Standardized Streamflow Index and Standardized Groundwater level Index

docs/references.bib

@@ -2153,7 +2153,7 @@ @article{droogers2002
 	type = {Article}
 }
 
-@Article{robin_2019,
+@article{robin_2019,
 	author = {Robin, Y. and Vrac, M. and Naveau, P. and Yiou, P.},
 	title = {Multivariate stochastic bias corrections with optimal transport},
 	journal = {Hydrology and Earth System Sciences},
@@ -2203,3 +2203,29 @@ @article{knol_1989
 	publisher = "Springer",
 	number = "1",
 }
+
+@article{vicente-serrano_2012,
+	author = {Sergio M. Vicente-Serrano  and Juan I. López-Moreno  and Santiago Beguería  and Jorge Lorenzo-Lacruz  and Cesar Azorin-Molina  and Enrique Morán-Tejeda},
+	title = {Accurate Computation of a Streamflow Drought Index},
+	journal = {Journal of Hydrologic Engineering},
+	volume = {17},
+	number = {2},
+	pages = {318-332},
+	year = {2012},
+	doi = {10.1061/(ASCE)HE.1943-5584.0000433},
+	URL = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000433},
+	eprint = {https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29HE.1943-5584.0000433},
+    abstract = "In this study, the authors investigated an approach to calculate the standardized streamflow index (SSI), which allows accurate spatial and temporal comparison of the hydrological conditions of a stream or set of streams. For this purpose, the capability of six three-parameter distributions (lognormal, Pearson Type III, log-logistic, general extreme value, generalized Pareto, and Weibull) and two different approaches to select the most suitable distribution the best monthly fit (BMF) and the minimum orthogonal distance (MD), were tested by using a monthly streamflow data set for the Ebro Basin (Spain). This large Mediterranean basin is characterized by high variability in the magnitude of streamflows and in seasonal regimes. The results show that the most commonly used probability distributions for flow frequency analysis provided good fits to the streamflow series. Thus, the visual inspection of the L-moment diagrams and the results of the Kolmogorov-Smirnov test did not enable the selection of a single optimum probability distribution. However, no single probability distribution for all the series was suitable for obtaining a robust standardized streamflow series because each of the distributions had one or more limitations. The BMF and MD approaches improved the results in the expected average, standard deviation, and the frequencies of extreme events of the SSI series in relation to the selection of a unique distribution for each station. The BMF and MD approaches involved using different probability distributions for each gauging station and month of the year to calculate the SSI. Both approaches are easy to apply and they provide very similar results in the quality of the obtained hydrological drought indexes. The proposed procedures are very flexible for analyses involving contrasting hydrological regimes and flow characteristics."
+}
+
+@article{bloomfield_2013,
+	AUTHOR = {Bloomfield, J. P. and Marchant, B. P.},
+	TITLE = {Analysis of groundwater drought building on the standardised precipitation index approach},
+	JOURNAL = {Hydrology and Earth System Sciences},
+	VOLUME = {17},
+	YEAR = {2013},
+	NUMBER = {12},
+	PAGES = {4769--4787},
+	URL = {https://hess.copernicus.org/articles/17/4769/2013/},
+	DOI = {10.5194/hess-17-4769-2013}
+}

tests/test_hydrology.py

@@ -1,8 +1,13 @@
 from __future__ import annotations
 
+from pathlib import Path
+
 import numpy as np
+import pytest
 
 from xclim import indices as xci
+from xclim import land
+from xclim.core.units import convert_units_to
 
 
 class TestBaseFlowIndex:
@@ -23,6 +28,37 @@ def test_simple(self, q_series):
         np.testing.assert_array_equal(out, 2)
 
