Search results for: monthly and daily rainfall

Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

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Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.

Keywords: recurrent neural networks, global solar radiation, multi-layer perceptron, gradient, root mean square error

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Marco Cifrodelli, Corrado Corradini

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The trend of magnitude and frequency of extreme rainfalls seems to be different depending on the investigated area of the world. In this work, the impact of climate change on extreme rainfalls in Umbria, an inland region of central Italy, is examined using data recorded during the period 1921-2015 by 10 representative rain gauge stations. The study area is characterized by a complex orography, with altitude ranging from 200 to more than 2000 m asl. The climate is very different from zone to zone, with mean annual rainfall ranging from 650 to 1450 mm and mean annual air temperature from 3.3 to 14.2°C. Over the past 15 years, this region has been affected by four significant droughts as well as by six dangerous flood events, all with very large impact in economic terms. A least-squares linear trend analysis of annual maximums over 60 time series selected considering 6 different durations (1 h, 3 h, 6 h, 12 h, 24 h, 48 h) showed about 50% of positive and 50% of negative cases. For the same time series the non-parametrical Mann-Kendall test with a significance level 0.05 evidenced only 3% of cases characterized by a negative trend and no positive case. Further investigations have also demonstrated that the variance and covariance of each time series can be considered almost stationary. Therefore, the analysis on the magnitude of extreme rainfalls supplies the indication that an evident trend in the change of values in the Umbria region does not exist. However, also the frequency of rainfall events, with particularly high rainfall depths values, occurred during a fixed period has also to be considered. For all selected stations the 2-day rainfall events that exceed 50 mm were counted for each year, starting from the first monitored year to the end of 2015. Also, this analysis did not show predominant trends. Specifically, for all selected rain gauge stations the annual number of 2-day rainfall events that exceed the threshold value (50 mm) was slowly decreasing in time, while the annual cumulated rainfall depths corresponding to the same events evidenced trends that were not statistically significant. Overall, by using a wide available dataset and adopting simple methods, the influence of climate change on the heavy rainfalls in the Umbria region is not detected.

Keywords: climate changes, rainfall extremes, rainfall magnitude and frequency, central Italy

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Authors: Aziez Ouahiba, Remini Boualam, Habi Mohamed

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the growth and the development of a pay are strongly related to the existence or the absence of water in this area, The sedentary lifestyle of the population makes that water demand is increasing and the different brandishing (dams, tablecloths or other) are increasingly solicited. In normal time rain and snow of the winter period reloads the slicks and the wadis that fill dams. Over these two decades, global warming fact that temperature is increasingly high and rainfall is increasingly low which induces a charge less and less important tablecloths, add to that the strong demand in irrigation. Our study will focus on the variation of rainfall and irrigation, their effects on the degree of pollution of the groundwater in this area based on statistical analyses by the Xlstat (ACP, correlation...) software for a better explanation of these results and determine the hydrochemistry of different groups or polluted areas pou be able to offer adequate solutions for each area.

Keywords: rainfall, groundwater of mitidja, irrigation, pollution

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Authors: Anshar Ajatasatru

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The decline of coal prices over past years have been significantly increasing the awareness of effective mining operation. A viable step must be undertaken in becoming more cost competitive while striving for best mining practice especially at Melak Coal Mine in East Kalimantan, Indonesia. This paper aims to show how effective dozer push measurement method can be implemented as it is controlled by contract rate on the unit basis of USD ($) per bcm. The method emerges from an idea of daily dozer push activity that continually shifts the overburden until final target design by mine planning. Volume calculation is then performed by calculating volume of each time overburden is removed within determined distance using cut and fill method from a high precision GNSS system which is applied into dozer as a guidance to ensure the optimum result of overburden removal. Accumulation of daily to weekly dozer push volume is found 95 bcm which is multiplied by average sell rate of $ 0,95, thus the amount monthly revenue is $ 90,25. Furthermore, the payment mechanism is then based on push distance and push grade. The push distance interval will determine the rates that vary from $ 0,9 - $ 2,69 per bcm and are influenced by certain push slope grade from -25% until +25%. The amount payable rates for dozer push operation shall be specifically following currency adjustment and is to be added to the monthly overburden volume claim, therefore, the sell rate of overburden volume per bcm may fluctuate depends on the real time exchange rate of Jakarta Interbank Spot Dollar Rate (JISDOR). The result indicates that dozer push measurement can be one of the surface mining alternative since it has enabled to refine method of work, operating cost and productivity improvement apart from exposing risk of low rented equipment performance. In addition, payment mechanism of contract rate by dozer push operation scheduling will ultimately deliver clients by almost 45% cost reduction in the form of low and consistent cost.

