Add new skills in stats (#666)

docs/api.rst

@@ -829,6 +829,7 @@ Statistics and performance metrics
 
    stats.skills.bias
    stats.skills.percentual_bias
+   stats.skills.volumetric_error
    stats.skills.nashsutcliffe
    stats.skills.lognashsutcliffe
    stats.skills.pearson_correlation
@@ -840,6 +841,7 @@ Statistics and performance metrics
    stats.skills.rsquared
    stats.skills.mse
    stats.skills.rmse
+   stats.skills.rsr
 
 Extreme Value Analysis
 =======================

docs/changelog.rst

@@ -14,6 +14,7 @@ Unreleased
 Added
 -----
 - Export CLI now also accepts time tuples (#660)
+- New stats.skills VE and RSR (#666)
 
 Changed
 -------
@@ -23,6 +24,7 @@ Fixed
 -----
 - Bug in `raster.set_crs` if input_crs is of type CRS. (#659)
 - Export CLI now actually parses provided geoms. (#660)
+- Bug in stats.skills for computation of pbias and MSE / RMSE. (#666)
 
 Deprecated
 ----------

hydromt/stats/skills.py

@@ -68,6 +68,35 @@ def percentual_bias(sim, obs, dim="time"):
     return pbias
 
 
+def volumetric_error(sim, obs, dim="time"):
+    r"""Return the volumetric error between two time series.
+
+        .. math::
+         VE= 1 - \\frac{\\sum_{i=1}^{N}(|sim_{i}-obs_{i}|)}{\\sum_{i=1}^{N}(obs_{i})}
+
+    Parameters
+    ----------
+    sim : xarray DataArray
+        simulations time series
+    obs : xarray DataArray
+        observations time series
+    dim : str, optional
+        name of time dimension in sim and obs (the default is 'time')
+
+    Returns
+    -------
+    xarray DataArray
+        volumetric error
+    """
+    # wrap numpy function
+    kwargs = dict(
+        input_core_dims=[[dim], [dim]], dask="parallelized", output_dtypes=[float]
+    )
+    ve = xr.apply_ufunc(_ve, sim, obs, **kwargs)
+    ve.name = "ve"
+    return ve
+
+
 def nashsutcliffe(sim, obs, dim="time"):
     r"""Return the Nash-Sutcliffe model efficiency.
 
@@ -351,6 +380,33 @@ def rmse(sim, obs, dim="time"):
     return rmse
 
 
+def rsr(sim, obs, dim="time"):
+    r"""Return the RMSE-observations standard deviation (RSR) between two time series.
+
+        .. math::
+         RSR=\\frac{RMSE}{STDEVobs}
+
+    Parameters
+    ----------
+    sim : xarray DataArray
+        simulations time series
+    obs : xarray DataArray
+        observations time series
+
+    Returns
+    -------
+    xarray DataArray
+        the root squared error
+    """
+    # wrap numpy function
+    kwargs = dict(
+        input_core_dims=[[dim], [dim]], dask="parallelized", output_dtypes=[float]
+    )
+    rsr = xr.apply_ufunc(_rsr, sim, obs, **kwargs)
+    rsr.name = "rsr"
+    return rsr
+
+
 def kge(sim, obs, dim="time"):
     r"""Return the Kling-Gupta Efficiency (KGE) of two time series.
 
@@ -458,15 +514,33 @@ def _bias(sim, obs, axis=-1):
 
 def _pbias(sim, obs, axis=-1):
     """Percentual bias."""
-    return np.nansum(sim - obs, axis=axis) / np.nansum(obs, axis=axis)
+    return 100 * np.nansum(sim - obs, axis=axis) / np.nansum(obs, axis=axis)
+
+
+def _ve(sim, obs, axis=-1):
+    """Volumetric Error."""
+    ve = 1 - np.nansum(np.absolute(sim - obs), axis=axis) / np.nansum(obs, axis=axis)
+    return ve
 
 
 def _mse(sim, obs, axis=-1):
     """Mean squared error."""
-    mse = np.nansum((obs - sim) ** 2, axis=axis)
+    mse = np.nansum((obs - sim) ** 2, axis=axis) / np.nansum(
+        np.isfinite(obs), axis=axis
+    )
     return mse
 
 
+def _rsr(sim, obs, axis=-1):
+    """RMSE-observations standard deviation."""
+    rmse = np.sqrt(_mse(sim, obs, axis=axis))
+    stdev = np.sqrt(
+        np.nansum((obs - np.nanmean(obs, axis=axis)) ** 2, axis=axis)
+        / np.nansum(np.isfinite(obs), axis=axis)
+    )
+    return rmse / stdev
+
+
 def _nse(sim, obs, axis=-1):
     """nash-sutcliffe efficiency."""
     obs_mean = np.nanmean(obs, axis=axis)

tests/test_stats_skill.py

@@ -9,7 +9,8 @@
 def test_skills(obsda):
     simda = obsda + 5.0
     assert np.isclose(skills.bias(simda, obsda).values, 5.0)
-    assert np.isclose(skills.percentual_bias(simda, obsda).values, 0.1033)
+    assert np.isclose(skills.percentual_bias(simda, obsda).values, 10.3299)
+    assert np.isclose(skills.volumetric_error(simda, obsda).values, 0.8967)
     assert np.isclose(skills.nashsutcliffe(simda, obsda).values, 0.97015)
     assert np.isclose(skills.lognashsutcliffe(simda, obsda).values, 0.8517)
     assert np.isclose(skills.pearson_correlation(simda, obsda).values, 1.0)
@@ -21,5 +22,6 @@ def test_skills(obsda):
         skills.kge_non_parametric_flood(simda, obsda)["kge_np_flood"].values, 0.8939
     )
     assert np.isclose(skills.rsquared(simda, obsda).values, 1.0)
-    assert np.isclose(skills.mse(simda, obsda).values, 9125.0)
-    assert np.isclose(skills.rmse(simda, obsda).values, 95.5249)
+    assert np.isclose(skills.mse(simda, obsda).values, 25.0)
+    assert np.isclose(skills.rmse(simda, obsda).values, 5.0)
+    assert np.isclose(skills.rsr(simda, obsda).values, 0.1727659)