Rain Patterns Research Articles

Statistical Analysis of Rainfall Data in Peninsular Malaysia using Strong Transformation of Cubic Spline Analysis

The constant rainfall is important for equatorial regions. The extreme rainfall shows our nature is facing climate change significantly and this needs to be paid attention by researchers hence the effects of disasters can be avoided as early as possible. Therefore, it is important to understand the rain pattern for the existing data so that forecasting can be done. This article discussed how to transform abnormal data into normal data and then analyze the data using the spline cubic analysis method. The study found that by using the transformation algorithm, the best r value is obtained. Cubic spline analysis shows that there are 30 rainfall stations in peninsular Malaysia where there are significant changes in rainfall patterns.

Amplification Efficiency and Template Accessibility as Distinct Causes of Rain in Digital PCR: Monte Carlo Modeling and Experimental Validation.

Partitions in digital PCR (dPCR) assays do not reach the detection threshold at the same time. This heterogeneity in amplification results in intermediate endpoint fluorescence values (i.e., rain) and misclassification of partitions, which has a major impact on the accuracy of nucleic acid quantification. Rain most often results from a reduced amplification efficiency or template inaccessibility; however, exactly how these contribute to rain has not been described. We developed and experimentally validated an analytical model that mechanistically explains the relationship between amplification efficiency, template accessibility, and rain. Using Monte Carlo simulations, we show that a reduced amplification efficiency leads to broader threshold cycle (Ct) distributions that can be fitted using a log-normal probability distribution. From the fit parameters, the amplification efficiency can be calculated. Template inaccessibility, on the other hand, leads to a different rain pattern, in which a distinct exponential tail in the Ct distribution can be observed. Using our model, it is possible to determine if the amplification efficiency, template accessibility, or another source is the main contributor of rain in dPCR assays. We envision that this model will facilitate and speed up dPCR assay optimization and provide an indication for the accuracy of the assay.

Impact of climate change on spatiotemporal variations in the vegetation cover and hydrology of district Nowshera

Abstract This research seeks to validate climate change in the district of Nowshera, as well as its impact on NDVI (vegetation) and hydrological events such as monsoon rains. Modified Mann-Kendall and Sen's statistics were used to examine climate parameters. Using climate factors and MODIS NDVI (Normalized Difference Vegetation Index) data, regression analysis was used to study a 20-year (2001–2020) spatiotemporal climate-vegetation relationship. Landsat 5 and 7 NDWI (Normalized Difference Water Index) data, as well as SRTM DEM (Digital Elevation Model) data, were used to map extreme weather events such as floods caused by climate change. For mapping and data processing, ArcGIS and R tools were used. The findings revealed significant trends in climate parameters, which has led to climate change in the area, affecting NDVI and water cycles. The NDVI showed a generally good trend, however some places were damaged. Monsoon rain patterns and rates have been substantially disrupted, resulting in flash and fluvial floods. The risk of future floods owing to the Kabul and Indus rivers was shown through watershed delineation, which is compounded by climate change and anthropogenic activities. It is advised that government officials and policymakers establish climate change mitigation plans for regional sustainable development and vegetation restoration.

Continuous learning deraining network based on residual FFT convolution and contextual transformer module

Single image deraining work has attracted a lot of attention. So an effective single image deraining network model, RFCTNet, is designed. Specifically, the residual Fast Fourier Transform convolution module is used as a baseline for the deraining network. The advantage is its ability to capture both long and short-term information interactions and to integrate high and low-frequency background information for delivery. And a novel Transformer module, the Contextual Transformer module, is introduced. Contextual knowledge of the rain streaks neighbourhood is learned to improve the recovery of textures and information in the image background based on focusing on the rain pattern features. Because of the overlap between rain streaks, removing them in one stage is difficult, so a multi-stage progressive rain removal method is used. Considering the catastrophic forgetting problem of deep learning, a continuous learning method, PIGWM, is adopted, which is c and enables the network model to memorize of the previous data set. Also, the rain removal method RFCTNet has been proven through extensive experiments to outperform other advanced methods' evaluation metrics on both synthetic and real datasets. The source code can be found on https://github.com/SUTwu/RFCTNet

