Search results for: rainfall variability coefficient

Authors: Mazhar B. Tayel, Eslam I. AlSaba

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The heart is the most important part in any body organisms. It effects and affected by any factor in the body. Therefore, it is a good detector of any matter in the body. When the heart signal is non-stationary signal, therefore, it should be study its variability. So, the Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and have become important dependent measure in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability. However, remain complex issues are fraught with pitfalls. This paper presents one of the non-linear techniques to analyze HRV. It discusses 'What Poincare plot is?', 'How it is work?', 'its usage benefits especially in HRV', 'the limitation of Poincare cause of standard deviation SD1, SD2', and 'How overcome this limitation by using complex correlation measure (CCM)'. The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2.

Keywords: heart rate variability, chaotic system, poincare, variance, standard deviation, complex correlation measure

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Soumiya Bhattacharjee, P. K. Champati Ray, Shovan L. Chattoraj, Mrinmoy Dhara

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The entire Himalayan range is globally renowned for rainfall-induced landslides. The prime focus of the study is to determine rainfall based threshold for initiation of landslides that can be used as an important component of an early warning system for alerting stake holders. This research deals with temporal dimension of slope failures due to extreme rainfall events along the National Highway-58 from Karanprayag to Badrinath in the Garhwal Himalaya, India. Post processed 3-hourly rainfall intensity data and its corresponding duration from daily rainfall data available from Tropical Rainfall Measuring Mission (TRMM) were used as the prime source of rainfall data. Landslide event records from Border Road Organization (BRO) and some ancillary landslide inventory data for 2013 and 2014 have been used to determine Intensity Duration (ID) based rainfall threshold. The derived governing threshold equation, I= 4.738D^-0.025, has been considered for prediction of landslides of the study region. This equation was validated with an accuracy of 70% landslides during August and September 2014. The derived equation was considered for further prediction of landslides of the study region. From the obtained results and validation, it can be inferred that this equation can be used for initiation of landslides in the study area to work as a part of an early warning system. Results can significantly improve with ground based rainfall estimates and better database on landslide records. Thus, the study has demonstrated a very low cost method to get first-hand information on possibility of impending landslide in any region, thereby providing alert and better preparedness for landslide disaster mitigation.

Keywords: landslide, intensity-duration, rainfall threshold, TRMM, slope, inventory, early warning system

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Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

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Authors: Omid Azimaraghi, Noushin Khazaei

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Background: Fluid therapy is an important aspect of the perioperative period and a major challenge for anesthesiologists. To authors best knowledge, there is a lack of strong guidance and evidence regarding the optimal approach to fluid therapy. Therefore a variety of medical devices have been introduced to help physicians. In this study, we aimed to evaluate the effectiveness of pleth variability index in guiding fluid therapy in different patient positions. Materials and Methods: Inclusion criteria consisted of patients aged 18-50 years old and classified as American Society of Anesthesiologists physical status I and II, who were candidates for elective thyroidectomy surgery. In total, 36 patients meeting the inclusion criteria were enrolled in the study. After induction of anesthesia and start of mechanical ventilation Pleth variability index was measured in the supine position, then patients were placed in Trendelenburg and reverse Trendelenburg position (30 degrees, 5 minutes); Pleth Variability Index has measured again in the mentioned positions. Results: Mean PVI (Pleth Variability Index) in the supine position was 14.3 ± 3.7 in comparison to 21.5 ± 4.3 in the reverse Trendelenburg position. The mean PVI in Trendelenburg position was 9.1 ± 2.0 in Trendelenburg position (p < 0.05). Conclusion: In conclusion, we found that Pleth Variability Index varies with patient position and this should be taken into account when using this index during fluid therapy.

