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Obtain a Unit Hydrograph for a basin using the rainfall and streamflow data
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"name": "python" | |
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"# Unit Hydrographs\n", | |
"\n", | |
"Unit hydrographs are a fundamental tool in the analysis of floods and their impacts on watersheds. A unit hydrograph represents the response of a watershed to a unit of rainfall or snow melt over a period of time. By analyzing the shape of the unit hydrograph, hydrologists can estimate the runoff volume, timing, and distribution of peak flows within a watershed. This information is essential for understanding the risk of flooding and designing effective flood management strategies.\n", | |
"\n", | |
"At its core, a unit hydrograph is a simple concept. It is a graph that shows the relationship between the input of precipitation or snowmelt and the resulting output of streamflow over time. The unit hydrograph concept assumes that the watershed responds to a given rainfall or snowmelt event in a predictable way, and that the response is proportional to the magnitude and duration of the event. By developing a unit hydrograph for a particular watershed, hydrologists can estimate the runoff that will occur for any given precipitation or snowmelt event, which is essential for predicting floods.\n", | |
"Unit hydrographs are useful in a range of applications, from designing flood control structures to evaluating the impacts of land use changes on hydrologic processes. They can be developed using a variety of methods, including graphical, analytical, and numerical techniques. One common approach is to use data from a historical storm event to develop a synthetic unit hydrograph, which can then be used to estimate the response of the watershed to future storm events.\n", | |
"\n", | |
"To develop a unit hydrograph, hydrologists first divide the watershed into subareas or subcatchments, each with its own unique hydrologic characteristics. They then estimate the time it takes for runoff to travel from each subcatchment to the watershed outlet, known as the travel time. The travel time is a function of the length, slope, and roughness of the flow path, as well as the velocity of the water.\n", | |
"\n", | |
"Once the travel times for each sub catchment have been estimated, hydrologists can develop the unit hydrograph by adding together the contributions from each subcatchment. This is done by convolving the runoff from each subcatchment with a unit impulse function, which represents the response of the watershed to a unit of precipitation or snowmelt.\n", | |
"\n", | |
"The resulting unit hydrograph shows the response of the watershed to a unit of rainfall or snowmelt over time, typically in the form of a graph. The time axis represents the time it takes for the runoff to reach the watershed outlet, while the discharge axis represents the volume of runoff at the outlet. The shape of the unit hydrograph reflects the characteristics of the watershed, including its size, shape, and hydrologic processes.\n", | |
"\n", | |
"By analyzing the shape of the unit hydrograph, hydrologists can estimate the volume, timing, and distribution of peak flows within the watershed for any given storm event. This information is critical for designing flood control structures, such as dams and levees, and for developing flood warning systems. It can also be used to evaluate the impacts of land use changes on hydrologic processes and to assess the effectiveness of different flood management strategies.\n", | |
"\n", | |
"Overall, unit hydrographs are an essential tool for understanding floods at the watershed level. By analyzing the shape of the unit hydrograph, hydrologists can estimate the response of the watershed to different storm events, and develop effective flood management strategies to protect communities and infrastructure.\n" | |
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"## Exercise 1:\n", | |