 
+class TestStandardizedStreamflow:
+    @pytest.mark.slow
+    def test_3d_data_with_nans(self, open_dataset):
+        nc_ds = Path("Raven", "q_sim.nc")
+        # test with data
+        ds = open_dataset(nc_ds)
+        q = ds.q_obs.sel(time=slice("2008")).rename("q")
+        qMM = convert_units_to(q, "mm**3/s", context="hydro")  # noqa
+        # put a nan somewhere
+        qMM.values[10] = np.nan
+        q.values[10] = np.nan
+
+        out1 = land.standardized_streamflow_index(
+            q,
+            freq="MS",
+            window=1,
+            dist="genextreme",
+            method="APP",
+            fitkwargs={"floc": 0},
+        )
+        out2 = land.standardized_streamflow_index(
+            qMM,
+            freq="MS",
+            window=1,
+            dist="genextreme",
+            method="APP",
+            fitkwargs={"floc": 0},
+        )
+        np.testing.assert_array_almost_equal(out1, out2, 3)
+
+
 class TestSnwMax:
     def test_simple(self, snw_series):
         a = np.zeros(366)

tests/test_indices.py

@@ -752,6 +752,141 @@ def test_standardized_precipitation_evapotranspiration_index(
 
         np.testing.assert_allclose(spei.values, values, rtol=0, atol=diff_tol)
 
+    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "freq, window, dist, method,  values, diff_tol",
+        [
+            # reference results: Obtained with R package `standaRdized`
+            (
+                "D",
+                1,
+                "genextreme",
+                "ML",
+                [0.5331, 0.5338, 0.5098, 0.4656, 0.4937],
+                9e-2,
+            ),
+            (
+                "D",
+                12,
+                "genextreme",
+                "ML",
+                [0.4414, 0.4695, 0.4861, 0.4838, 0.4877],
+                9e-2,
+            ),
+            # reference results : xclim version where the test was implemented
+            (
+                "D",
+                1,
+                "genextreme",
+                "ML",
+                [0.6105, 0.6167, 0.5957, 0.5520, 0.5794],
+                2e-2,
+            ),
+            (
+                "D",
+                1,
+                "genextreme",
+                "APP",
+                [-0.0259, -0.0141, -0.0080, -0.0098, 0.0089],
+                2e-2,
+            ),
+            ("D", 1, "fisk", "ML", [0.3514, 0.3741, 0.1349, 0.4332, 0.1724], 2e-2),
+            ("D", 1, "fisk", "APP", [0.3321, 0.3477, 0.3536, 0.3468, 0.3723], 2e-2),
+            (
+                "D",
+                12,
+                "genextreme",
+                "ML",
+                [0.5131, 0.5442, 0.5645, 0.5660, 0.5720],
+                2e-2,
+            ),
+            (
+                "D",
+                12,
+                "genextreme",
+                "APP",
+                [-0.0697, -0.0550, -0.0416, -0.0308, -0.0194],
+                2e-2,
+            ),
+            ("D", 12, "fisk", "ML", [0.2096, 0.2728, 0.3259, 0.3466, 0.2836], 2e-2),
+            ("D", 12, "fisk", "APP", [0.2667, 0.2893, 0.3088, 0.3233, 0.3385], 2e-2),
+            (
+                "MS",
+                1,
+                "genextreme",
+                "ML",
+                [0.7315, -1.4919, -0.5405, 0.9965, -0.7449],
+                2e-2,
+            ),
+            (
+                "MS",
+                1,
+                "genextreme",
+                "APP",
+                [0.0979, -1.6806, -0.5345, 0.7355, -0.7583],
+                2e-2,
+            ),
+            ("MS", 1, "fisk", "ML", [0.5331, -1.5777, -0.4363, 0.2525, -0.8149], 2e-2),
+            ("MS", 1, "fisk", "APP", [0.4663, -1.9076, -0.5362, 0.8070, -0.8035], 2e-2),
+            (
+                "MS",
+                12,
+                "genextreme",
+                "ML",
+                [-0.9795, -1.0398, -1.9019, -1.6970, -1.4761],
+                2e-2,
+            ),
+            (
+                "MS",
+                12,
+                "genextreme",
+                "APP",
+                [-0.9095, -1.0996, -1.9207, -2.2665, -2.1746],
+                2e-2,
+            ),
+            (
+                "MS",
+                12,
+                "fisk",
+                "ML",
+                [-1.0776, -1.0827, -1.9333, -1.7764, -1.8391],
+                2e-2,
+            ),
+            (
+                "MS",
+                12,
+                "fisk",
+                "APP",
+                [-0.9607, -1.1265, -1.7004, -1.8747, -1.8132],
+                2e-2,
+            ),
+        ],
+    )
+    def test_standardized_streamflow_index(
+        self, open_dataset, freq, window, dist, method, values, diff_tol
+    ):
+        ds = open_dataset("Raven/q_sim.nc")
+        q = ds.q_obs.rename("q")
+        q_cal = ds.q_sim.rename("q").fillna(ds.q_sim.mean())
+        if freq == "D":
+            q = q.sel(time=slice("2008-01-01", "2008-01-30")).fillna(ds.q_obs.mean())
+        else:
+            q = q.sel(time=slice("2008-01-01", "2009-12-31")).fillna(ds.q_obs.mean())
+        fitkwargs = {"floc": 0} if method == "APP" else {}
+        params = xci.stats.standardized_index_fit_params(
+            q_cal,
+            freq=freq,
+            window=window,
+            dist=dist,
+            method=method,
+            fitkwargs=fitkwargs,
+            zero_inflated=True,
+        )
+        ssi = xci.standardized_streamflow_index(q, params=params)
+        ssi = ssi.isel(time=slice(-11, -1, 2)).values.flatten()
+
+        np.testing.assert_allclose(ssi, values, rtol=0, atol=diff_tol)
+
     @pytest.mark.parametrize(
         "indexer",
         [

