4DHydro mHM Tier 2 simulations - exp20 0 15172

Project
hydrometeorological data
Description
Experiment 20 of the mHM Tier2 simulations, part of the 4DHydro ESA project (Work Package 5 deliverables).

This is a DOI reference for the STAC protocol of the 4DHydro project (tarball for all NetCDF files, identical to non-permanent storage under https://minio.ufz.de/4dhydro/mHM/tier2/exp20).

The mHM simulations are based on the Mesoscale Hydrological Model and SCC method (release_5.11.3-dev0, commit 05ece194). 

Simulation options:
- Period: 1990-2022
- Region: European setup forcing EMO1 and Tugela River basin setup forced by the CHIRPS product
- Settings: Multi-basin calibration with the objective function of 1.0 - KGE (Kling-Gupta efficiency measure); included modules: basic hydrologic modules. Snow processes are based on the day-degree approach, soil moisture is processed based on the Feddes equation for ET reduction, and a multi-layer infiltration capacity approach (Brooks-Corey-like); direct runoff is based on the linear reservoir exceedance approach; PET is based on the Hargreaves-Samani method; interflow is approximated by a storage reservoir with one outflow threshold and a nonlinear response; groundwater is assumed to be a linear reservoir; river routing is approximated by adaptive timestep, spatially varying celerity.
- Spin-up: Based on a 10-year simulation with 1990-2000 climatology for EMO-1 and a calibration period from 2005-2022.
- Source code: https://git.ufz.de/mhm/mhm, commit 05ece194
- Resolution: Spatial resolution of 0.0625 degrees (see STAC protocol above for more details)
- Temporal resolution: Hourly (with meteo inputs daily, internal weights based on sub-daily ERA5 values used for disaggregation to hourly) 

Input datasets for the mHM setup:
* See Rakovec et al. (2022) and Boeing et al. (2022) for more information.

- Meteorology (and pre-processing):
Precipitation, temperature, and reference potential evapotranspiration are based on two datasets: EMO-1 (Thieming et al., 2022; updated ~1 km resolution, see for more details: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/CEMS-EFAS/meteorological_forcings/README-EMO-1arcmin.txt) and CHIRPS (Funk et al., 2015). The latter was remapped on the 0.0625-degree grid using the nearest neighbor approach. Reference potential evapotranspiration was determined using the Hargreaves-Samani equation (Hargreaves and Samani, 1985). The internal model timestep of mHM is hourly, with temporal disaggregation (day to hour) based on weights derived from the native hourly ERA5 dataset.

- Soil and topography:
Soil parameters are based on the SoilGrids dataset (Hengl et al., 2014). Topography parameters are based on GMTED2010 (https://www.usgs.gov/coastal-changes-and-impacts/gmted2010), which was used to derive information about slope and aspect. It was further conditioned on the HydroSHEDS (Lehner et al., 2008) river network (https://www.hydrosheds.org/downloads) to derive flow direction and flow accumulation.

- Landcover and vegetation:
mHM uses three dominant land cover classes (forest, permeable, and impervious) retrieved from the GLOBCOVER database ESA (2009). Additionally, vegetation characteristics for interception processes are based on the Leaf Area Index (LAI) from the GIMMS MODIS from the Global Land Cover Facility (GLCF), available at http://iridl.ldeo.columbia.edu/SOURCES/.UMD/.GLCF/.GIMMS/.NDVIg/.global/index.html.

- Groundwater processes:
The baseflow recession constants are based on categorical classes derived from the GLIM data from Universität Hamburg (https://doi.org/10.1594/PANGAEA.788537); see Hartman and Moosdorf (2012) for more details.

- Output variables:
Discharge, actual evapotranspiration, soil moisture, total water, upstream area.


