ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. The GlobWat Model
2.2. Eref Methods and Data Processing
2.2.1. Eref Methods
De Bruin Method
Langbein Method
2.2.2. Processing Steps
2.3. Data
2.3.1. ERA5 and ERA-Interim
2.3.2. Observed Hydro-Climatic Variables
2.4. The Urmia Lake Basin, an Example Case Study
2.5. Evaluation Statistics
2.5.1. Correlation Coefficient (CC)
2.5.2. Standard Deviation (SD)
2.5.3. Root-Mean-Square Error (RMSE)
2.5.4. Nash–Sutcliffe Efficiency (NSE)
3. Results
4. Discussion
4.1. Development of Scripts for Processing Weather Input Data for GlobWat
4.2. Comparison of ERA5 and ERA-Interim Precipitation and Temperature with Observed Data in Urmia Lake Basin
4.3. GlobWat Discharge and Eact Estimates for the Urmia Lake Basin
5. Practical Implications of This Study
6. Conclusions
- Using developed scripts within the ESMValTool allowed the rapid analysis of the underlying causes of observed trends.
- The provenance made reproducibility easier by storing information from previous analyses. For example, in our study, we were able to look back and see which raw input files were used to create which files and so on.
- When compared to its previous version, ERA-Interim, the ERA5 representation of temperature has not improved significantly over the Urmia Lake Basin. However, the ERA5 precipitation representation has significantly improved over the Urmia Lake basin.
- In basins with low densities of meteorological stations, such as the Urmia Lake Basin, ERA5 can be a good source of weather data.
- In the Urmia Lake basin, the De Bruin radiation Eref method outperformed the Langbein temperature-based method when evaluated in terms of the modeled and measured discharge.
- The GlobWat discharge estimates are extremely sensitive to precipitation in (semi-) arid regions. Therefore, ERA5 would be preferable over ERA-Interim due to the better representation of precipitation.
- The GlobWat model does not capture human interactions in the basin.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Input Layer | Source |
---|---|
Global maps of monthly precipitation | ERA5 and ERA-Interim reanalysis products from ECMWF [28,57] |
Global maps of wet days per month | Provided by model developers from New et al. [30] |
Global maps of coefficient of variation of precipitation per month | Provided by model developers from New et al. [30] |
Global maps of monthly reference evaporation | Calculated according to Langbein and De Bruin methods with input data from ERA5 and ERA-Interim reanalysis products from ECMWF [28,57] |
Maximum soil moisture storage capacity | Provided by model developers from the Harmonised World Soil Database, FAO [34] |
Maximum groundwater recharge flux | Provided by model developers from WHYMAP, BGR, and UNESCO [33] |
Land use or vegetation type coefficient | Provided by model developers from FAO’s Global Agricultural Systems Map, FAO [32] |
Global map of irrigation areas | Provided by model developers from Siebert et al. [35] |
Global map of lakes and wetlands | Provided by model developers from Lehner and Döll [31] |
Global map of river basins and sub-basins | Provided by model developers from FAO [32] |
Appendix B
- Download the GlobWat model and its input data from the FAO’s AquaMaps website [17].
- Follow the instructions on the ESMValTool Website [87] to install the ESMValTool on your system.
- Using the reference method, select the variables needed for processing data for use in the GlobWat model. More information can be found in the ESMValTool Documentation [88].
- Using download_era_interim.py [51], download the variables you require from ERA-Interim for the time period you require.
- Using era5cli [52], download the variables you need for the desired time period.
- At recipe recipe_globwat.yml [49], select the time period and reference method with which you would like to process the data.
- Save the newly processed precipitation and reference evaporation to the GlobWat model input folder.
- Run the GlobWat model as described in How_to_use_GlobWatv1.0rev2.pdf [36].
- Using any CSV or ASCII reader, extract the results for your desired location.
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File Type | Columns (X) | Rows (Y) | Pixel Size (Degree) | No Data Value | Data Type | Reference System | Extent |
---|---|---|---|---|---|---|---|
AAIGrid: Arc/Info ASCII Grid | 4320 | 2160 | 0.08333 | −9999 | Float32 | GCS WGS84 | −180; −90: 180; 90 |
Time Period | Dataset | Eref Method | CC | RMSE (mm/yr) | Mean (mm/yr) |
---|---|---|---|---|---|
1986–2016 | Observed | 1 | 0 | 275 | |
ERA5 | De Bruin | 0.3 | 67 | 234 | |
ERA5 Eref | 0.4 | 74 | 222 | ||
Langbein | 0.5 | 93 | 197 | ||
ERA-Interim | De Bruin | 0.4 | 60 | 244 | |
Langbein | 0.5 | 83 | 208 | ||
1986–2006 | Observed | 1 | 0 | 257 | |
ERA5 | De Bruin | 0.3 | 55 | 234 | |
ERA5 Eref | 0.4 | 61 | 220 | ||
Langbein | 0.5 | 79 | 195 | ||
ERA-Interim | De Bruin | 0.2 | 54 | 241 | |
Langbein | 0.4 | 73 | 203 | ||
2006–2016 | Observed | 1 | 0 | 309 | |
ERA5 | De Bruin | 0.4 | 85 | 233 | |
ERA5 Eref | 0.4 | 93 | 224 | ||
Langbein | 0.5 | 115 | 200 | ||
ERA-Interim | De Bruin | 0.5 | 71 | 249 | |
Langbein | 0.4 | 100 | 217 |
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Abdollahi, B.; Alidoost, F.; Moshir Panahi, D.; Hut, R.; van de Giesen, N. ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model. Water 2022, 14, 1950. https://doi.org/10.3390/w14121950
Abdollahi B, Alidoost F, Moshir Panahi D, Hut R, van de Giesen N. ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model. Water. 2022; 14(12):1950. https://doi.org/10.3390/w14121950
Chicago/Turabian StyleAbdollahi, Banafsheh, Fakhereh Alidoost, Davood Moshir Panahi, Rolf Hut, and Nick van de Giesen. 2022. "ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model" Water 14, no. 12: 1950. https://doi.org/10.3390/w14121950