CIVIL ENGINEERING, HYDROLOGY, HYDRAULICS, SCIENCE, CYCLE, please break each sect
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CIVIL ENGINEERING, HYDROLOGY, HYDRAULICS, SCIENCE, CYCLE, please break each section down into steps on how to get the answer!!!!!! PLEASE NEED IT ASAP!!!!!
Question 1 (a) What changes do you see occurring in meteorological and collection methods over the next 20 years? meteorological and hydrological data (b) Describe briefly the principles of the Tipping Bucket an d the Current Meter approaches in hydro-meteorological data collection e terms "calibration", "validation" and "prediction "use types "used in for a (c) Define the ter hydrological modelling and list possible input and output data hydrological model (d) Illustrate (draw and name) three possible loss models that can be used in hydrological modelling e) Describe three possible methods in computing average rainfall of a ca tchment sitives and negatives of each method. Question 1-i) The bottom width of the ABC rectangular channel shown in Figure 1 is 5m. The Manning's roughness throughout the channel can be taken as 0015 slopes of the AB and BC are 1 in 1000 and 1 in 200 respectively. Flow at upstream of A is uniform while tail water level (where ABC delivers water to the crossing channel (at C) running perpendicular to ABC) is 3m. Uniform flow depth of the AB channel is also 3m. The flow depth at the vena contractor downstream of the gate is 500mm. The velocity at the upstream of the gate and energy losses at the gate can be considered as negligible. second conjugate depth of any hy The draulic jump formed in a mild slope, as well as the first conjugate depth of any hydraulic jump formed in a st betaken as the uniform fow depth of the respective channel. eep slope, canExplanation / Answer
(a)Drought has been a recurrent feature of the European climate. From 2006–2010, on average 15 % of the EU territory and 17 % of the EU population have been affected by meteorological droughts each year. In the 1990s and 2000s the drought hotspots were the Mediterranean area and the Carpathian Region. The frequency of meteorological droughts in Europe has increased since 1950 in parts of southern Europe and central Europe (Austria and Hungary), but droughts have become less frequent in northern Europe and parts of Eastern Europe. Trends in drought severity (based on a combination of Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI)) also show significant increases in the Mediterranean region (in particular the Iberian Peninsula, France, Italy and Albania) and parts of central and south-eastern Europe, and decreases in northern and parts of Eastern Europe. Most stream gauges in Europe show a decrease in summer low flows over the second half of the 20th century (Figure 2). However, current data availability is insufficient for attributing this trend to global climate change
An assessment of European meteorological droughts based on different drought indices and an ensemble of RCMs has projected drier conditions for southern Europe for the mid-21st century, with increases in the length, magnitude and area of drought events [iv]. In contrast, drought occurrence was projected to decrease in northern Europe. These projections showed the largest increases in frequency for extreme droughts in parts of the Iberian Peninsula, southern Italy and the eastern Mediterranean, especially at the end of the century with respect to the baseline period 1971–2000. The changes are most pronounced for the RCP8.5 high emissions scenario and slightly less extreme for the moderate scenario. The projected increases in droughts in large parts of southern Europe would increase competition between different water users, such as agriculture, industry, tourism and households. Droughts have severe consequences for Europe’s citizens and most economic sectors, including agriculture, energy production, industry and public water supply. However, the term ‘drought’ is used in different contexts, which may cause confusion when the language used is not carefully chosen. A persistent meteorological drought can propagate to a soil moisture (agricultural) drought affecting plant and crop growth, which may deepen into a hydrological drought affecting watercourses, water resources and natural ecosystems. Furthermore, hydrological droughts have a detrimental impact on freshwater ecosystems including vegetation, fish, invertebrates and riparian bird life. Hydrological droughts also have a significant impact on water quality by reducing the ability of a river to dilute pollution.
This indicator combines two types of droughts: meteorological droughts and hydrological droughts, focusing on river flow droughts in the case of the latter. A meteorological drought is defined in terms of precipitation deficiency, which may be exacerbated by high temperature associated with high evapotranspiration. Meteorological droughts are usually characterized using statistical indices, such as the standardized Precipitation Index (SPI), standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI). A river flow drought is characterized by unusually low river flow, which may result from a prolonged meteorological drought, possibly in combination with socio-economic factors.