Keywords: contract rate, cut-fill method, dozer push, overburden volume

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Mahdiyeh Zafaranchi, Ethan S. Cantor, William T. Riddell, Jess W. Everett

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The New Jersey Department of Military and Veteran’s Affairs (NJ DMAVA) operates over 50 facilities throughout the state of New Jersey, U.S. NJDMAVA is under a mandate to move toward decarbonization, which will eventually include eliminating the use of natural gas and other fossil fuels for heating. At the same time, the organization requires increased resiliency regarding electric grid disruption. These competing goals necessitate adopting the use of on-site renewables such as photovoltaic and geothermal power, as well as implementing power control strategies through microgrids. Planning for these changes requires a detailed understanding of current and future electricity use on yearly, monthly, and shorter time scales, as well as a breakdown of consumption by heating, ventilation, and air conditioning (HVAC) equipment. This paper discusses case studies of two buildings that were simulated using the QUick Energy Simulation Tool (eQUEST). Both buildings use electricity from the grid and photovoltaics. One building also uses natural gas. While electricity use data are available in hourly intervals and natural gas data are available in monthly intervals, the simulations were developed using monthly and yearly totals. This approach was chosen to reflect the information available for most NJ DMAVA facilities. Once completed, simulation results are compared to metrics recommended by several organizations to validate energy use simulations. In addition to yearly and monthly totals, the simulated peak demands are compared to actual monthly peak demand values. The simulations resulted in monthly peak demand values that were within 30% of the measured values. These benchmarks will help to assess future energy planning efforts for NJ DMAVA.

Keywords: building energy modeling, eQUEST, peak demand, smart meters

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Authors: Ali Razmi, Saeed Golian

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Climate change is known to have the potential to impact adversely hydrologic patterns for variables such as rainfall, maximum and minimum temperature and sea level rise. Long-term average of these climate variables could possibly change over time due to climate change impacts. In this study, trend analysis was performed on rainfall, maximum and minimum temperature and water level data of a coastal area in Manhattan, New York City, Central Park and Battery Park stations to investigate if there is a significant change in the data mean. Partial Man-Kendall test was used for trend analysis. Frequency analysis was then performed on data using common probability distribution functions such as Generalized Extreme Value (GEV), normal, log-normal and log-Pearson. Goodness of fit tests such as Kolmogorov-Smirnov are used to determine the most appropriate distributions. In flood frequency analysis, rainfall and water level data are often separately investigated. However, in determining flood zones, simultaneous consideration of rainfall and water level in frequency analysis could have considerable effect on floodplain delineation (flood extent and depth). The present study aims to perform flood frequency analysis considering joint probability distribution for rainfall and storm surge. First, correlation between the considered variables was investigated. Joint probability distribution of extreme water level and temperature was also investigated to examine how global warming could affect sea level flooding impacts. Copula functions were fitted to data and joint probability of water level with rainfall and temperature for different recurrence intervals of 2, 5, 25, 50, 100, 200, 500, 600 and 1000 was determined and compared with the severity of individual events. Results for trend analysis showed increase in long-term average of data that could be attributed to climate change impacts. GEV distribution was found as the most appropriate function to be fitted to the extreme climate variables. The results for joint probability distribution analysis confirmed the necessity for incorporation of both rainfall and water level data in flood frequency analysis.

Keywords: climate change, climate variables, copula, joint probability

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Authors: Olumuyiwa Idowu Ojo, Oluwatobi Peter Olowo

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The study developed a Geographical Information Systems (GIS) database and mapped inter-annual changes in crop yields of cassava, cowpea, maize, rice, melon and yam as a response to inter-annual rainfall and temperature variability in Southwest, Nigeria. The aim of this project is to study the comparative analysis of the weather variability impact of six crops yield (Rice, melon, yam, cassava, Maize and cowpea) in South Western States of Nigeria (Oyo, Osun, Ekiti, Ondo, Ogun and Lagos) from 1991 – 2007. The data was imported and analysed in the Arch GIS 9 – 3 software environment. The various parameters (temperature, rainfall, crop yields) were interpolated using the kriging method. The results generated through interpolation were clipped to the study area. Geographically weighted regression was chosen from the spatial statistics toolbox in Arch GIS 9.3 software to analyse and predict the relationship between temperature, rainfall and the different crops (Cowpea, maize, rice, melon, yam, and cassava).

