Distribution Of Rain Research Articles

Determining the onset and cessation of seasonal rains in Shire Valley, Southern Malawi

Background: Malawi's agricultural activities rely heavily on rainfall. Rain signals the start of a distribution of rain that is unsuitable for plant development, whereas lighting marks the end of rain's ability to support plants. Malawi's tropical savanna climate has a rainy season from November to April, accounting for 95% of total annual rainfall, and a dry season from May to October, with milder temperatures and occasional showers at higher elevations from May to July. Methods: This study examines the commencement and cessation of seasonal rainfall in the Shire Valley, southern Malawi, between 1990 and 2019. The objectives include identifying rainfall characteristics, examining trends in the start, closure, and length of the rainy season, and categorizing onset and narrative dates. The Department of Climate Change and Meteorological Services provided daily rainfall data, and dates were determined using R-Instat. Findings: Rain often begins in Nsanje on November 28, followed by Ngabu on December 7, and Nchalo on December 9. The advertising dates are comparable across stations: April 3 for Nchalo and Ngabu, and April 5 for Nsanje. While most years fall within the typical range, global phenomena like La Niña and El Niño can cause early or late results. Conclusion: These insights are crucial for sustainably managing and developing socioeconomic activities at the national and district levels in Malawi. Novelty/Originality of this Study: This study provides a comprehensive analysis of the onset and cessation dates of the rainy season, utilizing long-term rainfall data spanning from 1990 to 2019. This localized analysis addresses existing gaps in the understanding of regional rainfall characteristics, which is essential for enhancing agricultural planning and mitigating resource wastage in an area heavily reliant on rain-fed agriculture.

Analisis Spasial Indeks Kekeringan di Daerah Bandar Lampung

Drought is a routine case of natural disasters. Drought in a region can disrupt human activities. Drought occurs due to reduced rainfall, temperatures that are too high than normal, low soil moisture and insufficient surface water supply. The aim of this research is to obtain the distribution of rain and analyze the drought value index in the Bandar Lampung area. The cause of this drought is a step to predict the drought that will occur by analyzing the rain and temperature. This analysis is done by calculating the water balance and determining the drought level using drought index table by thorthwaite mather. The drought analysis is conducted in Bandar Lampung area, where the Bandar Lampung area is the capital city of Lampung province with an area of ± 160 km2 with an average population density of 8,316 people/km2. From the Drought Value Index, the level of drought in the Bandar Lampung area is predominantly low and the level of drought is moderate in the Peace District area. Keywords: Drought, Spatial Analysis, Water Balance

Data-driven analysis of annual rain distributions

Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. Published by the American Physical Society 2024

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

FMRNet: Image Deraining via Frequency Mutual Revision

The wavelet transform has emerged as a powerful tool in deciphering structural information within images. And now, the latest research suggests that combining the prowess of wavelet transform with neural networks can lead to unparalleled image deraining results. By harnessing the strengths of both the spatial domain and frequency space, this innovative approach is poised to revolutionize the field of image processing. The fascinating challenge of developing a comprehensive framework that takes into account the intrinsic frequency property and the correlation between rain residue and background is yet to be fully explored. In this work, we propose to investigate the potential relationships among rain-free and residue components at the frequency domain, forming a frequency mutual revision network (FMRNet) for image deraining. Specifically, we explore the mutual representation of rain residue and background components at frequency domain, so as to better separate the rain layer from clean background while preserving structural textures of the degraded images. Meanwhile, the rain distribution prediction from the low-frequency coefficient, which can be seen as the degradation prior is used to refine the separation of rain residue and background components. Inversely, the updated rain residue is used to benefit the low-frequency rain distribution prediction, forming the multi-layer mutual learning. Extensive experiments demonstrate that our proposed FMRNet delivers significant performance gains for seven datasets on image deraining task, surpassing the state-of-the-art method ELFormer by 1.14 dB in PSNR on the Rain100L dataset, while with similar computation cost. Code and retrained models are available at https://github.com/kuijiang94/FMRNet.

Attention‐generative adversarial networks for simulating rain field

AbstractThe synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors’ experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally.

