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# Land Indicators
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Land surface climate indicators focusing on snow, streamflow, and terrestrial processes for hydrological and agricultural applications. These indicators are essential for water resource management, flood forecasting, and agricultural planning.
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## Capabilities
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### Snow Indicators
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Snow depth and snow water equivalent analysis for hydrological modeling and water resource assessment.
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```python { .api }
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def snow_depth(snd, freq="YS"):
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    """
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    Mean snow depth over specified period.
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - freq: str, resampling frequency (default "YS" for yearly)
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    Returns:
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    xr.DataArray: Mean snow depth
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    """
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def snd_max(snd, freq="YS"):
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    """
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    Maximum snow depth over specified period.
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Maximum snow depth
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    """
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def snd_season_length(snd, thresh="2 cm", freq="YS"):
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    """
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    Length of snow season based on snow depth threshold.
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - thresh: str or float, snow depth threshold (default "2 cm")
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    - freq: str, resampling frequency
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    Returns:  
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    xr.DataArray: Snow season length in days
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    """
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def continuous_snow_season_start(snd, thresh="2 cm", window=5, freq="YS"):
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    """
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    Start date of continuous snow season.
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - thresh: str or float, snow depth threshold (default "2 cm")
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    - window: int, minimum number of consecutive days (default 5)
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Snow season start day of year
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    """
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def continuous_snow_season_end(snd, thresh="2 cm", window=5, freq="YS"):
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    """
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    End date of continuous snow season.
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - thresh: str or float, snow depth threshold (default "2 cm") 
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    - window: int, minimum number of consecutive days (default 5)
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Snow season end day of year
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    """
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def snw_max(snw, freq="YS"):
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    """
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    Maximum snow water equivalent.
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    Parameters:
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    - snw: xr.DataArray, daily snow water equivalent data
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Maximum snow water equivalent
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    """
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def melt_and_precip_max(snw, pr, window=3, freq="YS"):
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    """
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    Maximum snow melt and precipitation sum over window.
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    Parameters:
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    - snw: xr.DataArray, daily snow water equivalent data
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    - pr: xr.DataArray, daily precipitation data
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    - window: int, window size in days (default 3)
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Maximum combined melt and precipitation
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    """
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```
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### Streamflow Indicators
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Streamflow characteristics and hydrological indices for river flow analysis and flood risk assessment.
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```python { .api }
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def base_flow_index(q, freq="YS"):
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    """
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    Base flow index (ratio of base flow to total flow).
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    Parameters:
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    - q: xr.DataArray, daily streamflow data
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Base flow index (0-1)
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    """
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def rb_flashiness_index(q, freq="YS"):
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    """
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    Richards-Baker flashiness index for flow variability.
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    Parameters:
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    - q: xr.DataArray, daily streamflow data
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Flashiness index
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    """
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def snd_storm_days(snd, thresh="10 cm", freq="YS"):
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    """
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    Number of snow storm days (large daily snow depth increases).
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    Parameters:
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    - snd: xr.DataArray, daily snow depth data
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    - thresh: str or float, storm threshold (default "10 cm")
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Number of snow storm days
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    """
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def high_flow_frequency(q, thresh="90%", freq="YS"):
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    """
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    Frequency of high flow events above threshold.
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    Parameters:
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    - q: xr.DataArray, daily streamflow data
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    - thresh: str or float, flow threshold (default "90%" for 90th percentile)
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Number of high flow events
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    """
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def low_flow_frequency(q, thresh="10%", freq="YS"):
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    """
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    Frequency of low flow events below threshold.
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    Parameters:
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    - q: xr.DataArray, daily streamflow data
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    - thresh: str or float, flow threshold (default "10%" for 10th percentile)
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    - freq: str, resampling frequency
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    Returns:
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    xr.DataArray: Number of low flow events
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    """
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```
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## Usage Examples
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### Snow Analysis
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```python
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import xarray as xr
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import xclim.land as xcl
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# Load snow depth data
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snd = xr.open_dataset("snow_data.nc").snd
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# Calculate snow indicators
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max_snow = xcl.snd_max(snd, freq="YS")
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season_length = xcl.snd_season_length(snd, thresh="5 cm", freq="YS")
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season_start = xcl.continuous_snow_season_start(
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    snd, thresh="5 cm", window=10, freq="YS"
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)
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```
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### Streamflow Analysis
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```python
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# Load streamflow data
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q = xr.open_dataset("streamflow.nc").discharge
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# Calculate streamflow indicators
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base_flow = xcl.base_flow_index(q, freq="YS")
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flashiness = xcl.rb_flashiness_index(q, freq="YS")
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high_flows = xcl.high_flow_frequency(q, thresh="95%", freq="YS")
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```