Keywords: fluid therapy, Pleth Variability Index, position, surgery

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Authors: Lubaina Soni, Claire Farrell, Christopher Szota, Tim D. Fletcher

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Green roofs are a promising engineered ecosystem for reducing stormwater runoff and restoring vegetation cover in cities. Plants can contribute to rainfall retention by rapidly depleting water in the substrate; however, this increases the risk of plant drought stress. Green roof configurations, therefore, need to provide plants the opportunity to efficiently deplete the substrate but also avoid severe drought stress. This study used green roof modules placed in a rainout shelter during a six-month rainfall regime simulated in Melbourne, Australia. Rainfall was applied equally with an overhead irrigation system on each module. Aside from rainfall, modules were under natural climatic conditions, including temperature, wind, and radiation. A single species, Ficinia nodosa, was planted with five different treatments and three replicates of each treatment. In this experiment, we tested the impact of three plant cover treatments (0%, 50% and 100%) on rainfall retention and plant drought stress. We also installed two runoff zone treatments covering 50% of the substrate surface for additional modules with 0% and 50% plant cover to determine whether directing rainfall resources towards plant roots would reduce drought stress without impacting rainfall retention. The retention performance for the simulated rainfall events was measured, quantifying all components for hydrological performance and survival on green roofs. We found that evapotranspiration and rainfall retention were similar for modules with 50% and 100% plant cover. However, modules with 100% plant cover showed significantly higher plant drought stress. Therefore, planting at a lower cover/density reduced plant drought stress without jeopardizing rainfall retention performance. Installing runoff zones marginally reduced evapotranspiration and rainfall retention, but by approximately the same amount for modules with 0% and 50% plant cover. This indicates that reduced evaporation due to the installation of the runoff zones likely contributed to reduced evapotranspiration and rainfall retention. Further, runoff occurred from modules with runoff zones faster than those without, indicating that we created a faster pathway for water to enter and leave the substrate, which also likely contributed to lower overall evapotranspiration and retention. However, despite some loss in retention performance, modules with 50% plant cover installed with runoff zones showed significantly lower drought stress in plants compared to those without runoff zones. Overall, we suggest that reducing plant cover represents a simple means of optimizing green roof performance but creating runoff zones may reduce plant drought stress at the cost of reduced rainfall retention.

Keywords: green roof, plant cover, plant drought stress, rainfall retention

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Authors: Mariam Tsitsagi, Ana Berdzenishvili

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Rainfall intensity change is one of the main indicators of climate change. It has a great influence on agriculture as one of the main factors causing soil erosion. Splash and sheet erosion are one of the most prevalence and harmful for agriculture. It is invisible for an eye at first stage, but the process will gradually move to stream cutting erosion. Our study provides the assessment of rainfall erosivity potential with the use of modern research methods in Kakheti region. The region is the major provider of wheat and wine in the country. Kakheti is located in the eastern part of Georgia and characterized quite a variety of natural conditions. The climate is dry subtropical. For assessment of the exact rate of rainfall erosion potential several year data of rainfall with short intervals are needed. Unfortunately, from 250 active metro stations running during the Soviet period only 55 of them are active now and 5 stations in Kakheti region respectively. Since 1936 we had data on rainfall intensity in this region, and rainfall erosive potential is assessed, in some old papers, but since 1990 we have no data about this factor, which in turn is a necessary parameter for determining the rainfall erosivity potential. On the other hand, researchers and local communities suppose that rainfall intensity has been changing and the number of haily days has also been increasing. However, finding a method that will allow us to determine rainfall erosivity potential as accurate as possible in Kakheti region is very important. The study period was divided into three sections: 1936-1963; 1963-1990 and 1990-2015. Rainfall erosivity potential was determined by the scientific literature and old meteorological stations’ data for the first two periods. And it is known that in eastern Georgia, at the boundary between steppe and forest zones, rainfall erosivity in 1963-1990 was 20-75% higher than that in 1936-1963. As for the third period (1990-2015), for which we do not have data of rainfall intensity. There are a variety of studies, where alternative ways of calculating the rainfall erosivity potential based on lack of data are discussed e.g.based on daily rainfall data, average annual rainfall data and the elevation of the area, etc. It should be noted that these methods give us a totally different results in case of different climatic conditions and sometimes huge errors in some cases. Three of the most common methods were selected for our research. Each of them was tested for the first two sections of the study period. According to the outcomes more suitable method for regional climatic conditions was selected, and after that, we determined rainfall erosivity potential for the third section of our study period with use of the most successful method. Outcome data like attribute tables and graphs was specially linked to the database of Kakheti, and appropriate thematic maps were created. The results allowed us to analyze the rainfall erosivity potential changes from 1936 to the present and make the future prospect. We have successfully implemented a method which can also be use for some another region of Georgia.