References:

Boeing, F., Rakovec, O., Kumar, R., Samaniego, L., Schrön, M., Hildebrandt, A., Rebmann, C., Thober, S., Müller, S., Zacharias, S., Bogena, H., Schneider, K., Kiese, R., Attinger, S., and Marx, A.: High-resolution drought simulations and comparison to soil moisture observations in Germany, Hydrol. Earth Syst. Sci., 26, 5137–5161

Cornes, R., G. van der Schrier, E.J.M. van den Besselaar, and P.D. Jones. 2018: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res. Atmos., 123. doi:10.1029/2017JD028200

ESA (2009) Globcover v2 http://due.esrin.esa.int/page_globcover.php

Funk, Chris, et al. "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes." Scientific data 2.1 (2015): 1-21.

Hargreaves, G.H. and Samani, Z.A., 1985. Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1(2), pp.96-99.

Hartmann, J. and Moosdorf, N., 2012. The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12).

Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D. and Simmons, A., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), pp.1999-2049.

Kumar, R., Samaniego, L. and Attinger, S., 2013. Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resources Research, 49(1), pp.360-379.

Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., ... & Wisser, D. (2011). High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management. Frontiers in Ecology and the Environment, 9(9), 494-502. DOI: 10.1890/100125.

Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., Attinger, S., Schäfer, D., Schrön, M. and Samaniego, L., 2016. Multiscale and multivariate evaluation of water fluxes and states over European river basins. Journal of Hydrometeorology, 17(1), pp.287-307.

Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S., Hanel, M. and Kumar, R., 2022. The 2018–2020 multi‐year drought sets a new benchmark in Europe. Earth's Future, 10(3), p.e2021EF002394.

Samaniego, L., Kumar, R. and Attinger, S., 2010. Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale. Water Resources Research, 46(5).

Samaniego, L., Kumar, R., Zink, M., Cuntz, M., Juliane Mai, Stephan Thober, Christoph Schneider, Giovanni Dalmasso, Jude Musuuza, Oldrich Rakovec, John Craven, David Schäfer, Vladyslav Prykhodko, Martin Schrön, Diana Spieler, Johannes Brenner, Ben Langenberg, Lennart Schüler, Simon Stisen, Cüneyd M. Demirel, Miao Jing, Maren Kaluza, Robert Schweppe, Pallav Kumar Shrestha, Nicola Döring and Sebastian Müller (2023) mhm-ufz/mHM: v5.13.1, Zenodo [Online]. DOI: 10.5281/zenodo.8279545.

Thiemig, V., Gomes, G.N., Skøien, J.O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J.P., Kolbe, C., Pichon, D. and Schweim, C., 2022. EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe. Earth System Science Data, 14(7), pp.3249-3272.

Thober, S., Cuntz, M., Kelbling, M., Kumar, R., Mai, J. and Samaniego, L., 2019. The multiscale routing model mRM v1. 0: Simple river routing at resolutions from 1 to 50 km. Geoscientific Model Development, 12(6), pp.2501-2521.


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LICENCE

CC BY-NC-SA 4.0 DEED
Attribution-NonCommercial-ShareAlike 4.0 International

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DOI
https://doi.org/10.48758/ufz.15172
Cite as (APA)
Modiri, E., Rakovec, O., & Samaniego, L. (2024). 4DHydro mHM Tier 2 simulations - exp20. Helmholtz-Centre for Environmental Research. https://doi.org/10.48758/UFZ.15172
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Metadata Catalogues
Dublin Core
URI
https://www.ufz.de/record/dmp/archive/15172
DOI
10.48758/ufz.15172
Subject
4DHydro mHM tier2 simulations - exp20 for 3 regions of interest (Po, Rhine, and Tugela))
Publication Date
10.09.2024
Coverage
Po, Rhine, and Tugela basins
Type of data
model
Data quality
quality assured data
Status of data
processed data
Data format
netcdf
Creators
Modiri, E.
Rakovec, O.
Samaniego, L.
Publisher
Helmholtz-Centre for Environmental Research
Rights holders
Helmholtz-Centre for Environmental Research
Contact
ehsan.modiri@ufz.de
oldrich.rakovec@ufz.de
luis.samaniego@ufz.de
Time or Period of creation
01.01.1990 - 31.12.2022 24:00
Language of the content
English
Access permissions
free access