Average Annual Rainfall Research Articles

Influence of Design Variables on the Financial Feasibility of Rainwater Harvesting Systems

Extensive implementation of rainwater harvesting (RWH) systems can promote substantial improvement in urban water resource management. Therefore, establishing the financial feasibility of RWH systems is imperative for their dissemination. This study evaluated the influence of rainfall time series indicators (average annual rainfall, seasonality index and behaviour of periods without precipitation) and design variables (catchment area, rainwater demand, number of inhabitants, potable water demand and rainwater tank size) on the financial feasibility of RWH systems in eight Brazilian cities. Correlations between rainfall indicators and financial feasibility were introduced, along with sensitivity analysis of design variables. Financial feasibility was obtained in 30% to 70% of the simulated scenarios. Initial investment and operating costs varied significantly among the eight cities according to local prices. Systems with a catchment area of 200 m2 were capable of supplying, on average, 90.5% of the maximum rainwater consumption observed in this study. Local variation of potable water tariff schemes affected the financial feasibility of RWH systems. The number of inhabitants was the most influential design variable on the financial feasibility of RWH systems, followed by the rainwater tank size. Places with lower rainfall seasonality indexes and lower incidence and duration of dry periods are likely to lead to greater financial feasibility.

Yield stability analysis of pearl millet genotypes in arid region of India using AMMI and GGE biplot

Aim: To assess genotypes with ability to sustain yields under arid region and to understand the significance of interactive effects of genotype × environment to develop resilient cultivars for rainfed farming. Methodology: Performances of 12 pearl millet varieties and hybrids released for commercial cultivation in India, were estimated using RBD with three replications during rainy seasons of 2018, 2019 and 2020. The trials were conducted at the Agricultural Research Station, ANGRAU, Ananthapuramu, located in the Scarce Rainfall Zone of Andhra Pradesh, India, with an average annual rainfall of nearly 553 mm and 300 mm to 400 mm during rainy season. Results: The environment and genotype effects were highly significant in the Additive Main Effect and Multiplicative Interaction (AMMI) model, implying that environments are varied and genotypes performed differently in each environment offering a great scope for selecting better adaptive genotypes. Apart from moisture stress, the amount of rainfall received during both the anthesis and grain maturation stages were influenced grain yield through plant height, 1000-grain weight and dry fodder yield. Environment 3 was the best discriminating environment and the hybrids Pratap, 86M86 and NBH 5767 had outperformed the popular open pollinated varieties in ideal conditions. Interpretation: In terms of grain yield,Pusa Comp. 612 and Pratap have proved to be reliable and had a mean higher than the general mean over environments. As a result, Pusa Comp. 612 and Pratap are suggested for large-scale assessment in a wide range of locations in rainfed situations of arid regions. Key words: GT-biplot, Grain yield, Pearl millet, Stability, Trait associations

Effect of different MIT rainfall event division methods on volume capture ratio of annual rainfall based on bioretention assessment.

Volume capture ratio of annual rainfall (VCRAR) is the key parameter of low-impact development (LID) facilities design, which is significantly affected by the rainfall event division method. However, there is no universal agreement on how to determine an optimal division method to achieve it. A modified minimum inter-event time (MIT) method based on MATLAB software was proposed to find an optimal MIT value. The result showed that the optimal MIT value in Beijing is 200 min based on the daily rainfall data from 1987 to 2016, and the annual average rainfall events were 34.2 with an average rainfall depth of 13.7 mm. Taking bioretention facilities as an example, the errors of design VCRAR under different MIT values were compared based on a Stormwater Management Model (SWMM). The results showed that when design VCRAR was ≤50, 55-60, 60-75, 75-80 and >80%, the optimal MIT value for LID facilities design was 60, 120, 200, 360 and 1,440 min, respectively. Therefore, the optimal MIT should be flexibly selected with the changing of design VCRAR, to ensure that LID facilities meet the design goals.

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China.

