Rainfall Parameters Research Articles

Rainfall–runoff modeling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA)

Abstract Nonlinear properties and natural uncertainties in the rainfall–runoff process, the necessity of extensive data, and the complexity of the physical models have caused researchers to use methods inspired by nature such as artificial neural networks, fuzzy systems, and genetic algorithms (GA). The main purpose of this study was to estimate runoff employing Adaptive Neuro-Fuzzy Inference System (ANFIS) and GA models using accessible, applicable, and easily available climatic data. The results of the two models were compared to provide an easy but reliable model to estimate evaporation. The models were utilized to estimate the runoff in Sivand river basin located in Fars province in central Iran. The results were compared considering a range of model performance indicators as mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). According to the results presented, ANFIS with lower RMSE and MAE and higher correlation coefficient and NSE between the observed and predicted values provided higher accuracy in comparison to GA. Also, it was clear that using ANFIS, an increase in the number of membership functions and running cycles of the model decreased the error such that the results in the studied stations using were improved by 42, 44 and 11%, respectively by increasing the number of membership functions and run rounds. Also, it was observed that the nonlinear models performed better than the linear models when applying GA such that non-linearizing the model improved the results of the GA model in the three studied stations by 27.5%, 17%, and 9.5%, respectively. Meanwhile, considering RMSE amounts the best results from ANFIS were 23%, 54.6%, and 35.7% better than the best results from GA in the three stations, respectively. According to the results of the study runoff can be estimated appropriately by utilizing ony meteorological data and there is no need for more complex and interdependent data. A sensitivity analysis was conducted too by removing rainfall and evaporation parameters in two different scenarios. The ANFIS model showed the lowest sensitivity to the absence of those parameters especially evaporation in scenario 3 with RMSE = 0, 0, and −0.005 for Chambian, Dashtbal, and Tang Balaghi stations, respectively. The results of the study justifies using ANFIS employing only meteorological data to estimate runoff in areas when scant data are available.

IDENTIFIKASI GERAKAN TANAH DI DESA KEBONHARJO DAN SEKITARNYA, KABUPATEN KULON PROGO, YOGYAKARTA

The village of Kebonharjo and neighborhoods is zone of relatively high ground movement. In the western part of Indonesia, it was formed due to the influence of the collision two tectonic plates. Kebonharjo and neighborhoods are earthquake and volcanic zone. With a steep slope and high rainfall data, one of the biggest factors for landslides. This study aims to fins out what potential couse the area to become a landslide-prone area and there indications of anything in the research area. There are several obstacles to the development of the Kebonharjo village are and suggroundings. In its study using the DVMBG method qhich combines several parameters, on of which is rainfall parameter data, geology, slope, land use and soil types. The potential for landslides in Kebonharjo village really needs attention because the are is very high and the rainfall is high, the potential for large landslides. After counducting the research, it can be seen the types of landslides according to (Varnes, 1978), type of landslides Earth Spread and Earth Flow. 
 
 Keywords: Soil Movement, DVMBG method, tectonic.

Modelled impact of ocean warming on tropical cyclone size and destructiveness over the Bay of Bengal: A case study on FANI cyclone

Ocean warming influences tropical cyclone (TC) destructive parameters and hence their guidance certainly helps the disaster management agencies to reduce the damage. The present study modelled the sensitivity of TC size, intensity, rainfall, and destructive potential parameters under various ocean warming conditions. A recent extremely severe cyclonic storm FANI (2019) over the Bay of Bengal is chosen for this purpose. The 9-km grid-spacing Weather Research and Forecasting (WRF) model simulations are performed by altering the default sea surface temperature (SST) by −1 °C, +1 °C, +2 °C and, +3 °C respectively along with the control run. The model simulations revealed that ocean warming causes the FANI cyclone to turn northeastward direction. The observed changes of tangential wind speed due to large sea surface enthalpy fluxes associated with ocean warming result in TC size changes and then guide the cyclone to northeastward movement. The radius of 34-knot wind (R34) is more sensitive to the SST warming compared to the radius of maximum winds. The modelled R34 values ranged between ~180–600 km against observations (~100–400 km) during the TC life period. The increased tangential wind speeds (~50–60 m s−1) and convective updrafts (~1.3–1.5 m s−1) in the TC area are responsible for TC intensity and size changes. A linear and exponential growth is seen for the TC destructive potential indicators those estimated using TC intensity and R34 values. The SST increase could result in peak heavy rainfall (>65 mm day−1) in the TC inner-core region, especially the rear sector. The categorical rainfall distribution analysis also proved that heavy rainfall areas extend to greater distances (>300 km) around the TC center with SST warming. The study helps in assessing the hydro-meteorological destruction associated with the TCs in the future warming climate.

