Regional evaluation of global climate models for precipitation, maximum and minimum temperature over southern-part of India

Sreelatha, Koppala; AnandRaj, Pallakury

doi:10.1080/09715010.2020.1779137

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…When selecting appropriate GCMs, care should be taken as it creates significant uncertainty leading to either overestimation or underestimation of the projected climatic variables (Knutti and Sedláček, 2013; Shivam et al ., 2019). In India, numerous studies have been carried out to select appropriate GCMs for climatic parameters such as maximum temperature, minimum temperature, sea surface temperature, precipitation, and spatially and temporally mean surface temperature (Perkins et al ., 2007; Maxino et al ., 2008; Sreelatha and AnandRaj, 2019, 2020).…”

Section: Methodsmentioning

confidence: 99%

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Kumar

AnandRaj

Sreelatha

et al. 2021

Intl Journal of Climatology

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India is one of the most drought‐ravaged countries in the world and faces at least one drought in one region or another in every 3 years. There is no single reliable approach in characterizing future droughts. To understand future drought risk, potential changes of drought properties and characteristics are analysed in this study. Using Fuzzy c‐means clustering approach, homogeneous drought regions are identified in the Godavari river basin and therefore, optimum number of clusters were assigned as four. The 12‐month standardized precipitation index (SPI) using precipitation data from India Meteorological Department (IMD) and Global Climate Model (GCM)—MIROC‐ESM‐CHEM is calculated for the homogeneous regions of the Godavari basin. The best fit copula for observed and simulated severity and duration are: Region 1—Clayton, Regions 2 and 3—Gumbel, Region 4—Frank copula. Severity‐duration‐frequency (SDF) and severity‐area‐frequency (SAF) curves were developed and analysed using the best fit copulas. The research findings conclude that moderate and severe droughts are frequently increasing for future periods (2006–2099) compared to the historic period (1962–2005). Droughts with high severity and high mean interarrival time are observed as expected in the future. For the Godavari basin, the SDF curves were concave upwards indicating an increase in severity with an increase in duration. The rate of increase of severity is small for shorter durations compared to that of longer‐duration drought. Thus, more prolonged drought events in the 21st century are likely to occur. The SAF curves with steeper slopes and high variability in topographical and hydrological characteristics have been observed over the Godavari basin. From these curves, for a specified percentage of area and return period, the drought severity can be calculated and the information can be used for crop management and agricultural water demands. Overall, the findings of this research offer a view of likely scenarios of drought in the Godavari basin.

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Section: Methodsmentioning

confidence: 99%

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Kumar

AnandRaj

Sreelatha

et al. 2021

Intl Journal of Climatology

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“…Numerous models and simulations known as global climate models (GCMs) have been proposed to project the future climate as a cautious note for the aggressive development. These models are widely used in forecasting dry spell, precipitation and temperature [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling

et al. 2022

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Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation.

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Process informed selection of climate models for climate change impact assessment in the Western Coast of India

George

Athira

2022

Theor Appl Climatol

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Regional evaluation of global climate models for precipitation, maximum and minimum temperature over southern-part of India

Cited by 6 publications

References 31 publications

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Regional analysis of drought severity‐duration‐frequency and severity‐area‐frequency curves in the Godavari River Basin, India

Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling

Process informed selection of climate models for climate change impact assessment in the Western Coast of India

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