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Added water-demand-historic tool from Hylke E. Beck repository keeping git change history #47

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Added water-demand-historic tool from Hylke repository keeping change…
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Updated water-demand-historic documentation
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22 changes: 22 additions & 0 deletions README.md
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Expand Up @@ -42,6 +42,8 @@ Here's a list of utilities you can find in lisflood-utilities package.
* __compare__ is a package containing a set of simple Python classes that helps to compare
netCDF, PCRaster and TSS files.

* __water-demand-historic__ is a package allowing to generate sectoral (domestic, livestock, industry, and thermoelectric) water demand maps with monthly to yearly temporal steps for a range of past years, at the users’ defined spatial resolution and geographical extent. These maps are required by the LISFLOOD OS [water use module](https://ec-jrc.github.io/lisflood-model/2_18_stdLISFLOOD_water-use)

* __waterregions__ is a package containing two scripts that allow to create and verify a water regions map, respectively.

The package contains convenient classes for reading/writing:
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Output thresholds file
```

## water-demand-historic

This utility allows to create water demand maps at the desired resolution and for the desired geographical areas. The maps indicate, for each pixel, the time-varying water demand map to supply for domestic, livestock, industrial, and thermoelectric water consumption. The temporal discretization is monthly for domestic and energy demand, yearly for industrial and livestock demand. The maps of sectoral water demand are required by the LISFLOOD OS [water use module](https://ec-jrc.github.io/lisflood-model/2_18_stdLISFLOOD_water-use/). Clearly, the sectoral water demand maps and the scripts of this utility can be used also for other applications, as well as for stand-alone analysis of historical water demand for anthropogenic use.

#### Input
The creation of the sectoral water demand maps requires a template map that defines the desired geographical area and spatial resolution. The generation of the maps relies on a number of external datasets (examples are the [Global Human Settlement - Datasets - European Commission (europa.eu)](https://ghsl.jrc.ec.europa.eu/datasets.php) and [FAO AQUASTAT Dissemination System](https://data.apps.fao.org/aquastat/?lang=en)). The locations of the template map, of the input datasets and files, of the output folder, and other users’ choices (e.g. start year and end year) are specified in a configuration file. The syntax of the configuration file is pre-defined and an example is provided to the users. The complete list of external datasets, the instructions on how to prepare (i) the external dataset, (ii) the template map, (iii) the input folder, (iv) the output folder, and (v) the configuration file are explained into details [here](src/lisfloodutilities/water-demand-historic/README.md)

#### Output
Four sectoral water demand maps in netCDF-4 format. The geographical extent and the spatial resolution are defined by the template map (users-defined input file). Each netCDF-4 file has 12 months, for each year included in the temporal time span identified by the user. Sectoral water demand data with lower (yearly) temporal resolution are repeated 12 times.

#### Usage
The methodology includes five main steps. The instructions on how to retrieve the scrips, create the environment including all the required packages, and use the utility are provided [here](src/lisfloodutilities/water-demand-historic/README.md)

#### Important notes on documentation and data availability
The complete list of external datasets, the instructions on how to retrieve the external datasets, the methodology, and the usage of the scripts are explained into details [here](src/lisfloodutilities/water-demand-historic/README.md). The README file provides detailed technical information about the input datasets and the usage of this utility. The methodology is explained in the manuscript: Choulga, M., Moschini, F., Mazzetti, C., Grimaldi, S., Disperati, J., Beck, H., Salamon, P., and Prudhomme, C.: Technical note: Surface fields for global environmental modelling, EGUsphere, 2023 ([preprint](https://doi.org/10.5194/egusphere-2023-1306)).

The global sectoral water demand maps at 3 arcmin (or 0.05 degrees) resolution, 1979-2019, produced using the scripts of this utility can be downloaded from [Joint Research Centre Data Catalogue - LISFLOOD static and parameter maps for GloFAS - European Commission (europa.eu)](https://data.jrc.ec.europa.eu/dataset/68050d73-9c06-499c-a441-dc5053cb0c86)



## waterregions

The modelling of water abstraction for domestic, industrial, energetic, agricoltural and livestock use can require a map of the water regions. The concept of water regions and information for their definition are explained [here](htpst://ec-jrc.github.io/lisflood-model/2_18_stdLISFLOOD_water-use/).
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# Overview

This utility allows to create sectoral water demand maps at the desired spatial resolution and for the desired geographical areas. The maps indicate, for each pixel, the time-varying water demand map to supply for domestic, livestock, industrial, and energetic (cooling) water consumption. The temporal discretization of the information is monthly for domestic and energy demand, yearly for industrial and livestock demand. The users can select the historical temporal span of the output datasets (start year and end year). The maps of sectoral water demand are required by the LISFLOOD OS [water use module](https://ec-jrc.github.io/lisflood-model/2_18_stdLISFLOOD_water-use). Clearly, the scripts and the outputs of this utility can be used also for other applications, as well as for stand-alone analysis of historical water demand for anthropogenic use. Due to the large uncertainty of the global data sets, the distinction between demand and withdrawal is here omitted.