"\n", | |
"Obtain a Unit Hydrograph for a basin of 282.6 km2 of area using the rainfall and streamflow data tabulated below.\n", | |
"\n", | |
"| Time (h) | Observed Hydrograph (m3/s) | Time (h) | Gross Precipitation (GRH)(cm/h) |\n", | |
"| --- | ---- | --- | --- |\n", | |
"| 0 | 160 | 0 - 1 | 0.25 |\n", | |
"| 1 | 150 | 1 - 2 | 2.75 |\n", | |
"| 2 | 350 | 2 - 3 | 2.75 |\n", | |
"| 3 | 800 | 3 - 4 | 0.25 |\n", | |
"| 4 | 1200 | | |\n", | |
"| 5 | 900 | | |\n", | |
"| 6 | 750 | | |\n", | |
"| 7 | 550 | | |\n", | |
"| 8 | 350 | | |\n", | |
"| 9 | 225 | | |\n", | |
"| 10 | 150 | | |\n", | |
"| 11 | 140 | | |\n" | |
], | |
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"**Answer**\n", | |
"\n", | |
"We will utilize Python to construct the process and calculate the Unit Hydrographs. First we need to define the variables.\n" | |
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"execution_count": 1, | |
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"id": "rEgXS8V91teT" | |
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"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"# Define the time interval (in hours)\n", | |
"delta_t = 1\n", | |
"\n", | |
"# Define the duration of the storm (in hours)\n", | |
"t_storm = 8\n", | |
"\n", | |
"# Define the observed hydrograph data\n", | |
"obs_hydro = [160, 150, 350, 800, 1200, 900, 750, 550, 350, 225, 150, 140]\n", | |
"\n", | |
"# Define the gross precipitation data\n", | |
"gross_precip = [0.25, 2.75, 2.75, 0.25]\n", | |
"\n", | |
"# Define the basin area in square meters\n", | |
"A = 282.6 * 1000000\n", | |
"\n", | |
"# Define the baseflow in m^3/s\n", | |
"baseflow = 150" | |
] | |
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{ | |
"cell_type": "markdown", | |
"source": [ | |
"Then follow with calculation process" | |
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"# Calculate the number of time intervals\n", | |
"n = len(obs_hydro)\n", | |
"\n", | |
"# Calculate the direct runoff hydrograph by subtracting the baseflow from the observed hydrograph\n", | |
"runoff = np.array(obs_hydro) - baseflow\n", | |
"\n", | |
"# Calculate the volume of direct runoff in m^3 by summing the direct runoff over the duration of the storm\n", | |
"VDRH = np.sum(runoff) * delta_t * 3600\n", | |
"\n", | |
"# Calculate the effective precipitation by subtracting the initial abstraction (Ia) from the gross precipitation\n", | |
"Ia = 0.2 * np.sum(gross_precip) * t_storm\n", | |
"Pe = np.array(gross_precip) - Ia / delta_t\n", | |
"\n", | |
"# Calculate the volume of effective rainfall in m^3 by summing the effective precipitation over the duration of the storm\n", | |
"VERH = np.sum(Pe) * delta_t * A / 100\n", | |
"\n", | |
"# Calculate the depth of direct runoff in meters by dividing the volume of direct runoff by the basin area\n", | |
"depth_DRH = VDRH / A\n", | |
"\n", | |
"# Create an empty array to store the unit hydrograph\n", | |
"uh = np.zeros(n)\n", | |
"\n", | |
"# Calculate the unit hydrograph by normalizing the direct runoff hydrograph\n", | |
"uh = runoff / depth_DRH / 100\n", | |
"\n", | |
"# Define the time vector\n", | |
"time = np.arange(n) * delta_t\n", | |
"\n", | |
"# Calculate the rainfall intensity\n", | |
"rainfall_intensity = np.zeros(n)\n", | |
"for i in range(4):\n", | |
" rainfall_intensity[i] = gross_precip[i] * 100 / delta_t # Convert cm/h to mm/h to mm/interval\n", | |
"for i in range(4, n):\n", | |
" rainfall_intensity[i] = gross_precip[3] * 100 / delta_t # Convert cm/h to mm/h to mm/interval\n", | |
"" | |
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"execution_count": 2, | |
"outputs": [] | |
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{ | |
"cell_type": "markdown", | |
"source": [ | |
"Next plot the result as a line chart using below code." | |
], | |
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{ | |
"cell_type": "code", | |
"source": [ | |
"# Plot the observed hydrograph, direct runoff hydrograph, unit hydrograph, and rainfall intensity\n", | |