Analyzing the Reliability of Unstructured Data for Urban Rainfall Pattern Studies—A Case Study from Zhengzhou

Due to the insufficient number and uneven distribution of urban rainfall stations, research on urban flooding disasters is limited. With the development of big data research, many scholars have applied big data to natural disaster research. In this paper, we analyzed the reliability of unstructured data from the urban rainfall patterns studies using the measured rainfall data for Zhengzhou City. First, web crawler technology was used on Sina Weibo, one of China’s largest social platforms, to obtain the unstructured data related to rainfall. The fuzzy recognition method was used to analyze the rain patterns of the measured rainfall data and the unstructured data, which verified the reliability of the unstructured data in the analysis of the urban rainfall patterns. Taking Zhengzhou City as an example, it was found that the matching degree of rain pattern recognition results was 45%, between the unstructured data and measured data. This showed that the application of the unstructured data in the analysis of the urban rainfall patterns has a certain degree of reliability.

Canopy Interception of Different Rainfall Patterns in the Rocky Mountain Areas of Northern China: An Application of the Revised Gash Model

Canopy interception is an important part of forest ecosystem hydrological processes. It is the first stage of water distribution when rainfall reaches the canopy and has an important impact on nutrient inputs and water exchange. Pinus tabulaeformis is a main tree species in the rocky mountain areas of Northern China, and it is also a primary species for artificial afforestation. In previous studies of canopy interception, applications of the revised Gash model did not take rainfall characteristics into account. Therefore, in this study, rainfall patterns were divided according to the local rainfall characteristics in the rocky mountainous areas of Northern China. Rainfall was divided into three patterns. Rain pattern A was the main rainfall type. Rainfall patterns B and C were two types of rainstorms. Next, the revised Gash model was used to simulate Pinus tabulaeformis plantations under different rainfall patterns. The results showed that the canopy interception rate of Pinus tabulaeformis plantations in this area ranged from 14.7% to 17.9%. The revised Gash model can be used to simulate Pinus tabulaeformis plantations in the rocky mountainous areas of Northern China, with good simulation results for more than 80% of the conventional rainfall patterns. Furthermore, the canopy interception effect of simulated cumulative rainfall events was better than the individual rainfall event. The simulation effect for special rainfall patterns was not good, so it is necessary to improve the model parameters or collect more rainfall samples. These results can be used to explore the applicability of the revised Gash model in Pinus tabulaeformis plantations in the rocky mountain areas of Northern China. They also demonstrate different applicability of the model under different rainfall characteristics.

Application of Cultivation Technology for Rice (Oryza sativa L. ) in Three Sectors of Rain Patterns in South Sulawesi

This study aimed to determine the effect of each component of cultivation technology application on the production of rice (Oryza Sativa L.) in South Sulawesi. This research was carried out in three rain-type sectors within South Sulawesi Province, namely Maros for the West Sector, Bone for the East Sector, and North Luwu for the Transitional sector. A study was conducted from December 2019 to January 2020 in the form of a survey (study literature, observation, and interviews), with the selection of samples carried out purposively to 90 respondents. Data analysis employed multiple linear regression with SPSS software. The results showed that the average rice production in Maros, Bone and North Luwu regencies, South Sulawesi, was still relatively low, Maros Regency was 7,032 kg per ha, Bone was 5,020 kg per ha and North Luwu was 6,497 kg per ha. The multiple regression equation for rice production in Maros, Bone and North Luwu Regencies, South Sulawesi is Y = 3354,185 -17,751X1 – 4,044X2 +36,839X3 . Factors from the aspect of cultivation technology application can increase rice production. Variables of cropping system, irrigation system, and fertilizer dose had a significant effect on rice production, while the variables of varieties and harvest time did not have a significant effect on rice production.