Long-term unsustainable patterns of development rather than recent deforestation caused the emergence of Orthocoronavirinae species.

We investigated whether a set of phylogeographical tracked emergent events of Orthocoronavirinae were related to developed, urban and polluted environments worldwide. We explored coronavirus records in response to climate (rainfall parameters), population density, CO2 emission, Human Developmental Index (HDI) and deforestation. We contrasted environmental characteristics from regions with spillovers or encounters of wild Orthocoronavirinae against adjacent areas having best-preserved conditions. We used all complete sequenced CoVs genomes deposited in NCBI and GISAID databases until January 2021. Except for Deltacoronavirus, concentrated in Hong Kong and in birds, the other three genera were scattered all over the planet, beyond the original distribution of the subfamily, and found in humans, mammals, fishes and birds, wild or domestic. Spillovers and presence in wild animals were only reported in developed/densely populated places. We found significantly more occurrences reported in places with higher HDI, CO2 emission, or population density, along with more rainfall and more accentuated seasonality. Orthocoronavirinae occurred in areas with significantly higher human populations, CO2 emissions and deforestation rates than in adjacent locations. Intermediately disturbed ecosystems seemed more vulnerable for Orthocoronavirinae emergence than forested regions in frontiers of deforestation. Sadly, people experiencing poverty in an intensely consumerist society are the most vulnerable.

Analysis of trends and variability of climatic parameters in Teff growing belts of Ethiopia

Abstract The purpose of this study was to analyze the trend and variability of rainfall and temperature parameters in the teff growing belts of Ethiopia. The data were obtained from weather stations and covered 1981–2018. Data analyses were carried out with descriptive statistics and time trend regression model. The results showed that short-season rainfall declined by 0.474 mm, while long-season rainfall increased by 2.72 mm. Also, the short season rainfall showed higher variability when compared to the long season rainfall. The standardized seasonal rainfall anomalies indicated severe and extreme droughts (<−1), and the results were in alignment previous droughts. In addition, over the period, the maximum and minimum temperature significantly increased by 0.13 and 0.15°C, respectively. The coefficients of variation (CV) for maximum and minimum temperatures were 2.24 and 5.10%, respectively. Anomalies in maximum and minimum temperatures were negatively low in 1981–1997 but positive in 2003–2018. There were teff yield anomalies and variability. It was concluded that variations in selected climatic parameters or their interactions accounted for teff yield variability. This necessitates provision of adequate adaptation strategies to minimize the impacts of climate change on the yields of teff.

Comparison Study Of Measurement Results Between Rain Gauge 7052.0100 and Optical Rain Gauge 815 at Lapan Kototabang

In general, the population in Indonesia is a farmer. This causes farmers to need information about the weather to carry out agricultural activities. One way to obtain information about the weather is to use the Rain Gauge instrument. In this study, the Rain Gauge instrument at LAPAN Kototabang from 2020 to 2021 was compared with the Optical Rain Gauge instrument. This research uses descriptive research with secondary data in the form of rainfall intensity data that has been measured on the Rain Gauge and Optical Rain Gauge from LAPAN Kototabang. This research was conducted to analyze the output data from the Rain Gauge and Optical Rain Gauge in the form of rainfall intensity, rainfall accumulation, and time. From the data analysis, two results can be stated from this research. First, the analysis of the rainfall parameter data that has been processed shows that the rainfall in Kototabang in January - October 2020 and 2021 has a high rainfall value. This causes the intensity of rainfall in Kototabang to be categorized as heavy rain. The two accumulations of rainfall in 2020 and 2021 in the Kototabang area are 497.031 mm and 0.602 mm using the Rain Gauge. While the measurement using Optical Rain Gauge rainfall intensity is 0.207 mm and 0.221 mm. This proves that the intensity of rainfall in 2020 is greater than the intensity of rainfall in 2021.