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV-LR, IV-SVM, IV-RF, FR-LR, FR-SVM, and FR-RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR-RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR-RF coupling model had the highest accuracy. Under the optimal model FR-RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

Phenotypic Variation in Leaf, Fruit and Seed Traits in Natural Populations of Eucommia ulmoides, a Relict Chinese Endemic Tree

Eucommia ulmoides Oliver is a dioecious, pharmaceutically and economically important, and precious relict tree species endemic to China, and has been listed as a key protected tree species of national level II. Phenotypic variation in ten natural populations in some key traits is still obscure. In order to study the relationship between population variation in phenotypic traits and geoclimatic factors, 15 traits were analyzed in 117 female sampled tree from ten natural populations. The results showed that the coefficients of variation for all of the 15 traits widely ranged from 9.7% (fruit vertical diameter) to 49.0% (leaf thickness), with an average of 19.7%. The nested ANOVA revealed plentiful phenotypic variations within and among populations. The variation within population was the main source, with an average proportion of 42.8%, greater than that among the population (16.6%). The 15 traits were reduced to four principal components, which collectively accounted for 70.1% of phenotypic variation among trees. The ten populations were mainly divided into two groups: Group A included eight populations throughout the Wuling Mountains occurring in relatively close proximity to each other, and Group B which comprises two geographically distant populations in mountains further northern. There were significant level correlations between phenotypic differentiation among population of E. ulmoides and both geographic (r = 0.65, p < 0.05) and climatic (r = 0.73, p < 0.01) distance. Step-wise regression indicates average annual temperature and rainfall accounted for most of the phenotypic variation among populations, and mainly associated with differences in leaf, fruit and seed size. These results can have an important implication for genetic improvement, diversity conservation and resource management of the species in the future.

Coevolution of Climate Change, Surface Properties, Vegetation Cover, and Drainage Network in a Loess Covered Area.

Arid and semi-arid areas are often regarded as highly sensitive to climate change. Average annual rainfall is usually regarded as the most important factor controlling soil moisture regime, water availability, and therefore various environmental variables. The present study deals with the ecology of an area located in the semi-arid area of Israel, where average annual rainfall is ~280 mm. The area is located in a loess covered area. The loess cover was deposited during a wet climatic phase. However, pedological and ecological data point to a desertification effect. Data obtained show that non-climatic factors, such as local surface properties, can exert a strong influence on the environment. In addition, the global models disregard the characteristics of the local rainfall regime, such as the distribution of rain intensities, and the duration of individual rain-showers during a rainstorm. Under the present rainfall regime about 85% of the rains are below 5 mm/hr,1, rainfall is also highly intermittent, limiting runoff generation, and infiltration depth. Under such conditions, a high evaporation rate leads to a soil salinization process.

Statistical Analysis of Rainfall Distribution in Biratnagar, Nepal: A case study

Rainfall is a key component in the construction of many engineering projects, including canals, bridges, culverts, and road drainage systems. The primary goal of this study is to ascertain the annual, monthly and seasonal fluctuations in rainfall in Biratnagar, Nepal over a period of 10 years (2010-2019). The mean, standard deviation, coefficient of variation, skewness, kurtosis, Precipitation Concentration Index (PCI), normal rainfall along with Mann-Kendall test were calculated to check the rainfall variations. The annual data showed the biggest varieties in rainfall, with an average annual rainfall of 1542.43 mm during a ten years period. Furthermore, an erratic monthly rainfall pattern was found by the non-parametric Mann-Kendall test. However, the test value for each month was non-significant at the 5% level of significance. We also found that the rainfall is least in the winter and most during monsoon season. Based on these calculations, It is seen that the pattern of rainfall is inconsistent or unpredictable. However, present study will aid in the understanding of rainfall variations and trends, assisting in the management of water resources, ecosystems, irrigation, sustainable agricultural planning, engineering structural design, and other areas.

Hydraulic characteristics of the groundwater aquifer in Al-Dhibaei Area South Tikrit

In this study the ground water recharge was estimated, with studying the hydrological conditions Al-Dhubaei region which located 5 km to the southwest of Tikrit city. Depending on the data of Tikrit meteorological station, the averages monthly climatic elements (rainfall, temperature and evaporation) were studied for a period of (1989-2013), the average annual rainfall reached 166.72 mm, as well as the evaporation-transpiration rates were calculated using Thornthwait method. The water balance of the area was calculated and the results show that the ratio of water surplus were reached to 38.87% of the total annual rainfall, and the groundwater recharge equal to 4.66×106 m3/year.
 Also, the hydrogeological study of the region show that the main bearing formations of groundwater represented by Injana formation and Quaternary deposits, which are Confined Aquifers, the hydraulic properties were studied, and pumping test data show that the Transmissibility (T) ranged between (55.3 to 1140.48) m2/day, while the Hydraulic Conductivity (K) ranged between (0.58 to 43.38) m/day. The ground water table map and flow net were drawn, and show that the flow in northwest of the study area takes two trends, to the northeast and to the northwest, but in the middle and south of the area the flow was to the southeast.

