Impact Of Rainfall Research Articles

Spatiotemporal evolution of deformation and LSTM prediction model over the slope of the deep excavation section at the head of the South-North Water Transfer Middle Route Canal

Slope deformation is one of the focal issues of concern during the normal operation and maintenance of the South-North Water Transfer Middle Route Project. To study the slope deformation evolution in the deep excavation section at the head of the canal, we applied 88 views of Sentinel-1A ascending image data from 2017 to 2019 and MT-InSAR(Multi-temporal InSAR) deformation monitoring technology to obtain long-time series deformation rates and cumulative deformation fields over the slope in the study area. Based on the analysis of the time-series monitoring data of the deformation field sample points, a LSTM (Long Short Term Memory Network) slope deformation predictive model was constructed to predict the slope deformation for the next 12 months at 12 sample points of the deep excavation slope. The impact of rainfall on slope deformation was investigated, and the reliability of the LSTM model was verified by using the measured data. The results show that the average annual deformation rate of the slope ranges from 10mm/a to 25mm/a, the maximum cumulative deformation is about 60 mm, and the slope of the excavated section is generally in an uplifted state. The rainfall-induced repeated uplift or subsidence of the canal slopes together with the peak deformation was closely related to the amount of rainfall during the wet season, and the longer the duration of the wet season, the more obvious the crest. Among the12 sample sites, the minimum and maximum deformation predicted using the LSTM model were 51.7 mm and 73.9 mm respectively, with the lowest correlation coefficient of 0.994 and the highest of 0.999. The maximum and minimum values of RMSE (Root Mean Square Error) were 4.4 mm and 3.6 mm respectively, indicating reliable prediction results. The results of the study can provide reference for the prevention and control of geological hazards in the South-North Water Transfer Canal.

Impact of rainfall on the thermal performance of geothermal permeable pavement systems

Geothermal permeable pavement (GPP) systems are a nascent type of energy geo-structures. GPP combine ground-source heat pump (GSHP) systems with permeable pavements to enable the additional functionality of heating and cooling, through energy harvesting using roads alongside existing drainage controls. GPP thermal performance is relatively unknown, hence, this research focuses on the development of a detailed 3D finite element (FE) model that considers local thermal non-equilibrium (LTNE) for exploring the influence of rainfall infiltration on the thermal performance of GPPs. The 3D numerical model was successfully validated and was used to evaluate the short-term thermal response of GPPs during rainfall events in both cooling (summer) and heating (winter) demand conditions. Furthermore, the diurnal rainfall effects and the influence of raindrop temperature and rainfall duration on GPPs were also examined. Results show that rainfall can have significant impacts on the thermal performance of GPPs and the raindrop temperature is an important parameter in yield predictions. Compared to no-rainfall events, the GSHP system fluid temperature during rainfall can differ by up to 19% when in the cooling operation model (air conditioning) and 63% in heating mode. These results indicate the significantly better performance of GPPs than impervious geothermal pavements, depending on the climatic conditions and end-use.

Effects of rainfall on the karst-related carbon cycle due to carbonate rock weathering induced by H2SO4 and/or HNO3

To determine the mechanism underlying the effect of rainfall on the karst-related carbon cycle while controlled by that of H2SO4 and/or HNO3, high-resolution monitoring was conducted in Lanhuagou Spring, which is a typical epikarst spring in southwest China. During the monitoring period, the signals from major elements and stable isotopes showed that the CO2 generated by H2SO4 and/or HNO3 entirely escaped from the water. Therefore, the CO2 consumption induced by H2CO3 and CO2 emission induced by H2SO4 and/or HNO3 jointly affected the karst-related carbon cycle. The CO2 consumption induced by H2CO3 dropped following rainfall events and recovered gradually thereafter, which was only affected by the retention time of infiltrating water (dilution effect of rainfall). However, the CO2 emission induced by H2SO4 and/or HNO3 had showed a downward trend from the first rainfall event (R1) to the end of monitoring. The effects of rainfall on CO2 emission induced by H2SO4 and/or HNO3 were related to both the dilution effect and the removal of H2SO4 and/or HNO3. Although the average CO2 consumption induced by H2CO3 dropped from 2.8 mmol/L before rainfall to 2.21 mmol/L thereafter (decreased by 21.07 %), the average CO2 emission induced by H2SO4 and/or HNO3 dropped from 1.29 to 0.49 mmol/L (decreased by 62.02 %); the range of CO2 emission induced by H2SO4 and/or HNO3 decline (0.8 mmol/L) was larger than that of CO2 consumption induced by H2CO3 (0.59 mmol/L), resulting in higher net CO2 consumption (CO2 consumption induced by H2CO3 minus CO2 emission induced by H2SO4 and/or HNO3) after rainfall than before rainfall in the monitoring period. Therefore, in the model used to predict karst-related carbon flux caused by global climate change, it is necessary to consider the impact of rainfall on CO2 emission induced by H2SO4 and/or HNO3. Importantly, the net CO2 consumption rather than CO2 consumption induced by H2CO3, as well as runoff should be considered as two multipliers when carbonate weathering is interfered by H2SO4 and/or HNO3.

