added

aleaf · aleaf · commit 34be1c327e1a · 2015-12-30T11:05:23.000-06:00
diff --git a/baseflow.py b/baseflow.py
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+import numpy as np
+import pandas as pd
+
+def IHmethod(values, block_length=5, tp=0.9, interp_semilog=True):
+    """Baseflow separation using the Institute of Hydrology method, as documented in
+    Institute of Hydrology, 1980b, Low flow studies report no. 3--Research report: 
+    Wallingford, Oxon, United Kingdom, Institute of Hydrology Report no. 3, p. 12-19,
+    and
+    Wahl, K.L and Wahl, T.L., 1988. Effects of regional ground-water level declines
+    on streamflow in the Oklahoma Panhandle. In Proceedings of the Symposium on 
+    Water-Use Data for Water Resources Management, American Water Resources Association. 
+    
+    Parameters
+    ----------
+    values : pandas Series
+        Pandas time series (with datetime index) containing measured streamflow values.
+    block_length : int
+        N parameter in IH method. Streamflow is partitioned into N-day intervals;
+        a minimum flow is recorded for each interval.
+    tp : float
+        f parameter in IH method. For each three N-day minima, if f * the central value
+        is less than the adjacent two values, the central value is considered a 
+        turning point. Baseflow is interpolated between the turning points.
+    interp_semilog : boolean
+        If False, linear interpolation is used to compute baseflow between turning points
+        (as documented in the IH method). If True, the base-10 logs of the turning points
+        are interpolated, and the interpolated values are transformed back to 
+        linear space (producing a curved hydrograph). Semi-logarithmic interpolation
+        as documented in Wahl and Wahl (1988), is used in the Base-Flow Index (BFI)
+        fortran program.
+    
+    Returns
+    -------
+    Q : pandas DataFrame
+        DataFrame containing the following columns:
+        minima : N-day minima
+        ordinate : selected turning points
+        n : block number for each N-day minima
+        QB : computed baseflow
+        Q : discharge values
+    
+    Notes
+    -----
+    Whereas this program only selects turning points following the methodology above, 
+    the BFI fortran program adds artificial turning points at the start and end of
+    each calendar year. Therefore results for datasets consisting of multiple years
+    will differ from those produced by the BFI program.
+    
+    """
+    if values.dtype.name == 'object':
+        values = values.convert_objects(convert_numeric=True)
+    values = pd.DataFrame(values).resample('D')
+    values.columns = ['discharge']
+
+    # compute block numbers for grouping values on blocks
+    #nblocks = int(len(values) / float(block_length) + 1)
+    nblocks = int(np.floor(len(values) / float(block_length)))
+    n = []
+    for i in range(nblocks):
+        n += [i+1] * block_length
+    #values['n'] = n[:len(values)]
+    n += [np.nan] * (len(values) - len(n))
+    values['n'] = n
+    
+    # compute minima for block_length day blocks
+    Q = [np.min(values.discharge.values[i-block_length:i]) 
+         for i in np.arange(block_length, len(values))[::block_length]]
+
+    #  compute the minimum for each block
+    Q = values.groupby('n').min()
+    Q['datetime'] = values[['discharge', 'n']].groupby('n').idxmin() # include dates of minimum values
+
+    Q['ordinate'] = [np.nan] * len(Q)
+    #for i in range(len(Q))[:-2]:
+    #    end1, cv, end2 = Q.discharge[i:i+3]
+    for i in np.arange(1, len(Q)-1):
+        end1, cv, end2 = Q.discharge.values[i-1:i+2]
+        if tp * cv < end1 and tp * cv < end2:
+            Q.loc[Q.index[i], 'ordinate'] = cv
+    Q['n'] = Q.index
+    Q.index = Q.datetime
+
+    Q = Q.dropna(subset=['datetime'], axis=0).resample('D')
+    if interp_semilog:
+        QB = 10**(np.log10(Q.ordinate).interpolate()).values
+    else:
+        QB = Q.ordinate.interpolate(limit=100).values
+    Q['QB'] = QB
+    Q['Q'] = values.discharge[Q.index]
+    QBgreaterthanQ = Q.QB.values > Q.Q.values
+    Q.loc[QBgreaterthanQ, 'QB'] = Q.ix[QBgreaterthanQ, 'Q']
+    return Q
+
+def WI_statewide_eqn(Qm, A, Qr, Q90):
+    """Regression equation of Gebert and others (2007, 2011)
+    for estimating average annual baseflow from a field measurement of streamflow
+    during low-flow conditions.
+
+    Parameters
+    ----------
+    Qm : float or 1-D array of floats
+        Measured streamflow.
+    A : float or 1-D array of floats
+        Drainage area in watershed upstream of where Qm was taken.
+    Qr : float or 1-D array of floats
+        Recorded flow at index station when Qm was taken.
+    Q90 : float or 1-D array of floats
+        Q90 flow at index station.
+
+    Returns
+    -------
+    Qb : float or 1-D array of floats, of length equal to input arrays
+        Estimated average annual baseflow at point where Qm was taken.
+    Bf : float or 1-D array of floats, of length equal to input arrays
+        Baseflow factor. see Gebert and others (2007, 2011).
+
+    Notes
+    -----
+    Gebert, W.A., Radloff, M.J., Considine, E.J., and Kennedy, J.L., 2007,
+    Use of streamflow data to estimate base flow/ground-water recharge for Wisconsin:
+    Journal of the American Water Resources Association,
+    v. 43, no. 1, p. 220-236, http://dx.doi.org/10.1111/j.1752-1688.2007.00018.x
+
+    Gebert, W.A., Walker, J.F., and Kennedy, J.L., 2011,
+    Estimating 1970-99 average annual groundwater recharge in Wisconsin using streamflow data:
+    U.S. Geological Survey Open-File Report 2009-1210, 14 p., plus appendixes,
+    available at http://pubs.usgs.gov/ofr/2009/1210/.
+    """
+    Bf = (Qm / A) * (Q90 / Qr)
+    Qb = 0.907 * A**1.02 * Bf**0.52
+    return Qb.copy(), Bf.copy()