Keywords: GIS, crop yields, comparative analysis, temperature, rainfall, weather variability

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Authors: Tesfay Mekonnen Weldegerima

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Analysis of precipitation time series is a fundamental undertaking in meteorology and hydrology. The extreme precipitation scenario of the Kulfo River watershed is studied using wavelet analysis and atmospheric transport, a lagrangian trajectory model. Daily rainfall data for the 1991-2020 study periods are collected from the office of the Ethiopian Meteorology Institute. Meteorological fields on a three-dimensional grid at 0.5o x 0.5o spatial resolution and daily temporal resolution are also obtained from the Global Data Assimilation System (GDAS). Wavelet analysis of the daily precipitation processed with the lag-1 coefficient reveals some high power recurred once every 38 to 60 days with greater than 95% confidence for red noise. The analysis also identified inter-annual periodicity in the periods 2002 - 2005 and 2017 - 2019. Back trajectory analysis for 3-day periods up to May 19/2011, indicates the Indian Ocean source; trajectories crossed the eastern African escarpment to arrive at the Kulfo watershed. Atmospheric flows associated with the Western Indian monsoon redirected by the low-level Somali winds and Arabian ridge are responsible for the moisture supply. The time-localization of the wavelet power spectrum yields valuable hydrological information, and the back trajectory approaches provide useful characterization of air mass source.

Keywords: extreme precipitation events, power spectrum, back trajectory, kulfo watershed

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Authors: Oulad Naoui Noureddine, Cherif ELAmine, Djehiche Abdelkader

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Water resources management includes several disciplines; the modeling of rainfall-runoff relationship is the most important discipline to prevent natural risks. There are several models to study rainfall-runoff relationship in watersheds. However, the majority of these models are not applicable in all basins of the world.  In this study, a new stochastic method called The Only Corresponding Competitor method (OCC) was used for the hydrological modeling of M’ZAB   Watershed (South East of Algeria) to adapt a few empirical models for any hydrological regime.  The results obtained allow to authorize a certain number of visions, in which it would be interesting to experiment with hydrological models that improve collectively or separately the data of a catchment by the OCC method.

Keywords: modelling, optimization, rainfall-runoff relationship, empirical model, OCC

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Authors: Akinola Ikudayisi, Josiah Adeyemo

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The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.

Keywords: irrigation, principal component analysis, reference evapotranspiration, Vaalharts

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Authors: M. A. Wanzala, L. A. Ogallo, F. J. Opijah, J. N. Mutemi

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The usefulness and limitations in climate information are due to uncertainty inherent in the climate system. For any given region to have sustainable development it is important to apply climate information into its socio-economic strategic plans. The overall objective of the study was to assess the performance of the Coupled Model Inter-comparison Project (CMIP5) over the Lake Victoria Basin. The datasets used included the observed point station data, gridded rainfall data from Climate Research Unit (CRU) and hindcast data from eight CMIP5. The methodology included trend analysis, spatial analysis, correlation analysis, Principal Component Analysis (PCA) regression analysis, and categorical statistical skill score. Analysis of the trends in the observed rainfall records indicated an increase in rainfall variability both in space and time for all the seasons. The spatial patterns of the individual models output from the models of MPI, MIROC, EC-EARTH and CNRM were closest to the observed rainfall patterns.

Keywords: categorical statistics, coupled model inter-comparison project, principal component analysis, statistical downscaling

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Authors: G. Vishnu, T. V. Bharat

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Rainfall plays a major role in the stability of natural slopes in tropical and subtropical regions. These slopes usually have high slope angles and are stable during the dry season. The critical rainfall intensity that might trigger a landslide may not be the highest rainfall. In addition to geological discontinuities and anthropogenic factors, water content, suction, and hydraulic conductivity also play a role. A thorough geotechnical investigation with the principles of unsaturated soil mechanics is required to predict the failures in these cases. The study discusses three landslide events that had occurred in residual hills of Guwahati, India. Rainfall data analysis, history image analysis, land use, and slope maps of the region were analyzed and discussed. The landslide occurred on June (24, 26, and 28) 2020, on the respective sites, but the highest rainfall was on June (6 and 17) 2020. The factors that lead to the landslide occurrence is the combination of critical events initiated with rainfall, causing a reduction in suction. The sites consist of a mixture of rocks and soil. The slope failure occurs due to the saturation of the soil layer leading to loss of soil strength resulting in the flow of the entire soil rock mass. The land-use change, construction activities, other human and natural activities that lead to faster disintegration of rock mass may accelerate the landslide events. Landslides in these slopes are inevitable, and the development of an early warning system (EWS) to save human lives and resources is a feasible way. The actual time of failure of a slope can be better predicted by considering all these factors rather than depending solely on the rainfall intensities. An effective EWS is required with less false alarms in these regions by proper instrumentation of slope and appropriate climatic downscaling.