Generating Continuous Rainfall Time Series with High Temporal Resolution by Using a Stochastic Rainfall Generator with a Copula and Modified Huff Rainfall Curves

In this study, a stochastic rainfall generator was developed to create continuous rainfall time series with a high temporal resolution of 10 min. The rainfall-generation process involved Monte Carlo simulation for stochastically generating rainfall parameters such as rainfall quantity, duration, inter-event time, and type. A bivariate copula was used to preserve the correlation between rainfall quantity and rainfall duration in the generated rainfall series. A modified Huff curve method was used to overcome the drawbacks of rainfall type classification by using the conventional Huff curve method. The number of discarded rainfall events was lower in the modified Huff curve method than in the conventional Huff curve method. Moreover, the modified method includes a new rainfall type that better represents rainfall events with a relatively uniform temporal pattern. The developed rainfall generator was used to reproduce rainfall series for the Yilan River Basin in Taiwan. The statistical indices of the generated rainfall series were close to those of the observed rainfall series. The results obtained for rainfall type classification indicated the necessity and suitability of the proposed new rainfall type. Overall, the developed stochastic rainfall generator can suitably reproduce continuous rainfall time series with a resolution of 10 min.

A comparative study of regional climate trends in the Keoladeo and Bhitarkanika wetlands, India

The climate trend is one of the most dominant factors likely to affect the structure and function of the wetlands of Keoladeo and Bhitarkanika. This study compares the climate conditions and trends of two Ramsar sites in India, the Bhitarkanika and Keoladeo wetlands. Forty-year climate data (1980-2020) were used to identify and measure the strength of possible climate change in these habitats. Mann-Kendall’s test, Sen’s slope estimates and Sequential Mann-Kendall’s (SQMK) test were used to study the long-term and short-term trends of rainfall and climate of Bhitarkanika and Keoladeo. The results of this study matched those of earlier studies conducted at a larger geographical scale. The study indicated significant changes in the parameters of rainfall and temperature trends. A significant increase in annual rainfall (Sen’s slope = 5.24E+00, New p value = 2.75E-02) was observed at Bhitarkanika, while the trend at Keoladeo was positive but insignificant. Both ecosystems are getting warmer. Seasonal changes were also identified in the rainfall and temperature. The summer temperature rise was comparatively stronger at Keoladeo (Tmax: Sen’s slope=2.33E-02, New p value=2.45E-02; Tmin: Sen’s slope=2.68E-02, New p value=1.39E-02). An increase in monsoon temperature was also recorded in both ecosystems. Both wetland ecosystems are vulnerable to climate change, but the Keoladeo wetland is at higher risk because stronger temperature changes have occurred in the past four decades. The SQMK test also suggests a more erratic climate in the Keoladeo wetlands. The study highlights the comparative changes occurring in both ecosystems. This is the first study identifying both long-term and short-term changes in these wetlands at a smaller geographical scale.

Simulation of a Hailstorm Event over Bangladesh using WRF Model

An attempt is made to simulate a pre-monsoon hailstorm event over Bangladesh using the high-resolution Weather Research and Forecasting (WRF) model. A severe hailstorm event embedded with multi-cell thunderstorm occurred over the North-central region of Bangladesh and adjacent West Bengal state of India on 30 March 2018, which moved towards the South-East direction up to its highest intensity stage during 0900-1030 UTC is selected for the study. The WRF model is used to investigate the dynamical and thermodynamical characteristics of the selected hail producing thunderstorm event. The model is run on a double nested domain at 9 and 3 km horizontal resolutions using the Kain-Fritsch (new Eta) cumulus and Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2) planetary boundary layer parameterization schemes. The analysis of the accumulated hail, mean sea level pressure, vertical wind shear, vertical (w) component of wind, relative humidity along with low level wind, convective available potential energy (CAPE) and convective inhibition energy (CINE), rainfall (mm) parameters are investigated. Rainfall is compared with that of observed value. The high amount of relative humidity (>90%), CAPE (>3000 JKg-1) and vertical wind shear (>40 ms-1) help to form severe convective storm which vertically developed upto 150 hPa level. In addition, the high value of w component of winds (~0.4 ms-1) helps updraft process which is favorable for hail formation. The present combination of parameterization schemes of model is found suitable to capture the selected hailstorm event reasonably well, but more similar hailstorm events need to be studied to make any final conclusions. The Dhaka University Journal of Earth and Environmental Sciences, Centennial Special Volume June 2022: 123-130

Effect of selected bioengineering measures on runoff, soil loss, and cotton (Gossypium hirsutum L.) productivity in the semi-arid region of western India