Extreme Rainfall Pattern Analysis for Drought Prone Shali Reservoir Area in West Bengal of India

Rainfall is one of the major sources of needed water. Rainfall amount also determines the agricultural and industrial activities in the region. The present study has been conducted to examine the rainfall trend in the Shali reservoir area of the Gangajalghati block in Bankura district in West Bengal. The Shali reservoir was made to provide irrigation water in the Gangajalghati block. Water insufficiency is a habitual threat to the people of this area. Here the planting and production of crops highly depend on the constricted period of inconsistent rainfall. About 80-90 % of rainfall usually occurs in this area from June to September until the onset of heavy rains. Therefore, in this drought-prone area, the analysis of rain patterns is very vital to understand the change in rainfall at all times. The monthly and annual rainfall data have been taken from field surveys and secondary surveys for almost the last 40 years (1980 to 2020). The Mann Kendall (MK) and Sen’s Slope estimate (SSE) tests have been applied to recognize the standing trend and the SSE test has been used to discover the existing trend and extent of change in rainfall over time. The rainfall data has been taken from ten rain gauge stations surrounding the study area. The relevant maps were prepared using Remote Sensing and the rainfall distribution pattern was analyzed with the help of the kriging interpolation technique of GIS. The annual MK test shows a positive trend but the season-wise MK test is showing a negative trend during monsoon season. SPI result describes that the study area is not getting normal rainfall during the monsoon season, it may create harmful effects on agricultural productivity. The study suggests taking proper drought management programs to combat this situation.

Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.

Integrating cost-effectiveness optimization and robustness analysis for low impact development practices design

Current low impact development (LID) practices may suffer challenge when tackling extraordinarily variable rainfall. New framework was proposed by integrating robustness analysis and cost-effectiveness optimization under various rainfall events. The cost-effectiveness LID schemes were generated by coupling Storm Water Management Model (SWMM) with genetic algorithm. Stochastic rainfall algorithm was proposed for evaluating robustness of each scheme via joint distribution function of daily precipitation and antecedent dry days. The results show that current studies might overestimate nonpoint source pollution (NPS) based on limited events, resulting in less cost-effectiveness LIDs. This new framework, which generated 1087 additional events especially high- and low- frequency ones, extracted robust LIDs among several initial schemes. In addition, heavy rain pattern was suggested as the design criteria to increase robustness of LIDs. Large impervious areas were also suggested as priority area. This study provides guidance for designing LIDs, especially in response to rainfall events with high variability.

Stochastic daily rainfall generation on tropical islands with complex topography

Abstract. Stochastic rainfall generators are probabilistic models of rainfall space–time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in earth sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized precipitations with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity of rain statistics, we propose a new stochastic daily multi-site rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the distribution of rainfall through the island is modeled by combining a non-parametric resampling of past analogs of a latent field describing the spatial distribution of rainfall, and a parametric gamma transform function describing rain intensity. When applied to the stochastic simulation of rainfall on the islands of O`ahu (Hawai`i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating site-specific and island-scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resource management.

Contrastive learning-based generative network for single image deraining

The crux of image deraining stems from the challenge of recognizing the diverse rain patterns within the rainy image. Most methods for image deraining remain visible rain residuals in the restored image, which suffers from insufficient modeling of rain streaks. In this work, we propose contrastive learning-based generative network (CLGNet), which follows a coarse-to-fine framework. In the coarse phase, our CLGNet employs the hierarchical encoder–decoder structure to remove obvious rain patterns, and first generates the coarse background image. Then, we introduce a well-designed multiscale feature aggregation module in the refining phase to extract and integrate global information dependencies from different scales. In additon, to facilitate the intra-stage and cross-stage information interaction, we propose the intra-stage feature fusion module and the cross-stage feature fusion module to encode broad contextual information. More importantly, we propose an innovative contrastive learning strategy and apply it to each stage of our proposed CLGNet to enhance the decoupling ability of the encoder and help the model recognize complex rain patterns. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed CLGNet than other state-of-the-art methods for single image deraining on both the visual quality and quantitative evaluation.