Analysis and Correction of Precipitation Observation Error in Henan Province

Accurate precipitation data is the basis for studying regional hydrological cycle and climate change, and correcting the precipitation observation error can effectively improve its accuracy. Therefore, based on the daily observation data (including snow and rain types) of 111 meteorological stations in Henan Province from 1960 to 2017, the precipitation observation error correction equation obtained from the precipitation error comparison observation experiment conducted in the Urumqi River Basin of China in the International Cooperation Project on Solid Precipitation Observation carried out by the World Meteorological Organization in 1980 was used to systematically correct the dynamic loss, wetness loss and trace loss of precipitation at 111 meteorological stations from 1960 to 2017. The main conclusions are: (1) The absolute error of each season decreases from south to north, the average absolute error in summer is the largest, 47.30mm, and the winter is the smallest; while the relative error is the opposite, the largest in winter and the smallest in summer. (2) The absolute error of the average annual precipitation in the entire region is above 60mm, gradually increasing from north to south, and the error in most areas is 70-160mm; The relative error varies little from region to region, and the error range is 10%-16%. (3) The average annual rainfall before the correction was 761.92mm, and the average annual rainfall after the correction was 881.1mm, and the corrected value was 119.18mm. There was a decreasing trend in rainfall before and after the correction from 1960 to 2017, but the trend of reduction in the revised rainfall series was greater than that in the measured series.

Influence of Precipitation on the Spatial Distribution of 210Pb, 7Be, 40K and 137Cs in Moss

Mosses have been widely used as biomonitors of a variety of atmospheric pollutants, including radionuclides. Here we determine the radionuclide activity concentration of 210Pb, 137Cs, 7Be, and 40K in moss tissue (Hylocomium splendens) collected from 24 sites across Ireland and assess the influence of precipitation on radionuclide spatial distribution. Lead-210 was the most abundant radionuclide (range: 226–968 Bq kg–1), followed by 7Be (range: <DL—604 Bq kg–1), 40K (range: <DL—155 Bq kg–1), and 137Cs (range: <DL—41 Bq kg–1). Albeit nearly thirty years since the Chernobyl disaster, 137Cs activity concentration was detected at 67% of the study sites; however, the spatial distribution was not fully consistent with the 1986 Chernobyl deposition pattern. Rather, 137Cs was weakly correlated with rainfall, with higher concentrations along the west coast, suggesting that the 2011 Fukushima Dai-ichi nuclear accident was also a potential source. Average annual rainfall was a significant predictor of 210Pb activity (linear regression, R2 = 0.63, p < 0.001). As such, the highest radionuclide activity was observed for 210Pb (average: 541 Bq kg–1), owing to the high levels of precipitation across the study sites (average: 1585 mm). In contrast, 7Be or 40K were not correlated with precipitation; rather, 40K and 7Be were significantly correlated to each other (rs = 0.7), suggesting that both radionuclides were transferred from the substrate or through soil re-suspension. Precipitation is widely reported as an important factor in the spatial distribution of radionuclides; however, only 210Pb activity concentrations in moss were strongly influenced by precipitation in the current study.

District-Level Seasonal Rainfall Characteristics over Andhra Pradesh and its Global Teleconnections in Changing Climate