Keywords: early warning system, historic image analysis, slope instrumentation, unsaturated soil mechanics

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Authors: Messay Mulugeta, Degefa Tolossa

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The purpose of this paper is to scrutinize trends of climate risks in eastern and southeastern parts of Ethiopia. This part of the country appears severely affected by recurrent droughts, erratic rainfall, and increasing temperature condition. Particularly, erratic rains and moisture stresses have been forcibly threatening and shoving the people over many decades coupled with unproductive policy frameworks and weak institutional setups. These menaces have been more severe in dry lowlands where rainfall is more erratic and scarce. Long-term climate data of nine weather stations in eastern and southeastern parts of Ethiopia were obtained from National Meteorological Agency of Ethiopia (NMA). As issues related to climate risks are very intricate, different techniques and indices were applied to deal with the objectives of the study. It is concluded that erratic rainfall, moisture scarcity, and increasing temperature conditions have been the main challenges in eastern and southeastern Ethiopia. In fact, these risks can be eased by putting in place efficient and integrated rural development strategies, environmental rehabilitation plans of action in overworked areas, proper irrigation and water harvesting practices and well thought-out and genuine resettlement schemes.

Keywords: rainfall variability, erratic rains, precipitation concentration index (PCI), climatic pattern, Ethiopia

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Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

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Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: watershed simulation, WetSpa, stream flow, flood prediction

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: J. Bertrandt, A. Kłos, R. Waszkowski, T. Nowicki, R. Pytlak, E. Stęzycka, A. Gazdzinska

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The aim of the work was estimation of copper intake with the daily food rations used for alimentation of students of military high schools and soldiers doing compulsory military service in the Polish Army. An average planned copper content in daily food rations used for alimentation of students and soldiers amounted to 2.49±0.35 mg, and 2.44±0.25 mg respectively. The copper content in the daily food ration given for consumption to students amounted from 1.81±0.14 mg to 2.58±0.44 mg while daily food rations served to soldiers delivered from 2.06±0.45 mg to 2.13±0.33 mg. The copper content in the rations planned for students and soldiers’ alimentation was within the limits of the norms obligatory in Poland. Daily food rations given for consumption, except rations served for students, were within the limits of the recommended norms, but food rations really eaten by examined men didn’t cover the requirements for copper.

Keywords: copper, daily food ration, military service, food security, nutrition

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Authors: Asaad Y. Shamseldin

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Analytical probabilistic approach is an innovative approach for urban stormwater modeling. It can provide information about the long-term performance of a stormwater management facility without being computationally very demanding. This paper explores the application of the analytical probabilistic approach in New Zealand. The paper presents the results of a case study aimed at development of an objective way of identifying what constitutes a rainfall storm event and the estimation of the corresponding statistical properties of storms using two selected automatic rainfall stations located in the Auckland region in New Zealand. The storm identification and the estimation of the storm statistical properties are regarded as the first step in the development of the analytical probabilistic models. The paper provides a recommendation about the definition of the storm inter-event time to be used in conjunction with the analytical probabilistic approach.

