Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India

Rao, G. Venkata; Reddy, K. Venkata; Srinivasan, Raghavan; Sridhar, Venkataramana; Umamahesh, N. V.; Pratap, Deva

doi:10.1016/j.wace.2020.100265

Cited by 44 publications

(24 citation statements)

References 37 publications

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“…This inconsistency still exists when considering the IMD dataset (Supplementary Table 5). Note that there have been significant increasing trends in frequency and intensity of extreme heavy rainfall over central and southern India since 1950 where regional climates are controlled by the Asian monsoon system (Krishnan et al, 2015;Roxy et al, 2017;Venkata Rao et al, 2020) (i.e., the core monsoon zones). Interestingly, despite a poor station coverage over the Maritime Continent, we find significant positive trends in four out of six datasets.…”

Section: Discussionmentioning

confidence: 99%

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

Nguyen¹,

Bador²,

Alexander³

et al. 2020

Front. Clim.

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Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.

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Section: Discussionmentioning

confidence: 99%

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

Nguyen¹,

Bador²,

Alexander³

et al. 2020

Front. Clim.

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“…The trends in datasets can either be monotonic, where a variable consistently increases or decreases through time, or a step trend, where abrupt changes in data may occur at a specific time. Various studies on trend analysis of climate parameters [11][12][13][15][16][17][18][19][21][22][23], used the two non-parametric tests known as the Mann-Kendall trend test, and Theil-Sen slope test, to detect significant trends, and to quantify the magnitude of trends, respectively.…”

Section: Trend Analysismentioning

confidence: 99%

“…A set of standard measurements of the extreme climate indices based daily precipitation, and daily (minimum and maximum) temperatures were provided by the Expert Team on Climate Change Detection and Indices (ETCCDI) [9,10]. For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales.…”

Section: Introductionmentioning

confidence: 99%

“…For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales. While, majority of the studies focused on annual scales, and lesser on monthly scales, the former is insufficient in providing detailed information in a watershed, such as detecting shifting of precipitation patterns.…”

Section: Introductionmentioning

confidence: 99%

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Detailed Trend Analysis of Extreme Climate Indices in the Upper Geum River Basin

Felix

Kim

Choi³

et al. 2021

Water

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To investigate the recent effects of climate change in the upper Geum River basin in South Korea, a detailed trend analysis of 17 extreme climate indices based on 33 years (1988–2020) of daily precipitation, and daily (minimum and maximum) temperature data has been analyzed in this study. Out of the 17 extreme climate indices, nine (eight) indices were based on temperature (precipitation) data. Trend analysis based on detailed temporal scales (annual, seasonal, monthly) were performed through the Mann–Kendall trend test and the Theil–Sen slope method. Furthermore, the Mann–Whitney–Pettit test was also applied in this study, to detect abrupt changes in the extreme climate indices. Based on the results of this study, the climate conditions at the upper Geum River basin for the past three decades can be summarized as follows: general increase in temperature intensity, decrease in cold duration, increased heat duration, increased precipitation intensity, and increased consecutive wet and dry durations. Furthermore, a prolonged summer season (shorter spring, and autumn periods) and precipitation shifts, were detected based on trend analysis results of seasonal, and monthly time scales. The results presented in this study can provide supplementary data for improving watershed management strategies in the upper Geum River basin.

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“…On the other hand, as a result of global warming the capacity of the atmosphere to hold water has been increased which lead to an increase in the global precipitation [4][5][6][7][8][9]. In this context, a common way to have a knowledge of future extreme precipitation events is to investigate the trend in the longduration time series of historical records [10][11][12][13][14][15][16][17][18][19][20][21][22]. On the other hand, using the outputs of climate models such as global circulation models (GCMs) can provide a more reliable and more realistic picture of the future.…”

Section: Introductionmentioning

confidence: 99%

Influence of Climate Change on the Flood Disasters in Bursa, Turkey

Balov¹

2021

International Journal of Engineering Technologies IJET

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The recent increase in the frequency and intensity of the floods resulted from huge precipitation events in Bursa Province, Turkey, is the main motivation of this study. For this purpose, the daily precipitation values from the historical data recorded between 1971 and 2000 together with the projected data of the NorESM1-M general circulation model under RCP4.5 and RCP8.5 scenarios between 2031 and 2100 were used to calculate eight extreme precipitation events. The Spearman's rho and the Mann-Kendall trend tests were used to detect the trend in the indices. The results of the trend tests showed that any change is not expected in the total amount of the yearly precipitation amount, whereas the occurrence of the intense and short duration precipitation events is expected to be more probable by the end of the current century, compared to the last 30 years of the previous century. The positive significant trends were detected in some indices. However, the magnitude of the change in the climatic parameters and the increase in the number of extreme precipitation events cannot solely explain the increase in the number of destroying flood events in the province. Other parameters such as the dramatic increase in urbanization as a result of rapid population growth during the past years (since 1960) may have a more prominent effect on the increase of the flood risk in the province which is in need of comprehensive modeling and investigation.

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Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India

Cited by 44 publications

References 37 publications

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

Detailed Trend Analysis of Extreme Climate Indices in the Upper Geum River Basin

Influence of Climate Change on the Flood Disasters in Bursa, Turkey

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