ISIMIP3a simulation round simulation protocol - Water (regional)
Introduction
General concept
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.
ISIMIP is intended to be structured in successive rounds connected to the different phases of the climate model intercomparison CMIP (ISIMIP Mission & Implementation document).
The main components of the ISIMIP framework are:
- A common set of climate and other forcing data which will be distributed via a central database;
- A common modelling protocol to ensure consistency across sectors and scales in terms of data, format and scenario set-up;
- A central archive where the output data will be collected and made available to the scientific community.
ISIMIP3a
Historical model evaluation and attribution runs
The ISIMIP3a part of the third round framework is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are forced by observed climate and socio-economic information and a de-trended version of the observed climate allowing for the generation of a “no climate change” baseline (counterfactual).
You can find the ISIMIP3b protocol, which is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions, here.
Simulation protocol
In this protocol we describe the scenarios & experiments in ISIMIP3a simulation round, the different input datasets, the output variables, and how to report model results specifically for Water (regional). An overview of all sectors can be found at protocol.isimip.org.
Throughout the protocol we use specifiers that denote a particular scenario, experiment, variable or other parameter. We use these specifiers in the tables below, in the filenames of the input data sets, and ask you to use the same specifiers in your output files. More on reporting data can be found at the end of this document.
Model versioning
To ensure consistency between ISIMIP3a and ISIMIP3b as well as the different experiments within a simulation round, we require that modelling groups use the same version of an impact model for the experiments in ISIMIP3a and ISIMIP3b. If you cannot fulfill this, please indicate that by using a suffix for your model name (e.g. simple version numbering: MODEL-v1, MODEL-v2 or following semantic versioning: MODEL-2.0.0, see also reporting model results).
This versioning does not only apply to changes in the computational logic of the model, but also to input parameters, calibration or setup. If model versions are not reported, we will name them according to the simulation round (e.g. MODEL-isimip3a). We require the strict versioning to ensure that differences between model results are fully attributable to the changes in model forcings.
Scenarios & Experiments
Scenario definitions
| Scenario specifier | Description |
|---|---|
| obsclim | Observed climate and CO₂ forcing used for model evaluation and the detection and attribution task. |
| spinclim | Detrended version of the observed climate forcing used to spin-up the simulations based on a stable 1900 climate (see explanation below for details regarding the design of the spin-up) |
| counterclim | Detrended version of the observed climate forcing used for the "no climate change" baseline simulations in the context of the detection and attributions task. |
| Scenario specifier | Description |
|---|---|
| histsoc |
Varying direct human influences in the historical period (1850-2014) (e.g. observed changes in historical land use, nitrogen deposition and fertilizer input, fishing effort). Please label your model run |
| 2015soc |
Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort). Please label your simulations |
| nat |
No direct human influences (naturalized run). Please only label your model run |
| Scenario specifier | Description |
|---|---|
| default | For all experiments other than the sensitivity experiments. |
| 1901co2 | CO₂ concentration fixed at 1901 levels as a deviation from the “obsclim” climate + CO₂ forcing. |
| nowatermgt | No water management (e.g. no human water abstraction, no reservoirs) while other direct human forcings such as land use changes are considered according to histsoc or 2015soc. |
General note regarding sensitivity experiments
The sensitivity experiments are meant to be "artificial" deviations from the default settings. So for example if your model does not at all account for changes in CO₂ concentrations (no option to switch it on or off) the run should be labeled as “default” in the sensitivity specifier of the file name even if the run would be identical to the 1901co2 sensitivity setting.
The particular sensitivity scenario for an experiment is given in the experiments table below. For most experiments no sensitivity scenario is given, so the default label applies.