(b) The principle of this type of recording gauge is very simple. A light metal container is divided into two compartments and is balanced in unstable equilibrium about a horizontal axis. In its normal position the container rests against one of two stops, which prevents, it from tipping completely. The rain is led from a conventional collecting funnel into the uppermost compartment. After a predetermined amount of rain has fallen, the bucket becomes unstable in its present position and tips over to its other position of rest. The compartments of the container are so shaped that the water can now flow out of the lower one and leave it empty. Meanwhile, the rain falls into the newly positioned upper compartment. The movement of the bucket, as it tips over, is used to operate a relay contact and produce a record that consists of discontinuous steps. The amount of rain which causes the bucket to tip should not be greater than 0.2 millimeters. The main advantage of this type of instrument is that it has an electronic pulse output and can be recorded at a distance or for simultaneous recording of rainfall and river stage on a water stage recorder. Its disadvantages are:
- The bucket takes a small but finite time to tip, and during the first half of its motion, the rain is being fed into the compartment already containing the calculated amount of rainfall. This error is appreciable only in heavy rainfall
- With the usual design of the bucket, the exposed water surface is relatively large. Thus, significant evaporation losses can occur in hot regions. This will be most appreciable in light rains; and because of the discontinuous nature of the record, the instrument readings may not be satisfactory for use in light drizzle or very light rain. The time of beginning and ending of rainfall cannot be determined accurately.
The Tipping Bucket rain gauge is a widely used for recording rainfall amounts and intensities in remote and unattended places. Once the TBR is installed and calibrated, it is ready for use. The TBR is equipped with a data logger, which automatically stores the number of tipping per unit of time or the timings of each tipping. The data stored in the data logger is either transmitted through satellite or GPRS based telemetry system at the required location. Else the logger can be read out using data downloading device at any point of time or interval. The functioning of the equipment is to be checked as per instructions of the supplier on routine basis. Maintenance of TBR should be carried out in accordance with the instructions supplied with the equipment.
- The collector should be kept clear of obstructions and it should be gently cleaned for dust and debris without disturbing the tipping bucket switch. This should be carried out on regular interval basis.
- If the bucket does not tip, it is probably sticking on its bearings.
- If the bucket does tip but the counter reading fails to advance, the trouble may be due to a faulty counter or switch. For rectification of these defects, only an expert mechanic needs to attend.
The maximum and minimum atmospheric temperature plays a vital role in various natural processes including the hydrological cycle. The MAXIMUM / MINIMUM thermometer records the highest and lowest atmospheric temperatures seen by the thermometer between settings. A "U" shape tube holds a clear liquid and columns of mercury. As the temperature increases, the liquid expands forcing the mercury up the maximum scale. When the temperature falls, the liquid contracts and the mercury follows it back up the minimum scale. Small glass and wire floats called limit markers are pushed to the temperature limits by the two sides of the mercury column. The markers are held in position by a magnet in the back of the case and the maximum / minimum temperatures are read at the bottom point of the markers. A setting magnet will easily reset both scales so you can take periodic readings without worrying about recording the wrong information. The current or immediate temperature can always be read at the top of the mercury column as in a single tube thermometer. One limb of the thermometer reads the maximum temperature while the other limb is used to read the minimum temperature. The range of the thermometer should be between -35°C to +55°C with minimum readable graduation as 0.5°C.
(c) Hydrological models are important for a wide range of applications, including water resources planning, development and management, flood prediction and design, and coupled systems modelling including, for example, water quality, hydro-ecology and climate. However, due to resource constraints and the limited range of available measurement techniques, there are limitations to the availability of spatial-temporal data; hence a need exists to extrapolate information from the available measurements in space and time; in addition there is a need to assess the likely hydrological impact of future system response, for example to climate and land management change. The ability of a model to successfully predict catchment behavior relies on the reliability and representativeness of the data against which it is calibrated, the quality of the processes and parameters assumed internally within the model and the accuracy of the input datasets used to define the catchment. Furthermore, in the absence of high-quality “ground truth” data for soils, land-use and weather inputs, powerful automated calibration algorithms can alter model parameters to produce a structurally biased model which provides a good fit to specified calibration data, but may diverge significantly from true catchment behavior under other conditions.
This study explores a methodology for differentiating between various input datasets of unknown relative quality for a hydrological model of a typical semi-arid catchment. A start with the proposition that the combination of input datasets which produce the best fit to observed output data prior to full model calibration will yield a model that is less computationally intensive and which minimizes the potential for structural errors arising from systematic biases
Introduced during calibration. Objective is to test the specific hypothesis that different combinations of a small number of available datasets will result in a significant variation in pre-calibration model performance, allowing rapid estimation of relative input data quality. The aim is to develop a simple, resource-efficient and transferable method for use in the design and specification of catchment models to support water resource management where data are of uncertain quality and/or quantity, and decisions on where to invest efforts to improve them are limited by available resources.
