Monthly rainfall-distribution in southern California, with special reference to soil-erosion problems

1943 ◽  
Vol 24 (1) ◽  
pp. 144
Author(s):  
Maurice Donnelly
Keyword(s):  
Soil Erosion ◽  
Special Reference ◽  
Southern California ◽  
Monthly Rainfall ◽  
Rainfall Distribution

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.

scholarly journals Zoning of small catchment for restoration of natural forests with special reference to site productivity, soil erosion risks and riparian buffer strip

2013 ◽  
Vol 18 (2) ◽  
pp. 139-147 ◽  
Author(s):  
Satoshi Ito ◽  
Koji Kizaki ◽  
Yasushi Mitsuda ◽  
Ryoko Hirata ◽  
Hiromi Yamagawa ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Special Reference ◽  
Riparian Buffer ◽  
Natural Forests ◽  
Site Productivity ◽  
Small Catchment ◽  
Buffer Strip

scholarly journals Spatial-Temporal Trend Analysis of Rainfall Erosivity and Erosivity Density of Tropical Area in Air Bengkulu Watershed, Indonesia

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Supriyono Supriyono ◽  
Sugeng Utaya ◽  
Didik Taryana ◽  
Budi Handoyo
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Anthropogenic Activities ◽  
Monthly Rainfall ◽  
Rainfall Erosivity ◽  
Tropical Country ◽  
Distance Weighted ◽  
Average Monthly Rainfall ◽  
Research Findings ◽  
Parametric Statistics

Abstract There have been many studies on rainfall erosivity and erosivity density (ED). However, it was not widely developed in Indonesia as a tropical country and has unique precipitation patterns. They are indicators for assessing the potential risk of soil erosion. The Air Bengkulu Watershed is undergoing severe land degradation due to soil erosion. This study aimed to analyze spatial-temporal in rainfall erosivity and ED based on monthly rainfall data (mm). The data used consisted of 19 weather stations during the period 2006–2020 and which are sparsely distributed over the watershed. The analysis was done by using Arnold's equation. Then, the trend was tested using parametric and non-parametric statistics, and analysed with linear regression equation, and Spearman's Rho and Mann Kendall's tests. The spatial distribution of both algorithms was analysed using the inverse distance weighted (IDW) method based on the geographic information system (GIS). Unlike previous research findings, The long-term average monthly rainfall erosivity and ED revealed a general increase and decreasing trend, whereas it was found to be non-significant when both indices were observed. However, these results indicate a range from 840.94 MJ · mm−1 · ha−1 · h−1 · a−1, 552.42 MJ · mm−1 · ha−1 · h−1 · a−1 to 472.09 MJ · mm−1 · ha−1 · h−1 · a−1 in that November month followed by December and April are the most susceptible months for soil erosion. Therefore, The upstream area of the region shows that various anthropogenic activities must be managed properly by taking into account the rainfall erosivity on the environment and that more stringent measures should be followed in soil and water conservation activities.

Monthly Rainfall Analysis in West Part of Chongqing

2014 ◽  
Vol 580-583 ◽  
pp. 2019-2022
Author(s):  
Lin Qin ◽  
Jun Ying Jin ◽  
Qian Zhang ◽  
Da Ke Wang
Keyword(s):  
Large Scale ◽  
Global Scale ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
The West ◽  
Rainfall Distribution ◽  
Rainfall Analysis ◽  
Complex Interactions ◽  
West Part ◽  
Average Monthly Rainfall

Rainfall information is critical in understanding the hydrologic balance on a global scale and the complex interactions among the small-and large-scale components within the hydrologic cycle [1]. In this study, the monthly rainfall data from 1976 to 2006 at the stations of Rongchang, Dazu, Tongliang and Yongchuan were used to analyze the statistical characteristics and trends of rainfall changes in the west of Chongqing. The average monthly rainfall at Rongchang, Dazu, Tongliang and Yongchuan were 89.4mm, 83.5mm, 88.3mm and 84.8mm in thirty years. The probability distribution of rainfall is a normal distribution. Moreover, the histogram of frequencies showed a clear tendency toward a obvious seasonality of the rainfall distribution [2].

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