This README file provides detailed technical information about the input datasets and the usage of this utility. The methodology is explained in the manuscript: Choulga, M., Moschini, F., Mazzetti, C., Grimaldi, S., Disperati, J., Beck, H., Salamon, P., and Prudhomme, C.: Technical note: Surface fields for global environmental modelling, EGUsphere, 2023 ([preprint](https://doi.org/10.5194/egusphere-2023-1306)).

The global sectoral water demand maps at 3 arcmin (or 0.05 degrees) resolution, 1979-2019, produced using the scripts of this utility can be downloaded from [Joint Research Centre Data Catalogue - LISFLOOD static and parameter maps for GloFAS - European Commission (europa.eu)](https://data.jrc.ec.europa.eu/dataset/68050d73-9c06-499c-a441-dc5053cb0c86)

# Data

The utility consists of five scripts which require several following datasets and files. The full list of datasets is provided below. For each dataset, this documentation provides the link and the instructions relative to available version at the time of creation of the scripts. The correct functioning of the scripts is guaranteed for those datasets. Clearly, updated versions are regularly published: one relevant example is [Global Human Settlement - Datasets - European Commission (europa.eu)](https://ghsl.jrc.ec.europa.eu/datasets.php) (see Global Human Settlement Layer (GHSL)). Albeit the functioning of the utility is expected to be maintained, the users are invited to report problems or suggestions via GitHub issues.
1. [FAO AQUASTAT](http://fao.org/aquastat/statistics/query/index.html?lang=en) sectoral water withdrawal estimates. Select "All Countries" and the following seven fields in the variable groups "Geography and population" and "Water use": "Gross Domestic Product (GDP)", "Industry, value added to GDP", "Agricultural water withdrawal", "Industrial water withdrawal", "Municipal water withdrawal", "Total water withdrawal", and "Irrigation water withdrawal". Select All Years, then click on the "Show Data" button. On the new page, click on the "Download" button that will appear on the top right corner to download the xlsx file containing all data. Convert the Excel file to CVS and save it as `aquastat_clean.csv` in `aquastat_folder` specified in the configuration file.
1. [Global Human Settlement Layer (GHSL)](https://ghsl.jrc.ec.europa.eu/ghs_pop2019.php) POP R2019A residential population estimates for target years 1975, 1990, 2000, and 2015. You will find data as an archive [here]( https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_MT_GLOBE_R2019A/). For each year, download the 0.0025° (9 arcsec, folders ending by "_9ss", e.g. "GHS_POP_E2015_GLOBE_R2019A_4326_9ss") maps in WGS84 projection and resample them to 0.01° using `gdalwarp -t_srs EPSG:4326 -tr 0.01 0.01 -r average -of GTiff GHS_POP_E<year>_GLOBE_R2019A_4326_9ss_V1_0.tif GHS_POP_E<year>_GLOBE_R2019A_4326_9ss_V1_0_reprojected.tif`, where `<year>` is 1975, 1990, 2000, or 2015. We resample the maps using `average` instead of `sum` as the latter is broken. Put resampled files in `ghsl_folder`.
1. [Global Change Analysis Model (GCAM)](https://github.com/JGCRI/gcam-core/releases) regional water withdrawal and electricity consumption estimates. Download the Windows release package version 5.4, execute `run-gcam.bat`, wait for the model to finish. Execute `run-model-interface.bat`, click "File" > "Open" > "DB Open", select `output/database_basexdb`, and select all scenarios and all regions. Select `water demand`, select `water withdrawals by sector`, and click "Run query". Select `energy transformation`, select `electricity`, select `elec consumption by demand sector`, and click "Run query". For each tab, select all data with ctrl-a and click "Edit" > "Copy". Open a blank [Google Sheets](http://sheets.google.com/) spreadsheet, press ctrl-v, manually add the headers, click "File" > "Download" > "Comma-separated values", save as `water_demand.csv` and `elec_consumption.csv`, respectively, and put both files in `gcam_folder`.
1. [Gridded Livestock of the World (GLW 3)](https://doi.org/10.1038/sdata.2018.227) species distribution dataset. Download the eight zip files (each representing a different species) and extract them to `glw_folder`.
1. [Huang et al. (2018)](https://doi.org/10.5281/zenodo.1209296) global gridded water withdrawal estimates. These estimates are not incorporated in our dataset, but are only used for the sake of comparison. Download all 7z files and extract them to `huang_folder`.
