LamaH-CE

Overview

LamaH-CE is the Central Europe large-sample hydrological dataset. Large-sample hydrological dataset for Central Europe, covering diverse Alpine and pre-Alpine catchments with high-quality data.

Dataset Information

• Region: Central Europe
• Project: LamaH (Large-sample hydrological data and models)
• Module: hydrodataset.lamah_ce
• Class: LamahCe

About LamaH

LamaH (Large-sample hydrological data and models) provides comprehensive hydrological data for research and modeling:

Key Features

• High-quality, quality-controlled data
• Extensive catchment attributes
• Multiple temporal resolutions
• Detailed metadata
• Suitable for large-sample hydrology studies

Research Applications

• Hydrological model development and testing
• Climate change impact studies
• Regionalization studies
• Machine learning applications
• Comparative hydrology

Features

Static Attributes

Comprehensive static catchment attributes: - Basin geometry and area - Topographic characteristics (elevation, slope) - Land cover information - Soil properties and classes - Geological characteristics - Climate indices - Human influence indicators

Dynamic Variables

Timeseries variables available: - Streamflow (observed) - Precipitation - Temperature (min, max, mean) - Potential evapotranspiration - Snow water equivalent - Solar radiation - Humidity - And more...

Usage

Basic Usage

from hydrodataset.lamah_ce import LamahCe
from hydrodataset import SETTING

# Initialize dataset
data_path = SETTING["local_data_path"]["datasets-origin"]
ds = LamahCe(data_path)

# Get basin IDs
basin_ids = ds.read_object_ids()
print(f"Number of basins: {len(basin_ids)}")

# Check available features
print("Static features:", ds.available_static_features)
print("Dynamic features:", ds.available_dynamic_features)

# Check default time range
print(f"Default time range: {ds.default_t_range}")

# Read timeseries data
timeseries = ds.read_ts_xrdataset(
    gage_id_lst=basin_ids[:5],
    t_range=ds.default_t_range,
    var_lst=["streamflow", "precipitation"]
)
print(timeseries)

# Read attribute data
attributes = ds.read_attr_xrdataset(
    gage_id_lst=basin_ids[:5],
    var_lst=["area", "p_mean"]
)
print(attributes)

Advanced Analysis

# Read multiple variables for detailed analysis
ts_data = ds.read_ts_xrdataset(
    gage_id_lst=basin_ids[:10],
    t_range=["1990-01-01", "2020-12-31"],
    var_lst=[
        "streamflow",
        "precipitation", 
        "temperature_mean",
        "temperature_min",
        "temperature_max",
        "pet",
        "snow_water_equivalent"
    ]
)

# Analyze snow-influenced catchments
import xarray as xr
winter_months = ts_data.sel(time=ts_data.time.dt.month.isin([12, 1, 2]))
mean_swe = winter_months["snow_water_equivalent"].mean(dim="time")
print("Mean winter SWE:", mean_swe)

Reading Specific Variables

# Read with specific time range
ts_data = ds.read_ts_xrdataset(
    gage_id_lst=basin_ids[:10],
    t_range=["2000-01-01", "2010-12-31"],
    var_lst=["streamflow", "precipitation", "temperature_mean"]
)

# Read basin area
areas = ds.read_area(gage_id_lst=basin_ids[:10])

# Read mean precipitation
mean_precip = ds.read_mean_prcp(gage_id_lst=basin_ids[:10])

Data Quality and Completeness

LamaH datasets feature: - Rigorous quality control procedures - Documentation of data gaps - Metadata completeness - Peer-reviewed methodology - Regular updates

Regional Characteristics

LamaH-CE

• Alpine and pre-Alpine catchments
• Snow-influenced hydrology
• Elevation range from lowlands to high mountains
• Mixed land use patterns

LamaH-ICE

• Volcanic landscapes
• Glacial-influenced catchments
• Geothermal activity impact
• Unique geological conditions

API Reference

hydrodataset.lamah_ce.LamahCe

Bases: HydroDataset

LamaHCE dataset class extending HydroDataset.

This class provides access to the LamaHCE dataset, which contains hourly hydrological and meteorological data for various watersheds.

Attributes:

Name	Type	Description
region		Geographic region identifier
download		Whether to download data automatically
ds_description		Dictionary containing dataset file paths

Source code in hydrodataset/lamah_ce.py

class LamahCe(HydroDataset):
    """LamaHCE dataset class extending HydroDataset.

    This class provides access to the LamaHCE dataset, which contains hourly
    hydrological and meteorological data for various watersheds.

    Attributes:
        region: Geographic region identifier
        download: Whether to download data automatically
        ds_description: Dictionary containing dataset file paths
    """

    def __init__(
        self,
        data_path: str,
        region: Optional[str] = None,
        download: bool = False,
        cache_path: Optional[str] = None,
    ) -> None:
        """Initialize LamaHCE dataset.

