Temperature and precipitation trend over 139 major Indian cities: An assessment over a century

Sharma²,

Journal of Probability and Statistics

et al. 2021

Increasing temperature and declining and erratic rainfall is one of the greatest global challenges. This study presents the trend analysis of temperature and rainfall in five divisional headquarters of Rajasthan, namely, Bikaner, Jaipur, Jodhpur, Kota, and Udaipur. The historic data of minimum and maximum temperature and rainfall for a period of 49 years from 1971 to 2019 were collected from Climate Research and Services, India Meteorological Department, Pune. Detection of trends and change in magnitude was done using the Mann–Kendall (MK) test and Sen’s slope, respectively. The results of the study indicated a significant increase in both minimum and maximum temperature over time for all the five stations. However, rainfall showed a nonsignificant increasing trend for Kota and Udaipur district, whereas Bikaner, Jaipur, and Jodhpur detected a negative trend.

Section: Introductionmentioning

confidence: 99%

Trend Analysis of Temperature and Rainfall of Rajasthan, India

Sharma²,

Journal of Probability and Statistics

et al. 2021

“…The trends of hydrometeorological and climatological data such as streamflow, temperature, precipitation, and reference evapotranspiration (ET0), etc. are generally researched in different studies (Malik et al 2020;Amirataee et al 2016;Mohammad and Goswami 2019).…”

Section: Methodsmentioning

confidence: 99%

The application of piecewise ITA method in Oxford, 1870–2019

Şişman

Kizilöz

2021

Theor Appl Climatol

In this study, the trends and stabilities of temperature and precipitation hydro-meteorology time series recorded since 1870 in Oxford city of England were analyzed in detail. The Innovative Triangular Trend Analysis (ITTA) method has been inspired to identify and analyze the trends and stabilities of the selected time series. To compare the results obtained by the abovementioned method, the Classical Mann Kendall (MK) method has been applied to each series determined for ITTA design. Thanks to the innovative design of ITTA which is preferred by the Classic MK and Sen slope methods, the trends of time series could be analyzed in detail. In this study, the first draft structure has been improved with the help of ±5-±10 % percentage change levels which were added to the ITTA method, and thus more objective evaluations about the trend magnitudes in time series is possible. For the same draft, the monotonic trend slopes which were found by the classical MK were also calculated through the Sen slope method. The data trends could explain in more detail with the help of the draft used in this study, compared to the studies in the literature. Climate change, which has been the most important factor in trend formation in recent years, has been taken into consideration while determining the design series. The thirty-year period up to 2019, a year in which the climate change was felt much more, constitutes the most important reference years for the analysis beginning from 1990, a year in which the climate change effects started to emerge. When the data trends of one hundred fifty years are examined for the different sub-time series, it is seen that the temperature increase in during1990-2019 period is much higher than the past hundred and twenty years, according to the analysis results. The highest average precipitation occurred in the 1990-2019 and 1900-1929 periods, and their amounts and patterns are nearly similar.

“…Masih et al () incorporated a disaster loss database, literature review and a gridded drought indicator dataset to compile a descriptive and geospatial catalog of past drought events in Africa. Climate zones have been related to precipitation and temperature trends (Mohammad & Goswami, ), drought indices sensitivity (Vicente‐Serrano, Van der Schrier, Beguería, Azorin‐Molina, & Lopez‐Moreno, ), and river topography (S.‐A. Chen, Michaelides, Grieve, & Singer, ).…”

Section: Dataset Applicationsmentioning

confidence: 99%

“…Masih et al (2014) incorporated a disaster loss database, literature review and a gridded drought indicator dataset to compile a descriptive and geospatial catalog of past drought events in Africa. Climate zones have been related to precipitation and temperature trends (Mohammad & Goswami, 2019)…”

Section: Droughts and Floodsmentioning

confidence: 99%

“…Research on local scale may also benefit from global geospatial datasets, especially when located in data-poor regions. For instance, planetary-scale datasets have been used to analyze long-term meteorological changes in Pakistan (Ahmed, Shahid, Wang, Nawaz, & Khan, 2019), India (Mohammad & Goswami, 2019), Inner Mongolia (Wang et al, 2019), and Nigeria (Shiru, Shahid, Chung, & Alias, 2019). Similarly, global and long-term precipitation datasets have been utilized to derive time series of drought indices for locations such as Arkansas (Craig, Feng, & Gilbertz, 2019), Sweden (Campana et al, 2018), and Central Asia (Guo et al, 2018).…”

Section: Data-poor Areasmentioning

confidence: 99%

See 1 more Smart Citation

A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies

et al. 2020

The availability of planetary-scale geospatial datasets that can support the study of water-related disasters in the Anthropocene is rapidly growing. We review 124 global and free datasets allowing spatial (and temporal) analyses of floods, droughts and their interactions with human societies. Our collection of datasets is available in a descriptive list for download at https://zenodo.org/ record/3368882. The purpose of providing an overview of datasets across a wide range of hydrological and socioeconomic variables is to highlight research opportunities across scientific disciplines for the study of the watersociety interplay. Our collection of datasets confirms that the availability of geospatial data capturing hydrological hazards and exposure is far more mature than those capturing vulnerability aspects. We do not only highlight the unprecedented opportunities associated with these global datasets for the study of water-related disasters in the Anthropocene, but also discuss the challenges associated with their exploitation. These challenges include: (a) time varying datasets advised not to be used in time series analyses; (b) fine spatial resolution datasets advised not to be used in local scale studies; (c), datasets built by a wide variety of data sources prohibiting systematic uncertainty assessments; and (d) datasets built by covariate variables preventing interaction studies.