Pemanfaatan Teknologi Informasi HEC-HMS Pada Potensi Penerapan Rainwater Harvesting Di Perumahan Bumi Kilu Permai Kota Manado

Abstract - Flood disasters are a seasonal threat that always occurs, an increase in surface flow discharge which has the impact of flooding due to lost absorption capacity and drought in the dry season. Techniques or methods are needed to prevent this, such as the eco-drainage concept that can be applied, including Rainwater Harvesting (RWH). Community participation in maintenance is needed to maintain the success of the ecodrainage system. It is hoped that the people living in the Bumi Kilu Permai Housing Complex can set an example in environmental maintenance implemented in their respective homes in dealing with the possibility of flooding. This research evaluates the potential for implementing a rainwater harvesting system in managing environmentally sound drainage. The method used to calculate the capacity of rainwater harvesting facilities uses monthly storage simulations. Rain distribution uses the HEC-HMS application to determine flood discharge hydrographs, Log Pearson Type III frequency analysis method. Calculation of discharge in gutters and also calculation of rainwater harvesting. The application of rainwater harvesting in the Bumi Kilu Permai housing complex by applying HEC-HMS 4.2.1 software shows that the peak flow hydrograph is 390 mm/hour. The use of an open rectangular gutter of 15 cm x 15 cm is still safe for a return period of 2 to 100 years with discharge on the roof is 0.210 m3/s. The average rainfall calculated using HEC_HMS 4.2.1 software was 25,527.32 liters. The tower used for rainwater harvesting is 1000 liters in size. With the monthly amount of water that can be stored for 1 month, it is 186,000 liters, while the amount of water that can be stored in the tower is 180,440.73 liters. Implementing rainwater harvesting for 1 house can reduce 1000 liters of surface water runoff. If 300 can withstand 300,000 liters of surface water runoff or runoff. Of course, this can help minimize the probability of flooding in Paniki Satu Village, which is located in front of the Bumi Kilu Permai Housing Complex and is in the Paniki Watershed. Keywords: Rainwater Harvesting, HEC-HMS, Manado Abstrak - Bencana banjir adalah ancaman musiman yang selalu terjadi, peningkatan debit aliran permukaan yang membawa dampak terjadinya banjir akibat kapasitas resapan hilang dan kekeringan di musim kemarau. Diperlukan teknik atau metode untuk mencegah hal tersebut, seperti konsep ekodrainase yang dapat diterapkan diantaranya adalah Rainwater Harvesting (RWH). Peran serta masyarakat dalam hal pemeliharaan sangat dibutuhkan untuk tetap menjaga keberhasilan sistem ekodrainase.

POTENSI KETERSEDIAAN AIR DAS LOGAWA DENGAN MEMPERHITUNGKAN PERUBAHAN IKLIM

Water is one of the non-biological natural resources which can be renewed through the hydrological cycle. As the human population increases and the phenomenon of climate change, the availability of water tends to decrease both in terms of space, time, quality and quantity resulting in an imbalance between demand and availability. In addition to the increase in human population, climate change and the distribution of rain that is not always evenly distributed, the availability of groundwater is always different at any given time and place. The Logawa sub- watershed is one of the most influential watersheds in Banyumas Regency. Therefore, an analysis is needed regarding the impact of changes in rainfall on the condition of water availability in this watershed. One of the analyzes that can be done is to use the HEC-HMS software and a scenario neutral approach. The aims of this study were (i) to know the parameters of the Logawa watershed using the HEC-HMS model; (ii) Applying the HEC-HMS software to determine water availability in the Logawa watershed; (iii) Knowing the HEC-HMS model calibration process); and (iv) know how to calculate climate change using a scenario-neutral approach. The research method used is quantitative using secondary data. The data used are watershed geospatial data and watershed biogeophysical characteristics data. The HEC-HMS simulation method used is the User Hyetograph Model, Soil Moisture Accounting, Clark's Unit Hydrograph, Recession Baseflow. The results are (i) a hydrological model with a PBIAS value of -2.474% and an NSE of 0.153 is obtained; (ii) results of 130 rainfall scenarios; and (iii) reliable debit from the GPCC satellite rain data input and scenario-neutral approach rainfall data.

GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement

Abstract. Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %.

ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ ПРОЦЕССА РАССТАНОВКИ ДОЖДЕВАТЕЛЕЙ НА ВОДОПРОВОДЯЩЕМ ПОЯСЕ ДОЖДЕВАЛЬНОЙ МАШИНЫ