Keywords: hydrology, rainfall storm, storm inter-event time, New Zealand, stormwater management

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Authors: Rajarshi Sharma, Rashidul Alam, Visavino Seleyi, Yuvila Sangtam

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The Intensity-Duration-Frequency (IDF) relationship of rainfall amounts is one of the most commonly used tools in water resources engineering for planning, design and operation of water resources project, or for various engineering projects against design floods. The establishment of such relationships was reported as early as in 1932 (Bernard). Since then many sets of relationships have been constructed for several parts of the globe. The objective of this research is to derive IDF relationship of rainfall for western watershed of Guwahati, Assam. These relationships are useful in the design of urban drainage works, e.g. storm sewers, culverts and other hydraulic structures. In the study, rainfall depth for 10 years viz. 2001 to 2010 has been collected from the Regional Meteorological Centre Borjhar, Guwahati. Firstly, the data has been used to construct the mass curve for duration of more than 7 hours rainfall to calculate the maximum intensity and to form the intensity duration curves. Gumbel’s frequency analysis technique has been used to calculate the probable maximum rainfall intensities for a period of 2 yr, 5 yr, 10 yr, 50 yr, 100 yr from the maximum intensity. Finally, regression analysis has been used to develop the intensity-duration-frequency (IDF) curve. Thus, from the analysis the values for the constants ‘a’,‘b’ &‘c’ have been found out. The values of ‘a’ for which the sum of the squared deviation is minimum has been found out to be 40 and when the corresponding value of ‘c’ and ‘b’ for the minimum squared deviation of ‘a’ are 0.744 and 1981.527 respectively. The results obtained showed that in all the cases the correlation coefficient is very high indicating the goodness of fit of the formulae to estimate IDF curves in the region of interest.

Keywords: intensity-duration-frequency relationship, mass curve, regression analysis, correlation coefficient

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Authors: Zia Ullah, Ruh Ullah

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A research study was conceded in four locations (Swat, Dir, Kakul and Balakot) of Khyber Pakhtunkhwa, to find agro-climatic classes by using aridity index, Growing Degree Days of wheat and maize, crop growth index and Spatio-temporal analysis of rainfall by using long term climatic data (1970-2010). The climatic data used for research was acquired from Pakistan Meteorological Department (PMD) Islamabad, Agriculture Research Institute, Weather Station Peshawar and Tarnab Peshawar. Agro-climatic classes of each location were determined using three criteria mean temperature of the coldest month, mean temperature of the warmest month and aridity index. The agro-climatic classes of Dir, Swat, Kakul and Balakot were classified as Humid, Cold and very Warm (H-K-VW). Average aridity index of wheat for Dir, Swat, Kakul, and Balakot was 2.23, 2.67, 1.94 and 2.34 and for Maize was 1.31, 1.26, 1.97, and 2.83 respectively. The overall and decade-wise trend of GDD of Wheat and Maize was declined in Swat and Kakul while increased in Dir and Balakot.The average maximum CGI (1.26) and (0.73) of Wheat and Maize was observed for Balakot and Dir, while the minimum (1.09) and (0.62) was observed for Swat and Kakul. Spatio-temporal analysis of rainfall shows that the trend has increased in Swat while decreased in Dir, Kakul and Balakot. From the relation between rainfalls with altitude showed that there was an increasing trend between rainfalls with altitude. The maximum average rainfall was in Swat (2703mm) on altitude 2000m while the minimum average rainfall was observed in Kakul (1410mm) on altitude of 1255m.

Keywords: agro-climatic zones, aridity index, GDD, rainfall

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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The seasonal (January, April, July and October) and monthly soil respiration rate and the monthly litter fall amounts were examined in the laurel-leaved (B_B-1) and Cryptomeria japonica (B_B-2 and PW) forests in the Kasugayama Hill Primeval Forest (Nara, Japan). The change of the seasonal soil respiration rate corresponded to that of the soil temperature. The soil respiration rate was higher in October when fresh organic matter was supplied in the forest floor than in April in spite of the same temperature. The seasonal soil respiration rate of B_B-1 was higher than that of B_B-2, which corresponded to more numbers of bacteria and fungi counted by the dilution plate method and by the direct count method by microscopy in B_B-1 than that of B_B-2. The seasonal soil respiration rate of B_B-2 was higher than that of PW, which corresponded to more microbial biomass by the direct count method by microscopy in B_B-2 than that of PW. The correlation coefficient with the seasonal soil respiration and the soil temperature was higher than that of the monthly soil respiration. The soil respiration carbon was more than the litter fall carbon. It was suggested that the soil respiration included in the carbon dioxide which was emitted by the plant root and soil animal, or that the litter fall supplied to the forest floor included in animal and plant litter.

