Impact of land-use changes on the genesis and evolution of extreme rainfall event: a case study over Uttarakhand, India

Sahoo, Sanjeeb K.; Ajilesh, P.P.; Gouda, K. C.; Himesh, S.

doi:10.1007/s00704-020-03129-z

Cited by 17 publications

(6 citation statements)

References 55 publications

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“…Equally important is the suggestion from the various case‐studies (e.g. Badarinath et al ., 2012; Unnikrishnan et al ., 2016; Sahoo et al ., 2020) that the simulations with concurrent ISRO LULC datasets exhibit better fidelity related to those with the MODIS and/or USGS LULC. Given that these datasets are available from 2005, they provide a potentially ideal dataset to carry out sensitivity experiments in order to evaluate the implication of the local LULC changes for any changes in extremes of the heavy rainfall events over south India.…”

Section: Datasets and Methodologymentioning

confidence: 94%

Increasing heavy rainfall events in south India due to changing land use and land cover

Boyaj

Dasari

Hoteit

et al. 2020

Quart J Royal Meteoro Soc

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Through an analysis of land use and land cover (LULC) data for the years 2005 and 2017 from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, we find considerable changes in the LULC in three major states of south India, namely, Tamil Nadu, Telangana and Kerala. This change is mainly due to increasing urbanization, in addition to the change of prevalent mixed forest into deciduous needle/leaf forest in Kerala. Motivated by this finding, we study the impact of these LULC changes over a decade on the extremes of twelve heavy rainfall events in these states through several sensitivity experiments with a convection‐permitting Weather Research and Forecasting model, by changing the LULC boundary conditions. We particularly focus on three representative heavy rainfall events, specifically, over (a) Chennai (1 December 2015), (b) Telangana (24 September 2016), and (c) Kerala (15 August 2018). The simulated rainfall patterns of the three heavy rainfall events are found to be relatively better with the use of the 2017 LULC boundary conditions. The improvement is statistically significant in the case of the Chennai and Kerala events. On analysis of these simulations, and output from additional simulations we have conducted for nine other heavy rainfall events, we suggest that the recent LULC changes result in higher surface temperatures and sensible heat fluxes, and a deeper and moist boundary layer. This causes a relatively higher convective available potential energy and, consequently, heavier rainfall. We find the LULC changes in the three states, mainly dominated by the increasing urbanization in Telangana and Tamil Nadu, enhance the rainfall during the heavy rainfall events by 20–25%. This is the first extensive investigation of multiple and multi‐regional cases over the Indian region.

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Section: Datasets and Methodologymentioning

confidence: 94%

Increasing heavy rainfall events in south India due to changing land use and land cover

Boyaj

Dasari

Hoteit

et al. 2020

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

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“…The LULC data generated by the National Remote Sensing Centre (NRSC), ISRO, was derived from the Indian satellite IRS-P6, Advanced Wide Field Sensor (AWiFS) and is compatible with WRF pre-processing system. Many studies have demonstrated the improvement in the model outcomes, using this dataset over the Indian region (Kar et al, 2014;Sahoo et al, 2020a;Gupta et al, 2021), and also over the Bangalore region (Sahoo et al, 2020b). Navale and Singh (2020), Golzio et al (2021) have demonstrated the significant impact of variation in topography on the fine scale pattern of rainfall generated by the WRF model over complex topography.…”

Section: Experiments Design Using the Wrf Modelmentioning

confidence: 82%

A Flood Forecasting Framework Coupling a High Resolution WRF Ensemble With an Urban Hydrologic Model

et al. 2022

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Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (<4 km at 15-min intervals), has proved critical in reproducing the urban flood event. The flood forecasts created using the WRF ensemble indicate that flooding and water levels are comparable to the observed whereas the GFS underestimates these to a large extent. Thus, the coupled WRF–PCSWMM modelling framework is found effective in forecasting flood events over an Indian city.

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“…Almora, Dehradun, Pauri Garhwal, and Nainital show a negative trend in annual rainfall. The decrease in the rainfall in the lower Himalayas can be attributed to the increase in anthropogenic activities such as industrialization, intense agriculture, and deforestation, leading to land use and land cover change caused by the high demand for land resulting from the increase in the population [72][73][74][75][76].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA

et al. 2022

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The climate change scenarios, especially global warming, have raised significant concerns, and the Himalayan regions such as Uttarakhand are highly vulnerable to such climatic shifts. Considering 10 Coordinated Regional Climate Downscaling Experiments in South Asia (CORDEX-SA), experiments with 3 regional climate models (RCMs), driven by 13 global climate models, historical estimates and future projections are analyzed from the mid-century (MC) i.e., from 2021–2050 to the end of the century (EC) i.e., from 2070–2099 to characterize annual and seasonal variations in precipitation and temperature. The analysis shows a decrease in the annual average precipitation by 5.92% at MC and an increase of 5.97% at EC for the Representative Climate Pathway (RCP) 4.5, while precipitation may likely increase from 2.83% to 15.89% towards MC and EC in the RCP 8.5. The maximum temperature may likely increase from 0.42 °C to 3.07 °C from MC to EC in the RCP 4.5 and from 0.83 °C to 5.49 °C in the RCP 8.5. In addition, the minimum temperature may increase from 0.80 °C to 3.25 °C from MC to EC in the RCP 4.5 and from 0.30 °C to 5.86 °C from MC to EC in the RCP 8.5. Notably, a decrease in the pre-monsoon precipitation at EC and a higher increase in the maximum temperature during the monsoon season are observed. An increase in the maximum temperature along with precipitation may lead to an increase in the frequency of the monsoon season’s extreme rainfall events.

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Impact of land-use changes on the genesis and evolution of extreme rainfall event: a case study over Uttarakhand, India

Cited by 17 publications

References 55 publications

Increasing heavy rainfall events in south India due to changing land use and land cover

Increasing heavy rainfall events in south India due to changing land use and land cover

A Flood Forecasting Framework Coupling a High Resolution WRF Ensemble With an Urban Hydrologic Model

Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA

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