Purpose: to develop software for the process of arranging sprinklers on the water-conducting belt of a sprinkler to improve the irrigation quality. Materials and methods. As a theoretical basis for creating software, the traditional methods of algorithmization and programming based on the processes of algorithm design for solving the problem of calculating the optimal spacing and flow-pressure characteristics of deflector nozzles on the water-conducting belt of sprinklers, respectively, formalized in the form of a scheme, and writing the source code in the object-oriented programming language Python were used. Results. To replenish the domestic scientific and technical base for digitalization of engineering and reclamation work with information tools, software for the process of arranging sprinklers on water-conducting belt of a sprinkler for uniform distribution of artificial rain along the irrigated area has been developed. The algorithm graphical model is represented by three blocks. The “Source data input” block provides a choice of the type of sprinkler, the operating flow rate of irrigation water, as well as the material of the water conducting belt. In the “Intermediate and target values calculation” block, the irrigation water loss, head loss, distance between the end and first nozzles, as well as the total number of sprinklers are calculated. In the “Cyclic computation and output of results” block, head losses along the length and local pressure losses are determined for each section, the sum of which makes up the total head losses. Using the designed software, computations were performed using the example of a cantilever sprinkler with 4 sections of steel pipeline, a total flow rate of 6 l/s and a pressure at the end of the console of 10 m. It was determined that to ensure the uniform distribution of artificial irrigation along the irrigated area, it is necessary to install 9 sprinklers with variable spacing along the length console, and to provide a head at the beginning of the sprinkler machine of 23.8 m, taking into account the total head loss of 13.8 m. Conclusions: the software allows simulating the optimal arrangement of deflector nozzles of the same size on a water-conducting belt at the design stage of double-cantilever and multi-tower lateral sprinklers.

The Prediction of Debris Flow Based on Eruption and Rainfall Event for River Infrastructure Mitigation: Study Case Opak River, Sleman Regency

Mount Merapi is a volcano active in Indonesia. The eruption that occurred in 2010 was a major eruption with a return period of 100 years. Dominant debris to the Opak – Gendol Watershed; the biggest debris flood occurred in 2010 – 2011. One of the disaster mitigations on the Opak River is the Sabo Dam infrastructure. Currently, in the upper reaches of the Opak River, there are 5 Sabo Dams. In 2022, 2 additional Sabo Dams were built, namely OP RRC4 and OP RRC3a. This study will be used modelling 2D HEC-RAS Non-Newtonian before the construction of 2 Sabo Dams and after the construction of 2 Sabo Dams with river geometry measured in 2020 and flood discharge as measured from rainfall triggering debris flow on 03 January 2011, from this rainfall a 6-hour rain distribution was carried out using the PSA coefficient to be input in the HEC-HMS, the hydrological parameters used on 03 January 2011, were used to calculate the Q100 flood discharge. The results of modelling at the observation point using the Q100 showed that the elevation of the debris flow after the presence of the new Sabo Dam series could be reduced by up to 47.21 %, Sabo Dam effectively reduces the rate of debris flow to reduce the potential debris flood.

Transcriptomic Data Analysis Using the Galaxy Platform: Coffee (Coffea arabica L.) Flowers as Example.

Coffee, an important agricultural product for tropical producing countries, is facing challenges due to climate change, including periods of drought, irregular rain distribution, and high temperatures. These changes result in plant water stress, leading to significant losses in coffee productivity and quality. Understanding the processes that affect coffee flowering is crucial for improving productivity and quality. In this chapter, we describe a protocol for transcriptome analysis using available Internet software, mainly in the Galaxy Platform, using RNA-Seq data from flowers collected from different parts of the coffee tree. The methods presented in this chapter provide a comprehensive protocol for transcriptome analysis of differentially expressed genes from flowers of coffee plant. This knowledge can be utilized in coffee genetic improvement programs, particularly in the selection of cultivars that are tolerant to water deficit.

Determination of Impacts of Climate Change on Temperature and Rainfall Variations in Some Southern Iraqi Governorate Using GIS