Keywords: erosivity potential, Georgia, GIS, Kakheti, rainfall

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Authors: Zviad Ghadua, Biswa Bhattacharya

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The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina

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Authors: Ali M. Subyani

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The increase in the greenhouse gas emission has had a severe impact on global climate change and is bound to affect the weather patterns worldwide. This climate change impacts are among the future significant effects on any society. Rainfall levels are drastically increasing with flash floods in some places and long periods of droughts in others, especially in arid regions. These extreme events are causes of interactions concerning environmental, socio-economic and cultural life and their implementation. This paper presents the detailed features of dry and wet spell durations and rainfall intensity series available (1971-2012) on daily basis for the Jeddah area, Western, Saudi Arabia. It also presents significant articles for combating the climate change impacts on this area. Results show trend changes in dry and wet spell durations and rainfall amount on daily, monthly and annual time series. Three rain seasons were proposed in this investigation: high rain, low rain, and dry seasons. It shows that the overall average dry spell durations is about 80 continuous days while the average wet spell durations is 1.39 days with an average rainfall intensity of 8.2 mm/day. Annual and seasonal autorun analyses confirm that the rainy seasons are tending to have more intense rainfall while the seasons are becoming drier. This study would help decision makers in future for water resources management and flood risk analysis.

Keywords: climate change, daily rainfall, dry and wet spill, Jeddah, Saudi Arabia

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN Tool, disaggregation, exceedance probability, Kolmogorov-Smirnov test, rainfall

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Authors: Sudip Kumar Kundu, Charu Singh

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The climatic condition over Indian region is highly dependent on monsoon. India receives maximum amount of rainfall during southwest monsoon. Indian economy is highly dependent on agriculture. The presence of flood and drought years influenced the total cultivation system as well as the economy of the country as Indian agricultural systems is still highly dependent on the monsoon rainfall. The present study has been planned to investigate the flood and drought years for the north-west Himalayan region from 1951 to 2014 by using area average Indian Meteorological Department (IMD) rainfall data. For this investigation the Normalized index (NI) has been utilized to find out whether the particular year is drought or flood. The data have been extracted for the north-west Himalayan (NWH) region states namely Uttarakhand (UK), Himachal Pradesh (HP) and Jammu and Kashmir (J&K) to find out the rainy season average rainfall for each year, climatological mean and the standard deviation. After calculation it has been plotted by the diagrams (or graphs) to show the results- some of the years associated with drought years, some are flood years and rest are neutral. The flood and drought years can also relate with the large-scale phenomena El-Nino and La-Lina.

Keywords: IMD, rainfall, normalized index, flood, drought, NWH

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Authors: Benhammadi Hocine, Chaffai Hicham

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Urban development is often dependent on adequate land for expansion, except that sometimes these areas have vulnerability. This is the case of karst regions characterized by carbonate geological formations marked by the presence of cavities and cracks. The impact of climate variability in Cheria area marked by a growing shortage of rainfall, the impact resulted in the development of the vulnerability of these structures. This vulnerability has led to the appearance of collapse phenomena as well in both agricultural and urban areas. Two phenomena have emerged to explain the collapses, the first is assigned a filling process in the cavities, and the second is due to a weakening of the resistance that collapses limestone slab shear phenomenon. In urban areas, the weight of the buildings has increased the load on the limestone slab and accelerated the collapse. The analysis of the environmental process is in the context of our modest work, after which we indicate the appropriate methods for management policy of urban expansion. This management more preventive (upstream), much less expensive than remedial solutions (downstream) needed after the event and sometimes ineffective.