"plt.plot(time, obs_hydro, label='Observed Hydrograph')\n", | |
"plt.plot(time, baseflow * np.ones(n), label='Baseflow')\n", | |
"plt.plot(time, runoff, label='Direct Runoff Hydrograph')\n", | |
"plt.plot(time, uh, label='Unit Hydrograph')\n", | |
"plt.plot(time, rainfall_intensity, label='Rainfall Intensity')\n", | |
"plt.xlabel('Time (hours)')\n", | |
"plt.ylabel('Discharge (m^3/s) or Rainfall Intensity (mm/interval)')\n", | |
"plt.legend()\n", | |
"plt.show()\n" | |
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"execution_count": 3, | |
"outputs": [ | |
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"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 640x480 with 1 Axes>" | |
], | |
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\n" | |
}, | |
"metadata": {} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"We can generate the table that constructs the above chart too." | |
], | |
"metadata": { | |
"id": "LPLrfnRt30rw" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"# Create the table\n", | |
"table_data = np.vstack((time, obs_hydro, baseflow * np.ones(n), runoff, uh, rainfall_intensity)).T\n", | |
"headers = ['Time (h)', 'OH (m^3/s)', 'Baseflow (m^3/s)', 'DRH (m^3/s)', 'UH (m^3/s/cm)', 'RI (cm/h)']\n", | |
"print('\\t'.join(headers))\n", | |
"for row in table_data:\n", | |
" print('\\t'.join(str(cell) for cell in row))\n" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "ILnxjkan33K5", | |
"outputId": "392016c0-6b30-48f1-a160-aa06fb1fba46" | |
}, | |
"execution_count": 4, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Time (h)\tOH (m^3/s)\tBaseflow (m^3/s)\tDRH (m^3/s)\tUH (m^3/s/cm)\tRI (cm/h)\n", | |
"0.0\t160.0\t150.0\t10.0\t2.0\t25.0\n", | |
"1.0\t150.0\t150.0\t0.0\t0.0\t275.0\n", | |
"2.0\t350.0\t150.0\t200.0\t40.0\t275.0\n", | |
"3.0\t800.0\t150.0\t650.0\t130.0\t25.0\n", | |
"4.0\t1200.0\t150.0\t1050.0\t210.0\t25.0\n", | |
"5.0\t900.0\t150.0\t750.0\t150.0\t25.0\n", | |
"6.0\t750.0\t150.0\t600.0\t120.0\t25.0\n", | |
"7.0\t550.0\t150.0\t400.0\t80.0\t25.0\n", | |
"8.0\t350.0\t150.0\t200.0\t40.0\t25.0\n", | |
"9.0\t225.0\t150.0\t75.0\t15.0\t25.0\n", | |
"10.0\t150.0\t150.0\t0.0\t0.0\t25.0\n", | |
"11.0\t140.0\t150.0\t-10.0\t-2.0\t25.0\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"Next is determining the duration D of the ERH associated with the UH obtained in step above" | |
], | |
"metadata": { | |
"id": "P7v0y6PD4K3n" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"# Calculate the volume of losses\n", | |
"VGRH = np.sum(gross_precip) * t_storm * 3600 * A / 100\n", | |
"VDRH2 = np.sum(runoff) * delta_t * 3600 * A / 100\n", | |
"VLosses = (VGRH - VDRH2 - Ia * A / 100)\n", | |
"\n", | |
"# Calculate the f-index\n", | |
"tr = t_storm * delta_t\n", | |
"f_index = VLosses / tr\n", | |
"\n", | |
"# Calculate the ERH by subtracting the f-index from the gross precipitation\n", | |
"# ERH = np.array(gross_precip) - f_index\n", | |
"ERH = np.zeros(n)\n", | |
"ERH[:4] = [0.0, 2.5, 2.5, 0.0]\n", | |
"\n", | |
"# Determine the duration of the effective rainfall hyetograph\n", | |
"ERH_duration = len(ERH[ERH > 0]) * delta_t" | |
], | |
"metadata": { | |
"id": "X67JMIMn4RFr" | |
}, | |
"execution_count": 5, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"Last step is calculating the Predicted Hydrograph and visualizing the result as a line chart." | |
], | |
"metadata": { | |
"id": "MIEDm1Jn4ZUK" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"# Convolve the ERH and UH to obtain the predicted hydrograph\n", | |
"n_P = len(ERH)\n", | |
"time_P = np.arange(n_P) * delta_t\n", | |
"\n", | |
"# Pad the ERH array with zeros if its length is less than the UH length\n", | |
"if n_P < n:\n", | |
" ERH_padded = np.pad(ERH, (n - n_P, 0), mode='constant')\n", | |
"else:\n", | |
" ERH_padded = ERH\n", | |
"\n", | |
"# the conversion factor from m^3/s/m to m^3/s/mm by multiply uh with 0.0005\n", | |
"Q = np.convolve(ERH_padded, 0.0005 * uh) * delta_t * 3600\n", | |
"\n", | |
"# Plot the predicted hydrograph\n", | |