Keywords: field soil respiration rate, forest soil, litter fall, mineralization rate

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Authors: Priscila M. Lima, Corine A. P. de Almeida, Muriele R. de Lima, Fernando J. C. Magalhães Filho

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This work characterized the preliminary treatment in WWTPs in the state of Mato Grosso Do Sul (Brazil) and analyzed aspects of operation and maintenance of solid waste retained, and was evaluated the interference of this step in treatment efficiency beyond the relationship between solid waste generation with rainfall and seasonality in the region of each WTPs. The results shown that the standard setting in the preliminary treatment consists of grid along with Sand Trap, followed by Parshall that is used in 94.12% of WWTPs analyzed, and in 5.88% of WWTPs it was added the air-lift to the Sand Trap. Was concluded that the influence of rainfall, flow and seasonality associated with the rate of waste generation in the preliminary treatment, had little relation to the operation and maintenance of the primary treatment. But in some cases, precipitation data showed increased rainfall converging with increased flow and solid waste generation.

Keywords: pretreatment, sewage, solid waste, wastewater

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Authors: Chakrit Chotamonsak, Eric P. Salathé Jr, Jiemjai Kreasuwan

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Dynamical downscaling of the ECHAM5 global climate model is applied at 20-km horizontal resolution using the WRF regional climate model (WRF-ECHAM5), to project changes from 1990–2009 to 2045–2064 of temperature and precipitation over the Upper Ping River Basin. The analysis found that monthly changes in daily temperature and precipitation over the basin for the 2045-2064 compared to the 1990-2009 are revealed over the basin all months, with the largest warmer in December and the smallest warmer in February. The future simulated precipitation is smaller than that of the baseline value in May, July and August, while increasing of precipitation is revealed during pre-monsoon (April) and late monsoon (September and October). This means that the rainy season likely becomes longer and less intensified during the rainy season. During the cool-dry season and hot-dry season, precipitation is substantial increasing over the basin. For the annual cycle of changes in daily temperature and precipitation over the upper Ping River basin, the largest warmer in the mean temperature over the basin is 1.93 °C in December and the smallest is 0.77 °C in February. Increase in nighttime temperature (minimum temperature) is larger than that of daytime temperature (maximum temperature) during the dry season, especially in wintertime (November to February), resulted in decreasing the diurnal temperature range. The annual and seasonal changes in daily temperature and precipitation averaged over the basin. The annual mean rising are 1.43, 1.54 and 1.30 °C for mean temperature, maximum temperature and minimum temperature, respectively. The increasing of maximum temperature is larger than that of minimum temperature in all months during the dry season (November to April).

Keywords: climate change, regional climate model, upper Ping River basin, WRF

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Authors: Tabassum Riaz

Abstract:

This study provides a complete examination of the stock prices behavior in the Karachi stock exchange. It examines that whether Karachi stock exchange can be described as mean reversion or not. For this purpose daily, weekly and monthly index data from Karachi stock exchange ranging from period July 1, 1997 to July 2, 2011 was taken. After employing the Multiple variance ratio and unit root tests it is concluded that stock market follow mean reversion behavior and hence have reverting trend which opens the door for the active invest management. Thus technical analysis may be help to identify the potential areas for value creation.

Keywords: mean reversion, random walk, technical analysis, Karachi stock exchange

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Authors: J. Winkler, S. Chovancová

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The article deals with the relation between rainfall in selected months and subsequent weed infestation of spring barley. The field experiment was performed at Mendel University agricultural enterprise in Žabčice, Czech Republic. Weed infestation was measured in spring barley vegetation in years 2004 to 2012. Barley was grown in three tillage variants: conventional tillage technology (CT), minimization tillage technology (MT), and no tillage (NT). Precipitation was recorded in one-day intervals. Monthly precipitation was calculated from the measured values in the months of October through to April. The technique of canonical correspondence analysis was applied for further statistical processing. 41 different species of weeds were found in the course of the 9-year monitoring period. The results clearly show that precipitation affects the incidence of most weed species in the selected months, but acts differently in the monitored variants of tillage technologies.

Keywords: weeds, precipitation, tillage, weed infestation forecast

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Authors: Adilson Elias Xavier, Otto Corrêa Rotunno Filho, Paulo Canedo De Magalhães

Abstract:

This paper addresses the issue of automatic parameter estimation in conceptual rainfall-runoff (CRR) models. Due to threshold structures commonly occurring in CRR models, the associated mathematical optimization problems have the significant characteristic of being strongly non-differentiable. In order to face this enormous task, the resolution method proposed adopts a smoothing strategy using a special C∞ differentiable class function. The final estimation solution is obtained by solving a sequence of differentiable subproblems which gradually approach the original conceptual problem. The use of this technique, called Hyperbolic Smoothing Method (HSM), makes possible the application of the most powerful minimization algorithms, and also allows for the main difficulties presented by the original CRR problem to be overcome. A set of computational experiments is presented for the purpose of illustrating both the reliability and the efficiency of the proposed approach.

