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https://github.com/nismod/water_demand
A basic water demand model, where demand is a scalar multiple of population
https://github.com/nismod/water_demand
Last synced: 2 months ago
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A basic water demand model, where demand is a scalar multiple of population
- Host: GitHub
- URL: https://github.com/nismod/water_demand
- Owner: nismod
- License: mit
- Created: 2019-05-30T14:18:26.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-09T12:06:15.000Z (about 5 years ago)
- Last Synced: 2024-08-08T18:25:05.746Z (6 months ago)
- Language: Jupyter Notebook
- Size: 37.1 KB
- Stars: 2
- Watchers: 6
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://dev.azure.com/OxfordRSE/Nismod2%20Water%20Demand/_build/latest?definitionId=2&branchName=master)
[](https://codecov.io/gh/nismod/water_demand)
# A basic water demand model
A basic water demand model, where demand is a scalar multiple of population.
## Installation (user)
```
pip install git+https://github.com/nismod/water_demand.git@master#egg=water_demand
```
## Installation (developer)
```
git clone [email protected]:nismod/water_demand.git
pip install -e .[dev,docs]
```
## Basic usage
Create an instance of the `WaterDemand` class, constructing it with a sequence representing populations, a number or sequence representing the per capita water demand, and a number or sequence representing constant water demand.
The water demand is calculated as `constant + per_capita * population`.
### Global demand coefficients
```python
import water_demand
import numpy as np
# Define population (e.g. read from csv file)
pop = np.random.normal(size=20)
# Define the global per capita and constant demands
per_capita = 1.23
constant = 2.34
# Create the model
model = water_demand.WaterDemand(
population=pop,
per_capita_demand=per_capita,
constant_demand=constant
)
# Simulate the water demand
demand = model.simulate()
```
### Local demand coefficients
```python
import water_demand
import numpy as np
# Define population (e.g. read from csv file)
pop = np.random.normal(size=20)
# Define local demand coefficients (e.g. read from csv files)
per_capita = np.random.normal(size=20)
constant = np.random.normal(size=20)
# Create the model
model = water_demand.WaterDemand(
population=pop,
per_capita_demand=per_capita,
constant_demand=constant
)
# Simulate the water demand
demand = model.simulate()
```
### Data sources
Using 'Revised Draft Water Resources Management Plan 2019 Supply-Demand Data at Water Resource
Zone Level 2020/21 to 2044/45'
Provided by Environment Agency for research purposes (not shared publicly), last updated 09
August 2019. See related company level dataset published on
[gov.uk](https://data.gov.uk/dataset/fb38a40c-ebc1-4e6e-912c-bb47a76f6149/revised-draft-water-resources-management-plan-2019-supply-demand-data-at-company-level-2020-21-to-2044-45).
Relevant variables:
ea_code | measurement | unit
--- | --- | ---
11BL | Distribution input | Ml/d
11FP | Distribution Input | Ml/d
29BL | Measured Household - PCC | l/h/d
29FP | Measured Household - PCC | l/h/d
30BL | Unmeasured Household - PCC | l/h/d
30FP | Unmeasured Household - PCC | l/h/d
31BL | Average Household - PCC | l/h/d
31FP | Average Household - PCC | l/h/d
49BL | Measured Non Household - Population | 000's
49FP | Measured Non Household - Population | 000's
50BL | Unmeasured Non Household - Population | 000's
50FP | Unmeasured Non Household - Population | 000's
51BL | Measured Household - Population | 000's
51FP | Measured Household - Population | 000's
52BL | Unmeasured Household - Population | 000's
52FP | Unmeasured Household - Population | 000's
53BL | Total Resource Zone Population | 000's
53FP | Total Resource Zone Population | 000's
Note on scenarios:
>The baseline scenario (with BL suffix) represents 'business as usual'. This scenario reflects:
>
>- forecast changes to the amount of water available, through planned changes to abstraction
> licences and through reductions to supply from climate change
>- continuation of current policies in demand management (including the committed leakage
> levels, metering policies and implementation of companies’ water efficiency plans)
>- forecast changes to properties and population.
>
>For some companies, forecast demand exceeds the available supply over the planning period.
>Where this occurs, a water company must propose schemes (termed 'options') to increase
>supplies and/or reduce the demand for water. Detailed option data is not included in this
>dataset because it commercially sensitive.
>
>The final planning scenario (with FP suffix) represents the company's forecast supply and
>demand as a result of the implementation of the additional supply and/or reduced demand
>options incorporated into its forecasts. This is the scenario that represents what a water
>company intends to doover the planning period to maintain the balance of supply and demand.
Section 1.3 *Scenarios explained* in [Water Resources Planning Components
Guide](https://data.gov.uk/dataset/fb38a40c-ebc1-4e6e-912c-bb47a76f6149/revised-draft-water-resources-management-plan-2019-supply-demand-data-at-company-level-2020-21-to-2044-45)
## Running in NISMOD
This model is integrated with population scenarios and a water supply model in
[nismod/nismod2](https://github.com/nismod/nismod2).
```bash
bash provision/get_data_water_demand.sh .
bash provision/install_water_demand.sh .
smif csv2parquet data/scenarios/water_demand/
smif run -b batch/arc_wd.batch -i local_binary
```