation to topography and climatic dynamical processes. In recent years, several studies used RCMs in order to assess climate change effect on hydrology [31][32][33][34][35]. The effectiveness of these models is mostly dependent on their inputs, especially the past climate data [36].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

Stefanidis

Dafis

Stathis

2020

Water

View full text Add to dashboard Cite

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus (Greece). To this end, observed precipitation data from ground-based rain gauge stations were compared with RCMs grid point’s simulations for the baseline period 1974–2000. Statistical indexes such as root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient, and standard deviation (SD) were used in order to evaluate the model’s performance. The results demonstrated that RCMs fail to represent the temporal variability of precipitation time series with exception of REMO. Although, concerning the model’s prediction accuracy, it was found that better performance was achieved by the RegCM3 model in the study area. In addition, regarding a future projection (2074–2100), it was highlighted that precipitation will significantly decrease by the end of the 21st century, especially in spring (−30%). Therefore, adaption of mountainous catchment management to climate change is crucial to avoid water scarcity.

show abstract

A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales

Cited by 4 publications

References 48 publications

Assessment of Irrigation Needs in Sugar Beet (Beta vulgaris L.) in Temperate Climate of Kujawsko-Pomorskie Region (Poland)

Assessment of Irrigation Needs in Sugar Beet (Beta vulgaris L.) in Temperate Climate of Kujawsko-Pomorskie Region (Poland)

Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models

Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

Contact Info

Product

Resources

About