Soil, climate, time and site factors as drivers of soil structure evolution in agricultural soils from a temperate-boreal region

The evolution of soil structure in agricultural soils is driven by natural and anthropogenic factors including inherent soil properties, climate and soil management interventions, all acting at different spatial and temporal scales. Although the causal relationships between soil structure and these individual factors are increasingly understood, their relative importance and complex interactive effects on soil structure have so far not been investigated across a geo-climatic region.Here we present the first attempt to identify the relative importance of factors that drive the evolution of soil structure in agricultural soils as well as their direction of effect with a focus on the temperate-boreal zone. This was done using a random forest (RF) approach including soil, climate, time, and site factors as covariates. Relative entropy, as quantified by the Kullback-Leibler (KL) divergence, was used as a quantitative index of soil structure, which is derived from the particle-size distribution and soil water retention data, and integrates the effects of soil structure on pores from the micrometre-scale to large macropores. Our dataset includes 431 intact topsoil and subsoil samples from 89 agricultural sites across Sweden and Norway, which were sampled between 1953 and 2017. The relative importance of covariates for the evolution of soil structure was identified and their non-linear and non-monotonic effects on the KL divergence were investigated through partial dependence analysis. To reveal any differences between topsoils (0–30 cm; n = 174) and subsoils (30–100 cm; n = 257), the same analysis was repeated separately on these two subsets.The covariates were able to explain on average more than 50% of the variation in KL divergence for all soil samples and when only subsoil samples were included. However, the predictions were poorer for topsoil samples (≈ 35%), underlining the complex dynamics of soil structure in agricultural topsoils. Parent material was the most important predictor for the KL divergence, followed by clay content for all soil samples and sampling year for only subsoil samples. Mean annual air temperature ranked third and annual precipitation ranked fourth for subsoil samples. However, it remains unclear whether the effects of climate factors are direct (e.g., freezing and thawing, wetting and drying, rainfall impact) or indirectly expressed through interactions with soil management. The partial dependence analysis revealed a soil organic carbon threshold of around 3% below which soil structure starts to deteriorate. Besides this, our results suggest that subsoil structure in the agricultural land of Sweden deteriorated steadily during the 1950′s to 1970′s, which we attribute to traffic compaction as a consequence of agricultural intensification. We discuss our findings in the light of data bias, laboratory methods and multicollinearity and conclude that the approach followed here gave valuable insights into the drivers of soil structure evolution in agricultural soils of the temperate-boreal zone. Theses insights will be of use to inform soil management interventions that address soil structure or soil properties and functions related to it.

Three-dimensional stability analysis of two-layer slope based on GIS under rainfall conditions

ABSTRACT Geographical Information System (GIS), as a computer-based tool, allows easy data processing, and complex geospatial data management and analysis, making it a suitable environment for 3D slope stability analysis. The study proposed an integral scheme for 3D stability analysis of a two-layer slope under rainfall conditions based on GIS. To construct the 3D model of the slope and the sliding surface in GIS, a method was proposed in which a grid is divided into four sections to improve the accuracy of calculation, and the extension of the Hovland model and the Janbu model in unsaturated soil was discussed. The transformation equations between the global coordinates and the ellipsoid local coordinates were derived to locate the critical sliding surface, and how to apply constraints of parameters to improve search efficiency was put forward. An infiltration model for a two-layer slope was built so that the changes in water content in slope at the both stages of rainwater infiltration and redistribution can be determined, and the model was integrated into the GIS-based scheme to assess the impact of rainfall on slope stability. Finally, two case studies were implemented to demonstrate the validity and correctness of the proposed scheme.

