CAMELS-CH

Overview

CAMELS-CH is the Switzerland hydrological dataset implementation. Swiss CAMELS dataset covering Alpine and pre-Alpine catchments in Switzerland.

Dataset Information

• Region: Switzerland
• Module: hydrodataset.camels_ch
• Class: CamelsCh

Features

Static Attributes

Static catchment attributes include: - Basin area - Mean precipitation - Topographic characteristics - Land cover information - Soil properties - Climate indices

Dynamic Variables

Timeseries variables available (varies by dataset): - Streamflow - Precipitation - Temperature (min, max, mean) - Potential evapotranspiration - Solar radiation - And more...

Usage

Basic Usage

from hydrodataset.camels_ch import CamelsCh
from hydrodataset import resolve_data_path

# Initialize dataset
data_path = resolve_data_path("camels_ch")
ds = CamelsCh(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)

# 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)

Reading Specific Variables

# Read with specific time range
ts_data = ds.read_ts_xrdataset(
    gage_id_lst=basin_ids[:10],
    t_range=["1990-01-01", "1995-12-31"],
    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 Sources

The dataset supports multiple data sources for certain variables. Check the class documentation for available sources and use tuple notation to specify:

# Request specific data source
ts_data = ds.read_ts_xrdataset(
    gage_id_lst=basin_ids[:5],
    t_range=["1990-01-01", "1995-12-31"],
    var_lst=[
        ("precipitation", "era5land"),  # Specify ERA5-Land source
        "streamflow"  # Use default source
    ]
)

API Reference

hydrodataset.camels_ch.CamelsCh

Bases: HydroDataset

CAMELS-CH dataset class extending RainfallRunoff.

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

This class overrides the default CSV reading methods from AquaFetch to use comma separators instead of semicolon separators, and updates the download URL to the latest Zenodo record.

Source code in hydrodataset/camels_ch.py

class CamelsCh(HydroDataset):
    """CAMELS-CH dataset class extending RainfallRunoff.

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

    This class overrides the default CSV reading methods from AquaFetch to use
    comma separators instead of semicolon separators, and updates the download
    URL to the latest Zenodo record.
    """

    # Raw CSV column →?zarr variable name (matches NC cache)
    _COL_MAP = {
        "discharge_vol(m3/s)": "q_cms_obs",
        "discharge_spec(mm/d)": "q_mm_obs",
        "waterlevel(m)": "waterlevel",
        "precipitation(mm/d)": "pcp_mm",
        "temperature_min(degC)": "temperature_min",
        "temperature_mean(degC)": "temperature_mean",
        "temperature_max(degC)": "temperature_max",
        "rel_sun_dur(%)": "rel_sun_dur",
        "swe(mm)": "swe_mm",
    }
    _STATIC_FILES = [
        "CAMELS_CH_climate_attributes_obs.csv",
        "CAMELS_CH_geology_attributes.csv",
        "CAMELS_CH_glacier_attributes.csv",
        "CAMELS_CH_humaninfluence_attributes.csv",
        "CAMELS_CH_hydrogeology_attributes.csv",
        "CAMELS_CH_hydrology_attributes_obs.csv",
        "CAMELS_CH_landcover_attributes.csv",
        "CAMELS_CH_soil_attributes.csv",
        "CAMELS_CH_topographic_attributes.csv",
    ]

    def __init__(
        self,
        uri: str,
        region: Optional[str] = None,
        download: bool = False,
        version: str = "v0.9",
    ) -> None:
        super().__init__(uri)
        self.region = region
        self.download = download
        self.version = version

        if str(uri).startswith("s3://"):
            return

        # Define updated URL for the new dataset version
        new_url = "https://zenodo.org/records/15025258"

        # Create custom methods that override the AquaFetch CSV reading
        def custom_climate_attrs(self) -> pd.DataFrame:
            """Returns 14 climate attributes of catchments with comma separator."""
            df = pd.read_csv(
                self.clim_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype={
                    "gauge_id": str,
                    "p_mean": float,
                    "aridity": float,
                    "pet_mean": float,
                    "p_seasonality": float,
                    "frac_snow": float,
                    "high_prec_freq": float,
                    "high_prec_dur": float,
                    "high_prec_timing": str,
                    "low_prec_timing": str,
                },
            )
            return df

        def custom_geol_attrs(self) -> pd.DataFrame:
            """15 geological features with comma separator."""
            df = pd.read_csv(
                self.geol_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype=np.float32,
            )
            df.index = df.index.astype(int).astype(str)
            return df