Experiments
| Experiment | Short description |
Transition from Spin-up to experiment 1850-1900, only if spin-up is needed |
Historical 1901-2016 |
|---|---|---|---|
model evaluationhistsoc 1st priority |
CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards |
spinclim |
obsclim |
| DHF: Fixed 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
histsoc |
|
model evaluation2015soc 1st priority |
CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards |
spinclim |
obsclim |
| DHF: Fixed 2015 levels of direct human forcing for the entire time period |
2015soc |
2015soc |
|
model evaluationnat 2nd priority |
CF: No climate change before 1901, observed forcing afterwards; 1850 levels of CO₂ before 1850 and based on observations afterwards |
spinclim |
obsclim |
| DHF: No direct human influences |
nat |
nat |
|
counterfactual climatehistsoc 1st priority |
CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level | "histsoc" version of the transition period of the model evaluation run |
counterclim |
| DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
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counterfactual climate2015soc 1st priority |
CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level | "2015soc" version of the transition period of the model evaluation run |
counterclim |
| DHF: fixed 2015 levels of direct human influences for the entire time period |
2015soc |
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counterfactual climatenat 2nd priority |
CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level | "nat" version of the transition period of the model evaluation run |
counterclim |
| DHF: No direct human influences |
nat |
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CO₂ sensitivityhistsoc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "histsoc" version of the transition period of the model evaluation run |
obsclim Sensitivity scenario: 1901co2 |
| DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
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CO₂ sensitivity2015soc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "2015soc" version of the transition period of the model evaluation run |
obsclim Sensitivity scenario: 1901co2 |
| DHF: fixed 2015 levels of direct human influences for the entire time period |
2015soc |
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Water management sensitivityhistsoc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "histsoc" version of the transition period of the model evaluation run |
obsclim |
| DHF: no accounting for water management but representation of other direct human influences such as land use changes according to "histsoc" |
histsoc Sensitivity scenario: nowatermgt |
||
Water management sensitivity2015soc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "2015soc" version of the transition period of the model evaluation run |
obsclim |
| DHF: no accounting for water management but representation of other direct human influences such as land use patterns according to "2015soc" |
2015soc Sensitivity scenario: nowatermgt |
Note regarding models requiring spin-up
For models requiring spin-up, we provide 100 years of spinclim data which is identical with the first 100 years of the counterclim data (files climate/atmosphere/spinclim/<dataset>/<dataset>_spinclim_<variable>_global_daily_<start-year>_<start-year>.nc). If more than 100 years of spin-up are needed, these data can be repeated as often as needed.
- For
historicalruns, use historical CO2 concentration and varying DHF, for the transition period from spin-up to the start of the experiment (1850-1900). When using a longer spin-up period that (nominally) extends back further than 1850, please keep CO2 concentration and DHF constant at 1850 level until reaching the year corresponding to 1850. - For experiments with fixed year-2015 direct human influences (
2015soc), the spin-up should be based on the 2015 DHF. - For experiments with no direct human influences (
nat), the spin-up should be not using DHF as well.
Input data
The base directory for input data at DKRZ is:
/work/bb0820/ISIMIP/ISIMIP3a/InputData/
Further information on accessing ISIMIP data can be found at ISIMIP - getting started.
Some of the datasets are tagged as mandatory. This does not mean that the data must be used in all cases, but if your models uses input data of this kind, we require to use the specified dataset. If an alterntive data set is used instead, we cannot consider it an ISIMIP simulation. If the mandatory label is not given, you may use alternative data (please document this clearly).
Climate forcing
The climate forcing input files can be found on DKRZ using the following pattern:
climate/atmosphere/<climate-scenario>/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc
| Title | Specifier | Time period | Reanalysis | Bias adjustment target | Comments | Priority |
|---|---|---|---|---|---|---|
| GSWP3-W5E5 | gswp3-w5e5 | 1901-2016 | ERA5 | GPCC, CRU | Combination of W5E5 (Lange, 2019a; Cucchi et al., 2020) for 1979-2016 with GSWP3 (Dirmeyer et al., 2006) homogenized to W5E5 for 1901-1978. The homogenization reduces discontinuities at the 1978/1979 transition and was done using the ISIMIP3BASD v2.4.1 bias adjustment method (Lange, 2019b; Lange, 2020). | 1 |
| GSWP3 | gswp3 | 1901-2010 | 20CR | GPCC, GPCP, CPC-Unified, CRU, SRB | Dynamically downscaled and bias-adjusted 20th Century Reanalysis (20CR; Compo et al., 2011) from the Global Soil Wetness Project Phase 3 (GSWP3; Dirmeyer et al., 2006). | 2 |
Note on climate forcing priority
The priority for the different climate forcing datasets is from top to bottom. If you cannot use all climate forcing datasets, please concentrate on those at the top of the table.