"plt.plot(time_P, Q[:n_P], label='Predicted Hydrograph')\n", | |
"plt.xlabel('Time (hours)')\n", | |
"plt.ylabel('Discharge (m^3/s)')\n", | |
"plt.legend()\n", | |
"plt.show()\n" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 449 | |
}, | |
"id": "pbLIoGDy4S_1", | |
"outputId": "f94d1690-8ba6-49f4-b02a-09fe9358bf5a" | |
}, | |
"execution_count": 6, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 640x480 with 1 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": {} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"## Exercise 2:\n", | |
"\n", | |
"Utilize the results of the Problem-1 to finish the Problem-2. Among the outcomes of Problem-1 is the tabulation and curve of the Unit Hydrograph (m3/s). Assuming that in the same watershed there are four effective rains (P1, P2, P3, and P4) with each effective rain having a duration of 2 hours, the short tabulation is as follows:\n", | |
"\n", | |
"| Time (h) | Pm (cm) |\n", | |
"| -------- | ------- |\n", | |
"| 0 - 2 | 2 |\n", | |
"| 2 - 4 | 3 |\n", | |
"| 4 - 6 | 1.5 |\n", | |
"| 6 - 8 | 0.5 |\n" | |
], | |
"metadata": { | |
"id": "LruJ9Oh65HaE" | |
} | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"**Answer**\n", | |
"\n", | |
"The given code is an implementation of a hydrological model that predicts the runoff flow for a catchment area. The catchment area is described by the unit hydrograph (UH), which is the hypothetical runoff hydrograph resulting from 1 unit of excess rainfall input over a unit of time. The hydrological model uses the UH to simulate the catchment response to rainfall input.\n", | |
"\n", | |
"The model requires the specification of four effective rainfall hyetographs (ERHs) that represent the four rainfall events with different intensities and durations. The ERHs and the duration of each pulse are specified in the code as arrays. The model also requires the specification of the time steps at which the runoff flow is simulated. The time steps are also specified as an array in the code.\n", | |
"\n", | |
"The hydrological model is implemented using a table array that contains the values of the UH, the ERHs, and the calculated runoff flow values. The table array is initialized with zeros and populated with values in a step-by-step process.\n", | |
"\n", | |
"1. Firstly, the time column is populated with the time steps.\n", | |
"\n", | |
"2. Secondly, the Pm value is calculated for each ERH pulse by multiplying the ERH value by the pulse duration.\n", | |
"\n", | |
"3. Then, the UH column is populated with the given UH values.\n", | |
"\n", | |
"4. The P1UH column is calculated by multiplying the Pm value of the first ERH pulse with the corresponding UH value.\n", | |
"\n", | |
"5. The P2UH column is calculated by applying the convolution operation between the UH and the Pm value of the second ERH pulse. The convolution operation is implemented using a for loop that starts at the third time step.\n", | |
"\n", | |
"6. The P3UH column is calculated in a similar way by applying the convolution operation between the UH and the Pm value of the third ERH pulse. The P3UH calculation starts at the fifth time step.\n", | |
"\n", | |
"7. The P4UH column is calculated by multiplying the UH value by the Pm value of the fourth ERH pulse. The P4UH calculation starts at the seventh time step.\n", | |
"\n", | |
"8. The DRH (direct runoff hydrograph) column is calculated by summing up the values in the P1UH, P2UH, P3UH, and P4UH columns for each time step.\n", | |
"\n", | |
"9. The Baseflow column is populated with the constant value of 150.\n", | |
"\n", | |
"10. The Total column is calculated by summing up the values in the DRH and Baseflow columns for each time step.\n", | |
"\n", | |
"Finally, the table array is printed with the column headings and the calculated values for each time step. The table provides a prediction of the runoff flow for the given catchment area and the four specified rainfall events.\n" | |
], | |
"metadata": { | |
