Figure 3. Co-occurrence probability between AMAX streamflow and
(a) AMAX precipitation, (b) AMAX wet-month precipitation, (c) AMAX
effective precipitation across all CAMELS catchments; and (d) when
grouped into six categories using the fraction of precipitation falling
as snow (fsnow ). Note that Peffwere available for only 638 catchments.
The vast majority of catchments (more than 95%) have a co-occurrence
probability that is much higher than random chance (i.e., 2%), which is
generally expected. Catchments with a relatively high co-occurrence
probability are mostly located in coastal regions (e.g., South
Atlantic-Gulf Region, Texas Gulf Region, California Region, and Pacific
Northwest Region) while co-occurrence probability tends to be low
whenever the fraction of precipitation falling as snow
(fsnow ) is high (e.g., Upper Colorado Region and
Great Basin Region). More importantly, only a small fraction (14%) of
all catchments having a co-occurrence probability between
QDOYMAX and PDOYMAX (Figure 3a; see
Figure S6 for regional results) of at least 0.5, indicating a weak
causal linkage.
The co-occurrence probability between QDOYMAX and
Psm.DOYMAX (Figure 3b; see Figure S7 for regional
results) is higher than or equal to 0.5 over 7% of all catchments,
indicating a weaker causal linkage relative to that between
QDOYMAX and PDOYMAX. A possible reason
is that soil moisture has a relatively strong seasonal cycle
[Eltahir , 1998; Findell and Eltahir , 1997],
contrasting to a weak seasonality of short-duration precipitation
extremes [Do et al. , 2020a]. Using Psm has
potentially masked out many short-duration flood-induced rainfall events
that spread throughout the years, leading to a lower co-occurrence
probability. This finding suggests that precipitation extremes
constrained over wet months has a potentially lower predictability for
changes in annual floods.
Among the three precipitation extreme metrics, effective precipitation
(Figure 3c; see Figure S8 for regional results) is the variable with the
strongest causal relationship to floods. Specifically, 26% of all
catchments have a co-occurrence probability between
Peff.DOYMAX and QDOYMAX of at least 0.5.
A simple approach to take into account snow-soil interaction has led to
a substantial increase in co-occurrence probability, suggesting that
catchment processes potentially play a more important role in modulating
floods relative to precipitation intensity.
When catchments are divided into different categories usingfsnow (Figure 3d), there is a notable decrease of
co-occurrence probability when fsnow increases.
We note that the co-occurrence probability between precipitation
extremes and floods is not consistently low across all catchments with a
high fsnow . For instance, of all 73 catchments
with an fsnow of higher than or equal to 0.5, six
catchments (located in the Pacific Northwest Region) have a
co-occurrence probability between PDOYMAX and
QMAX of at least 0.5. As a result, catchments with a
high snow-to-rain ratio are likely to have floods not driven by
precipitation extremes, but there are exceptions such as catchments
strongly influenced by atmospheric rivers which are responsible for
flood-induced rainfall events.
3.3 To what extent are changes in precipitation extremes useful to
explain changes in floods?
The co-variation between precipitation extremes and QMAXis relatively low, with 81%, 85%, and 66% of all catchments having an
R2 of less than 0.5 for PMAX,
Psm.MAX, and Peff.MAX respectively. When
catchments are grouped into different categories according to
co-occurrence probability, a strong positive correlation between
co-variation and co-occurrence probability is observed (Figure 4). Of
all catchments with co-occurrence probability of less than 0.5, the
averaged R2 is 0.28 (for PMAX), 0.24
(for Psm.MAX) and 0.33 (for Peff.MAX)
respectively, indicating that only about 30% of the temporal
variability of floods can be explained by precipitation extremes.
Focusing on the catchments with the strongest causal relationship (i.e.,
a co-occurrence probability of at least 0.6), a low-to-moderate
correlation is observed, with the median of R2 between
QMAX and precipitation extremes is 0.41, 0.43 and 0.52
for PMAX, Psm.MAX and
Peff.MAX respectively. The causal relationship between
Peff.MAX and QMAX is the strongest, with
34 out of 63 catchments (54%) have an R2 of above
0.5. The relationship between PMAX and
QMAX is the lowest, with 10 out of 28 catchments (36%)
have an R2 value of above 0.5, further confirming the
need for considering catchment processes (e.g., snow-soil interaction)
to explain changes in annual floods.