        def custom_glacier_attrs(self) -> pd.DataFrame:
            """Returns a dataframe with glacier attributes using comma separator."""
            df = pd.read_csv(
                self.glacier_attr_path,
                sep=",",  # Changed from ';' to ','
                skiprows=1,
                index_col="gauge_id",
                dtype=np.float32,
            )
            df.index = df.index.astype(int).astype(str)
            return df

        def custom_human_inf_attrs(self) -> pd.DataFrame:
            """14 anthropogenic factors with comma separator."""
            df = pd.read_csv(
                self.hum_inf_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype={
                    "gauge_id": str,
                    "n_inhabitants": int,
                    "dens_inhabitants": float,
                    "hp_count": int,
                    "hp_qturb": float,
                    "hp_inst_turb": float,
                    "hp_max_power": float,
                    "num_reservoir": int,
                    "reservoir_cap": float,
                    "reservoir_he": float,
                    "reservoir_fs": float,
                    "reservoir_irr": float,
                    "reservoir_nousedata": float,
                },
            )
            return df

        def custom_hydrogeol_attrs(self) -> pd.DataFrame:
            """10 hydrogeological factors with comma separator."""
            df = pd.read_csv(
                self.hydrogeol_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype=float,
            )
            df.index = df.index.astype(int).astype(str)
            return df

        def custom_hydrol_attrs(self) -> pd.DataFrame:
            """14 hydrological parameters + 2 useful infos with comma separator."""
            df = pd.read_csv(
                self.hydrol_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype={
                    "gauge_id": str,
                    "sign_number_of_years": int,
                    "q_mean": float,
                    "runoff_ratio": float,
                    "stream_elas": float,
                    "slope_fdc": float,
                    "baseflow_index_landson": float,
                    "hfd_mean": float,
                    "Q5": float,
                    "Q95": float,
                    "high_q_freq": float,
                    "high_q_dur": float,
                    "low_q_freq": float,
                },
            )
            return df

        def custom_landcolover_attrs(self) -> pd.DataFrame:
            """13 landcover parameters with comma separator."""
            return pd.read_csv(
                self.lc_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                dtype={
                    "gauge_id": str,
                    "crop_perc": float,
                    "grass_perc": float,
                    "scrub_perc": float,
                    "dwood_perc": float,
                    "mixed_wood_perc": float,
                    "ewood_perc": float,
                    "wetlands_perc": float,
                    "inwater_perc": float,
                    "ice_perc": float,
                    "loose_rock_perc": float,
                    "rock_perc": float,
                    "urban_perc": float,
                    "dom_land_cover": str,
                },
            )

        def custom_soil_attrs(self) -> pd.DataFrame:
            """80 soil parameters with comma separator."""
            df = pd.read_csv(
                self.soil_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
            )
            df.index = df.index.astype(int).astype(str)
            return df

        def custom_topo_attrs(self) -> pd.DataFrame:
            """Topographic parameters with comma separator."""
            df = pd.read_csv(
                self.topo_attr_path,
                skiprows=1,
                sep=",",  # Changed from ';' to ','
                index_col="gauge_id",
                encoding="unicode_escape",
            )
            df.index = df.index.astype(int).astype(str)
            return df

        def custom_static_data(self) -> pd.DataFrame:
            """Concatenate all static attributes without supp_geol_attrs."""
            df = pd.concat(
                [
                    self.climate_attrs(),
                    self.geol_attrs(),
                    # self.supp_geol_attrs(),  # Removed as requested
                    self.glacier_attrs(),
                    self.human_inf_attrs(),
                    self.hydrogeol_attrs(),
                    self.hydrol_attrs(),
                    self.landcolover_attrs(),
                    self.soil_attrs(),
                    self.topo_attrs(),
                ],
                axis=1,
            )
            df.index = df.index.astype(str)
            df.rename(columns=self.static_map, inplace=True)
            return df

        def custom_read_stn_dyn(self, station: str) -> pd.DataFrame:
            """Reads daily dynamic data for one catchment with comma separator."""
            df = pd.read_csv(
                os.path.join(self.dynamic_path, f"CAMELS_CH_obs_based_{station}.csv"),
                sep=",",  # Changed from ';' to ','
                index_col="date",
                parse_dates=True,
                dtype=np.float32,
            )
            df.rename(columns=self.dyn_map, inplace=True)
            return df

        def custom_stations(self) -> list:
            """Returns station ids for catchments with comma separator."""
            stns = pd.read_csv(
                self.glacier_attr_path, sep=",", skiprows=1  # Changed from ';' to ','
            )["gauge_id"].values.tolist()
            return [str(stn) for stn in stns]

        def custom_dynamic_path(self):
            """Return the correct path for dynamic data (timeseries not time_series)."""
            return os.path.join(self.camels_path, "timeseries", "observation_based")

        def do_nothing(self, *args, **kwargs):
            """Placeholder method to disable certain operations."""
            pass