| Variable | Variable specifier | Unit | Resolution | Datasets | ||
|---|---|---|---|---|---|---|
| Atmospheric variables mandatory | ||||||
| Near-Surface Relative Humidity | hurs | % |
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| Near-Surface Specific Humidity | huss | kg kg-1 |
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| Precipitation | pr | kg m-2 s-1 |
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| Surface Air Pressure | ps | Pa |
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| Surface Downwelling Longwave Radiation | rlds | W m-2 |
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| Surface Downwelling Shortwave Radiation | rsds | W m-2 |
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| Near-Surface Wind Speed | sfcwind | m s-1 |
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| Near-Surface Air Temperature | tas | K |
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| Daily Maximum Near-Surface Air Temperature | tasmax | K |
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| Daily Minimum Near-Surface Air Temperature | tasmin | K |
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Other climate datasets
| Variable | Variable specifier | Unit | Resolution | Datasets | ||
|---|---|---|---|---|---|---|
| Atmospheric composition mandatory | ||||||
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Atmospheric CO2 concentration |
climate/atmosphere_composition/co2/<climate-scenario>/co2_<climate-scenario>_annual_<start_year>_<end_year>.txt
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| co2 | ppm |
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Meinshausen et al. (2011) for 1850-2005 and Dlugokencky & Tans (2019) from 2006-2018. |
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Socioeconomic forcing
| Dataset | Included variables (specifier) | Covered time period | Resolution | Reference/Source and Comments | ||
|---|---|---|---|---|---|---|
| Land use mandatory | ||||||
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Landuse totals |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-totals_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review, see also https://luh.umd.edu). |
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Downscaling to 5 crops |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-5crops_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu). |
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Downscaling to 15 crops |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-15crops_annual_<start_year>_<end_year>.nc
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The C4 perennial crops are not further downscaled from the "5 crops" data set and currently only include sugarcane. Similarly, the C3 perennial crops are not downscaled either. The data is derived from the "5 crops" LUH2 data, and the crops have been downscaled to 15 crops according to the ratios given by the Monfreda data set (Monfreda, Ramankutty, & Foley, 2008). |
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Managed pastures and rangeland |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-pastures_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review., see also https://luh.umd.edu). |
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Urban areas |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-urbanareas_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu). |
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| N-fertilizer mandatory | ||||||
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Nitrogen deposited by fertilizers on croplands |
socioeconomic/n-fertilizer/<soc_scenario>/<soc_scenario>_n-fertilizer-5crops_annual_<start_year>_<end_year>.nc
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Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al., in in review, see also https://luh.umd.edu). |
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| N-deposition | ||||||
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Reduced nitrogen deposition |
socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc
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Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018) |
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Oxidized nitrogen deposition |
socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc
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Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018) |
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| Reservoirs & dams | ||||||
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Reservoirs & dams |
socioeconomic/reservoir_dams/reservoirs-dams_1850_2025.xls
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Lehner et al. (2011a, https://doi.org/10.7927/H4N877QK), Lehner et al. (2011b, https://dx.doi.org/10.1890/100125), and Jida Wang et al. (KSU/Kansas State University, personal communication). Because the data from KSU is yet unpublished, modeling teams using it are asked to offer co-authorship to the team at KSU on any resulting publications. Please contact info@isimip.org in case of questions. |
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| Water abstraction | ||||||
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Water abstraction for domestic and industrial purposes |
socioeconomic/water_abstraction/[domw|indw][w|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc
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For modelling groups that do not have their own representation, we provide files containing the multi-model mean of domestic and industrial water withdrawal and consumption generated by WaterGAP, PCR-GLOBWB, and H08. This data is based ISIMIP2a varsoc simulations for 1901-2005, and on RCP6.0 simulations from the Water Futures and Solutions project (Wada et al., 2016, http://www.geosci-model-dev.net/9/175/2016/) for after 2005. Years before 1901 have been filled with the value for year 1901. |
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| Population mandatory | ||||||
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Population 5' grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
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Population 0.5° grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
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Population national |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
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Population density 5' grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
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Population density 0.5° grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
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Population density national |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
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| GDP mandatory | ||||||
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GDP |
socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
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Historic country-level GDP data are an extension of the data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html), and are derived mainly from the Maddison Project database. Gridded GDP data will be provided by c. 07/2021. |
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Geographic data and information
| Dataset | Included variables (specifier) | Resolution | Reference/Source and Comments | |||
|---|---|---|---|---|---|---|
| Land/Sea masks | ||||||
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landseamask |
geo_conditions/landseamask/landseamask.nc
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0.5° grid | This is the land-sea mask of the W5E5 dataset (Lange, 2019a; Cucchi et al., 2020). Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land. |
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landseamask_no-ant |
geo_conditions/landseamask/landseamask_no-ant.nc
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0.5° grid | Same as landseamask but without Antarctica. |
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landseamask_water-global |
geo_conditions/landseamask/landseamask_water-global.nc
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0.5° grid | This is the generic land-sea mask from ISIMIP2b that is to be used for global water simulations in ISIMIP3. It marks more grid cells as land than landseamask. Over those additional land cells, the climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are not guaranteed to represent climate conditions over land. Instead they may represent climate conditions over sea or a mix of conditions over land and sea. |
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| Soil | ||||||
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gswp3_hwsd |
geo_conditions/soil/gswp3_hwsd.nc
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0.5° grid | One fixed pattern to be used for all simulation periods. Upscaled Soil texture map (30 arc sec. to 0.5°x0.5° grid) based on Harmonized World Soil Database v1.1 (HWSD) using the GSWP3 upscaling method A (http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html) |
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hwsd_soil_data_all_land |
geo_conditions/soil/hwsd_soil_data_all_land.nc
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0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on all land. |
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hwsd_soil_data_on_cropland |
geo_conditions/soil/hwsd_soil_data_on_cropland.nc
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0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on current cropland (MIRCA2000 at 5 arc-minutes). |
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| River routing | ||||||
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basins |
geo_conditions/river_routing/ddm30_basins_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
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flowdir |
geo_conditions/river_routing/ddm30_flowdir_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
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slopes |
geo_conditions/river_routing/ddm30_slopes_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
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Output data
ISIMIP output variables are usually reported with the dimensions (time,lat,lon). For variables with a number of levels (e.g. layers or depth), an alternative set of dimensions is given in the comment column in the table below. More information about level dimensions can be found here and here on the ISIMIP webpage.
Please note that unless otherwise defined, the variables in ISIMIP should be reported relative to the grid cell land area.