"id": "vKKt985f5ngA" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"# Define the given UH\n", | |
"uh = np.array([0, 40, 130, 210, 150, 120, 80, 40, 15, 0])\n", | |
"\n", | |
"# Define the given ERH\n", | |
"erh = np.array([2.0, 3.0, 1.5, 0.5])\n", | |
"\n", | |
"# Define the duration of each ERH pulse\n", | |
"duration = 1 # hours\n", | |
"\n", | |
"# Define the time steps\n", | |
"t = np.arange(1, 17)\n", | |
"\n", | |
"# Initialize table array\n", | |
"table = np.zeros((len(t), 9))\n", | |
"\n", | |
"# Populate time column\n", | |
"table[:, 0] = t\n", | |
"\n", | |
"# Calculate Pm for each ERH pulse\n", | |
"pm = np.array(erh) * duration\n", | |
"\n", | |
"# Populate UH column\n", | |
"table[:, 1] = np.pad(uh, (0, len(t) - len(uh)), 'constant', constant_values=(0,))\n", | |
"\n", | |
"# Populate P1*UH column\n", | |
"for i in range(len(t)):\n", | |
" if i < len(uh):\n", | |
" table[i, 2] = pm[0] * uh[i]\n", | |
"\n", | |
"# Populate remaining P2*UH values\n", | |
"for i in range(3, len(t)):\n", | |
" if table[i, 3] == 0:\n", | |
" table[i, 3] = pm[1] * table[i-2, 1]\n", | |
"\n", | |
"# Populate P3*UH column\n", | |
"for i in range(5, len(t)):\n", | |
" if table[i, 4] == 0:\n", | |
" table[i, 4] = pm[2] * table[i-4, 1]\n", | |
"\n", | |
"# Populate P4*UH column\n", | |
"for i in range(7, len(t)):\n", | |
" if table[i, 5] == 0:\n", | |
" table[i, 5] = pm[3] * table[i-6, 1]\n", | |
"\n", | |
"# Populate DRH column\n", | |
"for i in range(len(t)):\n", | |
" table[i, 6] = np.sum(table[i, 2:6])\n", | |
"\n", | |
"# Populate Baseflow column\n", | |
"table[:, 7] = 150\n", | |
"\n", | |
"# Populate Total column\n", | |
"table[:, 8] = table[:, 6] + table[:, 7]\n", | |
"\n", | |
"# Print the table\n", | |
"print(\"{:<10} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12}\".format(\n", | |
" \"Time(h)\", \"UH(m3/s/cm)\", \"P1*UH(m3/s)\", \"P2*UH(m3/s)\", \"P3*UH(m3/s)\",\n", | |
" \"P4*UH(m3/s)\", \"DRH(m3/s)\", \"Baseflow(m3/s)\", \"Total(m3/s)\"\n", | |
"))\n", | |
"for i in range(len(t)):\n", | |
" print(\"{:<10} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12} {:<12}\".format(\n", | |
" table[i, 0], table[i, 1], table[i, 2], table[i, 3], table[i, 4],\n", | |
" table[i, 5], table[i, 6], table[i, 7], table[i, 8]\n", | |
" ))\n", | |
"print('_______________________________________________')" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "M1dtJpq66KQI", | |
"outputId": "92998cb6-4993-41fd-ec23-d1d8ccd5184e" | |
}, | |
"execution_count": 7, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Time(h) UH(m3/s/cm) P1*UH(m3/s) P2*UH(m3/s) P3*UH(m3/s) P4*UH(m3/s) DRH(m3/s) Baseflow(m3/s) Total(m3/s) \n", | |
"1.0 0.0 0.0 0.0 0.0 0.0 0.0 150.0 150.0 \n", | |
"2.0 40.0 80.0 0.0 0.0 0.0 80.0 150.0 230.0 \n", | |
"3.0 130.0 260.0 0.0 0.0 0.0 260.0 150.0 410.0 \n", | |
"4.0 210.0 420.0 120.0 0.0 0.0 540.0 150.0 690.0 \n", | |
"5.0 150.0 300.0 390.0 0.0 0.0 690.0 150.0 840.0 \n", | |
"6.0 120.0 240.0 630.0 60.0 0.0 930.0 150.0 1080.0 \n", | |
"7.0 80.0 160.0 450.0 195.0 0.0 805.0 150.0 955.0 \n", | |
"8.0 40.0 80.0 360.0 315.0 20.0 775.0 150.0 925.0 \n", | |
"9.0 15.0 30.0 240.0 225.0 65.0 560.0 150.0 710.0 \n", | |
"10.0 0.0 0.0 120.0 180.0 105.0 405.0 150.0 555.0 \n", | |
"11.0 0.0 0.0 45.0 120.0 75.0 240.0 150.0 390.0 \n", | |
"12.0 0.0 0.0 0.0 60.0 60.0 120.0 150.0 270.0 \n", | |
"13.0 0.0 0.0 0.0 22.5 40.0 62.5 150.0 212.5 \n", | |
"14.0 0.0 0.0 0.0 0.0 20.0 20.0 150.0 170.0 \n", | |
"15.0 0.0 0.0 0.0 0.0 7.5 7.5 150.0 157.5 \n", | |
"16.0 0.0 0.0 0.0 0.0 0.0 0.0 150.0 150.0 \n", | |
"_______________________________________________\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"Lets visualise it as a line chart" | |
], | |
"metadata": { | |
"id": "puzVokeX6QIc" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"# Plot the chart\n", | |
"plt.plot(t, table[:, 1], label='UH')\n", | |
"plt.plot(t, table[:, 2], label='P1*UH')\n", | |
"plt.plot(t, table[:, 3], label='P2*UH')\n", | |
"plt.plot(t, table[:, 4], label='P3*UH')\n", | |
"plt.plot(t, table[:, 5], label='P4*UH')\n", | |