Keywords: Cheria, urban, climate variability, vulnerability karst collapse, extension, management

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: concrete, FEM, pavement, sensitivity, simulation

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Authors: Tasir Khan, Yejuan Wang

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The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management.

Keywords: RAMSE, multiple frequency analysis, annual maximum rainfall, L-moments

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Authors: M. T. Islam, S. H. Shakif, R. Hasan, S. H. Kobi

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Investigation of rainfall trends is crucial considering climate change, food security, and the economy of a particular region. This research aims to study seasonal and annual precipitation trends and their abrupt changes over time in the south-central climatic zone of Bangladesh using monthly time series data of 50 years (1970-2019). A trend-free pre-whitening method has been employed to make necessary adjustments for autocorrelations in the rainfall data. Trends in rainfall and their intensity have been observed using the non-parametric Mann-Kendall test and Theil-Sen estimator. Significant changes and fluctuation points in the data series have been detected using the sequential Mann-Kendall test at the 95% confidence limit. The study findings show that most of the rainfall stations in the study area have a decreasing precipitation pattern throughout all seasons. The maximum decline in the rainfall intensity has been found for the Tangail station (-8.24 mm/year) during monsoon. Madaripur and Chandpur stations have shown slight positive trends in post-monsoon rainfall. In terms of annual precipitation, a negative rainfall pattern has been identified in each station, with a maximum decrement (-) of 14.48 mm/year at Chandpur. However, all the trends are statistically non-significant within the 95% confidence interval, and their monotonic association with time ranges from very weak to weak. From the sequential Mann-Kendall test, the year of changing points for annual and seasonal downward precipitation trends occur mostly after the 90s for Dhaka and Barishal stations. For Chandpur, the fluctuation points arrive after the mid-70s in most cases.

Keywords: trend analysis, Mann-Kendall test, Theil-Sen estimator, sequential Mann-Kendall test, rainfall trend

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Tommaso Picciafuoco, Corrado Corradini

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A considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimate of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships and in determining if climate change is producing effects on extreme event intensities and frequencies. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results indicate that: 1) in the worst conditions, for d=ta, the estimation of a single Hd value can be affected by an underestimation error up to 50%, while the average underestimation error for a series with at least 15-20 Hd values, is less than or equal to 16.7%; 2) the underestimation error values follow an exponential probability density function; 3) each very long time series of Hd contains many underestimated values; 4) relationships between the non-dimensional ratio ta/d and the average underestimate of Hd, derived from continuous rainfall data observed in many stations of Central Italy, may overcome this issue; 5) these equations should allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions.

Keywords: central Italy, extreme events, rainfall data, underestimation errors

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Authors: L. Mouzai, M. Bouhadef

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Raindrops and overland flow both are erosive parameters but they do not act by the same way. The overland flow alone tends to shear the soil horizontally and concentrates into rills. In the presence of rain, the soil particles are removed from the soil surface in the form of a uniform sheet layer. In addition to this, raindrops falling on the flow roughen the water and soil surface depending on the flow depth, and retard the velocity, therefore influence shear velocity and Manning’s factor. To investigate this part, agricultural sandy soil, rainfall simulator and a laboratory soil tray of 0.2x1x3 m were the base of this work. Five overland flow depths of 0; 3.28; 4.28; 5.16; 5.60; 5.80 mm were generated under a rainfall intensity of 217.2 mm/h. Sediment concentration control is based on the proportionality of depth/microtopography. The soil loose is directly related to the presence of rain splash on thin sheet flow. The effect of shear velocity on sediment concentration is limited by the value of 5.28 cm/s. In addition to this, the rain splash reduces the soil roughness by breaking the soil crests. The rainfall intensity is the major factor influencing depth and soil erosion. In the presence of rainfall, the shear velocity of the flow is due to two simultaneous effects. The first, which is horizontal, comes from the flow and the second, vertical, is due to the raindrops.

