Analysis of rainfall trends over Tripura

Rainfall in India has very high temporal and spatial variability. The rainfall variability affects the livelihood and food habits of people from different regions. In this study, the rainfall trends in two stations in the north-eastern state of Tripura, namely Agartala and Kailashahar have been studied for the period 1955-2017. The state experiences an annual mean of more than 2000 mm of rainfall, out of which, about 60% occurs during the monsoon season and about 30% in pre-monsoon. An attempt has been made to analyze the trends in seasonal and annual rainfall, rainy days and heavy rainfall in the two stations, during the same period.Non-parametric Mann-Kendall test has been used to find out the significance of these trends. Both increasing and decreasing trends are observed over the two stations. Increasing trends in rainfall, rainy days and heavy rainfall are found at Agartala during pre-monsoon season and decreasing trends in all other seasons and at annual scale. At Kailashahar, rainfall amount (rainy days & heavy rainfall) is found to be increasing during pre-monsoon and monsoon seasons (pre-monsoon season). At annual scale also, rainfall and rainy days show increasing trends at Kailashahar. The parameters are showing decreasing trends during all other seasons at the station. Rainy days over Agartala show a significantly decreasing trend in monsoon, whereas no other trend is found to be significant over both the stations.

Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study has been tried to analyze the most important climatic variables, viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.

Changes in Terrestrial Evaporation across Poland over the Past Four Decades Dominated by Increases in Summer Months

Given the importance of terrestrial evaporation (ET) for the water cycle, a fundamental understanding of the water quantity involved in this process is required. As recent observations reveal a widespread ET intensification across the world, it is important to evaluate regional ET variability. The specific objectives of this study are the following: (1) to assess annual and monthly ET trends across Poland, and (2) to reveal seasons and regions with significant ET changes. This study uses the ET estimates acquired from the Global Land Evaporation Amsterdam Model (GLEAM) dataset allowing for multi-year analysis (1980–2020). The Mann–Kendall test and the Sen’s slope were applied to estimate the significance and magnitude of the trends. The results show that a rising temperature, along with small precipitation increase, led to the accelerated ET of 1.36 mm/y. This was revealed by increased transpiration and interception loss not compensated by a decrease in bare soil evaporation and sublimation. The wide-spread higher water consumption especially occurred during the summer months of June, July, and August. Comparing the two subperiods of 1980–2020, it was found that in 2007–2020, the annual ET increased by 7% compared to the reference period of 1980–2006. These results can serve as an important reference for formulating a water resources management strategy in Poland.

Sea surface temperature trend analysis by Mann-Kendall test and sen’s slope estimator: a study of the Hai Phong coastal area (Vietnam) for the period 1995-2020

Based on the Mann-Kendall test and Sen’s slope method, this study investigates the monthly, seasonal, and annual sea surface temperature (SST) trends in the coastal area of Hai Phong (West of Tonkin Gulf) based on the measurements at Hon Dau Station from 1995 to 2020. The results show a sea surface warming trend of 0.02°C/year for the period 1995-2020 (significant level α = 0.1) and of 0.093°C/year for the period 2008-2020 (significant level α = 0.05). The monthly SSTs in June and September increased by 0.027°C/year and 0.036°C/year, respectively, for the period 1995-2020, and by 0.080°C/year and 0.047°C/year, respectively, for the period 2008-2020. SST trends in winter, summer, and other months were either different for the two periods or not significant enough. This may be due to the impact of ENSO, which caused interannual SST variability in the Hai Phong coastal with two intrinsic mode functions (IMF) signals a period of ~2 (IMF3) and ~5.2 years cycle (IMF4). A combination of these signals had a maximum correlation of 0.22 with ONI (Oceanic Niño Index) delayed by 8 months. ENSO events took ~8 months to affect SST at Hai Phong coastal area for 1995-2020 and caused a variation of SST within 1.2°C.

Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

Does precipitation pattern foretell climatic shift over Punjab state ?