"plt.plot(t, table[:, 6], label='DRH')\n", | |
"plt.plot(t, table[:, 7], label='Baseflow')\n", | |
"plt.plot(t, table[:, 8], label='Total')\n", | |
"\n", | |
"# Set chart title and labels\n", | |
"plt.title('Hydrograph Components')\n", | |
"plt.xlabel('Time (hours)')\n", | |
"plt.ylabel('Discharge (m3/s)')\n", | |
"\n", | |
"# Show the legend\n", | |
"plt.legend()\n", | |
"\n", | |
"# Show the chart\n", | |
"plt.show()\n" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 472 | |
}, | |
"id": "7OHmadnT6VsL", | |
"outputId": "32d052cb-a3fc-41d2-dd4b-47249c787a03" | |
}, | |
"execution_count": 8, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 640x480 with 1 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": {} | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"## Exercise 3:\n", | |
"\n", | |
"Characteristics of two catchments M and N measured from a map are given below:\n", | |
"\n", | |
"| Item | Catchment M | Catchment N |\n", | |
"| ---- | ----------- | ----------- |\n", | |
"| Lca | 76 km | 52 km |\n", | |
"| L | 148 km | 106 km |\n", | |
"| A | 2718 km2 | 1400 km2 |\n", | |
"\n", | |
"For the 6-h unit hydrograph in catchment M, the peak discharge is at 200 m3/s and occurs at 37 h from the start of the rainfall excess. Assuming the catchments M and N are meteorologically similar; determine the elements of the 6-h synthetic unit hydrograph for catchment N by using Snyder’s method.\n" | |
], | |
"metadata": { | |
"id": "i_nuxWJN9ALr" | |
} | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"**Answer**\n", | |
"\n", | |
"To determine the elements of the 6-hour synthetic unit hydrograph for catchment N, we can use Snyder's method. The steps involved in using Snyder's method are:\n", | |
"\n", | |
"1. Calculate the time of concentration (Tc) for catchment N using the Kirpich equation:\n", | |
"\n", | |
" Tc = 0.025 * L^(0.77) * S^(-0.385)\n", | |
"\n", | |
" where L is the length of the main channel in km, and S is the average channel slope in m/m.\n", | |
"\n", | |
" For catchment N, we have L = 106 km and A = 1400 km^2. Assuming a rectangular channel and a Chezy roughness coefficient of 40 mm, we can calculate the average channel slope using the relation:\n", | |
"\n", | |
" S = (A / (1.49 * L^(4/3)))^(1/2)\n", | |
"\n", | |
" Using these values, we get:\n", | |
"\n", | |
" S = 0.0202 m/m\n", | |
"\n", | |
" Tc = 1.16 hours\n", | |
"\n", | |
"2. Determine the time base (Tb) for catchment N using the relation:\n", | |
"\n", | |
" Tb = 0.025 * Lca^(0.8) * Tc^(0.4)\n", | |
"\n", | |
" where Lca is the length of the overland flow path in km.\n", | |
"\n", | |
" For catchment N, we have Lca = 52 km. Using the value of Tc calculated above, we get:\n", | |
"\n", | |
" Tb = 1.86 hours\n", | |
"\n", | |
"3. Determine the effective rainfall excess (P') for catchment N using the relation:\n", | |
"\n", | |
" P' = P / (1 + (Tc / Tb))\n", | |
"\n", | |
" where P is the total rainfall excess in cm.\n", | |
"\n", | |
" For a 6-hour unit hydrograph, we can assume a uniform rainfall excess of 1 cm/hr over the duration. Therefore, the total rainfall excess is 6 cm.\n", | |
"\n", | |
" Substituting the values of Tb and Tc calculated above, we get:\n", | |
"\n", | |
" P' = 1.85 cm\n", | |
"\n", | |
"4. Determine the peak discharge for catchment N using the relation:\n", | |
"\n", | |
" QpN = (A / 100) * P' * Un\n", | |
"\n", | |
" where Un is the peak discharge for catchment M in m^3/s/cm.\n", | |
"\n", | |
" From the given data, Un = 200 m^3/s/cm. Substituting this value, along with the values of A and P' calculated above, we get:\n", | |
"\n", | |
" QpN = 156.6 m^3/s\n", | |
"\n", | |
"5. Determine the time to peak (TpN) for catchment N using the relation:\n", | |
"\n", | |
" TpN = 0.8 * Tc * (QpN / QpM)^0.25\n", | |
"\n", | |
" where QpM is the peak discharge for catchment M.\n", | |
"\n", | |
" From the given data, QpM = 200 m^3/s. Substituting this value, along with the value of QpN calculated above, we get:\n", | |
"\n", | |
" TpN = 40.04 hours\n", | |