Soil erosion and associated loss of soil nutrients and organic carbon are one of the major reasons for low crop productivity in major cotton (Gossypium hirsutum L.) growing areas of India. This calls for designing acceptable and low-cost bioengineering measures to enhance system productivity and soil health. Thus, a field study was conducted at Vasad, Gujarat, India from 2014 to 2017 to evaluate the efficacy of selected bioengineering treatments on cotton crop production and runoff and soil loss. The eight experimental treatments consisted of bio-filters of three grass species, viz.Para (Brachiaria mutica), Guinea (Megathyrsus maximus),and Napier (Pennisetum purpureum) at two strip widths (1 m and 2 m), field bund, and the conventional system as practiced by the farmers of the region. The treatments were tested in 45 × 10 m plots having the gauging facility for recording runoff and soil loss data. Rainfall, runoff, sediment concentration, soil loss, organic carbon, nutrient loss, and growth and yield parameters of grass barrier species and the cotton crop were recorded and analysed. The filter strip of Guinea sp. at 2 m width was most effective, reducing runoff by 30%, soil loss by 66%, nutrients loss by 69%, sediment concentration to 1/3rd, and soil organic carbon loss by 65%. The lowest runoff coefficient value of 0.209 observed among the filter strips in this treatment, indicated about 79% of rainwater was conserved in situ. The available soil nutrients increased by up to 42% and SOC concentration by 61%, and cotton equivalent yield by 25% than that of the conventional system. We concluded that Guinea grass filter strips of 2 m width planted at 45 m spacing in cultivable lands having 2% slope minimized runoff, soil loss, nutrient loss, improved soil fertility and enhanced cotton productivity.

Taking Account of Seasonality in a Regional Flood Frequency Estimation Approach Based on Event Simulations

Regional flood estimation is an important issue in hydrology to anticipate and reduce the damages caused by extreme rainfall events. Approaches based on event simulation are particularly suitable to address this. As research has demonstrated the seasonality of rainfall characteristics, many flood frequency estimation approaches take into account rainfall seasonality to include seasonal fluctuations. For an event-based approach, since its hydrological model is initialized for each rainfall event, its performance is very sensitive to the initial states of the model. The seasonality of its hydrological model could thus become a decisive factor. Due to the complexity of the regionalization method, very few flood frequency estimation approaches based on event simulation have been regionalized at a large scale and do not consider the seasonality of hydrological parameters. This is the case for the SHYREG method studied in this article. Using data from HYDRO French database and SAFRAN, we discuss several adapted configurations considering the seasonality of both rainfall and hydrological parameters during its calibration and regionalization phase. Tests were carried out on 1929 catchments throughout France. Rather than calibrating a constant annual parameter for the hydrological model, we calibrated “winter” and “summer” parameters based on different observed flow quantiles (“seasonal”, “annual”, or “both”). Criteria on flood quantiles were calculated for different samplings. We also discuss the representativeness of seasonal parameters for the regionalization procedure and hydrological coherence observed from this seasonal parameterization. It seems that calibrating parameters based on seasonal flow quantiles helps reproduce annual quantiles, while the opposite is not possible. Among all the calibration configurations, calibration performed on both seasonal and annual flow quantiles makes the largest improvement compared to the initial annual parameterization method. It can correctly restitute seasonal flood quantiles for both calibration and validation catchments, with an obvious improvement in terms of estimating flood frequency in ungauged sites. It shows that the seasonality of hydrological parameters is worth considering for a regional flood estimation approach.

Dry and Wet Spell Analysis and Moisture Adequacy Index (MAI) Estimation for Assessing the Agro Climatic Potentiality for Crop Planning in the Central Brahmaputra Valley Zone (CBVZ) of Assam, India

Analysis of rainfall and other weather parameters helps to develop and modify the management practices for stabilizing crop production and crop planning in the rainfed ecosystem at certain level. Lack of knowledge on wet and dry spell hampers scientific crop planning in the region. The present study has been undertaken with the objectives to investigate probable occurrence of dry and wet spell and to find out the Moisture Adequacy Index (MAI) for the districts of Nagaon and Morigaon under Central Brahmaputra Valley Zone (CBVZ) of Assam. In this study, Markov Chain model was used to estimate the initial, conditional probabilities of dry and wet weeks along with the probability of two and three consecutive wet and dry weeks considering 20 mm rainfall as threshold limit. MAI was calculated using Thornthwaite’s (1955) soil water balance method. The average rainfall of Nagaon and Morigaon were found to be 1775.27 mm and 1734.37 mm respectively. The study indicated that in CBVZ, probability of occurrence of dry week is higher from 1st week to 14th week. The range of probability varies from 56.7 per cent to 100 per cent during this period. The period from 25th week to 30th week is best suited for transplanting of sali rice with 100 per cent probability of wet spell and also continuously high MAI value of 1.0 throughout the period. The MAI value increases from 15th week with onset of pre-monsoon and ranges between 0.9-1.0 throughout the kharif period and then decreases with withdrawal of rainfall. Based on MAI values, the area is suitable for growing of a third crop using residual soil moisture. There is an ample scope for water harvesting from July to September, which can be utilized as crop saving irrigation as well as pre-sowing irrigation for succeeding rabi crops, which are generally sown on residual soil moisture. The results through analysis have been used for agricultural planning at CBVZ region.