xclim/data/fr.json

@@ -269,6 +269,12 @@
     "title": "Précipitation liquide accumulée totale",
     "abstract": "Précipitation liquide accumulée totale. Les précipitations sont considérées liquides lorsque la température quotidienne moyenne est au-dessus de 0°C."
   },
+  "SGI": {
+    "long_name": "Indice d'eau souterraine standardisé (SGI)",
+    "description": "Niveau de l'eau souterraine sur une fenêtre mobile de {window} {freq:nom}, normalisé telle que la moyenne du SGI est 0 pour les données de calibration. L'unité de la fenêtre 'X' est l'unité de temps déterminée par la fréquence de rééchantillonage,",
+    "title": "Indice d'eau souterraine standardisé (SGI)",
+    "abstract": "Niveau de l'eau souterraine sur une fenêtre mobile, normalisée telle que la moyenne du SGI est 0 pour les données de calibration. L'unité de la fenêtre est l'unité de temps déterminée par la fréquence de rééchantillonnage."
+  },
   "SOLIDPRCPTOT": {
     "long_name": "Précipitation totale lorsque la température moyenne est en dessous ou égale à {thresh}",
     "description": "Précipitation solide totale {freq:f}, estimée comme la précipitation lorsque la température moyenne est en dessous ou égale à {thresh}.",
@@ -287,6 +293,12 @@
     "title": "Indice de précipitation évapotranspiration standardisé (SPEI)",
     "abstract": "Budget d'eau (précipitation - évapotranspiration) sur une fenêtre mobile, normalisé tel que la moyenne du SPEI est 0 pour les données de calibration. L'unité de la fenêtre est l'unité de temps déterminée par la fréquence de rééchantillonnage."
   },
+  "SSI": {
+    "long_name": "Indice d'écoulement standardisé (SSI)",
+    "description": "Écoulement sur une fenêtre mobile de {window} {freq:nom}, normalisée telle que la moyenne du SSI est 0 pour les données de calibration. L'unité de la fenêtre 'X' est l'unité de temps déterminée par la fréquence de rééchantillonage,",
+    "title": "Indice d'écoulement standardisé (SSI)",
+    "abstract": "Écoulement sur une fenêtre mobile, normalisée telle que la moyenne du SSI est 0 pour les données de calibration. L'unité de la fenêtre est l'unité de temps déterminée par la fréquence de rééchantillonnage."
+  },
   "DC": {
     "long_name": "Indice de sécheresse",
     "description": "Code numérique estimant la teneur en humidité moyenne des couches organiques.",