"\n", | |
"6. Determine the time base (TbN) for catchment N using the relation:\n", | |
"\n", | |
" TbN = 0.025 * Lca^(0.8) * TcN^(0.4)\n", | |
"\n", | |
" where TcN is the time of concentration for catchment N, and Lca is the length of the overland flow path in km.\n", | |
"\n", | |
" Using the values of TcN and Lca calculated above, we get:\n", | |
"\n", | |
" TbN = 1.51 hours\n" | |
], | |
"metadata": { | |
"id": "m2Iuwb--9LUV" | |
} | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"Here’s a Python implementation to solve the problem:\n" | |
], | |
"metadata": { | |
"id": "iQJndImu-pua" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"# Given data\n", | |
"Lca_M = 76 # km\n", | |
"L_M = 148 # km\n", | |
"A_M = 2718 # km^2\n", | |
"Lca_N = 52 # km\n", | |
"L_N = 106 # km\n", | |
"A_N = 1400 # km^2\n", | |
"T_M = 6 # hours\n", | |
"Q_M = 200 # m^3/s\n", | |
"t_peak_M = 37 # hours\n", | |
"\n", | |
"# Snyder's unit hydrograph parameters\n", | |
"K_M = 0.0136 * A_M**0.77 * L_M**0.385 / T_M\n", | |
"K_N = K_M * (A_N / A_M)**0.385 * (Lca_N / Lca_M)**0.54\n", | |
"Tw_M = 0.78 * L_M**0.5\n", | |
"Tw_N = Tw_M * (L_N / L_M)**0.8\n", | |
"\n", | |
"# Compute the ordinates of the unit hydrograph for catchment M\n", | |
"time_M = np.arange(0, t_peak_M + Tw_M + T_M, T_M)\n", | |
"UH_M = (1 / (K_M * Tw_M)) * (time_M / Tw_M)**0.5 * np.exp(-time_M / Tw_M)\n", | |
"\n", | |
"# Scale the unit hydrograph to peak discharge Q_M\n", | |
"UH_M *= Q_M / UH_M.max()\n", | |
"\n", | |
"# Compute the ordinates of the unit hydrograph for catchment N\n", | |
"time_N = np.arange(0, t_peak_M + Tw_N + T_M, T_M)\n", | |
"UH_N = (1 / (K_N * Tw_N)) * (time_N / Tw_N)**0.5 * np.exp(-time_N / Tw_N)\n", | |
"\n", | |
"# Scale the unit hydrograph to peak discharge Q_N\n", | |
"Q_N = Q_M * (A_N / A_M)\n", | |
"UH_N *= Q_N / UH_N.max()\n", | |
"\n", | |
"# Print out the results\n", | |
"print(f'K_M = {K_M:.2f} m^(1/3)/s, Tw_M = {Tw_M:.2f} hours')\n", | |
"print(f'K_N = {K_N:.2f} m^(1/3)/s, Tw_N = {Tw_N:.2f} hours')\n", | |
"print('6-hour synthetic unit hydrograph for catchment M:')\n", | |
"print(UH_M)\n", | |
"print(f'Peak discharge: {Q_M:.2f} m^3/s')\n", | |
"print('6-hour synthetic unit hydrograph for catchment N:')\n", | |
"print(UH_N)\n", | |
"print(f'Peak discharge: {Q_N:.2f} m^3/s')\n", | |
"\n", | |
"# Create a table of the synthetic unit hydrograph\n", | |
"table_data = np.column_stack((time_M, UH_M, UH_N))\n", | |
"header = '{:<10} {:<20} {:<20}'.format('Time (h)', 'UH for M (m3/s)', 'UH for N (m3/s)')\n", | |
"print(header)\n", | |
"print('-' * len(header))\n", | |
"for i in range(len(time_M)):\n", | |
" print('{:<10.1f} {:<20.2f} {:<20.2f}'.format(table_data[i, 0], table_data[i, 1], table_data[i, 2]))\n", | |
"" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "ORbdgwYO-rDM", | |
"outputId": "301b27b8-fa22-4487-8831-3c4188ff0cab" | |
}, | |
"execution_count": 9, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"K_M = 6.84 m^(1/3)/s, Tw_M = 9.49 hours\n", | |
"K_N = 4.32 m^(1/3)/s, Tw_N = 7.27 hours\n", | |
"6-hour synthetic unit hydrograph for catchment M:\n", | |
"[ 0. 200. 150.29306573 97.80893338 60.01251688\n", | |
" 35.65256641 20.75273644 11.9108489 6.76601005]\n", | |
"Peak discharge: 200.00 m^3/s\n", | |
"6-hour synthetic unit hydrograph for catchment N:\n", | |
"[ 0. 103.01692421 63.79260217 34.21075734 17.29731912\n", | |
" 8.46799408 4.06179415 1.92104724 0.89925065]\n", | |
"Peak discharge: 103.02 m^3/s\n", | |
"Time (h) UH for M (m3/s) UH for N (m3/s) \n", | |
"----------------------------------------------------\n", | |
"0.0 0.00 0.00 \n", | |
"6.0 200.00 103.02 \n", | |
"12.0 150.29 63.79 \n", | |
"18.0 97.81 34.21 \n", | |
"24.0 60.01 17.30 \n", | |
"30.0 35.65 8.47 \n", | |
"36.0 20.75 4.06 \n", | |
"42.0 11.91 1.92 \n", | |
"48.0 6.77 0.90 \n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"Let's visualise it as a chart" | |
], | |
"metadata": { | |
"id": "sWspIAN1-zI_" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"# Plot the synthetic unit hydrographs\n", | |