        Args:
            data_path: Path to the LamaHCE data directory
            region: Geographic region identifier (optional)
            download: Whether to download data automatically (default: False)
            cache_path: Path to the cache directory
        """
        super().__init__(data_path, cache_path=cache_path)
        self.region = region
        self.download = download
        # Use the custom LamaHCE class defined at module level
        self.aqua_fetch = LamaHCE(data_path)

    @property
    def _attributes_cache_filename(self):
        return "lamahce_attributes.nc"

    @property
    def _timeseries_cache_filename(self):
        return "lamahce_timeseries.nc"

    @property
    def default_t_range(self):
        return ["1981-01-01", "2019-12-31"]

    # get the information of features from table 3 in "https://doi.org/10.5194/essd-13-4529-2021"
    # Static variable definitions based on inspected data
    _subclass_static_definitions = {
        "p_mean": {"specific_name": "p_mean", "unit": "mm/day"},
        "area": {"specific_name": "area_km2", "unit": "km^2"},
    }

    # Dynamic variable mapping based on inspected data
    _dynamic_variable_mapping = {
        StandardVariable.STREAMFLOW: {
            "default_source": "observations",
            "sources": {
                "observations": {"specific_name": "q_cms_obs", "unit": "m^3/s"},
            },
        },
        StandardVariable.PRECIPITATION: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "pcp_mm", "unit": "mm"},
            },
        },
        StandardVariable.TEMPERATURE_MAX: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "airtemp_c_max", "unit": "°C"},
                "dp": {"specific_name": "dptemp_c_max_2m", "unit": "°C"},
            },
        },
        StandardVariable.TEMPERATURE_MIN: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "airtemp_c_min", "unit": "°C"},
                "dp": {"specific_name": "dptemp_c_min_2m", "unit": "°C"},
            },
        },
        StandardVariable.TEMPERATURE_MEAN: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "airtemp_c_mean", "unit": "°C"},
                "dp": {"specific_name": "dptemp_c_mean_2m", "unit": "°C"},
            },
        },
        StandardVariable.EVAPOTRANSPIRATION: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "total_et", "unit": "mm"},
            },
        },
        StandardVariable.SNOW_WATER_EQUIVALENT: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "swe_mm", "unit": "mm"},
            },
        },
        StandardVariable.SOLAR_RADIATION: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "solrad_wm2", "unit": "W/m^2"},
            },
        },
        StandardVariable.SOLAR_RADIATION_MAX: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "solrad_wm2_max", "unit": "W/m^2"},
            },
        },
        StandardVariable.THERMAL_RADIATION: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "thermrad_wm2", "unit": "W/m^2"},
            },
        },
        StandardVariable.THERMAL_RADIATION_MAX: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "thermrad_wm2_max", "unit": "W/m^2"},
            },
        },
        StandardVariable.SURFACE_PRESSURE: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "airpres_hpa", "unit": "Pa"},
            },
        },
        StandardVariable.U_WIND_SPEED: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "windspeedu_mps", "unit": "m/s"},
            },
        },
        StandardVariable.V_WIND_SPEED: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "windspeedv_mps", "unit": "m/s"},
            },
        },
        StandardVariable.VOLUMETRIC_SOIL_WATER_LAYER1: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "volsw_123", "unit": "m^3/m^3"},
            },
        },
        StandardVariable.VOLUMETRIC_SOIL_WATER_LAYER4: {
            "default_source": "era5",
            "sources": {
                "era5": {"specific_name": "volsw_4", "unit": "m^3/m^3"},
            },
        },
    }

default_t_range property

__init__(data_path, region=None, download=False, cache_path=None)

Initialize LamaHCE dataset.

Parameters:

Name	Type	Description	Default
data_path	str	Path to the LamaHCE data directory	required
region	Optional[str]	Geographic region identifier (optional)	None
download	bool	Whether to download data automatically (default: False)	False
cache_path	Optional[str]	Path to the cache directory	None

Source code in hydrodataset/lamah_ce.py

def __init__(
    self,
    data_path: str,
    region: Optional[str] = None,
    download: bool = False,
    cache_path: Optional[str] = None,
) -> None:
    """Initialize LamaHCE dataset.

    Args:
        data_path: Path to the LamaHCE data directory
        region: Geographic region identifier (optional)
        download: Whether to download data automatically (default: False)
        cache_path: Path to the cache directory
    """
    super().__init__(data_path, cache_path=cache_path)
    self.region = region
    self.download = download
    # Use the custom LamaHCE class defined at module level
    self.aqua_fetch = LamaHCE(data_path)