        # Create class attributes dictionary to override CAMELS_CH methods
        class_attrs = {
            "url": new_url,
            "dynamic_path": property(custom_dynamic_path),
            "climate_attrs": custom_climate_attrs,
            "geol_attrs": custom_geol_attrs,
            "glacier_attrs": custom_glacier_attrs,
            "human_inf_attrs": custom_human_inf_attrs,
            "hydrogeol_attrs": custom_hydrogeol_attrs,
            "hydrol_attrs": custom_hydrol_attrs,
            "landcolover_attrs": custom_landcolover_attrs,
            "soil_attrs": custom_soil_attrs,
            "topo_attrs": custom_topo_attrs,
            "_static_data": custom_static_data,
            "_read_stn_dyn": custom_read_stn_dyn,
            "stations": custom_stations,
            "_maybe_to_netcdf": do_nothing,
        }

        # Create custom CAMELS_CH class with overridden methods
        CustomCamelsCh = type("CAMELS_CH", (CAMELS_CH,), class_attrs)

        try:
            self.aqua_fetch = CustomCamelsCh(uri)
        except Exception as e:
            print(e)
            check_zip_extract = False
            # The zip files that should be downloaded for CAMELS-CH
            zip_files = ["camels_ch.zip", "Caravan_extension_CH.zip"]
            for filename in tqdm(zip_files, desc="Checking zip files"):
                # The extracted directory name (without .zip extension)
                extracted_dir = self.data_source_dir.joinpath(
                    "CAMELS_CH", filename[:-4]
                )
                if not extracted_dir.exists():
                    check_zip_extract = True
                    break
            if check_zip_extract:
                from hydroutils import hydro_file

                hydro_file.zip_extract(self.data_source_dir.joinpath("CAMELS_CH"))
            self.aqua_fetch = CustomCamelsCh(uri)

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

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

    @property
    def default_t_range(self):
        return ["1981-01-01", "2020-12-31"]

    def read_object_ids(self) -> np.ndarray:
        uri = str(self.data_source_dir).rstrip("/")
        rel = "CAMELS_CH/camels_ch/camels_ch/static_attributes/CAMELS_CH_glacier_attributes.csv"
        if self._is_cloud():
            fs = self._make_s3fs()
            with fs.open(f"{uri}/{rel}".removeprefix("s3://")) as fh:
                df = pd.read_csv(fh, comment="#")
        else:
            df = pd.read_csv(os.path.join(uri, *rel.split("/")), comment="#")
        return np.array(sorted(df["gauge_id"].astype(str).tolist()))

    def cache_attributes_to_zarr(self) -> None:
        import zarr
        fs = self._make_s3fs()
        uri = str(self.data_source_dir).rstrip("/")
        attr_base = f"{uri}/CAMELS_CH/camels_ch/camels_ch/static_attributes"
        dfs = []
        for fname in self._STATIC_FILES:
            path = f"{attr_base}/{fname}".removeprefix("s3://")
            try:
                with fs.open(path) as fh:
                    raw = fh.read()
                import io
                for enc in ("utf-8", "latin-1"):
                    try:
                        df = pd.read_csv(io.BytesIO(raw), comment="#",
                                         index_col="gauge_id",
                                         dtype={"gauge_id": str}, encoding=enc)
                        break
                    except UnicodeDecodeError:
                        continue
                df.index = df.index.astype(str)
                dfs.append(df)
            except Exception as e:
                print(f"  WARN {fname}: {e}")
        static = pd.concat(dfs, axis=1)
        static = static.loc[~static.index.duplicated(keep="first")]
        static.columns = self._clean_feature_names(list(static.columns))
        static = static.rename(columns={"area": "area_km2", "gauge_lat": "lat"})

