Analysis of historical changes in rainfall in the Indian Himalayas

Basistha, Ashoke; Arya, D. S.; Goel, N. K.

doi:10.1002/joc.1706

Cited by 232 publications

(115 citation statements)

References 99 publications

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“…For the European regions the seasons are spring (SP, March to May), summer (SU, June to August), autumn (AU, September to November) and winter (WI, December to February). As suggested by Basistha et al (2009) for the South Asian regions they are summer (SU, March to May), monsoon (MO, June to September), post-monsoon (PM, October to November) and winter (WI, December to February). The projections were normalized with respect to the reference period 1971-2000.…”

Section: Regional Climate Projectionsmentioning

confidence: 87%

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Dobler

Sharma

et al. 2011

Adv. Sci. Res.

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Abstract. Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB).This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe.Different climate change scenarios were downscaled for the time period 1960-2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks.

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Section: Regional Climate Projectionsmentioning

confidence: 87%

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Dobler

Sharma

et al. 2011

Adv. Sci. Res.

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“…Mann-Kendall test is robust to the influence of extremes, performs well with skewed variables and can handle missing values [7]. This method has been widely used in rainfall trend detection studies [8,9,10] and has produced reliable results. The purpose of this test is to determine whether the value of a random variable is generally increasing or decreasing in statistical terms over a certain period of time.…”

Section: Methodsmentioning

confidence: 99%

Changes in Rainfall in Sri Lanka during 1966 – 2015

Karunathilaka

Dabare²,

Nandalal

2017

Engineer

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Abstract:The aim of this study was to analyse the annual and seasonal rainfall trends in Sri Lanka during the 50 year period from 1966 to 2015 using rainfall data collected at 32 rain gauging stations. The seasons considered were the two principal monsoon seasons and the two inter-monsoon seasons. Non-parametric Mann-Kendall trend test and Sen's Slope estimator method were used to examine the rainfall trends and determine their magnitudes. According to the analysis which was based on annual rainfall data, 21 of the gauging stations were showing increasing trends. The balance 11 stations were showing decreasing trends. Four of the stations, namely those at Anuradhapura, Batticaloa, Mapakadawewa and Pottuvil had significantly increasing trends. The three stations at Chilaw, Dandeniya Tank and Iranamadu Tank showed significantly decreasing trends. In general, the eastern region of the country has shown over the last half century an increasing rainfall trend and the western, northern and south western regions and the central hills of the country have shown a decreasing rainfall trend during the same period. The seasonal rainfall indicated increasing trends during the First-Inter Monsoon, Second-Inter Monsoon and Northwest Monsoon seasons at a majority of the stations. In contrast, during the Southwest Monsoon season, most of the gauging stations have shown downward rainfall trends.

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“…O MK foi aplicado em cada uma das 12 séries mensais assumindo-se a inexistência de autocorrelações significativas uma vez que a temperatura mensal de um ano não deve ter correlação com a do ano seguinte ou do ano anterior, e assim não é necessário usar o teste modificado de Mann-Kendall (Hamed e Rao, 1998;Basistha et al, 2009;Shahid, 2009). Para os meses em que o MK indicou tendências significativas, realizou-se uma regressão linear simples considerando como variável dependente a Tmr e como variável independente os anos (1 a 40).…”

Section: Methodsunclassified

Temperatura mínima de relva em Santa Maria, RS: climatologia, variabilidade interanual e tendência histórica

Streck¹,

Gabriel²,

Heldwein³

et al. 2011

Bragantia

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ResumoO objetivo do trabalho foi calcular as estatísticas descritivas para caracterizar a climatologia da temperatura mínima de relva, a mínima do ar a 5 cm do solo coberto com grama (Tmr), em Santa Maria (RS), associar a variabilidade interanual desse parâmetro com o fenômeno ENOS e verificar se há tendência de alteração na série histórica deste elemento meteorológico. Foram usados dados diários de temperatura mínima de relva, medidos no período de 1. o de maio de 1970 a 31 de dezembro de 2009, registrados na Estação Climatológica Principal de Santa Maria (latitude: 29º43'S, longitude: 53º43'W e altitude: 95 m). Classificaram-se os anos em anos de El Niño (EN), La Niña (LN) e Neutros (N), considerando-se o período de 1. o /7 a 30/6 do ano seguinte. A média mensal de temperatura mínima de relva variou de 6,9 ºC em julho a 17,3 ºC em janeiro, com mínima absoluta de -8,4 ºC em junho e máxima absoluta de 28,6 ºC em outubro. Há sinal do ENOS sobre a temperatura mínima de relva nos meses de outono, inverno e primavera, de modo que esta é maior em anos de EN, menor em anos de LN e intermediária em anos Neutros. Houve aumento na temperatura mínima de relva no período de 1970-2009 em abril, junho, outubro, novembro e dezembro, com início do aumento principalmente na década de 80. Nos demais meses não foram verificadas significativas tendências de ordem climática.Palavras-chave: El Niño, La Niña, variabilidade climática, mudança climática, tendência das mínimas.Minimum temperature over short grass in Santa Maria, RS: climatology, interannual variability, and historical trend AbstractThe objective of this study was to calculate the descriptive statistics to characterize the climatology of minimum air temperature at 5 cm over short grass (Tmr) in Santa Maria, RS. A further objective was to associate the interannual variability of Tmr with the ENSO phenomenon and also to verify if there is a trend in the historical series for this meteorological variable. Data used in this study were daily minimum air temperature over short grass measured at the Meteorological Station in Santa Maria, RS, Brazil (29º43'S, 53º43'W and altitude of 95 m) from

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Analysis of historical changes in rainfall in the Indian Himalayas

Cited by 232 publications

References 99 publications

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Changes in Rainfall in Sri Lanka during 1966 – 2015

Temperatura mínima de relva em Santa Maria, RS: climatologia, variabilidade interanual e tendência histórica

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