In general several levels of evaluation are necessary before the model should be applied to estimate the output from a catchment these are: (i) model selection – choice of working hypotheses (ii) model calibration - estimation of the parameter values (iii) model validation - testing the fitted model to verify its accuracy; and (iv) estimation of its range of applicability.
Conceptually, these evaluations are distinct and follow in sequence. In practice, the boundaries for many types of models are often blurred. Of the four types of evaluation, estimation of the parameter values generally receives most attention. Nevertheless, it is important to recognize that all four evaluations are of equal fundamental importance, and neglect of any one can lead to serious error. Hydrological practice would be improved if models were objectively chosen on
the basis of making the best use of the information available and following some systematic procedure of selection and verification. The choice of the best model depends to a large extent on the problem. Generally speaking, items that should be considered in the selection process include:
(a) The nature of the physical processes involved,
(b) The use to be made of the model,
(c) The quality of the data available and
(d) The decisions that rest on the outcome of the model's use.
Finally, in model selection, decisions that may rest upon the outcome of the model’s use must be considered. To a great extent, these decisions will dictate the criteria that should be used to judge the quality of the model’s performance. As an example, suppose that streamflow sequences will be used to determine the size of a dam to be used for water supply. In this case, the model is selected and its parameters estimated in such a way as to minimize the costs of uncertainty inherent in decisions regarding the size of the dam. Alternatively, suppose aerial rainfall data were used to study the spatial variability of soil moisture in assessing crop conditions. In this case, the model and its parameters must be selected to minimize the costs inherent in either over irrigation or losses in productivity brought on by drought induced growth stress. These are rather simplistic examples, but they serve to show the needs of the decision maker, who may not know how to judge the quality of a model’s response.
Physically based distributed models of the hydrological cycle can in principle be applied to almost any kind of hydrological problem. These models are based on our understanding of the physics of the hydrological processes which control catchment response and use physically based equations to describe these processes. Some typical examples of field applications include study of effect of catchment changes, prediction of behavior of ungauged catchment, of spatial variability in catchment inputs and outputs, movement of pollutants and sediment etc. Hydrological modelling is a powerful technique of hydrologic system investigation for both the research hydrologists’ system investigation for both the research hydrologists and the practicing water resources engineers involved in the planning and development of integrated approach for management of water resources. he availability of remote sensing data and application of Geographical Information system provide very useful input data requirement for physically based hydrological models. The use of remote sensing and GIS facilitates hydrologists to deal with large scale, complex and spatially distributed hydrological processes.
Hydrological modelling is a powerful technique of hydrologic system investigation for both the research hydrologists and the practicing water resources engineers involved in the planning and development of integrated approach for management of water resources.
When seen at the node points of this discretized representation. Physically based distributed models do not consider the transfer of water in a catchment to take place in a few defined storage as in case of lumped conceptual models. From their physical basis such models can simulate the complete runoff regime, providing multiple outputs (e.g. river discharge, phreatic surface level and evaporation loss) while black box models can offer only one output. In these models transfer of mass, momentum and energy are calculated directly from the governing partial differential equations which are solved using numerical methods, for example the St. Venant equations for surface flow, the Richards equation for unsaturated zone flow and the Boussinesq equation for ground water flow. As the input data and computational requirements are enormous, the use of these models for real-time forecasting has not reached the ‘production stage’ so far, particularly for data availability situations prevalent in developing countries like India.
(e) To compute the average rainfall over a catchment area or basin, rainfall is measured at a number of gauges by suitable type of measuring devices. A rough idea of the number of the needed rain gauges to be installed in a practical area is depending on experience of the hydrologist although this was determined by the regulation of the World Meteorological
Organization (WMO). In areas where more than one rain gauge is established, following methods may be employed to compute the average rainfall:
- Arithmetic average method
This is the simplest method of computing the average rainfall over a basin. As the name suggests, the result is obtained by the division of the sum of rain depths recorded at different rain gauge stations of the basin by the number of the stations. If the rain gauges are uniformly distributed over the area and the rainfall varies in a very regular manner, the results obtained by this method will be quite satisfactory and will not differ much than those obtained by other methods. This method can be used for the storm rainfall, monthly or annual rainfall average computations.
- Weighing mean method or Thiessen polygon method
This is the weighted mean method. The rainfall is never uniform over the entire area of the basin or catchment, but varies in intensity and duration from place to place. Thus the rainfall recorded by each rain gauge station should be weighted according to the area, it represents. This method is more suitable under the following conditions:
- For areas of moderate size.
- When rainfall stations are few compared to the size of the basin.