Investigating the trends and changes in rainfall over vulnerable regions is of huge importance in this global warming era. The present study intensively investigates the rainfall over the Indian state, Andhra Pradesh (AP), and its 13 districts using a high-resolution (0.25°×0.25°) gridded rainfall analysis dataset from India Meteorological Department (IMD) for the study period of 118 years (1901-2018). For this, normality, homogeneity, persistence, and change-point tests are performed and changes in the district-level rainfall in the present global warming period (1991-2018) as compared to the pre-global warming period (1901-1990) is also analyzed.
 The results suggest that the long-term average annual rainfall over AP is 882 mm and most of the rainfall is contributed by the monsoon (55.7%) and the post-monsoon rainfall (32.8%). The coefficient of variation is low (high) during monsoon (winter). The coastal region receives more rainfall than the inland districts. The post-monsoon rainfall over AP is more consistent than in other seasons, and the persistence is only during the southwest monsoon season. The southwest monsoon and post-monsoon rainfall have increased (by about 10%) over most of the districts in the recent period. The Nino3.4 region SST (South Oscillation Index; SOI) has a significant negative (positive) relationship with southwest summer monsoon rainfall in most of the districts. The relationship of Nino 3.4 SST and DMI is strikingly similar for post-monsoon and has significantly weakened in recent decades. This study is useful for proper planning and mitigation measures for the agricultural and water resources sector at the district level over AP in this global warming era.

Spatial downscaling of precipitation for hydrological modelling: Assessing a simple method and its application under climate change in Britain

AbstractNational or regional grid‐based hydrological models are usually run at relatively fine spatial resolutions. But the meteorological data necessary to drive such models are often coarser resolution, so some form of spatial downscaling is generally required. A 1 km hydrological model for Great Britain is used to test the performance of a simple method of downscaling precipitation based on 1 km patterns of long‐term mean annual rainfall (Standard Average Annual Rainfall; SAAR). For a range of coarser resolutions (5, 10, 25 and 50 km), a 1 km grid of multiplicative scaling factors is derived as the ratio of the 1 km grid box SAAR divided by the mean SAAR of the coarser resolution grid box that contains it. A dataset of 1 km daily observation‐based precipitation is then degraded to the coarser resolutions, and application of SAAR scaling factors is compared to no downscaling and direct use of 1 km data, for simulating river flows for a large set of catchments. SAAR‐based downscaling provides a clear improvement over no downscaling. Using monthly rather than annual long‐term mean rainfall patterns provides minimal further improvement. There are no strong relationships between performance and catchment properties, but performance using 50 km precipitation without downscaling tends to be worse for smaller, steeper catchments and those with a more south‐westerly aspect; these benefit more from SAAR‐based downscaling. An assessment using high‐resolution convection‐permitting model data shows relatively small changes in derived SAAR scaling factors between a baseline and far‐future period, suggesting that use of historical scaling factors for future periods is reasonable. Applicability of this simple downscaling method for other parts of the world should be similarly assessed, for both historical and future periods. While use of annual patterns seems to be sufficient in Britain, areas where spatial rainfall patterns are more variable through the year may require use of sub‐annual patterns.

Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model

The spatial heterogeneity of landslide influencing factors is the main reason for the poor generalizability of the susceptibility evaluation model. This study aimed to construct a comprehensive explanatory framework for landslide susceptibility evaluation models based on the SHAP (SHapley Additive explanation)-XGBoost (eXtreme Gradient Boosting) algorithm, analyze the regional characteristics and spatial heterogeneity of landslide influencing factors, and discuss the heterogeneity of the generalizability of the models under different landscapes. Firstly, we selected different regions in typical mountainous hilly region and constructed a geospatial database containing 12 landslide influencing factors such as elevation, annual average rainfall, slope, lithology, and NDVI through field surveys, satellite images, and a literature review. Subsequently, the landslide susceptibility evaluation model was constructed based on the XGBoost algorithm and spatial database, and the prediction results of the landslide susceptibility evaluation model were explained based on regional topography, geology, and hydrology using the SHAP algorithm. Finally, the model was generalized and applied to regions with both similar and very different topography, geology, meteorology, and vegetation, to explore the spatial heterogeneity of the generalizability of the model. The following conclusions were drawn: the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor on the occurrence of landslides has obvious regional characteristics and spatial heterogeneity. The generalizability of the landslide susceptibility evaluation model is spatially heterogeneous and has better generalizability to regions with similar regional characteristics. Further explanation of the XGBoost landslide susceptibility evaluation model using the SHAP method allows quantitative analysis of the differences in how much various factors contribute to disasters due to spatial heterogeneity, from the perspective of global and local evaluation units. In summary, the integrated explanatory framework based on the SHAP-XGBoost model can quantify the contribution of influencing factors on landslide occurrence at both global and local levels, which is conducive to the construction and improvement of the influencing factor system of landslide susceptibility in different regions. It can also provide a reference for predicting potential landslide hazard-prone areas and for Explainable Artificial Intelligence (XAI) research.