1. [Multi-Source Weather (MSWX)](http://www.gloh2o.org/mswx) daily and monthly mean air temperature. Download using rclone as explained in the FAQ on the web page. Put the daily and monthly netCDFs in `<mswx_folder>/Past/Temp/Daily` and `<mswx_folder>/Past/Temp/Monthly`, respectively, where `mswx_folder` is specified in the configuration file.
1. [US Census Bureau](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html) state borders shape file. Download `cb_2018_us_state_500k.zip`, unzip it, open the shape file in QGIS, open the Field Calculator, enter `STATEFP` in "Expression", enter `STATEFP_` in "Output field name", and click OK. Rasterize to 0.01° using `gdal_rasterize -l cb_2018_us_state_500k -a STATEFP_ -tr 0.01 0.01 -a_nodata 0.0 -te -180.0 -90.0 180.0 90.0 -ot Float32 -of GTiff cb_2018_us_state_500k.shp cb_2018_us_state_500k_rasterized.tif`. Put `cb_2018_us_state_500k_rasterized.tif` in `us_states_folder`.
1. [USGS NWIS](https://waterdata.usgs.gov/nv/nwis/wu) water withdrawal estimates for 1985--present. For each state in the "Geographic Area" drop-down menu, select "State Data", "ALL Years", "State Total", and "ALL Categories" and click "Submit". Then select "Tab-separated data" and click "Submit". Do this for each state and put files in `usgs_water_use_folder`.
1. USGS water withdrawal data files for [1985](https://water.usgs.gov/watuse/data/1985/index.html) and [1990](https://water.usgs.gov/watuse/data/1990/index.html) (to supplement the NWIS data). Download "Data file of state-level data" for each year (`us85st.txt` and `us90st.txt`) and put the files in `usgs_water_use_folder`.
1. [Vassolo and Döll (2005)](https://doi.org/10.1029/2004WR003360) industrial and thermoelectric water withdrawal maps (included in this repository with permission from Petra Döll). The industrial withdrawal shape file is rasterized using `gdal_rasterize -l "industry_paper_vassolo&doell" -a MANUF_WWD -tr 0.5 0.5 -a_nodata 0.0 -te -180.0 -90.0 180.0 90.0 -ot Float32 -of GTiff "industry_paper_vassolo&doell.shp" manuf_wwd.tif`. Thermoelectric shape file rasterized using same command but with `WWD_PS` and `wwd_ps.tif`. Put the files in `vassolo_doll_folder`.
1. [Thematic Mapping](https://thematicmapping.org/downloads/world_borders.php) country borders shape file. Download `TM_WORLD_BORDERS-0.3.zip`, unzip, and rasterize to 0.01° using `gdal_rasterize -l TM_WORLD_BORDERS-0.3 -a UN -tr 0.01 0.01 -a_nodata 0.0 -te -180.0 -90.0 180.0 90.0 -ot Float32 -of GTiff TM_WORLD_BORDERS-0.3.shp TM_WORLD_BORDERS_UN_rasterized.tif`. Put the result in `world_borders_folder`.
1. [World Bank](https://data.worldbank.org/) manufacturing value added and gross domestic product data. Search for "Manufacturing, value added (constant 2010 US$)" and "GDP (constant 2010 US$)", download as CSV, and put in `world_bank_folder` (Remove Metadata csv files, if any).
<!---
1. USGS Circular xxx 1980 (https://pubs.usgs.gov/circ/1983/1001/report.pdf) Table 7 provided in ancillary_data folder
1. [Global Power Plant Database](https://datasets.wri.org/dataset/globalpowerplantdatabase)
1. [Lohrmann et al. (2019) power plant database](https://doi.org/10.1038/s41560-019-0501-4) Supplementary Data 1
1. [UN Industrial Commodity Statistics Database](http://data.un.org/), enter for example <beer>, then under "Select filters" select "Industry" and click "Apply filter", click "Download", click "Comma", extract and rename.
1. EIA [electricy capacity](https://www.eia.gov/international/data/world/electricity/), Select World, select Electricity, select Capacity and Generation, click View Data, click Sort by Energy Source/Activity, click Download Options, select Export CSV (table).
-->

The locations of the datasets and files are specified in the configuration file (see the `config.cfg` example). The script also requires a template map which defines the output resolution and area. The template map should be in netCDF-4 format and contain `lat` and `lon` variables and a data variable (any name). The location of the template map is specified using `templatemap_path` in the configuration file. The data are produced for the period spanning `year_start` to `year_end` and saved in netCDF-4 format to `output_folder` (all specified in the configuration file).

# Methods

The module consists of five scripts:
1. `step1_population_density.py`:
1. Resamples the native resolution of the GHSL population grids to the template map resolution.
1. Computes population grids (in density per km<sup>2</sup>) for each year using linear interpolation and nearest-neighbor extrapolation.