Output variables
| Variable long name | Variable specifier | Unit | Resolution | Comments | ||
|---|---|---|---|---|---|---|
| Hydrological variables | ||||||
| Total Runoff | qtot | kg m-2 s-1 |
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Total (surface + subsurface) runoff (qtot = qs + qsb). Please provide both daily and monthly resolution. |
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| Surface Runoff | qs | kg m-2 s-1 |
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Water that leaves the surface layer (top soil layer) e.g. as overland flow / fast runoff. |
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| Groundwater Recharge | qr | kg m-2 s-1 |
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Water that percolates through the soil layer(s) into the groundwater layer. In case seepage is simulated but no groundwater layer is present, report seepage as qr and qg. |
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| Discharge | dis | m3 s-1 |
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River discharge or streamflow. Please provide both daily and monthly resolution. |
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| Evapotranspiration | evap-total | kg m-2 s-1 |
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Sum of transpiration, evaporation, interception and sublimation. |
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| Potential Evapotranspiration | potevap | kg m-2 s-1 |
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As evap, but with all resistances set to zero, except the aerodynamic resistance. |
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| Total Soil Moisture Content | soilmoist | kg m-2 |
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Level dimensions: (time, depth, lat, lon). Please provide soil moisture for all depth layers (i.e. 3D-field), and indicate depth in m. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files. |
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| Soil Moisture Content at Root Zone | rootmoist | kg m-2 |
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Level dimensions: (time, depth, lat, lon). Total simulated soil moisture available for evapotranspiration. Please indicate the depth of the root zone for each vegetation type in your model. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files. |
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| Snow Water Equivalent | swe | kg m-2 |
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Total water mass of the snowpack (liquid or frozen) averaged over grid cell. Please also deliver for the permafrost sector. |
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| Water management variables (for models that consider water management/human impacts) | ||||||
| Potential Irrigation Water Withdrawal (assuming unlimited water supply) | pirrww | kg m-2 s-1 |
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Irrigation water withdrawn in case of optimal irrigation (in addition to rainfall). |
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| Actual Irrigation Water Withdrawal | airrww | kg m-2 s-1 |
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Irrigation water withdrawal, taking water availability into account; please provide if computed. |
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| Potential Irrigation Water Consumption | pirruse | kg m-2 s-1 |
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Portion of withdrawal that is evapo-transpired, assuming unlimited water supply. |
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| Actual Irrigation Water Consumption | airruse | kg m-2 s-1 |
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Portion of withdrawal that is evapo-transpired, taking water availability into account; if computed. |
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| Actual Irrigation Green Water Consumption on Irrigated Cropland | airrusegreen | kg m-2 s-1 |
|
Actual evapotranspiration from rainwater over irrigated cropland; if computed. |
||
| Potential Irrigation Green Water Consumption on Irrigated Cropland | pirrusegreen | kg m-2 s-1 |
|
Potential evapotranspiration from rainwater over irrigated cropland; if computed and different from AIrrUseGreen. |
||
| Actual Green Water Consumption on Rainfed Cropland | arainfusegreen | kg m-2 s-1 |
|
Actual evapotranspiration from rainwater over rainfed cropland; if computed. |
||
Catchment gauging stations