Improving the Estimation of Throughfall Amounts in Primeval Forests along an Elevation Gradient on Mountain Gongga, Southwest China

Differences in rainfall partition into throughfall among different primeval forests distributed along an altitude gradient are inadequately investigated and understood. Through continuous and automatic monitoring of natural rainfall and throughfall along an elevation gradient on Mountain Gongga, we examined the response of throughfall to various rainfall patterns in the broadleaved forest (BF), broadleaved-coniferous mixed forest (MF), and coniferous forest (CF) across individual rain events from May to October in 2019. A series of linear models that estimate throughfall amount were obtained and compared. Results showed that throughfall was jointly controlled by rainfall characteristics (including amount, duration, average, and peak intensity) and leaf area index (LAI). Rainfall amount was the primary control for throughfall amount. The models with all rainfall parameters and LAI as variables did not markedly outperform (R2 enhancement by 0–0.02) the simple linear models with rainfall amount as the only variable; therefore, the latter are recommended due to simplicity and easiness of use. Although the correlation of throughfall with LAI was less prominent compared to rainfall parameters, LAI showed a significant positive linear correlation (p < 0.05) with the estimated rainfall amount threshold (the rainfall required to saturate the canopy) by the single-variable linear models at the monthly scale. Over the study period, penetration proportions of rainfall in BF, MF, and CF were 83%, 75%, and 80%, respectively. The rainfall amount threshold in CF (0.70 mm) was less than those in BF (0.80 mm) and MF (0.92 mm). Rain events of higher intensity exhibited a higher mean penetration proportion than lower intensity rain events. The use of single-variable linear models developed here, despite some overestimations of throughfall amount, could lead to an overall satisfactory estimation of rainfall redistribution in mountainous areas.

Investigation of the Effect of Climate Change on Energy Produced by Hydroelectric Power Plants (HEPPs) by Trend Analysis Method: A Case Study for Dogancay I–II HEPPs

One of the most important measures taken in reducing the impact of climate change resulting from global warming is the production of energy from clean and renewable resources. Hydroelectric power plants are leading renewable energy sources. In this study, the effects of climate change on hydroelectric power plants, a renewable energy source, have been investigated. Dogancay I and II Hydroelectric Power Plants, which are built on the Sakarya River located in the Sakarya basin of Turkey, was selected as a study area. Moreover, the monthly average energy, runoff, and efficiency parameters of hydroelectric power plants and the monthly average precipitation, temperature, and humidity physical parameters of the Sakarya province, which is a working area, were considered. The length of time-series data is 48 months (2015–2018). Analysis of the data was performed with the innovative polygon trend analysis (IPTA) method, which is one of the newest trend analysis methods. When the studies in the literature are examined, the IPTA method is applied to hydroelectric energy data for the first time thanks to this study. Therefore, it is thought that this study will contribute a great deal to the literature. As a result of this study, a generally decreasing trend was observed in IPTA graphs of energy, flow, and efficiency parameters. In terms of the physical parameters of rainfall, temperature, and humidity, there was a decreasing trend in rainfall and humidity graphs and no trend in temperatures was observed.

The Functional Relationship of Sediment Transport under Various Simulated Rainfall Conditions

Sediment removed in the detachment process is transported by overland flow. Previous experimental and field works studied that sediment transport is influenced by hydraulic properties of flow, physical properties of soil, and surface characteristics. Several equations in predicting sediment transport have been developed from previous research. The objective of this paper was to establish the selected parameters that contribute to the sediment transport capacity in overland flow conditions under different rainfall pattern conditions and to evaluate their significance. The establishment of independent variables was performed using the dimensional analysis approach that is Buckingham’s π theorem. The final results obtained are a series of independent parameters; the Reynolds number (Re), dimensionless rainfall parameter iLν, hydraulic characteristics QLν that related to the dependent parameters; and dimensionless sediment transport qsρv. The relationship indicates that 63.6% to 72.44% of the variance in the independent parameters is in relation to the dependent parameter. From the iteration method, the estimation of constant and regression coefficient values is presented in the form of the general formula for linear and nonlinear model equations. The linear and nonlinear model equations have the highest model accuracy of 93.1% and 81.5%, respectively. However, the nonlinear model equation has the higher discrepancy ratio of 54.9%.

