Overview of the mHM Model
The mesoscale hydrologic model (mHM) is a spatially explicit distributed hydrologic model that uses grid cells as a primary hydrologic unit and accounts for the following
processes: canopy interception, snow accumulation, and melting, soil moisture dynamics, infiltration and surface runoff, evapotranspiration, subsurface storage and discharge
generation, deep percolation and baseflow and discharge attenuation and flood routing. The detail information about the model can be found here.
Figure 2: The concept of the mesoscale hydrologic model, mHM. (Samaniego et al., 2010)
What is SMI?
The soil moisture index (SMI) is calculating by estimating the percentile of the soil moisture output from the model. The daily soil moisture is estimated as the average
of the soil conditions of the preceding 30 days. Therefore, it represents values that correspond to a time period of 1 month. The SMI is estimated using a non-parametric
kernel-based cumulative distribution function based on a 38-year historic soil moisture reconstruction (1982–2019), as described by Samaniego et al (2013).
The classes of SMI are derived using the thresholds of the SMI. These thresholds reflect the occurrence of similar conditions in the past and thus indicate the potential impacts of these conditions. For example, the class of exceptional drought is defined by an upper threshold of 0.02. This implies that the soil moisture conditions were observed in less than 2% of the time within the 60 year reference period at this grid cell and time of the year.
-
Abnormally Dry
0.3 ≤ SMI < 0.2
Short-term dry conditions before or after a preceding drought. -
Moderate Drought
0.2 ≤ SMI < 0.1
Damages to crops and pastures possible. -
Severe Drought
0.1 ≤ SMI < 0.05
Losses in crops and pastures are likely. -
Extreme Drought
0.05 ≤ SMI < 0.02
High probability of major losses in crops and pasture. -
Exceptional Drought
SMI ≤ 0.02 =
High probability of exceptional losses in crops and pastures.
Description: The classification of droughts based on the soil moisture index (SMI) (Matthias Zink et al. 2016)
About the Data
A number of meteorological, morphological, and land cover data have been used to estimate the soil moisture. The data were collected from the following sources.
| Data Type | Data-set name | Processed Resolution | Developing Institute/ URL |
|---|---|---|---|
| DEM (+ derivatives) | Global Multi-resolution Terrain Elevation Data (GMTED2010) | 1/512° | U.S. Geological Survey (USGS) and the National Geospatial-Intelligence Agency (NGA) |
| Soil | SoilGrids | 1/512° | Wageningen University |
| Geology | Global Lithological Map (GLiM) | 1/512° | Institute for Biogeochemistry and Marine Chemistry, KlimaCampus, Universitt Hamburg |
| Land cover | Global Land Cover (GlobCover) | 1/512° | European Space Agency (ESA), Universit Catholique de Louvain |
| Leaf Area Index (LAI) | Global Inventory Modeling and Mapping Studies (GIMMS) | 1/512° monthly climatology | Global Land Cover Facility, University of Maryland |
| Meteorological forcings | ERA5 | 0.25° daily | Copernicus Climate Change Service |
Contributors
Katie Baldwin
DeveloperUndergraduate Student
- Princeton University (A.B. Computer Science)
Toma Rani Saha
DeveloperGuest Researcher
- UFZ Department of Computational Hydrosystems
Pallav Kumar Shrestha
Service Manager, Hydroclimate Team PhD Researcher
- UFZ Department of Computational Hydrosystems
Dr. Oldrich Rakovec
Hydroclimate TeamScientist
- UFZ Department of Computational Hydrosystems
Dr. Stephan Thober
Hydroclimate TeamScientist
- UFZ Department of Computational Hydrosystems
Dr. Luis Samaniego
Hydroclimate TeamDeputy Head
- UFZ Department of Computational Hydrosystems
Acknowledgment
We would like to acknowledge the Alexander von Humboldt Foundation for providing the fund to accomplish this research through the
International Climate Protection Fellowship (2019, ID = BGD1202252IKS).
We would like acknowledge the providers of input data namely U.S. Geological Survey (USGS) and the National Geospatial-Intelligence
Agency (NGA) for Terrain Elevation Data; Wageningen University for Soil data; Institute for Biogeochemistry and Marine Chemistry,
KlimaCampus, Universitt Hamburg for geological map; European Space Agency (ESA), Universit Catholique de Louvain for land cover data;
Global Land Cover Facility for Leaf Area Index (LAI); University of Maryland, Climate Hazard Group (CHG) UC Santa Barbara for precipitation
data; and Princeton University for temperature data.
Contact
- Dr. Luis Samaniego
- Email: luis.samaniego@ufz.de
- Fax: +49 341 235 45 1971
- Address: Helmholtz Centre for Environmental Research – UFZ, Permoserstraße 15 | 04318 Leipzig, Germany
- Main Website: UFZ Department of Computational Hydrosystems