Understanding Changes in the Hydrometeorological Conditions towards Climate-Resilient Agricultural Interventions in Ethiopia

Climate resilient agriculture (CRA) is very important to achieve long-term improvement in productivity and farm incomes under climate uncertainty. The present study focuses on investigating the plausible changes in the hydrometeorological conditions using big-data analysis techniques in the study of Ethiopia. The original contribution of this work envisages the importance of the CRA system in water-scarce areas for sustainable agriculture planning and management under changing climatic conditions. In the present research, a TerraClimate model was the basis for weather (precipitation and temperature) and hydrological data (runoff, actual evapotranspiration, potential evapotranspiration, vapor pressure deficit and climate water deficit); these data were used to determine the spatial distribution of the standardized anomaly index (SAI) and the slope of the linear regression for long-term (1958–2020) trend analysis. Future climate trend analysis (2021–2100) has been performed through the CMIP6 (EC-Earth3) shared socio-economic pathway (SSP 2) 4.5 dataset. Gravity Recovery and Climate Experiment (GRACE) with CSR and JPL data were utilized for the generation of water storage heat maps from 2002 to 2021. The results show that the average annual rainfall data for over 62 years was found to be 778.42 mm and the standard deviation is 81.53 mm. The results also show that the western part of the study area has the highest temperature trend, which diminishes as one moves eastward; the minimum temperature trend has been found in the western part of the study area. It was found that the equivalent water thickness (EWT) range of both CSR and JPL products was −15 to 40 cm. These results can help local climate-resilient development planning and enhance coordination with other institutions to access and manage climate finance.

Observed trends and coherent changes in daily rainfall extremes over Greater Mumbai, 1985–2020

Climate extremes are predicted to become more frequent and severe in the future, owing to rising greenhouse gas emissions in the atmosphere. Using appropriate trend indices, this study intends to analyze the effects of climate change on rainfall variability and extreme rainfall events in the millennium city of Mumbai. The analysis was carried out for the period 1985–2020 for 2 stations and 23 stations were selected (2006–2020) later from the dense rain gauge network of Municipal Corporation of Greater Mumbai (MCGM). To comprehend the trend and behaviour of rainfall and extreme events, three statistical models were used: Mann–Kendall test, Sen’s slope estimator, and simple linear regression; for abrupt change point detection, Pettitt’s test, standard normal homogeneity test, Buishand’s range test, and von Neumann ratio test were used. The results indicated that average annual rainfall in the study area is 2208.82 mm which is increasing at the rate of 5.18 mm/year (at 99% confidence level). The frequency of heavy (> 120 mm/day) and extreme heavy rainfall events (250 mm/day) increased over Santacruz after 1994 and in the case of Colaba, it increased after 2005. The examination of rainfall and temperature data series for abrupt change point identification suggests that several change points exist, with the highest change points occurring between 2001 and 2005. Extreme climate event scenarios shown here could have large-scale negative consequences for the ecosystem and ecological resources of the study area.

The Effect of Atmospheric Parameters and Climate Changes on the NDYI Index in the Hyrcanian Forests

The increase in the production and entry of greenhouse gases into the earth’s atmosphere has disturbed the balance of the environment. The signs of climate change (caused by global warming) are clearly visible and its effects are tangible. According to the United Nations, if the current trend continues, the global average temperature will increase by 3.2°C by the end of the century, which will have terrible consequences if it happens. This research aimed to investigate the effects of greenhouse gases and climate changes resulting from them on Hyrcanian forests. The Hyrcanian forests, as one of the oldest forest areas (remaining from the Paleogene era), were studied by telemetry from 2013 to 2021. The analysis of the images taken from the Landsat 8 satellite showed that during 9 years, the NDVI index decreased by 0.6 units and the average air temperature increased by 0.5°C. Although the average relative humidity has only increased by 2%, the average annual rainfall has recorded an increase of 25mm. The analysis of the statistics showed that the rains occur irregularly and are often torrential. Therefore, it is predicted that as the average temperature continues to increase, the NDVI index will further decrease, and as a result, the forest cover will become weaker and the soil will lose more water absorption power, and due to the increase in average rainfall. Successive floods will occur. Therefore, soil erosion increases and the extinction and migration of plant and animal species increase significantly.