The concept of temperature change is closely related to climate change. Global warming is one of its most prominent manifestations, and the effects of this will be significant on the climate. It will lead to an increase in the intensity of the air cycle, the encroachment of climatic regions towards the poles, an increase in the frequency of extreme weather phenomena, a change in the distribution of heat and rain in different cities of the world, and a rise in sea levels and the threat of most coastal areas. The research aims to determine the variation in temperature, the amount of rain, and its distribution system in the (Basra, Thi Qar, and Missan) during the period (1998 – 2020), and diagnosis of changes in temperature, rain amount, and their cause, as well as its distribution system by analyzing the trend of change in the annual averages of temperature and annual rainfall amounts for the same study period. The results of spatial analysis using GIS for monthly temperature average data for the same period indicated that the general trend of temperature distribution in all study stations is high, and the peaks during the months (June, July, August, and September), where we notice a large and clear increase, especially in recent years, if compared with rates in previous years. As for the rain data analysis in Iraq, the study found that it was limited to five months (Nov., Dec., Jan., March, April), and October month, which represents the beginning of the rainy season in most monitoring stations, and the month of March, which represents its end, did not record any amount of rain, and this means that the depressions which Iraq exposed to during these two months are unable to produce heavy amounts of the rain because of their shallowness, from the spatial distribution of the amount of rain in the study area that the rainfall rates are reduced to the highest level and its irregular distribution, and therefore reflected in the decrease in water resources in the areas under study. A large amount of daily precipitation in Iraq is due to the recurrence of three atmospheric depressions the (Mediterranean, combined, and Sudanese) depression. These responsible depressions are distinguished by their differences from one region to another, the short duration of their stay and recurrence, the weather conditions that dominate them, and the pressure patterns accompanying them in the upper atmosphere layers. Or some of these depressions reach dryness or are accompanied by non-rainy clouds. This makes Iraq’s rains few, leading to many repercussions on climate, the environment, humans, and various activities.

Simple Analytical Expressions for Steady-State Vapor Growth and Collision–Coalescence Particle Size Distribution Parameter Profiles

Abstract This study derives simple analytical expressions for the theoretical height profiles of particle number concentrations (Nt) and mean volume diameters (Dm) during the steady-state balance of vapor growth and collision–coalescence with sedimentation. These equations are general for both rain and snow gamma size distributions with size-dependent power-law functions that dictate particle fall speeds and masses. For collision–coalescence only, Nt (Dm) decreases (increases) as an exponential function of the radar reflectivity difference between two height layers. For vapor deposition only, Dm increases as a generalized power law of this reflectivity difference. Simultaneous vapor deposition and collision–coalescence under steady-state conditions with conservation of number, mass, and reflectivity fluxes lead to a coupled set of first-order, nonlinear ordinary differential equations for Nt and Dm. The solutions to these coupled equations are generalized power-law functions of height z for Dm(z) and Nt(z) whereby each variable is related to one another with an exponent that is independent of collision–coalescence efficiency. Compared to observed profiles derived from descending in situ aircraft Lagrangian spiral profiles from the CRYSTAL-FACE field campaign, these analytical solutions can on average capture the height profiles of Nt and Dm within 8% and 4% of observations, respectively. Steady-state model projections of radar retrievals aloft are shown to produce the correct rapid enhancement of surface snowfall compared to the lowest-available radar retrievals from 500 m MSL. Future studies can utilize these equations alongside radar measurements to estimate Nt and Dm below radar tilt elevations and to estimate uncertain microphysical parameters such as collision–coalescence efficiencies. Significance Statement While complex numerical models are often used to describe weather phenomenon, sometimes simple equations can instead provide equally good or comparable results. Thus, these simple equations can be used in place of more complicated models in certain situations and this replacement can allow for computationally efficient and elegant solutions. This study derives such simple equations in terms of exponential and power-law mathematical functions that describe how the average size and total number of snow or rain particles change at different atmospheric height levels due to growth from the vapor phase and aggregation (the sticking together) of these particles balanced with their fallout from clouds. We catalog these mathematical equations for different assumptions of particle characteristics and we then test these equations using spirally descending aircraft observations and ground-based measurements. Overall, we show that these mathematical equations, despite their simplicity, are capable of accurately describing the magnitude and shape of observed height and time series profiles of particle sizes and numbers. These equations can be used by researchers and forecasters along with radar measurements to improve the understanding of precipitation and the estimation of its properties.

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

Hybrid CNN-Transformer Feature Fusion for Single Image Deraining

Since rain streaks exhibit diverse geometric appearances and irregular overlapped phenomena, these complex characteristics challenge the design of an effective single image deraining model. To this end, rich local-global information representations are increasingly indispensable for better satisfying rain removal. In this paper, we propose a lightweight Hybrid CNN-Transformer Feature Fusion Network (dubbed as HCT-FFN) in a stage-by-stage progressive manner, which can harmonize these two architectures to help image restoration by leveraging their individual learning strengths. Specifically, we stack a sequence of the degradation-aware mixture of experts (DaMoE) modules in the CNN-based stage, where appropriate local experts adaptively enable the model to emphasize spatially-varying rain distribution features. As for the Transformer-based stage, a background-aware vision Transformer (BaViT) module is employed to complement spatially-long feature dependencies of images, so as to achieve global texture recovery while preserving the required structure. Considering the indeterminate knowledge discrepancy among CNN features and Transformer features, we introduce an interactive fusion branch at adjacent stages to further facilitate the reconstruction of high-quality deraining results. Extensive evaluations show the effectiveness and extensibility of our developed HCT-FFN. The source code is available at https://github.com/cschenxiang/HCT-FFN.