        zarr_name = self._attributes_cache_filename.replace(".nc", ".zarr")
        out, opts = self._zarr_path_and_opts(zarr_name)
        ids = static.index.tolist()
        n = len(ids)
        root = zarr.open_group(out, mode="w", storage_options=opts, zarr_format=2)
        for col in static.columns:
            vals = static[col].values.astype(str) if static[col].dtype == object else static[col].values
            arr = root.create_array(col, shape=(n,), chunks=(n,), dtype=vals.dtype)
            arr[:] = vals
            arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]
        basin_arr = root.create_array("basin", shape=(n,), chunks=(n,), dtype=str)
        basin_arr[:] = ids
        basin_arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]
        root.attrs["coordinates"] = "basin"
        self._write_zarr_units(root, "static")
        print(f"Attributes zarr written to: {out}")

    def cache_timeseries_to_zarr(self) -> None:
        import zarr
        fs = self._make_s3fs()
        uri = str(self.data_source_dir).rstrip("/")
        ts_base = f"{uri}/CAMELS_CH/camels_ch/camels_ch/timeseries/observation_based"

        stations = self.read_object_ids().tolist()
        all_times = pd.date_range(self.default_t_range[0], self.default_t_range[1], freq="D")
        n, nt = len(stations), len(all_times)
        times_ns = all_times.asi8
        all_vars = list(self._COL_MAP.values())

        data: dict[str, np.ndarray] = {vn: np.full((n, nt), np.nan) for vn in all_vars}
        print(f"Reading {n} stations from OSS...")
        for i, stn in enumerate(tqdm(stations, desc="CAMELS_CH zarr")):
            path = f"{ts_base}/CAMELS_CH_obs_based_{stn}.csv".removeprefix("s3://")
            try:
                with fs.open(path) as fh:
                    df = pd.read_csv(fh, index_col="date", parse_dates=True)
                df = df.reindex(all_times)
                for raw_col, zarr_vn in self._COL_MAP.items():
                    if raw_col in df.columns:
                        data[zarr_vn][i] = df[raw_col].values.astype(float)
            except Exception as e:
                print(f"  WARN {stn}: {e}")

        zarr_name = self._timeseries_cache_filename.replace(".nc", ".zarr")
        out, opts = self._zarr_path_and_opts(zarr_name)
        root = zarr.open_group(out, mode="w", storage_options=opts, zarr_format=2)
        for vn in all_vars:
            arr = root.create_array(vn, shape=(n, nt), chunks=(min(n, 100), min(nt, 365)), dtype="float64")
            arr[:] = data[vn]
            arr.attrs["_ARRAY_DIMENSIONS"] = ["basin", "time"]

        time_arr = root.create_array("time", shape=(nt,), chunks=(min(nt, 365),), dtype="int64")
        time_arr[:] = times_ns
        time_arr.attrs["_ARRAY_DIMENSIONS"] = ["time"]
        time_arr.attrs["units"] = "nanoseconds since 1970-01-01"
        time_arr.attrs["calendar"] = "proleptic_gregorian"

        basin_arr = root.create_array("basin", shape=(n,), chunks=(n,), dtype=str)
        basin_arr[:] = stations
        basin_arr.attrs["_ARRAY_DIMENSIONS"] = ["basin"]

        root.attrs["coordinates"] = "basin time"
        self._write_zarr_units(root, "dynamic")
        print(f"Timeseries zarr written to: {out}")

    _subclass_static_definitions = {
        "p_mean": {"specific_name": "p_mean", "unit": "mm"},
        "area": {"specific_name": "area_km2", "unit": "km^2"},
    }
    # get the information of features from dataset file"camels_ch_data_description.pdf"
    _dynamic_variable_mapping = {
        StandardVariable.STREAMFLOW: {
            "default_source": "vol",
            "sources": {
                "vol": {"specific_name": "q_cms_obs", "unit": "m^3/s"},
                "spec": {"specific_name": "q_mm_obs", "unit": "mm/d"},
            },
        },
        StandardVariable.PRECIPITATION: {
            "default_source": "sfo",
            "sources": {
                "sfo": {"specific_name": "pcp_mm", "unit": "mm/day"},
            },
        },
        StandardVariable.TEMPERATURE_MAX: {
            "default_source": "sfo",
            "sources": {"sfo": {"specific_name": "airtemp_C_max", "unit": "°C"}},
        },
        StandardVariable.TEMPERATURE_MIN: {
            "default_source": "sfo",
            "sources": {
                "sfo": {"specific_name": "airtemp_C_min", "unit": "°C"},
            },
        },
        StandardVariable.TEMPERATURE_MEAN: {
            "default_source": "sfo",
            "sources": {
                "sfo": {"specific_name": "airtemp_C_mean", "unit": "°C"},
            },
        },
        StandardVariable.RELATIVE_DAYLIGHT_DURATION: {
            "default_source": "sfo",
            "sources": {
                "sfo": {"specific_name": "rel_sun_dur(%)", "unit": "%"},
            },
        },
        StandardVariable.SNOW_WATER_EQUIVALENT: {
            "default_source": "wsl",
            "sources": {"wsl": {"specific_name": "swe_mm", "unit": "mm"}},
        },
    }