Impact of climate change on the agriculture sector and its adaptation strategies

Climate change in Indonesia, including erratic rainfall intensity, extreme temperatures, El Nino and La Nina events, can cause farmers not to implement agricultural efforts optimally. Multiplier effect caused is the risk of decreased agricultural production and even crop failure that causes losses for farmers. The impact can broadly reduce farmers’ welfare, food security and increase poverty, for which most Indonesians livelihood as farmers. The negative consequences of climate change require efforts to minimize the impact and anticipate through adaptation strategies. These research aims are to analyze the impact of climate change on the agriculture sector and develop an adaptation strategy to address the impact of climate change on the agricultural sector. This method uses review literature related to the impact of climate change on agricultural products and adaptation strategies to climate change in the agricultural sector. The impact of climate change caused a decline in agricultural production and even crop failure. The main actors of climate change that affect the agricultural sector include rain patterns, extreme climate events (floods and droughts), and increased air temperatures. Recommendations for adaptation strategies are using superior plants, adjusting planting time and pattern, water management technology (rain harvesting and irrigation), and biotic environmental strategies.

Genetic characterization of flowering and phytochrome genes in peanut (Arachis hypogaea L.) for early maturity.

Peanut (Arachis hypogaea L.) production and cropping pattern is highly influenced by the climatic factors including temperature and rain pattern fluctuations. It is one of the most important cash crop in the rain fed areas of Pakistan and its production, under changing climatic conditions, that can be improved by developing short duration varieties. The present study was based on the molecular characterization of the maturity associated gene families in the peanut under two light conditions. Genomic analysis based on the in silico study of important gene families for early maturity associated attributes like flowering time, their pattern, duration and photoperiodism was done for a comprehensive mapping of maturity related genes. Phytochromes genes Phy A, Phy B and Phy E and flowering genes FT2a, Ft5a and COL2 were selected for in silico characterization for protein based analysis including Multiple Sequence Alignment (MSA), and Neighbor Joining (NJ) tree. MSA and NJ trees of the peanut with Arabidopsis thaliana and Glycine max showed a clear picture of the phylogenetic relationship on the basis of selected gene proteins. Expression profile of phytochrome and flowering genes revealed that photoperiod conditions i.e. short and long days, have great influence on the Phy A, Phy B and Phy E, Ft2a, FT5a and COL2 gene expression pattern. In current study, the relative expression of all studied genes was found higher in short day light condition at flower initiation stage of the plants than in the long light day condition with exception of COL2 gene protein. The molecular characterization based on the in silico study of the particular genes and qPCR based gene expression profiling of the selected genes provided an evidence of the role of these genes and their comparative analysis under two photoperiodic conditions.

Adaptation Strategies to Climate Change and Impacts on Household Income and Food Security: Evidence from Sahelian Region of Niger

Sahelian countries, particularly Niger, are more vulnerable to climate change due to the high dependence of most of their populations on rain-fed agriculture and limited capacities to respond to climate variability and change. This paper examines the factors influencing climate change adaptation strategies and the impacts on household income and food security in rural Niger. For this purpose, we collected data from 1783 valid rural households in four main agricultural regions of Niger. The results showed that crop diversification (72.74%), income diversification (67.97%) and changing planting times (55%) are the main adaptation strategies adopted by households. The majority of respondents had noticed changes in rain patterns (93.21%), in the amount of rain (91.25%) and in the intensity of rain (81.82%) during the last five years. We categorized these adaptation strategies into six major groups namely climate-resilient crop varieties, improved agronomic practices, irrigation and water conservation practices, crop diversification, income diversification, and agroforestry. We ran logit regression to identify the determinants of each individual group. The results show mixed effects of independent variables on these categories of adaptation strategies. Using matching techniques, we found adaptation strategies have positive and significant impact on both household income and food security. The farmers who adopt climate change adaptation strategies are more likely to increase household income by 7721.526 FCFA compared to those households with zero adaptation strategies. Similarly, the adapters have 7% to 9% more chance to be food secure compared to those who did not adopt strategies. These results suggest that strengthening the awareness of the effects of climate change on farmers and the choice of appropriate adaptation strategies are necessary to enhance household resilience. Strengthening institutional factors such as access to credit and market, extension services, and using drought-resilient crop varieties would surely improve agricultural production.

Reduced Potential for Consecutive Truck-Mounted Ultra-Low Volume Mosquito Adulticide Applications in Chicago Metropolitan Area Due to Weather Conditions.