1. `step2_domestic_demand.py`:
1. Produces a map of the R parameter (measures the relative withdrawal difference between the warmest and coldest months).
1. Computes annual time series of population for each country.
1. Loads the country-scale AQUASTAT municipal water withdrawal estimate and produces annual time series using linear interpolation and population-based forward and backward extrapolation.
1. Loads the state-level USGS withdrawal data and computes annual values using linear interpolation and nearest-neighbour extrapolation.
1. For each year, computes country- and state-scale per capita water demand and produces a global map.
1. Annual per capita water demand for countries without any AQUASTAT information is estimated using nearest-neighbour interpolation.
1. Spatially disaggregates the country- and state-scale annual per capita water demand estimates using the population estimates to obtain global annual withdrawal grids.
1. Temporally downscales the annual withdrawal grids using monthly MSWX air temperature grids and the map of the R parameter.
1. `step3a_industrial_demand.py`:
1. Computes country-scale values of the Vassolo and Döll (2005) industrial and thermoelectric withdrawal grids.
1. Processes the country-scale AQUASTAT industrial withdrawal data.
1. Processes the country-scale World Bank manufacturing value added (MVA) data.
1. Processes the GCAM regional industry and thermoelectric withdrawals and spatially downscales them to the country scale based on population.
1. Processes the GCAM regional electricity consumptions and assigns values to each country.
1. Computes annual country-scale manufacturing and thermoelectric withdrawal time series as follows:
1. If AQUASTAT and GCAM data are available, temporally gap-fills the AQUASTAT industrial withdrawals using linear interpolation and MVA- or population-based extrapolation, temporally gap-fills the GCAM manufacturing and thermoelectric withdrawals using linear interpolation and nearest-neighbor extrapolation, rescales the GCAM manufacturing and thermoelectric withdrawals to match the Vassolo and Döll (2005) estimates, and temporally disaggregates the AQUASTAT industrial withdrawals into manufacturing and thermoelectric using GCAM.
1. If only GCAM data are available, simply use the rescaled GCAM manufacturing and thermoelectric withdrawals.
1. If neither AQUASTAT nor GCAM data are available, set the manufacturing and thermoelectric withdrawals to zero.
1. Computes monthly heating degree days (HDD) and cooling degree days (CDD) grids using daily MSWX air temperature.
1. `step3b_industrial_demand.py`:
1. Loads the annual country-scale manufacturing and thermoelectric withdrawal time series, the USGS manufacturing and thermoelectric withdrawal estimates, and the GCAM electricity consumptions from the preceding scripts.
1. Gap-fills the annual USGS manufacturing and thermoelectric withdrawal time series using linear interpolation and nearest-neighbor extrapolation.
1. Spatially downscales the country- and state-scale manufacturing and thermoelectric withdrawals using population grids to obtain annual withdrawal grids.
1. Temporally downscales the annual thermoelectric withdrawal grids based on the GCAM electricity consumptions and the HDD and CDD grids.
1. `step4_livestock_demand.py`:
1. Computes country-scale livestock withdrawals by taking the difference between the AQUASTAT agriculture and irrigation withdrawals.
1. Processes the raw GLW grids for the different species and estimates the total livestock mass.
1. Computes the total livestock mass for each country.
1. Spatially downscales the GCAM regional livestock withdrawals to the country scale using the GLW-based total livestock mass estimates.
1. Loops through countries and gap-fills the annual GCAM time series using linear interpolation and nearest-neighbor extrapolation, and rescales the time series to match the AQUASTAT estimate (if available).
1. Gap-fills the annual USGS manufacturing and thermoelectric withdrawal time series using linear interpolation and nearest-neighbor extrapolation.
1. For each year, spatially downscales the country- and state-scale livestock withdrawals using the GLW total livestock mass grid.

# System requirements

The script can be run on a normal desktop PC, support is provided only for Linux. Physical memory requirements depend on the desired geographical extent and resolution of the maps dataset. For instance, the generation of the sectoral water demand dataset for the global domain at 3arcmin (or 0.05 degrees) resolution required 32 GB.

# Instructions

Clone the repository:
```
git clone https://github.com/ec-jrc/lisflood-utilities/water-demand-historic
cd lisflood-utilities/water-demand-historic
```
Produce a configuration file with the correct paths and folders (based on the [included example](config_global_0p1deg.cfg)).

Create and activate a Conda environment and run the scripts as follows:
```
conda create --name <env> --file requirements.txt
conda activate <env>
python step1_population_density.py <config file>
python step2_domestic_demand.py <config file>
...
```
If the environment creation step fails, the following might work:
```
conda create -n <env> -c conda-forge geopandas h5py pandas numpy netcdf4 matplotlib rasterio scikit-image openpyxl geopy
```
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