- In moderate rugged areas.
- Isohyetal method.
An isohyetal is a line joining places where the rainfall amounts are equal on a rainfall map of a basin. An isohyetal map showing contours of equal rainfall is more accurate picture of the rainfall over the basin. This method is more suited under the following conditions:
- For hilly and rugged areas.
- For large areas over 5000 km2.
- For areas where the network of rainfall stations within the storm area is sufficiently dense, isohyetal method gives more accurate distribution of rainfall.
Comparison Between the Three Methods:
Arithmetic mean method:
1- This is the simplest and easiest method to compute average rainfall.
2- In this method every station has equal weight regardless its location.
3- If the recording stations and rainfall is uniformly distributed over the entire catchment, then this method is equally accurate.
Thiessen method
1-This method is also mechanical
2-In this method the rainfall stations located at a short distance beyond the boundary of drainage are also used to determine the mean rainfall of the basin, but their influence diminishes as the distance from the boundary increases.
3-It is commonly used for flat and low rugged areas.
Isohyetal method:
1- It is the best method for rugged areas and hilly regions.
2- It is the most accurate method if the contours are drawn correctly. However to obtain the best results good judgment in drawing the isohyets and in assigning the proper mean rainfall values to the area between them is required.
3- Other points are as for Thiessen method.
(d) In hydrology, loss estimation is necessary to provide input for two main applications: (1) real-time flood forecasting; and (2) design flood estimation. Loss models adopted for real-time flood forecasting are usually simple lumped models, examples include initial loss combined with continuing loss and proportional loss rate or runoff coefficient. Estimating temporal variability of losses across storm events is a crucial part of in real-time flood forecasting. For design flood estimation, either statistical, design storm derivation or continuous simulation approaches can be used. he process of calculating losses involves: (1) extracting events; (2) baseflow separation; and (3) calculating IL, CL and PL components. For hydrological loss estimations, baseflow should be separated from the original streamflow data set. The Lyne and Hollick algorithm ompared this method of baseflow separation with several other rigorous algorithms and concluded that the Lyne and Hollick algorithm was simpler and produced as good results as the alternatives. It also shows agreement with the most recent baseflow separation method. The Lyne and Hollick algorithm is given = qfi = sigma qf(i-1) + (qi – qi-1 )1+ sigma /2
where
qf(i) is the filtered quickflow for the ith sampling instant
qf(i1) is the filtered quickflow for the previous sampling instant
q(i) is the original streamflow for the ith sampling instant
q(i1) is the original streamflow for the previous sampling instant
is a filter parameter.
The Total Rainfall (TR) can be expressed according to below rquation:
TR = IL + CL * t + QF
CF = TR-IL-QF / t
where TR, IL and QF are in mm, CL is in mm/h and t is the time (in hours) elapsed between the start of the surface runoff and end of the rainfall event.
Quickflow (QF) calculated using Eq. (1) was in the units of m3/s. In order to substitute values of quickflow into Eq. (4), quickflow values need to be converted into mm using QF = sigma qfi * 1000 delta t / A, where delta t is streamflow duration in seconds and A is catchment area in m2.
A bivariate analysis was performed to investigate the relationship between the loss components and the selected variables. The loss components (IL, CL and PL) were considered as dependent variables (Y) and the TR, D and AW were considered as independent variables (X). Scatter plots were constructed to visually demonstrate the results. If both Y and X variables are random and if Y shows significant variability for a given value of X, then the Random X model can be used for analyzing variability. The Random X model shows the mean value of Y against a given value of X. In this analysis, the variability of the losses with D and AW were investigated using Random X models. Multiple-regression was carried out to estimate the combined effect of the independent variables (TR, AW and D) on the dependent variable (IL, CL). However, as the multiple-regression analysis was not accurate enough to enable IL to be estimated, further analysis of IL was carried out as described below:
1) The relationship between IL and TR was further assessed by calculating IL over TR (IL/TR).
2)Contour and dot maps were used to examine the effect of the variables on each other. On these maps each loss value was mapped in the third dimension as it improves the interpretation of the loss distribution patterns. The possibility of forming either a contour map or a dot map, and the patterns of distribution of each loss component compared to two other independent variables were investigated.
3)The k-coloured dot maps were developed by plotting the observed IL values against (AW and TR), (TR and D) and (AW and D). In order to identify clusters, all the observed IL values were ranked and aggregated to 8 arrays, as shown in Table 2. Then, the aggregations were plotted against various other combinations of the independent variables. These combinations included (IL/D and TR), (TR/D and D) and (TR/D and AW).