Keywords: rainfall-runoff models, automatic calibration, hyperbolic smoothing method

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Authors: Maria Dora Horvath, Norbert Buzas, Zsanett Tesch

Abstract:

Diabetes management poses many unique challenges for children and their parents. The use of a diabetes management device should not be one of these challenges as the purpose of these devices is to make the management more convenient. The objective of our study was to examine how demographical, psychological and diabetes-related factors determine the choices parents make regarding their child’s diabetes management technologies and how they perceive advanced devices. We conducted the study using an online questionnaire with 318 parents (mostly mothers). The questions of the survey were about demographical, diabetes-related and psychological factors (diabetes management problems, diabetes management competence). In addition, we asked the parents opinions about advanced diabetes management devices. We expanded our data with semi-structured in-depth interviews. 61 % of the participants Self-Monitored Blood Glucose (SMBG), and 39 % used a Continuous Glucose Monitoring System (CGM). Considering insulin administration, 58 % used Multiple Daily Insulin Injections (MDII) and 42 % used Continuous Subcutaneous Insulin Infusion (CSII). Parents who used diverse combinations of diabetes management devices showed significant differences in age (parents’ and child’s), the monthly cost of diabetes, the duration of diabetes, the highest level of education and average monthly household income. CGM users perceived diabetes management problems significantly more severe than SMBG users and CSII users felt significantly more competent in diabetes management than MDII users. Avoiding CGM use due to lack of financial resources was determined by diagnosis duration. While avoiding its use by the cause of the child rejecting, it was determined by the child’s age and diabetes competence. Using MDII instead of CSII because of the child’s rejection was determined by the monthly cost of diabetes and child’s age. We conducted a complex empirical study in which we examined perceptions and experiences of advanced and less advanced diabetes management technologies comprehensively. Our study highlights the factors that fundamentally influence parents’ motivations and choices about diabetes management technologies. These results could contribute to developing diabetes management technologies more suitable for children living with type 1 diabetes and their parents.

Keywords: advanced diabetes management technologies, children living with type 1 diabetes, diabetes management, motivation, parents

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Authors: Dewi Retno Sari Saputro, Purnami Widyaningsih, Hendrika Handayani

Abstract:

Extreme data on an observation can occur due to unusual circumstances in the observation. The data can provide important information that can’t be provided by other data so that its existence needs to be further investigated. The method for obtaining extreme data is one of them using maxima block method. The distribution of extreme data sets taken with the maxima block method is called the distribution of extreme values. Distribution of extreme values is Gumbel distribution with two parameters. The parameter estimation of Gumbel distribution with maximum likelihood method (ML) is difficult to determine its exact value so that it is necessary to solve the approach. The purpose of this study was to determine the parameter estimation of Gumbel distribution with quasi-Newton BFGS method. The quasi-Newton BFGS method is a numerical method used for nonlinear function optimization without constraint so that the method can be used for parameter estimation from Gumbel distribution whose distribution function is in the form of exponential doubel function. The quasi-New BFGS method is a development of the Newton method. The Newton method uses the second derivative to calculate the parameter value changes on each iteration. Newton's method is then modified with the addition of a step length to provide a guarantee of convergence when the second derivative requires complex calculations. In the quasi-Newton BFGS method, Newton's method is modified by updating both derivatives on each iteration. The parameter estimation of the Gumbel distribution by a numerical approach using the quasi-Newton BFGS method is done by calculating the parameter values that make the distribution function maximum. In this method, we need gradient vector and hessian matrix. This research is a theory research and application by studying several journals and textbooks. The results of this study obtained the quasi-Newton BFGS algorithm and estimation of Gumbel distribution parameters. The estimation method is then applied to daily rainfall data in Purworejo District to estimate the distribution parameters. This indicates that the high rainfall that occurred in Purworejo District decreased its intensity and the range of rainfall that occurred decreased.

Keywords: parameter estimation, Gumbel distribution, maximum likelihood, broyden fletcher goldfarb shanno (BFGS)quasi newton

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Authors: Safayat Ali Shaikh

Abstract:

Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

Procedia PDF Downloads 175