Keywords: flow resistance, laboratory experiments, rainfall simulator, sediment concentration, shear velocity, soil erosion

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Authors: N. V. Nikhil, S. R. Lee, Do Won Park

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Korean peninsula receives about two third of the annual rainfall during summer season. The extreme rainfall pattern due to typhoon and heavy rainfall results in severe mountain disasters among which 55% of them are debris flows, a major natural hazard especially when occurring around major settlement areas. The basic mechanism underlined for this kind of failure is the unsaturated shallow slope failure by reduction of matric suction due to infiltration of water and liquefaction of the failed mass due to generation of positive pore water pressure leading to abrupt loss of strength and commencement of flow. However only an empirical model cannot simulate this complex mechanism. Hence, we have employed an empirical-physical based approach for hazard analysis of debris flow using TRIGRS, a debris flow initiation criteria and DAN3D in mountain Woonmyun, South Korea. Debris flow initiation criteria is required to discern the potential landslides which can transform into debris flow. DAN-3D, being a new model, does not have the calibrated values of rheology parameters for Korean conditions. Thus, in our analysis we have used the recent 2011 debris flow event in mountain Woonmyun san for calibration of both TRIGRS model and DAN-3D, thereafter identifying and predicting the debris flow initiation points, path, run out velocity, and area of spreading for future extreme rainfall based scenarios.

Keywords: debris flow, DAN-3D, extreme rainfall, hazard analysis

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Authors: J. B. Clapp, S. Croarkin, C. Dolphin, S. K. Lyons

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Chronic pain or stress in farm animals impacts both on their welfare and productivity. Measuring chronic pain or stress can be problematic using hormonal or behavioural changes because hormones are modulated by homeostatic mechanisms and observed behaviour can be highly subjective. We propose that heart rate variability (HRV) can quantify chronic pain or stress in farmed animal and represents a more robust and objective measure of their welfare.

Keywords: dairy calf, welfare, heart rate variability, non-invasive, biomonitor

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Authors: Hemant Kumar

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Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position.

Keywords: aerosol, change detection, spatial analysis, trend analysis

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Authors: Suparat Niwitpong, Sa-aat Niwitpong

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Statistical inference of normal mean with known coefficient of variation has been investigated recently. This phenomenon occurs normally in environment and agriculture experiments when the scientist knows the coefficient of variation of their experiments. In this paper, we constructed new confidence intervals for the normal population mean with known coefficient of variation. We also derived analytic expressions for the coverage probability of each confidence interval. To confirm our theoretical results, Monte Carlo simulation will be used to assess the performance of these intervals based on their coverage probabilities.

Keywords: confidence interval, coverage probability, expected length, known coefficient of variation

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Authors: Salif Koné

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We analyze hydrometric data at the Koulikoro station on the Niger River; this basin drains 120600 km2 and covers three countries in West Africa, Guinea, Mali, and Ivory Coast. Two subsequent decadal cycles are highlighted (1925-1936 and 1929-1939) instead of the presumed single decadal one from literature. Moreover, the observed hydrometric data shows a multidecadal 40-year period that is confirmed when graphing a spatial coefficient of variation of runoff over decades (starting at 1901-1910). Spatial runoff data are produced on 48 grids (0.5 degree by 0.5 degree) and through semi-distributed versions of both SimulHyd model and GR2M model - variants of a French Hydrologic model – standing for Genie Rural of 2 parameters at monthly time step. Both extremal decades in terms of runoff coefficient of variation are confronted: 1951-1960 has minimal coefficient of variation, and 1981-1990 shows the maximal value of it during the three months of high-water level (August, September, and October). The mapping of the relative variation of these two decadal situations allows hypothesizing as following: the scale of variation between both extremal situations could serve to fix boundary conditions for further simulations using data from climate scenario.