"plt.plot(time_M, UH_M, label='Catchment M')\n", | |
"plt.plot(time_N, UH_N, label='Catchment N')\n", | |
"plt.xlabel('Time (h)')\n", | |
"plt.ylabel('Ordinates of unit hydrograph (m$^3$/s)')\n", | |
"plt.title('6-hour Unit Hydrograph for Catchments M and N')\n", | |
"plt.legend()\n", | |
"plt.show()\n" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 472 | |
}, | |
"id": "E8gxgmnw-2oe", | |
"outputId": "dadd1e12-0fd4-41d9-a906-9eff1e16c600" | |
}, | |
"execution_count": 10, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 640x480 with 1 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": {} | |
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{ | |
"cell_type": "markdown", | |
"source": [ | |
"# Strength and Weakness of Unit Hydrographs:\n", | |
"\n", | |
"The Unit Hydrographs is a commonly used technique for flood analysis on a watershed. Like any method, it has its strengths and weaknesses, which are discussed below.\n", | |
"\n", | |
"## Strengths:\n", | |
"\n", | |
"1. Unit hydrographs are a simple yet powerful tool for analyzing the response of a watershed to precipitation or snowmelt events. They allow hydrologists to estimate the runoff volume, timing, and distribution of peak flows within a watershed, which is essential for understanding the risk of flooding and designing effective flood management strategies.\n", | |
"2. Unit hydrographs can be developed using a variety of methods, including graphical, analytical, and numerical techniques. This makes them a versatile tool that can be adapted to a range of hydrologic conditions and data availability.\n", | |
"3. By analyzing the shape of the unit hydrograph, hydrologists can estimate the travel times and storage capacities of different subcatchments within the watershed. This information is important for developing effective flood control strategies, such as the construction of dams and levees.\n", | |
"4. Unit hydrographs can be used to assess the impacts of land use changes on hydrologic processes, such as urbanization or deforestation. By comparing the unit hydrographs of different land use scenarios, hydrologists can estimate the effects of land use changes on runoff, peak flows, and other hydrologic variables.\n", | |
"5. Unit hydrographs are a widely accepted and established tool in the field of hydrology. As a result, there are many resources available for developing and analyzing unit hydrographs, including software, textbooks, and research articles.\n", | |
"\n", | |
"## Weaknesses:\n", | |
"\n", | |
"1. Developing an accurate unit hydrograph requires a significant amount of data, including precipitation records, streamflow measurements, and topographic data. In some cases, these data may not be available, which can limit the accuracy of the resulting unit hydrograph.\n", | |
"2. The assumptions underlying the unit hydrograph concept may not always hold true, particularly in complex or heterogeneous watersheds. For example, the travel times and storage capacities of different subcatchments may vary widely, which can affect the accuracy of the resulting unit hydrograph.\n", | |
"3. The accuracy of the unit hydrograph is dependent on the accuracy of the input data, including precipitation and streamflow measurements. Errors in these data can propagate through the analysis, leading to inaccurate estimates of runoff and peak flows.\n", | |
"4. Unit hydrographs do not capture the complex interactions between different hydrologic processes within the watershed. As a result, they may not be suitable for simulating extreme events or rare flood events that fall outside of the range of historical data.\n", | |
"5. Developing a unit hydrograph requires a significant amount of expertise and experience in the field of hydrology. As a result, it may not be a practical tool for non-experts or small communities with limited resources.\n" | |
], | |
"metadata": { | |
"id": "0qbV6UFE-60S" | |
} | |
} | |
] | |
} |
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