Regionalizing Streamflow Regime Function through Integrations of Geographical Controls in Mountainous Basins

Flow duration curves (FDCs) that represent streamflow regime function through an empirical relationship between the FDC parameters and basin descriptors are widely adopted for hydrologic applications. However, the applications of this method are highly dependent on the availability of observation data. Hence, it is still of great significance to explore the process controls of underpinning regional patterns on streamflow regimes. In this study, we developed a new regionalization method of FDCs to solve the problem of runoff prediction for ungauged mountainous basins. Five empirical equations (power, exponential, logarithmic, quadratic, and cubic) were used to fit the observed FDCs in the 64 mountainous basins in eastern China, and the power model outperforms other models. Stepwise regression was used to explore the differentiated control of 23 basin descriptors on the 13 percentile flows of FDCs, and seven descriptors remained as independent variables for further developing the regional FDCs. Application results with different combinations of these selected descriptors showed that five indices, i.e., average annual rainfall (P), average elevation (H), average gradient (β), average topographic index (TI), and maximum 7d of annual rainfall (Max7d), were the main control factors of FDCs in these areas. Through the regional method, we found that 95.31% of all the basins have NSE values greater than 0.60 and ε (namely the relative mean square error) values less than 20%. In conclusion, our study can guide runoff predictions to help manage booming demands for water resources and hydropower developments in mountainous areas.

Joint modeling of rainfall and temperature in Bahir Dar, Ethiopia: Application of copula

ObjectiveClimate change has effects on the economy development of any country. This paper aimed to fit the best marginal and joint distribution models of rainfall with minimum and maximum temperatures.MethodsThe average values of minimum and maximum monthly temperature, and rainfall were used in this study. For the marginal model, five probability distributions and five families of copula models were employed to show the interdependence between the maximum and minimum average annual temperature with rainfall. The Kendall's tau (τ) correlation coefficient was used to find out the correlations between rainfall with minimum and maximum temperature. Both the Akaki Information Criteria (AIC) and Bayesian information criteria (BIC) were used to select the best marginal and copula.ResultsThe result revealed that there is a significant negative relationship between the maximum temperature and rainfall. The maximum average rainfall was obtained from June to August and the maximum temperature is almost consistent in all months. Based on AIC/BIC, the Weibull distribution for rainfall, the Beta for minimum, and the Gaussian for maximum temperature were identified as the best marginal distributions. The Clayton copula distribution was identified as the best copula for rainfall and minimum temperature (with parameter of θ =1. 21, tau correlation = −0.41, p < 0.001), and Frank copula was identified for rainfall and maximum temperature (with unique Frank parameter of θ = −3.94, correlation = −0.38, p < 0.001).ConclusionThe result showed that there is a significant positive relationship between the average annual minimum temperature and rainfall; whereas a negative relationship occurred between the maximum temperature and rainfall. The Clayton and Frank copula were found to be the most appropriate to the model of a bivariate distribution of mean annual rainfall with minimum/maximum temperature respectively.

Economic Efficiency of Solar and Rainwater Systems—A Case Study

The study deals with the analysis of data from a selected tourism facility and the implementation of a solar system and a rainwater system, which are an alternative to commonly used energy sources. The objective is to evaluate the potential savings from the use of the solar system for water heating and the rainwater system for purposes other than potable use with respect to local conditions of sunlight and rainwater variability. The facility holds 257 beds allocated in 124 rooms on 5 floors. The result of the contribution is an economic evaluation of the efficiency of investments in the proposed systems and the resulting recommendations in the field of energy flows in the selected facility. Appropriate formulas including the consumption of hot water, the amount of energy needed to heat water, the cost of the consumed energy necessary for heating and the average annual rainfall in the location, water consumption for clearly identified purposes of alternation with rainwater, the initial investment costs and operating costs of the solar and rainwater systems, were used for an economic evaluation of the investment into the solar and rainwater systems. Based on the economic evaluation, the economic efficiency of the proposed systems and the return on investment was calculated. The payback periods for the solar system used for hot water heating and the rainwater system used for non-potable purposes, accepting the local conditions, are 7 and 15 years, respectively.