Keywords: internal cycles, hydrometric data, niger river, gr2m and simulhyd framework, runoff coefficient of variation

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Authors: Bumjo Kim, Hyun Jin Kim, Joon Ha Kim

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The separate sewer system is that collects the wastewater as a sewer pipe and rainfall as a stormwater pipe separately, and then sewage is treated in the wastewater treatment plant, the stormwater is discharged to rivers or lakes through stormwater drainage pipes. Unfortunately, even for separate sewer systems, it is not possible to prevent Rainfall Driven Inflow/Infiltration(RDII) completely to the sewer pipe. Even if the sewerage line is renovated, there is an ineluctable RDII due to the combined sewer system in the house or the difficulty of sewage maintenance in private areas. The basic statistical analysis was performed using environmental data including rainfall, sewage, water qualities and groundwater level in the strict of Gwangju in ​South Korea. During rainfall in the target area, RDII showed an increased rate of 13.4 ~ 53.0% compared to that of a clear day and showed a rapid hydrograph response of 0.3 ~ 3.0 hr. As a result of water quality analysis, BOD5 concentration decreased by 17.3 % and salinity concentration decreased by 8.8 % at the representative spot in the project area compared to the sunny day during rainfall. In contrast to the seasonal fluctuation range of 0.38 m ~ 0.55 m in groundwater in Gwangju area and 0.58 m ~ 0.78 m in monthly fluctuation range, while the difference between groundwater level and the depth of sewer pipe laying was 2.70 m on average, which is larger than the range of fluctuation. Comprehensively, it can be concluded that the increasing of flowrate at sewer line is due to not infiltration water caused by groundwater level rise, construction failure, cracking due to joint failure or conduit deterioration, rainfall was directly inflowed into the sewer line rapidly. Acknowledgements: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: ground water, rainfall, rainfall driven inflow/infiltration, separate sewer system

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Authors: Ping-Ben Liu, Chien-Chou Tseng

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The objective of this study is to investigate the relationship between the lift coefficient and dynamic behaviors of cavitating flow around a two-dimensional Clark Y hydrofoil at 8° angle of attack, cavitation number of 0.8, and Reynolds number of 7.10⁵. The flow field is investigated numerically by using a vapor transfer equation and a modified turbulence model which applies the filter and local density correction. The results including time-averaged lift/drag coefficient and shedding frequency agree well with experimental observations, which confirmed the reliability of this simulation. According to the variation of lift coefficient, the cycle which consists of growth and shedding of cavitation can be divided into three stages, and the lift coefficient at each stage behaves similarly due to the formation and shedding of the cavity around the trailing edge.

Keywords: Computational Fluid Dynamics, cavitation, turbulence, lift coefficient

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Authors: Radha Krishan, Deepak Khare, Bhaskar R. Nikam, Ayush Chandrakar

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Every irrigation project is planned considering long-term historical climatic conditions; however, the prompt climatic shift and change has come out with such circumstances which were inconceivable in the past. Considering this fact, scrutiny of rainfall and temperature trend has been carried out over the command area of Eastern Ganga Canal project for pre-climate shift period and post-climate shift periods in the present study. Non-parametric Mann-Kendall and Sen’s methods have been applied to study the trends in annual rainfall, seasonal rainfall, annual rainy day, monsoonal rainy days, average annual temperature and seasonal temperature. The results showed decreasing trend of 48.11 to 42.17 mm/decade in annual rainfall and 79.78 tSo 49.67 mm/decade in monsoon rainfall in pre-climate to post-climate shift periods, respectively. The decreasing trend of 1 to 4 days/decade has been observed in annual rainy days from pre-climate to post-climate shift period. Trends in temperature revealed that there were significant decreasing trends in annual (-0.03 ºC/yr), Kharif (-0.02 ºC/yr), Rabi (-0.04 ºC/yr) and summer (-0.02 ºC/yr) season temperature during pre-climate shift period, whereas the significant increasing trend (0.02 ºC/yr) has been observed in all the four parameters during post climate shift period. These results will help project managers in understanding the climate shift and lead them to develop alternative water management strategies.

