Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Vijay, Anjali; Sivan, Sruthi D.; Mudbhatkal, Amogh; Mahesha, A.

doi:10.1061/(asce)he.1943-5584.0002070

Cited by 15 publications

(5 citation statements)

References 58 publications

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“…On the other hand, RCP 8.5 portrayed an increasing trend of mean temperature in both the catchments ranging between 4.69 to 14.11% for Aghanashini and 4.69 to 14.45% for the Gurupura river catchment. The increase in mean temperature for RCP 8.5 could be attributed to warming patterns globally and have been reported in several watersheds of the Western Ghats of India (Mudbhatkal et al, 2017;Mudbhatkal & Amai, 2018b;Sharannya et al, 2018;Sinha & Eldho, 2018;Vijay et al, 2021).…”

Section: Climate Scenariomentioning

confidence: 66%

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Sharannya

Venkatesh

Mudbhatkal

et al. 2021

Environ Monit Assess

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CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006-2040), mid-future (2041-2070), and farfuture (2071-2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the presentday LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment.

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Section: Climate Scenariomentioning

confidence: 66%

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Sharannya

Venkatesh

Mudbhatkal

et al. 2021

Environ Monit Assess

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“…The maximum and minimum annual mean temperature over the region is 32.70°C, 21.20°C approximately. The highest annual rainfall in the Central part (Karnataka) of the WG than other parts and decreases in the intensity towards the north of the Ghat (Shetty et al, 2022; Vijay et al, 2021) are intently captured in all the ensemble models. The apprehension of the heterogeneity in the distribution of temperature and rainfall by the RFR, XGBR and ETR ensemble of CMIP6 directs the use of GCMs even in the river basin scale.…”

Section: Resultsmentioning

confidence: 98%

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Shetty

Umesh

Shetty

2023

Intl Journal of Climatology

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The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.

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“…The rest of the seasons and annual rainfall did not show any significant trend over the years. A study by Vijay et al [62] revealed a declining trend of annual and seasonal rainfall for the entire state of Kerala over 118 years ending in 2018. Adarsh and Janga Reddy [63] indicated that the annual rainfall in the Kerala meteorological sub-division shows a significant decreasing trend, along with a similar decreasing trend in Jun rainfall as well.…”

Section: Rainfall Trend and Distribution Over Keralamentioning

confidence: 99%

Climate Dynamics over Kerala, India: Insight from a Century-long Temperature and Rainfall Data Analysis

Jamaludheen A.,

Kumar,

Singh

et al. 2024

Int. J.Environ. Clim. Change.

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Historical climate data analysis is of great significance in climate change adaptation and mitigation planning at global as well as regional levels. This article attempted to study the long-term trends of temperature and rainfall across the districts in Kerala, India. CRU monthly time series data of rainfall and temperature data spanning from 1901-2022 were used for the analysis. Mann-Kendall test and Sen’s Slop estimator were applied to detect the presence and magnitude of the trend, and Pettitt’s homogeneity test was used to find the climate change point in temperature time series data. The analysis found a significant and positive temperature trend across all districts in Kerala, with temperature increases ranging from 0.0086°C/year to 0.0102°C/year. In terms of rainfall trends, June and January experienced a significant decrease, while July and September saw a significant increase over the years. The year 1976 was identified as the point of climate change. It was observed that there was an increase in the southwest and Northeast monsoons in the post-period, with higher variability in the latter. Winter rainfall notably decreased during the post-period. The variability in climate parameters identified in this study could impact crop cycles and agricultural productivity, requiring further investigation at a micro-level for effective adaptation and mitigation strategies for the state.

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Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Cited by 15 publications

References 58 publications

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Climate Dynamics over Kerala, India: Insight from a Century-long Temperature and Rainfall Data Analysis

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