default_t_range property

__init__(uri, region=None, download=False, version='v0.9')

Source code in hydrodataset/camels_ch.py

def __init__(
    self,
    uri: str,
    region: Optional[str] = None,
    download: bool = False,
    version: str = "v0.9",
) -> None:
    super().__init__(uri)
    self.region = region
    self.download = download
    self.version = version

    if str(uri).startswith("s3://"):
        return

    # Define updated URL for the new dataset version
    new_url = "https://zenodo.org/records/15025258"

    # Create custom methods that override the AquaFetch CSV reading
    def custom_climate_attrs(self) -> pd.DataFrame:
        """Returns 14 climate attributes of catchments with comma separator."""
        df = pd.read_csv(
            self.clim_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype={
                "gauge_id": str,
                "p_mean": float,
                "aridity": float,
                "pet_mean": float,
                "p_seasonality": float,
                "frac_snow": float,
                "high_prec_freq": float,
                "high_prec_dur": float,
                "high_prec_timing": str,
                "low_prec_timing": str,
            },
        )
        return df

    def custom_geol_attrs(self) -> pd.DataFrame:
        """15 geological features with comma separator."""
        df = pd.read_csv(
            self.geol_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype=np.float32,
        )
        df.index = df.index.astype(int).astype(str)
        return df

    def custom_glacier_attrs(self) -> pd.DataFrame:
        """Returns a dataframe with glacier attributes using comma separator."""
        df = pd.read_csv(
            self.glacier_attr_path,
            sep=",",  # Changed from ';' to ','
            skiprows=1,
            index_col="gauge_id",
            dtype=np.float32,
        )
        df.index = df.index.astype(int).astype(str)
        return df

    def custom_human_inf_attrs(self) -> pd.DataFrame:
        """14 anthropogenic factors with comma separator."""
        df = pd.read_csv(
            self.hum_inf_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype={
                "gauge_id": str,
                "n_inhabitants": int,
                "dens_inhabitants": float,
                "hp_count": int,
                "hp_qturb": float,
                "hp_inst_turb": float,
                "hp_max_power": float,
                "num_reservoir": int,
                "reservoir_cap": float,
                "reservoir_he": float,
                "reservoir_fs": float,
                "reservoir_irr": float,
                "reservoir_nousedata": float,
            },
        )
        return df

    def custom_hydrogeol_attrs(self) -> pd.DataFrame:
        """10 hydrogeological factors with comma separator."""
        df = pd.read_csv(
            self.hydrogeol_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype=float,
        )
        df.index = df.index.astype(int).astype(str)
        return df

    def custom_hydrol_attrs(self) -> pd.DataFrame:
        """14 hydrological parameters + 2 useful infos with comma separator."""
        df = pd.read_csv(
            self.hydrol_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype={
                "gauge_id": str,
                "sign_number_of_years": int,
                "q_mean": float,
                "runoff_ratio": float,
                "stream_elas": float,
                "slope_fdc": float,
                "baseflow_index_landson": float,
                "hfd_mean": float,
                "Q5": float,
                "Q95": float,
                "high_q_freq": float,
                "high_q_dur": float,
                "low_q_freq": float,
            },
        )
        return df

    def custom_landcolover_attrs(self) -> pd.DataFrame:
        """13 landcover parameters with comma separator."""
        return pd.read_csv(
            self.lc_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            dtype={
                "gauge_id": str,
                "crop_perc": float,
                "grass_perc": float,
                "scrub_perc": float,
                "dwood_perc": float,
                "mixed_wood_perc": float,
                "ewood_perc": float,
                "wetlands_perc": float,
                "inwater_perc": float,
                "ice_perc": float,
                "loose_rock_perc": float,
                "rock_perc": float,
                "urban_perc": float,
                "dom_land_cover": str,
            },
        )

    def custom_soil_attrs(self) -> pd.DataFrame:
        """80 soil parameters with comma separator."""
        df = pd.read_csv(
            self.soil_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
        )
        df.index = df.index.astype(int).astype(str)
        return df