A study on siting of emergency shelters for dam failure floods considering population distribution and weather effects

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

MODELING THE IMPACT OF RAINFALL AND TEMPERATURE ON STERILE INSECT CONTROL STRATEGIES IN A TROPICAL ENVIRONMENT

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and study their impacts on sterile males’ releases strategies. Our model is as simple as possible to avoid complexity while being able to capture the temporal variations of an Aedes albopictus mosquito population in a domain treated by SIT, located in Réunion island. The main objective is to determine what period of the year is the most suitable to start a SIT control to minimize the duration of massive releases and the number of sterile males to release, either to reduce the mosquito nuisance, or to reduce the epidemiological risk. Since sterilization is not [Formula: see text] efficient, we also study the impact of different levels of residual fertility within the released sterile males population. Our study shows that rainfall plays a major role in the dynamics of the mosquito and the SIT control, that the best period to start a massive SIT treatment lasts from July to December, that residual fertility has to be as small as possible, at least for nuisance reduction. Indeed, when the main objective is to reduce the epidemiological risk, we show that residual fertility is not necessarily an issue. Increasing the size of the releases is not always interesting. We also highlight the importance of combining SIT with mechanical control, i.e., the removal of breeding sites, in particular when the initial mosquito population is large. Last but not least our study shows the usefulness of the modeling approach to derive various simulations to anticipate issues and demand in terms of sterile insects’ production.

Impact of Rainfall on 5G Millimeter Wave Channels

Impact of Rainfall on 5G Millimeter Wave Channels

Archimedes Screw Pump Efficiency Based on Three Design Parameters using Computational Fluid Dynamics Software – Ansys CFX

Utilization of Archimedes screw pumps as water lifting pumps has become widespread in past decade due to frequent occurrences of floods in Malaysia. The problem of insufficient drainage in various urban areas exacerbates the impact of heavy rainfall, prompting efforts to mitigate this issue with minimal maintenance cost and low impact to the environment. Thus, this study is aiming to study the design parameters of screw pump to obtain the optimal efficiency of the Archimedes screw pump specifically for flood mitigation in Malaysia. The main design parameters affecting pump’s efficiency are rotor profile, pitch length, length of the pump, rotational speed, inclination angle, and material selection. However, only three design parameters were considered in the study, that are the angle of inclination, the number of blades, and the angular velocity of the rotating pump. These three design parameters are selected as many previous findings focusing on varying angle of inclination with number of blades with constant rotational speed. Thus, this study will find the highest efficiency when these three design parameters are integrated with variation of rotational speeds at 25, 30 and 40 RPM. Basically, screw pump is designed using SOLIDWORKS and simulations with specific boundary conditions are conducted using the ANSYS-CFX software, which utilizes computational fluid dynamics (CFD) techniques. These boundary conditions are based on previous study by Rosly et al in 2016. The inlet flow rate of 0.002 m3/s and diameter of the screw pump are constant while the other three main parameters are varying within the acceptable ranges which are reported from prior studies. The outcomes found that the highest torque is generated by a single rotating blade at 5.65 Nm which rotates at 30 RPM at 30° angle of inclination. Meanwhile, the highest efficiency of 24.04% is obtained with a single rotating blade at 40 RPM with 20° angle of inclination. Based on the findings, it is concluded that these three main design parameters of screw pump may not be sufficient to obtain the optimal efficiency for the specific boundary conditions used in the simulation study. Thus, several combinations of design parameters should be considered in the future to increase the screw pump’s efficiency.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Analysis of Pluviograms Recorded in the Area of a Phosphogypsum Heap at Wiślinka, Poland

Abstract The paper is devoted to the long-term project concerning monitoring and stability analysis of a 40 m high phosphogypsum heap located at Wiślinka, Poland. The research presented in the paper focused on collecting and processing rainfall data, which subsequently allows us to perform numerical simulation of rainfall impact on heap’s behaviour. Such analysis requires time history of rainfall intensity, that is recorded by an automatic precipitation station. Since this is not common monitoring equipment, the characteristics of the station installed in the immediate vicinity of the heap are presented, and the data obtained in two experiments conducted in the laboratory are discussed. The analysis revealed that di erences between introduced and measured total rain are as large as 10% for very heavy rains. Moreover, the recorded maximum rainfall intensity often includes errors. The data processing procedure to obtain time history of rainfall intensity is presented on the basis of data collected in the first half of the hydrological year 2023 (the first period of the station’s operation). The total precipitation registered was 107 mm, and the maximum daily rain was only 26 mm. However, first single and multi-stage rainfall models for the Wiślinka region that would be applied into numerical simulations are presented.

Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions

During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.

Wet Tropospheric Correction for the HY-2C Altimeter over the Coastal Ocean Based on GNSS and ERA5 Data

For the impact of land, sea ice, rainfall and strong wind, the wet tropospheric correction (WTC) of HY-2C (the third marine dynamic environmental monitoring satellite of China) altimeter cannot be obtained accurately by the calibration microwave radiometer (CMR) over the coastal ocean. The conducted evaluation of the HY-2C CMR WTC over the coastal ocean by the Global Navigation Satellite System (GNSS) and ECMWF Reanalysis v5 (ERA5) data showed that the CMR WTC is abnormal or invalid. To solve this issue, a new method to determine the WTC of the HY-2C altimeter based on objective analysis (OA) by combining the GNSS WTC, ERA5 WTC and valid CMR WTC is developed. The OA WTC and HY-2C CMR WTC are compared to the Integrated Global Radiosonde Archive (IGRA) WTC to validate the OA method. The results show that the RMSE of the WTC differences significantly decreases and the correlation coefficient increases. The number of available WTC data increases over the open ocean. The variances of sea level anomaly corresponding to the OA WTC in most areas are significantly smaller than those of the HY-2C CMR WTC. In general, the availability and accuracy of HY-2C altimeter data are largely enhanced with the OA WTC.

Increased aridity and chronic anthropogenic disturbance reduce litter productivity in a Caatinga dry forest

Tropical dry forest productivity is thought to be driven by a combination of natural factors, particularly total water availability and seasonality. However, most of the world’s dry forests have been converted into forest successional mosaics by a combination of slash-and-burn agriculture and exploitation of forest products, which has potential impacts for forest productivity. Here, we examined litter production, nutrient concentrations and fluxes across regenerating and old-growth stands of a Caatinga dry forest during a 48-mo period, assessing the impact of annual rainfall, forest aboveground biomass, soil fertility, forest successional status, stand age and chronic anthropogenic disturbance. We found our sites to have low litter productivity, producing 0.193 ± 0.016 Mg ha−1 yr−1 (mean ± SE) of litter. Moreover, litter production was highly seasonal (> 90% during the dry season), and varied substantially across forest stands (from 0.0035 to 0.564 Mg ha−1 yr−1), but with no differences relative to forest successional status; i.e. similar litter production in regenerating and old-growth stands. Litter production was affected by precipitation, soil fertility and chronic anthropogenic disturbance. Litter nutrient concentration was highly variable across forest stands, but concentration responded very little to our focal drivers. Litter N-concentration was relatively high, while P was very low resulting in a small flux to the soil via litter decomposition. In contrast to litter nutrient concentrations, nutrient fluxes were more predictable, particularly considering forest successional status. Our results suggest that Caatinga dry forest on sandy soils is a low-productivity ecosystem, probably due to a combination of natural and anthropogenic drivers.

ANALISIS KARAKTERISTIK IKLIM DAN HUJAN PADA LAHAN PERTANIAN DI KECAMATAN KEDIRI KABUPATEN LOMBOK BARAT

The availability of water on Earth's surface is influenced by rainfall and climate in a particular region. Water is a basic necessity for human beings and is used for various daily life purposes. One of these purposes is irrigation in agricultural lands. The agricultural sector has traditionally relied on the availability of water from rainfall and the suitability of the climate. Climate factors include air temperature, humidity, sunlight intensity, and wind speed. This research aims to determine the impact of rainfall and climate conditions on agricultural lands. The study utilizes descriptive analysis of climate data and its influence on agricultural lands. The research findings reveal that the average monthly rainfall in Kediri district, West Lombok Regency, is approximately 222.75 mm. The average temperature is 26.7 degrees Celsius, with an average humidity of 84.5%, average sunlight intensity of 65.9%, and an average wind speed of 3.1 knots. These rainfall and climate conditions can affect water availability, soil microbe growth, plant photosynthesis processes, and plant pollination processes in agricultural lands.