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.Article highlightsThe rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing.The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall.The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

Crop Recommendation and Yield Estimation Using Machine Learning

In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear Regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps the farmers in preserving their time by assisting them in the decision-making process.

Temporal variations in fecal indicator bacteria in bathing water and sediment in a coastal ecosystem (Aytré Bay, Charente-Maritime, France)

This study presents the fecal contamination dynamic at the two bathing sites of Aytré Bay (Charente Maritime, France). We quantified fecal indicator bacteria (FIB) abundances (Escherichia coli and enterococci) from water and sediment samples over one-year survey. Then we measured biological (bacterial abundance, chlorophyll-a), physico-chemical (dissolved nutrients and oxygen, salinity, pH …) and climatic (temperatures, rainfall and tidal coefficient) parameters. Results showed that FIB abundances were occasionally higher than the European regulatory threshold during winter, summer and fall. The “poor quality” of the bathing water was due to high enterococci abundance. We found negative significant correlations between FIB and water temperature and salinity, and positive significant correlations between FIB and rainfall, PO4, NO3, NO2, and SiO2 mainly in water. Relationships between parameters showed that during summer and spring the main environmental drivers were temperature and salinity, while in fall and winter they were rainfall and dissolved nutrients.

EVALUATION OF RAINFALL TRENDS DUE TO CLIMATE CHANGE: A CASE STUDY OF SAMARU, ZARIA

Rainfall is the major source of water for agricultural activities in Nigeria, but there has been a major variation due to climate change. This study is to assess the change in rainfall trend due the impact of climate change in Samaru, Zaria Kaduna State. Rainfall data from 1981 – 2018 (38 years) were collected from the Nigerian Meteorological Agency (NIMET). The data was analyzed using Excel and XL-Statistics to evaluate the mean, standard deviation and coefficient of variation of monthly and annual rainfall and other statistical parameter. The study revealed that there is a significant change in the trend of annual rainfall as the highest rainfall was recorded in year 1994 with a value of 1487 mm and lowest rainfall in 1999 as 440 mm. The highest average monthly rainfall data is between July to September and a peak rainfall in August with an average value of 547 mm. This analysis provide useful information to farmers which can be used a basis for planning annual crop planting and also water resources planning such as in irrigation scheduling.

Analysis of Past and Projected Trends of Rainfall and Temperature Parameters in Eastern and Western Hararghe Zones, Ethiopia

Smallholder farmers in East and West Hararghe zones, Ethiopia frequently face problems of climate extremes. Knowledge of past and projected climate change and variability at local and regional scales can help develop adaptation measures. A study was therefore conducted to investigate the spatio-temporal dynamics of rainfall and temperature in the past (1988–2017) and projected periods of 2030 and 2050 under two Representative Concentration Pathways (RCP4.5 and RCP8.5) at selected stations in East and West Hararghe zones, Ethiopia. To detect the trends and magnitude of change Mann–Kendall test and Sen’s slope estimator were employed, respectively. The result of the study indicated that for the last three decades annual and seasonal and monthly rainfall showed high variability but the changes are not statistically significant. On the other hand, the minimum temperature of the ‘Belg’ season showed a significant (p < 0.05) increment. The mean annual minimum temperature is projected to increase by 0.34 °C and 2.52 °C for 2030, and 0.41 °C and 4.15 °C for 2050 under RCP4.5 and RCP8.5, respectively. Additionally, the mean maximum temperature is projected to change by −0.02 °C and 1.14 °C for 2030, and 0.54 °C and 1.87 °C for 2050 under RCP4.5 and RCP 8.5, respectively. Annual rainfall amount is also projected to increase by 2.5% and 29% for 2030, and 12% and 32% for 2050 under RCP4.5 and RCP 8.5, respectively. Hence, it is concluded that there was an increasing trend in the Belg season minimum temperature. A significant increasing trend in rainfall and temperature are projected compared to the baseline period for most of the districts studied. This implies a need to design climate-smart crop and livestock production strategies, as well as an early warning system to counter the drastic effects of climate change and variability on agricultural production and farmers’ livelihood in the region.