| River Basin | Station | Specifier | Coordinates (Lat, Lon) | GRDC Station Code | Data availability (monthly discharge) | Data availability (daily discharge) | Area upstream of gauge (km2) according to GRDC or GIS |
|---|---|---|---|---|---|---|---|
| Amazon | São Paulo de Olivenca | amazon-sao-paulo-de-olivenca | -3.45, -68.75 | 3623100 | 1979-1993 | 1973-2010 | 990781 |
| Blue Nile | El-Deim, Sudan Border | blue-nile-el-diem | 11, 35 | 1961-2002 | 1900-1982 | 160000 | |
| Blue Nile | Khartoum | blue-nile-khartoum | 15.62, 32.55 | 1663100 | 1900-1982 | 325000 | |
| Danube | Wien-Nußdorf | danube-wien-nussdorf | 48.25, 16.3 | 6242500 | 1828-1899 | 1900-to date | 101700 |
| Ganges | Farakka | ganges-farakka | 25, 87.92 | 2846800 | 1949-1973 | 835000 | |
| Godavari | Tekra | godavari-tekra | 19, 80 | 1964-2017 | 1964-2017 | 119781 | |
| Indus | Tarbela Reservoir | indus-tarbela | 72.86, 34.33 | 2000-2016 | 2000-2016 | 173345 | |
| Lena | Krestovski | lena-krestovski | 59.73, 113.17 | 2903427 | 1936-2002 | 1936-1999 | 440000 |
| Lena | Stolb | lena-stolb | 72.37, 126.8 | 2903430 | 1978-1994 | 1951-2002 | 2460000 |
| Mackenzie | Arctic Red River | mackenzie-arctic-red-river | 67.4583, -133.745 | 4208025 | 1972-1996 | 1972-2015 | 1660000 |
| Mississippi | Alton | mississippi-alton | 38.885, -90.1809 | 4119800 | 1928-1984 | 1933-1987 | 444185 |
| Darling | Louth | darling-louth | -30.5318, 145.1144 | 5204250 | 1954-2000 | 1954-2008 | 489300 |
| Niger | Dire | niger-dire | 16.2667, -3.3833 | 1134700 | 1924-2012 | 1924-2003 | 340000 |
| Niger | Koulikoro | niger-koulikoro | -30.5318, -7.55 | 1134100 | 1907-2012 | 1907-2006 | 120000 |
| Niger | Lokoja | niger-lokoja | 7.8, 6.7667 | 1834101 | 2007-2012 | 1970-2006 | 2074171 |
| Niger | Tossaye | niger-tossaye | 16.9416, -0.579166 | 1134850 | 1954-1992 | 1954-1992 | 348000 |
| Pajeu | Floresta | pajeu-floresta | -8.6089, -38.5767 | 12266 | |||
| Rhine | Lobith | rhine-lobith | 51.84, 6.11 | 6435060 | 1901-1996 | 1901-2010 | 160800 |
| Tagus | Almourol | tagus-almourol | 39.47, -8.37 | 6113050 | 1973-1990 | 1982-1990 | 61490 |
| Tagus | Trillo | tagus-trillo | 40.7, -2.58 | 6213800 | 1977-1984 | 1977-1984 | 3253 |
| Yangtze | Cuntan | yangtze-cuntan | 29.616667, 106.6 | 1987-2006 | 1987-2006 | 804859 | |
| Yellow, Huang He | Tangnaihai | yellow-tangnaihai | 35.5, 100.15 | 1971-2002 | 1971-2002 | 121000 |
Reporting model results
The specification on how to submit the data, as well as further information and instructions are given on the ISIMIP website at:
https://www.isimip.org/protocol/preparing-simulation-files
It is important that you comply precisely with the formatting specified there, in order to facilitate the analysis of your simulation results in the ISIMIP framework. Incorrect formatting can seriously delay the analysis. The ISIMIP Team will be glad to assist with the preparation of these files if necessary.
File names consist of a series of identifier, separated by underscores. Things to note:
- Report one variable per file.
- In filenames, use lowercase letters only.
- Use underscore (
_) to separate identifiers. - Variable names consist of a single word without hyphens or underscores.
- Use hyphens (
-) to separate strings within an identifier, e.g. in a model name. - If no specific
sens-scenariois given in the experiments table, usedefault. - NetCDF file extension is
.nc. - The reference year for the NetCDF files for ISIMIP3a is
1901.
Please name the files in the Water (regional) sector according to the following pattern:
<model>_<climate-forcing>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>(-<crop>-<irrigation>|-<pft>)_<river-basin>_<time-step>_<start-year>_<end-year>.nc
and replace the identifiers with the specifiers given in the tables of this document. Examples would be:
lpjml_gswp3_obsclim_histsoc_default_qtot_global_annual_1901_1910.nc
lpjml_gwsp3_counterclim_2015soc_1901co2_yield-mai-noirr_global_annual_2006_2010.nc
The following regular expression can be used to validate and parse the file name for the water (regional) sector:
(?P<model>[a-z0-9-]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<climate_scenario>[a-z0-9]+)_(?P<soc_scenario>[a-z0-9]+)_(?P<sens_scenario>[a-z0-9-]+)_(?P<variable>[a-z0-9]+)(-(?P<crop>[a-z0-9]+)-(?P<irrigation>(firr|cirr|noirr))|-(?P<pft>[a-z0-9-]+))?_(?P<river_basin>[a-z-]+)_(?P<time_step>[a-z]+)_(?P<start_year>\d{4})_(?P<end_year>\d{4}).nc
For questions or clarifications, please contact info@isimip.org or the data managers directly (isimip-data@pik‐potsdam.de) before submitting files.
References
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