Does the Sierra Madre Mountain Range in Luzon Act as a Barrier to Typhoons?

Tropical cyclones (TCs) making landfall in Luzon weaken due to the surface friction of Luzon’s mountainous terrain and the reduced ocean heat, momentum, and moisture fluxes over the landmass. Landmass also influences TC rain distribution by orographic enhancement or blocking of a TC’s moisture-laden circulation. In this study, we investigated the influence of the effects of the Sierra Madre and the Cordillera Mountains Ranges (SMMR and CMR) on TC-associated wind and rainfall hazards to answer the question of whether the SMMR or the CMR mitigates TC hazards. We used the Weather Research and Forecasting (WRF) model with modified SMMR and CMR terrains to disentangle the effects of the orography with flat land. Results show that Luzon-passing TCs maintain their intensities at landfall regardless of the frictional effects of the mountain ranges but the CMR inhibits the re-intensification of westward-moving TCs emerging from landmass after traversing Luzon. The SMMR reduces wind exposure and basin-wide rainfall of the Cagayan Valley. Hence, the SMMR can be considered a barrier for that region. In addition, the weakening effect of the SMMR reduces the wind exposure of the island of Catanduanes and eastern Bicol the most. However, for the rest of Luzon, the SMMR enhances rainfall which will likely compensate for the slight decrease in wind exposure – especially considering that most TC-related damages are water/rainfall related. The CMR, overall, has a larger hazard-mitigating effect than the SMMR. In any case, we believe that shifting the discourse to these mountains’ biodiversity conservation and restoration – rather than their purported TC mitigating effects – will be more strategically constructive.

Modeling the Yogyakarta International Airport (YIA) Area Flood Control System Using EPA SWMM 5.1

Yogyakarta International Airport (YIA) is a new airport that operates in the Special Region of Yogyakarta, located in Temon District, Kulon Progo Regency. The presence of the airport will undoubtedly bring various benefits, such as increasing economic activities in the area around the airport. However, the location has a problem, namely the risk of flooding. Flooding can affect activities around the airport, such as freight forwarding activities and access to or from the airport. Based on this problem, efforts are needed to prevent the flood that occurs does not have an impact on the activities of YIA airport and the surrounding area. In this case, the researcher used the EPA SWMM program in modeling the drainage system at the site under study. Modeling the point of occurrence of flooding will make it easier to normalize and add embankments to the channel. Modeling with EPA SWMM 5.1 requires input data such as rain distribution data, watershed characteristics, and the transverse appearance of the drainage channel. The results showed an overflow in the 15 drainage channels studied. The causes of overflow are high surface runoff and sedimentation based on channels and backwaters. Efforts to overcome by normalizing and adding embankments are sufficient to overcome flooding in several channels. Meanwhile, for channels that are still experiencing flooding, other countermeasures, such as retention ponds, are needed.

Combining Ability of Maize (Zea mays L.) Line for Grain Yield in Mali

Mali experiences irregular and poor distribution of rains which limit maize production greatly. The study objective was to evaluate the performance of various maize hybrids as well as determine general and specific combining ability of the lines under water limited conditions. Two trials were conducted at Sotuba and Kassela in South-Western Mali with 23 genotypes in the first trial and 15 genotypes in the second trial. The experimental design was a completely random Fisher block design. Morphological and agronomic traits were evaluated in a lines/testers crossing scheme where the testers were females and the lines were males. Results showed significant difference (P< 0.05) for ear height, days to anthesis, days to silking, grain yield, leaf width, plant aspect, and 1000 grain weight. In both sites, the general combining ability was significantly different (P< 0.05) between testers whereas the specific combining ability differed significantly (P< 0.05) between the lines. The best performing hybrid at Sotuba in term of grain yield was observed with TZI1876/9071 (6,003 kg/ha), and at Kassela the highest yielding was TZI1876/CML442 (6,320 kg/ha).

The results of studies of agrotechnical indicators of the uniformity of rain distribution by a jet fan rain-forming nozzle when watering mushrooms

The materials of the article consider the studies carried out to determine the agrotechnical indicators of the uniformity of rain distribution when using the proposed design of a jet fan rain-forming nozzle installed on an automated plant for watering champignons. The flow-pressure characteristics of rain created by jet fan-shaped rain-forming nozzles with various nozzle cross-section shapes are presented.