  When fitting statistical models to variables in geoscientific disciplines
such as hydrology, it is a customary practice to regionalize - to divide a
large spatial domain into multiple regions and study each region separately -
instead of fitting a single model on the entire data (also known as
unification). Traditional wisdom in these fields suggests that models built for
each region separately will have higher performance because of homogeneity
within each region. However, by partitioning the training data, each model has
access to fewer data points and cannot learn from commonalities between
regions. Here, through two hydrologic examples (soil moisture and streamflow),
we argue that unification can often significantly outperform regionalization in
the era of big data and deep learning (DL). Common DL architectures, even
without bespoke customization, can automatically build models that benefit from
regional commonality while accurately learning region-specific differences. We
highlight an effect we call data synergy, where the results of the DL models
improved when data were pooled together from characteristically different
regions. In fact, the performance of the DL models benefited from more diverse
rather than more homogeneous training data. We hypothesize that DL models
automatically adjust their internal representations to identify commonalities
while also providing sufficient discriminatory information to the model. The
results here advocate for pooling together larger datasets, and suggest the
academic community should place greater emphasis on data sharing and
compilation.