xclim/indicators/land/_streamflow.py

@@ -5,13 +5,21 @@
 from xclim.core.cfchecks import check_valid
 from xclim.core.indicator import ResamplingIndicator
 from xclim.core.units import declare_units
-from xclim.indices import base_flow_index, generic, rb_flashiness_index
+from xclim.indices import (
+    base_flow_index,
+    generic,
+    rb_flashiness_index,
+    standardized_groundwater_index,
+    standardized_streamflow_index,
+)
 
 __all__ = [
     "base_flow_index",
     "doy_qmax",
     "doy_qmin",
     "rb_flashiness_index",
+    "standardized_groundwater_index",
+    "standardized_streamflow_index",
 ]
 
 
@@ -28,6 +36,13 @@ def cfcheck(q):
         check_valid(q, "standard_name", "water_volume_transport_in_river_channel")
 
 
+class StandardizedStreamflowIndexes(ResamplingIndicator):
+    """Resampling but flexible inputs indicators."""
+
+    src_freq = ["D", "MS"]
+    context = "hydro"
+
+
 base_flow_index = Streamflow(
     title="Base flow index",
     identifier="base_flow_index",
@@ -74,3 +89,35 @@ def cfcheck(q):
     compute=declare_units(da="[discharge]")(generic.select_resample_op),
     parameters={"op": generic.doymin, "out_units": None},
 )
+
+
+standardized_streamflow_index = StandardizedStreamflowIndexes(
+    title="Standardized Streamflow Index (SSI)",
+    identifier="ssi",
+    units="",
+    standard_name="ssi",
+    long_name="Standardized Streamflow Index (SSI)",
+    description="Streamflow over a moving {window}-X window, normalized such that SSI averages to 0 for "
+    "calibration data. The window unit `X` is the minimal time period defined by resampling frequency {freq}.",
+    abstract="Streamflow over a moving window, normalized such that SSI averages to 0 for the calibration data. "
+    "The window unit `X` is the minimal time period defined by the resampling frequency.",
+    cell_methods="",
+    keywords="streamflow",
+    compute=standardized_streamflow_index,
+)
+
+
+standardized_groundwater_index = StandardizedStreamflowIndexes(
+    title="Standardized Groundwater Index (SGI)",
+    identifier="sgi",
+    units="",
+    standard_name="sgi",
+    long_name="Standardized Groundwater Index (SGI)",
+    description="Groundwater over a moving {window}-X window, normalized such that SGI averages to 0 for "
+    "calibration data. The window unit `X` is the minimal time period defined by resampling frequency {freq}.",
+    abstract="Groundwater over a moving window, normalized such that SGI averages to 0 for the calibration data. "
+    "The window unit `X` is the minimal time period defined by the resampling frequency.",
+    cell_methods="",
+    keywords="groundwater",
+    compute=standardized_groundwater_index,
+)

xclim/indices/_agro.py

@@ -1171,7 +1171,7 @@ def standardized_precipitation_index(
     >>> from xclim.indices import standardized_precipitation_index
     >>> ds = xr.open_dataset(path_to_pr_file)
     >>> pr = ds.pr
-    >>> cal_start, cal_end = "1990-05-01", "1990-08-31"
+    >>> cal_start, cal_end = "1990-05-01", "1992-06-01"
     >>> spi_3 = standardized_precipitation_index(
     ...     pr,
     ...     freq="MS",