Hydrology is shifting from process-based to deep learning models. Entity-aware (EA) deep learning models with static features (predominantly physiographic proxies) merged to dynamic forcing features show significant performance improvements. However, recent studies challenge the notion that combining dynamic forcings with static attributes make such models entity aware, suggesting static features are not effectively leveraged for generalization. We examine entity awareness using state-of-the-art Long-Sort Term Memory (LSTM) networks with the CAMELS-US dataset. We compare EA models provided with physiographic static features with ablated variants not provided with static inputs. Findings indicate that the superior performance of EA models is largely due to information provided by meteorological data, with minimal contributions by physiographic static features, particularly when tested out-of-sample. These results challenge previously held assumptions regarding how physiographic proxies are used to achieve generalization ability in EA Models, highlighting the need for new approaches for robust generalization in deep learning models.

Sap flow observations provide a basis for estimating transpiration and understanding forest water use dynamics and plant-climate interactions. This study developed a continental modeling approach using Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe based on the SAPFLUXNET database. We developed models with varying levels of training sets to evaluate performance in unseen conditions. The average Kling-Gupta Efficiency was 0.77 for gauged models trained on 50 % of time series across all forest stands and was 0.52 for ungauged models trained on 50 % of the forest stands. Continental models matched or exceeded performance of specialized and baseline models for all genera and forest stands, demonstrating the potential of LSTMs to generalize hourly sap flow across tree, climate, and forest types. This work highlights hence the potential of deep learning models to generalize sap flow for enhancing tree to continental ecohydrological investigations.

We numerically study how the interactions between surface, subsurface, and rainfall create complex runoff behavior in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to understand the effect of surface--subsurface interactions on the hydrological response, we compare model variations with different soil permeability. We compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the domain. Our results show that changing surface--subsurface interactions trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Increased surface--subsurface interaction leads to a higher degree of connectivity in the catchment. Temporal dynamics of both distributed and aggregated hydrological response did not change significantly, which may be because the structural connectivity of the catchment remained untouched. Our results indicate that in higher order catchments, the effect of changes in distributed hydrological response may not always be observable through aggregated hydrological signatures.