    def custom_topo_attrs(self) -> pd.DataFrame:
        """Topographic parameters with comma separator."""
        df = pd.read_csv(
            self.topo_attr_path,
            skiprows=1,
            sep=",",  # Changed from ';' to ','
            index_col="gauge_id",
            encoding="unicode_escape",
        )
        df.index = df.index.astype(int).astype(str)
        return df

    def custom_static_data(self) -> pd.DataFrame:
        """Concatenate all static attributes without supp_geol_attrs."""
        df = pd.concat(
            [
                self.climate_attrs(),
                self.geol_attrs(),
                # self.supp_geol_attrs(),  # Removed as requested
                self.glacier_attrs(),
                self.human_inf_attrs(),
                self.hydrogeol_attrs(),
                self.hydrol_attrs(),
                self.landcolover_attrs(),
                self.soil_attrs(),
                self.topo_attrs(),
            ],
            axis=1,
        )
        df.index = df.index.astype(str)
        df.rename(columns=self.static_map, inplace=True)
        return df

    def custom_read_stn_dyn(self, station: str) -> pd.DataFrame:
        """Reads daily dynamic data for one catchment with comma separator."""
        df = pd.read_csv(
            os.path.join(self.dynamic_path, f"CAMELS_CH_obs_based_{station}.csv"),
            sep=",",  # Changed from ';' to ','
            index_col="date",
            parse_dates=True,
            dtype=np.float32,
        )
        df.rename(columns=self.dyn_map, inplace=True)
        return df

    def custom_stations(self) -> list:
        """Returns station ids for catchments with comma separator."""
        stns = pd.read_csv(
            self.glacier_attr_path, sep=",", skiprows=1  # Changed from ';' to ','
        )["gauge_id"].values.tolist()
        return [str(stn) for stn in stns]

    def custom_dynamic_path(self):
        """Return the correct path for dynamic data (timeseries not time_series)."""
        return os.path.join(self.camels_path, "timeseries", "observation_based")

    def do_nothing(self, *args, **kwargs):
        """Placeholder method to disable certain operations."""
        pass

    # Create class attributes dictionary to override CAMELS_CH methods
    class_attrs = {
        "url": new_url,
        "dynamic_path": property(custom_dynamic_path),
        "climate_attrs": custom_climate_attrs,
        "geol_attrs": custom_geol_attrs,
        "glacier_attrs": custom_glacier_attrs,
        "human_inf_attrs": custom_human_inf_attrs,
        "hydrogeol_attrs": custom_hydrogeol_attrs,
        "hydrol_attrs": custom_hydrol_attrs,
        "landcolover_attrs": custom_landcolover_attrs,
        "soil_attrs": custom_soil_attrs,
        "topo_attrs": custom_topo_attrs,
        "_static_data": custom_static_data,
        "_read_stn_dyn": custom_read_stn_dyn,
        "stations": custom_stations,
        "_maybe_to_netcdf": do_nothing,
    }

    # Create custom CAMELS_CH class with overridden methods
    CustomCamelsCh = type("CAMELS_CH", (CAMELS_CH,), class_attrs)

    try:
        self.aqua_fetch = CustomCamelsCh(uri)
    except Exception as e:
        print(e)
        check_zip_extract = False
        # The zip files that should be downloaded for CAMELS-CH
        zip_files = ["camels_ch.zip", "Caravan_extension_CH.zip"]
        for filename in tqdm(zip_files, desc="Checking zip files"):
            # The extracted directory name (without .zip extension)
            extracted_dir = self.data_source_dir.joinpath(
                "CAMELS_CH", filename[:-4]
            )
            if not extracted_dir.exists():
                check_zip_extract = True
                break
        if check_zip_extract:
            from hydroutils import hydro_file

            hydro_file.zip_extract(self.data_source_dir.joinpath("CAMELS_CH"))
        self.aqua_fetch = CustomCamelsCh(uri)

read_object_ids()

Source code in hydrodataset/camels_ch.py

def read_object_ids(self) -> np.ndarray:
    uri = str(self.data_source_dir).rstrip("/")
    rel = "CAMELS_CH/camels_ch/camels_ch/static_attributes/CAMELS_CH_glacier_attributes.csv"
    if self._is_cloud():
        fs = self._make_s3fs()
        with fs.open(f"{uri}/{rel}".removeprefix("s3://")) as fh:
            df = pd.read_csv(fh, comment="#")
    else:
        df = pd.read_csv(os.path.join(uri, *rel.split("/")), comment="#")
    return np.array(sorted(df["gauge_id"].astype(str).tolist()))