ABSTRACT. Statistical analysis of 82-years (1901-1982) record of precipitation from 27 rain-recording stations in Punjab state of lndia has been carried out to assess the climate shift if any in the state. The central part of the study is the trend and spectrum analysis of annual. monsoon and winter rainfall of different stations in the region. It is seen that frequency distribution of 19 rainfall series out of 81 series is normally distributed. Maikov linear type of persistence is observed in some of the rainfall series. Marin-Kendall test indicates the decreasing trend in winter rainfall of all the stations and is found to be significant in case of Amritsar, Taran Taran, Tanda, Ludhiana and Ranike. Low-pass filter reveals that trend is not linear but oscillatory consisting of periods of 10 years or more. It is seen that winter rainfall of most of the stations exhibits the decreasing trend from 1935-40 to 1965-70. It is also revealed by the low-pass filter curves that winter rainfall of all t1le sla1ions remained below average from 1960 till the end of the study period. The spectral analysis indicates a significant cycle of 4.1 to 27 years in some of the stations and Quasi-Biennial Oscillations (QBO) over many stations.

Analysis of Runoff Variation Characteristics and Influencing Factors in the Wujiang River Basin in the Past 30 Years

Changes in climate and the underlying surface are the main factors affecting runoff. Quantitative assessment of runoff characteristics, and determination of the climate and underlying surface contribution to changes in runoff are critical to water resources management and protection. Based on the runoff data from the Wulong Hydrological Station, combined with the Mann-Kendall test, Indicators of Hydrologic Alteration (IHA), Budyko hypothesis, and changes in climate and the underlying surface, this study comprehensively analyzed the runoff in the Wujiang River Basin (WRB). The results showed that: (1) The annual runoff of Wujiang River showed a downward trend, and an abrupt change occurred in 2005. (2) The overall hydrological change in WRB is 46%, reaching a moderate change. (3) The contribution rates of precipitation (P), potential evaporation (ET0), and underlying surface to runoff changes are 61.5%, 11.4%, and 26.9%, respectively. (4) After 2005, the WRB has become more arid, human activities have become more active, vegetation coverage has increased, and the built-up land has increased significantly.

Analysis of Non-Parametric Trend and Climatic Parameter Homogeneity Tests in a Data-scarce Region: A Spatio-Temporal Perspective in the Tawang River Basin, Eastern Himalayas

This study is aimed at studying long–term historical and future (1950-2099) trends for the RCP 4.5 and RCP 8.5 on approximately 30-year timescale at annual and seasonal for precipitation and at annual, seasonal, monthly, and diurnal temperature range (DTR) for temperature maximum (T_max), temperature minimum (T_min) variations using statistical trend analysis techniques– Mann–Kendall test (MK) and Sen's slope estimator (S) and the homogeneity test using Pettitt’s test. The study is carried out in three spatial points across the Tawang Chu in the district of Tawang, Arunachal Pradesh. The summer mean precipitation for RCP 4.5 (2006-2065) shows a positive trend with a rise in precipitation between 1.56 mm to 9.94 mm in all the study points. The mean annual precipitation statistics for all the points show an increase of RCP 4.5 in 2006-2052 and 2053-2099 timescale. Both RCP 4.5 and 8.5 scenarios exhibit a uniform rise in T_min and T_max during investigation. For all the points, the results likewise reveal a rising trend in mean annual T_min and T_max. Still, the inter-decadal temperature statistical analysis shows that the increase in mean annual T_min is greater than the increase in T_max, indicating a decreasing trend in DTR. It is anticipated that this study's outcomes will contribute to a better understanding of the relationship between change in climate and the regional hydrological behaviour and will be benefitting the society to develop a regional strategy for water resource management, can serve as a resource for climate impact research scope- assessments, adaptation, mitigation, and disaster management strategies for India's north-eastern region.

Forecast of weather parameters using time series data

The present study is undertaken to develop area specific weather forecasting models based on time series data for Pantnagar, Uttarakhand. The study was carried out by using time series secondary monthly weather data of 27 years (from 1981-82 to 2007-08). The trend analysis of weather parameters was done by Mann-Kendall test statistics. The methodologies adopted to forecast weather parameters were the winter’s exponential smoothing model and Seasonal Autoregressive Integrated Moving Average (SARIMA). Comparative study has been carried out by using forecast error percentage and mean square error. The study showed that knowledge of this trend is likely to be helpful in planning and production of enterprises/crops. The study of forecast models revealed that SARIMA model is the most efficient model for forecasting of monthly maximum temperature, monthly minimum temperature and monthly humidity I. The Winter’s model was found to be the most efficient model for forecasting Monthly Humidity II but no model was found to be appropriate to forecast monthly total rainfall.