Keywords: climate shift, rainfall trend, temperature trend, Mann-Kendall test, sen slope estimator, eastern Ganga canal command

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Authors: Sanjay Kumar Srivastava, Anu Rani Sharma, Kamna Sachdeva

Abstract:

The aim of the paper is to analyze the characteristics of winter fog in terms of its trend and spatial-temporal variability over Indo-Gangetic plains. The study reveals that during last four and half decades (1971-2015), an alarming increasing trend in fog frequency has been observed during the winter months of December and January over the study area. The frequency of fog has increased by 118.4% during the peak winter months of December and January. It has also been observed that on an average central part of IGP has 66.29% fog days followed by west IGP with 41.94% fog days. Further, Empirical Orthogonal Function (EOF) decomposition and Mann-Kendall variation analysis are used to analyze the spatial and temporal patterns of winter fog. The findings have significant implications for the further research of fog over IGP and formulate robust strategies to adapt the fog variability and mitigate its effects. The decision by Delhi Government to implement odd-even scheme to restrict the use of private vehicles in order to reduce pollution and improve quality of air may result in increasing the alarming increasing trend of fog over Delhi and its surrounding areas regions of IGP.

Keywords: fog, climatology, spatial variability, temporal variability

Procedia PDF Downloads 315

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

Abstract:

Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Racha El Kadiri, Mohamed Sultan, Henrique Momm, Zachary Blair, Rachel Schultz, Tamer Al-Bayoumi

Abstract:

The Mediterranean Basin is a very diverse region of nationalities and climate zones, with a strong dependence on agricultural activities. Predicting long term (with a lead of 1 to 12 months) rainfall, and future droughts could contribute in a sustainable management of water resources and economical activities. In this study, an integrated approach was adopted to construct predictive tools with lead times of 0 to 12 months to forecast rainfall amounts over nine subzones of the Mediterranean Basin region. The following steps were conducted: (1) acquire, assess and intercorrelate temporal remote sensing-based rainfall products (e.g. The CPC Merged Analysis of Precipitation [CMAP]) throughout the investigation period (1979 to 2016), (2) acquire and assess monthly values for all of the climatic indices influencing the regional and global climatic patterns (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); (3) delineate homogenous climatic regions and select nine pilot study zones, (4) apply data mining methods (e.g. neural networks, principal component analyses) to extract relationships between the observed rainfall and the controlling factors (i.e. climatic indices with multiple lead-time periods) and (5) use the constructed predictive tools to forecast monthly rainfall and dry and wet periods. Preliminary results indicate that rainfall and dry/wet periods were successfully predicted with lead zones of 0 to 12 months using the adopted methodology, and that the approach is more accurately applicable in the southern Mediterranean region.

Keywords: rainfall, neural networks, climatic indices, Mediterranean

Procedia PDF Downloads 285

Authors: M. Sharif Imam Ibne Amir, Mohammad Masud Kamal Khan, Mohammad Golam Rasul, Raj H. Sharma, Fatema Akram

Abstract:

Future flood can be predicted using the probable maximum flood (PMF). PMF is calculated using the historical discharge or rainfall data considering the other climatic parameter stationary. However, climate is changing globally and the key climatic variables are temperature, evaporation, rainfall and sea level rise (SLR). To develop scenarios to a basin or catchment scale these important climatic variables should be considered. Nowadays scenario based on climatic variables is more suitable than PMF. Six scenarios were developed for a large Fitzroy basin and presented in this paper.

Keywords: climate change, rainfall, potential evaporation, scenario, sea level rise (SLR), sub-catchment

Procedia PDF Downloads 488