scholarly journals Some hydrometeorological studies over Teesta basin in north Bengal

MAUSAM ◽  
2021 ◽  
Vol 42 (4) ◽  
pp. 385-392
Author(s):  
S. K. PRASAD ◽  
A. K. DAS ◽  
I. SENGUPTA
Keyword(s):  
Spatial Distribution ◽  
Extreme Rainfall ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
Precipitation Depth ◽  
Areal Precipitation ◽  
Rainy Days ◽  
North Bengal

Based on data of 40 rainfall stations located within and in the neighbourhood of Teesta basin in north Bengal for period ranging between 7 & 23 years, hydrometeorological informations of the spatial distribution of monthly rainfall, umber of rainy days and extreme rainfall distribution over Teesta basin have been determined and presented on basin maps for the months of May to October.  The average monthly areal precipitation depth as wi1l as extreme areal precipitation depth for a day have been discussed for 6 sectors of the basin. The pentads rainfall for 22 selected stations in the catchment during May to October have also been evaluated and discussed.

scholarly journals Seasonal features of the spatial distribution of rainfall in pre-partitioned India

MAUSAM ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 47-56
Author(s):  
K. L. SINHA
Keyword(s):  
Spatial Distribution ◽  
India Meteorological Department ◽  
Rainfall Distribution ◽  
Common Features ◽  
Four Seasons ◽  
Rainy Days

The spatial distribution of rainfall in accordance with the practice prevalent in the India Meteorological Department, viz., "few falls", "local" and "widespread" during the four seasons and the whole in the different meteorological subdivisions of the pre-partitioned India have been studied with a view, to find any common features that may exist between the three types of rainfall distribution. Distribution of total number of rainy days in the various meteorological subdivisions during the four seasons and the year have been discussed.

scholarly journals Rainfall distribution over Delhi State during monsoon

MAUSAM ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 57-66
Author(s):  
D. A. MOOLEY
Keyword(s):  
Spatial Distribution ◽  
Heavy Rainfall ◽  
Extreme Values ◽  
Monsoon Season ◽  
The State ◽  
Rainfall Distribution ◽  
Mean Values ◽  
The Mean ◽  
Rainy Days

Based on the data for the period 1939-1954, the mean values of rainfall and number of rainy days during the, monsoon season at the various raingauge stations as well as the extreme values of these have been given; spatial distribution of heavy, rainfall over the State and the incidence of heavy rainfall at the various location have been studied. From a study of the synoptic charts on days prior to the days on which local heavy rainfall over was reported, an attempt has been made to indicate the topical synoptic situations which usually lead to local heavy rainfall over Delhi State during the next 24hours.Typical situation ‘have been illustrated by charts.  

scholarly journals An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

2020 ◽  
Vol 12 (4) ◽  
pp. 709 ◽  
Author(s):  
Abhishek Banerjee ◽  
Ruishan Chen ◽  
Michael E. Meadows ◽  
R.B. Singh ◽  
Suraj Mal ◽  
...  
Keyword(s):  
River Basin ◽  
Temporal Trends ◽  
Google Earth ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Google Earth Engine ◽  
Spatio Temporal ◽  
Uttarakhand Himalaya ◽  
Rainy Days

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

scholarly journals Effects of rainfall manipulation and nitrogen addition on plant biomass allocation in a semiarid sandy grassland

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jing Zhang ◽  
Xiaoan Zuo ◽  
Xueyong Zhao ◽  
Jianxia Ma ◽  
Eduardo Medina-Roldán
Keyword(s):  
Biomass Allocation ◽  
Plant Biomass ◽  
Extreme Rainfall ◽  
Growing Season ◽  
Nitrogen Addition ◽  
Extreme Drought ◽  
N Addition ◽  
Rainfall Distribution ◽  
Grassland Plants ◽  
Sandy Grassland

Abstract Extreme climate events and nitrogen (N) deposition are increasingly affecting the structure and function of terrestrial ecosystems. However, the response of plant biomass to variations to these global change drivers is still unclear in semi-arid regions, especially in degraded sandy grasslands. In this study, a manipulative field experiment run over two years (from 2017 to 2018) was conducted to examine the effect of rainfall alteration and nitrogen addition on biomass allocation of annuals and perennial plants in Horqin sandy grassland, Northern China. Our experiment simulated extreme rainfall and extreme drought (a 60% reduction or increment in the growing season rainfall with respect to a control background) and N addition (20 g/m2) during the growing seasons. We found that the sufficient rainfall during late July and August compensates for biomass losses caused by insufficient water in May and June. When rainfall distribution is relatively uniform during the growing season, extreme rainfall increased aboveground biomass (AGB) and belowground biomass (BGB) of annuals, while extreme drought reduced AGB and BGB of perennials. Rainfall alteration had no significant impacts on the root-shoot ratio (R/S) of sandy grassland plants, while N addition reduced R/S of grassland species when there was sufficient rainfall in the early growing season. The biomass of annuals was more sensitive to rainfall alteration and nitrogen addition than the biomass of perennials. Our findings emphasize the importance of monthly rainfall distribution patterns during the growing season, which not only directly affect the growth and development of grassland plants, but also affect the nitrogen availability of grassland plants.

scholarly journals Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

2021 ◽  
Author(s):  
Nawinda Chutsagulprom ◽  
Kuntalee Chaisee ◽  
Ben Wongsaijai ◽  
Papangkorn Inkeaw ◽  
Chalump Oonariya
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
Mathematical Concepts ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Fold Cross Validation ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.

scholarly journals Rainfall Distribution in the Upper East Region of Ghana, 1976 – 2016

2020 ◽  
Vol 6 (2) ◽  
pp. 45-59
Author(s):  
Boateng AMPADU ◽  
Isaac SACKEY ◽  
Eugene CUDJOE
Keyword(s):  
Agricultural Sector ◽  
Moving Average ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
East Region ◽  
Dry Spells ◽  
Early Maturing ◽  
Upper East Region ◽  
The Mean ◽  
High Precipitation

The knowledge and understanding of rainfall distribution of a region are very essential and useful in determining the overall impacts of climate change, especially to the agricultural sector. Monthly rainfall data from 1976-2016 for five selected stations were acquired and subjected to various statistical techniques namely coefficient of variation, 5-year moving average and departure from the mean to obtain the variability and trends in the data. The results showed that the selected stations have uni-modal rainfall distribution and that the rain mostly starts in May and ends in September. High precipitation occurs in July, August and September, with August recording the highest amount with a low variability, indicating the reliable occurrence of precipitation within this period of the year. This is of high importance to farmers and the recharging of aquifers. The wettest station was Zuarungu, with a mean total monthly rainfall of 89.55 mm followed by Navrongo, Bolgatanga, Garu and Manga-Bawku with their respective mean total monthly rainfall as 81.08 mm, 80.59 mm, 79.64 mm and 78.86 mm. High annual variability was found in all the stations and long dry spells were observed from November to March. The rainfall season wet period is between July and September at all the stations and it is recommended that farmers should cultivate early-maturing crops and adopt irrigation farming practices as well as practices which utilize water efficiently.

Sign in / Sign up

Export Citation Format

Share Document