  A large fraction of major waterways have dams influencing streamflow, which
must be accounted for in large-scale hydrologic modeling. However, daily
streamflow prediction for basins with dams is challenging for various modeling
approaches, especially at large scales. Here we took a divide-and-conquer
approach to examine which types of basins could be well represented by a long
short-term memory (LSTM) deep learning model using only readily-available
information. We analyzed data from 3557 basins (83% dammed) over the contiguous
United States and noted strong impacts of reservoir purposes,
capacity-to-runoff ratio (dor), and diversion on streamflow on streamflow
modeling. Surprisingly, while the LSTM model trained on a widely-used
reference-basin dataset performed poorly for more non-reference basins, the
model trained on the whole dataset presented a median test Nash-Sutcliffe
efficiency coefficient (NSE) of 0.74, reaching benchmark-level performance. The
zero-dor, small-dor, and large-dor basins were found to have distinct
behaviors, so migrating models between categories yielded catastrophic results.
However, training with pooled data from different sets yielded optimal median
NSEs of 0.73, 0.78, and 0.71 for these groups, respectively, showing noticeable
advantages over existing models. These results support a coherent, mixed
modeling strategy where smaller dams are modeled as part of rainfall-runoff
processes, but dammed basins must not be treated as reference ones and must be
included in the training set; then, large-dor reservoirs can be represented
explicitly and future work should examine modeling reservoirs for fire
protection and irrigation, followed by those for hydroelectric power
generation, and flood control, etc.