To increase the effectiveness of adult mosquito control, more frequent ultra-low volume (ULV) applications have been suggested. In the Chicago area, weather may be a limiting factor to more frequent applications. To investigate this, ULV adulticidal application dates from the Northwest Mosquito Abatement District during June 2011 to September 2020 were compared to historical weather data-wind and rain patterns to determine optimum conditions for conducting ULV treatments. During the 85 applications performed during 2011-20, there would have been an opportunity, based on weather conditions, for an additional consecutive night for about half (48.2%) of the time. These opportunities diminished with each additional application. The most intensive response possible would have been a spray of 5 consecutive nights, which occurred once in 3 of the 10 years investigated.

Assessment of Sponge City Flood Control Capacity According to Rainfall Pattern Using a Numerical Model after Muti-Source Validation

Urban floods are a common urban disaster that threaten the economy and development of cities. Sponge cities can improve flood resistance ability and reduce floods by setting low-impact development measures (LID). Evaluating flood reduction benefits is the basic link in the construction of sponge cities. Therefore, it is of great significance to evaluate the benefits of sponge cities from the perspective of different rain patterns. In this study, we investigated the urban runoff of various rainfall patterns in Mianyang city using the Strom Water Management Model (SWMM). We employed 2–100-year return periods and three different temporal rainfall downscaling methods to evaluate rain patterns and simulate urban runoff in Mianyang, with and without the implementation of sponge city measures. After calibration, model performance was validated using multi-source data concerning flood peaks and inter-annual variations in flood magnitude. Notably, the effects of peak rainfall patterns on historical floods were generally greater than the effects of synthetic rainfalls generated by temporal downscaling. Compared to the rainfall patterns of historical flood events, the flood protection capacities of sponge cities can be easily overestimated when using the synthetic rainfall patterns generated by temporal downscaling. Overall, an earlier flood peak was associated with better flood sponge city protection capacity. In this context, the results obtained in this study provide useful reference information about the impact of rainfall pattern on urban flood control by LID, and can be used for sponge city design in other part of China.

Research on the Characteristics and Influence Factors of Autumn Continuous Rain in Qinghai Province

Using daily precipitation data of national meteorological stations in Qinghai Province and reanalysis data, the main patterns of autumn continuous rain (ACR) are extracted, and key SST areas and circulation factors are searched for to analyze the possible effects on the seasonal and sub-seasonal predictability of ACR in Qinghai Province. The factors with strong precursors mainly include sea surface temperature (SST), Arctic vortex, and subtropical high. The influence of oceanic factors on ACR is relatively independent, while there is an interaction between atmospheric circulation factors. The precursors in spring are from the equatorial east-central Pacific for the main pattern with consistent precipitation distribution, and the Indian Ocean for the pattern with “saddle type” distribution. SST anomalies from the Pacific and Indian Oceans force different circulation patterns that can enhance meridional circulation over Europe-Asia and lead to different precipitation pattern of ACR. The signals from the Arctic vortex and subtropical high in June to July are indicative for ACR. The southward, westward and strong Arctic vortex, together with the south-westward extension of subtropical high over the Atlantic and Pacific can promote more ACR in Qinghai Province. Under the SST forcing and configuration of high and low latitude circulation, the meridional circulation continues to be weak, and the latitudinal circulation to be enhanced over the Eurasia in the early period, and the circulation pattern changes from type-W to type-C, which lead to positive anomaly of ACR in Qinghai Province.

Women and Girls’ Vulnerability to Climate Change: Gender-Based Policy Strategies for Sustainable Development

Climate change which is a change in weather taking the form of rising sea levels, shifting rain patterns, drought and flooding affect both men and women. However, the impacts of climate change on these sexes differ just as their expected roles in the society differ. Female folks face the brunt of climate change more than their male counterpart due largely to their differentiated roles; sources of livelihood, level of education which combines and shape their abilities to cope with climate change impacts. From all indications the Nigerian government’s climate change policy has not acknowledged the different impacts of climate change on male and female sexes to incorporate gender-based policies and programmes to boost women and girls’ ability to cope with climate change vulnerability. The paper aims at examining the impact of climate change on the female gender, and recommends the incorporation of a gender-based approach in the national policies and programmes to ease the impact of the changing climate on the female folks for the attainment of sustainable development and better livelihood.