  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 examined which types of dammed
basins could be well represented by long short-term memory (LSTM) models using
readily-available information, and delineated the remaining challenges. We
analyzed data from 3557 basins (83% dammed) over the contiguous United States
and noted strong impacts of reservoir purposes, degree of regulation (dor), and
diversion on streamflow modeling. While a model trained on a widely-used
reference-basin dataset performed poorly for non-reference basins, the model
trained on the whole dataset presented a median Nash-Sutcliffe efficiency
coefficient (NSE) of 0.74. The zero-dor, small-dor (with storage of
approximately a month of average streamflow or less), and large-dor basins were
found to have distinct behaviors, so migrating models between categories
yielded catastrophic results, which means we must not treat small-dor basins as
reference ones. However, training with pooled data from different sets yielded
optimal median NSEs of 0.72, 0.79, and 0.64 for these respective groups,
noticeably stronger than existing models. These results support a coherent
modeling strategy where smaller dams (storing about a month of average
streamflow or less) are modeled implicitly as part of basin rainfall-runoff
processes; then, large-dor reservoirs of certain types can be represented
explicitly. However, dammed basins must be present in the training dataset.
Future work should examine separate modeling of large reservoirs for fire
protection and irrigation, hydroelectric power generation, and flood control.