Consistency of the Troposphere Wet Delay From Water Vapor Radiometer and Co‐Located GPS Station

AbstractThe tropospheric delay acquired by the Global Navigation Position System (GPS) Precise Point Positioning (PPP) is continuous and steady, less affected by rainfall. The Water Vapor Radiometer (WVR) can provide real‐time meteorological parameters but is more sensitive to high‐frequency information in troposphere. To explore the use of WVR‐retrieved tropospheric delay and assist other geodetic techniques for atmospheric correction, the tropospheric delay from WVR and co‐located GPS at Shanghai, Beijing, Kunming, and Urumqi stations in China are compared. For the inconsistent values of WVR‐PPP zenith wet delay, the variations of the tropospheric delay from WVR and GPS before and after the rainfall were statistically analyzed. The results suggest that, for the rain rate ranging from 0.1 to 50 mm/hr, the impact of rainfall on WVR could last from 10 min before to 30 min after the rainfall. With filtering WVR data based on meteorological parameters and rain rate, the zenith wet delay between WVR and PPP at Shanghai shows good consistency, the root mean square (RMS) is 6.11 mm, correlation is 0.997, and the RMS in the other three stations ranges from 16.35 to 25.16 mm (correlation ranges in 0.794–0.951). The analysis indicates that the tropospheric delay of WVR is reliable to be applied to space geodetic techniques correction in real‐time with filtering to reduce the effect of rainfall, water vapor, and liquid water variability.

Precision Rainfall Predictions: A Daily Weather Data Approach using Machine Learning

Rainfall prediction is an important task since a lot of individuals rely on it, especially in agriculture. The study attempts to predict rainfall using machine learning algorithms, taking into account the impact of shortages or excessive rainfall on rural and urban life. Several techniques and approaches for predicting rain have been developed; however, there is still a lack of precise outcomes. The comparative study focused on incorporating Machine Learning (ML) models, analyzing various situations and time horizons, and predicting rainfall by using three different approaches. This research uses data preprocessing, feature selection, and machine learning methods like Random Forest, K-nearest neighbor (KNN), and Logistic Regression. This study shows the usefulness of machine-learning approaches in forecasting rainfall. In comparison, Random Forest performs better when compared to other models with a high precision rate.

Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China.

Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier. In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China. Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days. These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

The Contribution of Climate Factors on the Availability of Hydropower Energy in West Java

West Java has excellent potential in hydropower energy development, partly because it has high-intensity rainfall. However, some things could be improved with the water supply to produce electricity, mainly due to rainfall and temperature variability. Therefore, it is necessary to analyze the magnitude of the contribution of climate factors to the availability of hydropower energy, as well as the sensitivity of the total potential loss of water supply due to variability and changes in rainfall and temperature. Data analysis was carried out by modeling the impact of rainfall and temperature on the availability of power supply for hydropower operations. The results show that rainfall greatly contributes to the loss of hydropower supply with an R2 value of 0.9. However, the temperature does not contribute to the Cirata and Saguling hydropower plants, seen from the R2 value of only 0.03. In contrast, for the Kracak hydropower plant, the temperature contributes to the loss of power supply with an R2 value of 0.7. Loss of power supply in 2021 to 2040 will tend to be higher and occur more frequently than the observation year, with the highest potential loss of 1 MW (5%) at the Kracak, 300 MW (29%) at the Cirata, and 300 MW (42%) ) at the Saguling. The total potential loss of power supply for the Kracak, Cirata, and Saguling hydropower plants is sensitive to changes in rainfall and temperature in these areas.

Influence of Some Selected Climatic Variables on Yam Production in Bosso Local Government of Niger State

This study focused on the influence of Some Selected Climatic Variables on Yam Production in Bosso Local Government of Niger State. Secondary data was obtained from the Nigerian Meteorological Agency, Minna (NIMET). The data acquired include rainfall and humidity data. Also, data for crop was obtained from the Agricultural development program Minna. The result of the study therefore shows that positive relationship exists between yam production and rainfall/relative humidity in the area under study. As for the rainfall, it was observed that in 2020, where the rainfall value was maximum, the accompanying yam production was also very high, also in 2010 when the rainfall amount was recorded least, the yam production for that year was not much. Any variation in result could be associated with the source of data and the study areas. Summarily, it can be stated that there is an established positive relationship between climatic data (rainfall and relative humidity) and the production of yam for the period under study. The study recommended that there is a need to develop and promote yam varieties that can withstand drought conditions given the impact of rainfall and humidity on yam production. Farmers should also be encouraged to adopt improved irrigation systems to mitigate the impact of low rainfall on yam production. This could include the use of drip irrigation systems or rainwater harvesting. Finally, farmers should be advised to plant yam at optimal times, taking into consideration the climatic conditions of their region.