Hourly Differentiable Modeling Arrives in the NGIAB-NRDS NextGen Ecosystem
In October 2025, Penn State's Multi-scale Hydrology, Processes and Intelligence group (MHPI), led by Dr. Chaopeng Shen, and the Alabama Water Institute (AWI), led by Steve Burian and Arpita Patel, achieved a milestone R2O effort: the preliminary integration of δHBV 2.0 [4] -- a daily-scale, high-resolution, distributed differentiable model -- into a NextGen ecosystem. This resulted in the first adoption of a differentiable model into NextGen In A Box (NGIAB) [2] and provided an opportunity for CIROH researchers to fine-tune the δHBV 2.0 architecture for NextGen operation.
Having proven viability for daily timescale predictions on high-resolution river networks [4], MHPI researchers recently adapted δHBV 2.0 into a multi-timescale architecture designed to parameterize HBV and simulate streamflow at hourly intervals, at scale, across the NextGen HydroFabric. This new model, δHBV 2.0 MTS (Multi-TimeScale) [5], is a fusion of a daily and hourly δHBV 2.0 model designed to efficiently handle ML training with high geospatial and temporal complexity. (See MTS Architecture for more details about this construction.)
With δHBV 2.0 MTS maintaining similar forecasting skill compared to its daily-scale counterpart [5], Penn State and AWI were once again reunited in a joint effort to embed hourly scale differentiable modeling within AWI's operational ecosystem as a demonstration of model viability and to facilitate open access to its runtime.
Differentiable Models
δHBV 2.0 and δHBV 2.0 MTS differentiable model constructions are briefly outlined here to contextualize the development efforts. For further detail, see each model's respective citation. At their core, differentiable models embed traditional process-based equations (here, the HBV rainfall-runoff model) inside a machine learning training loop. Because these models are designed to be differentiable (e.g., in PyTorch), gradients flow end-to-end from the loss function back through the physical equations and into the neural networks that supply their parameters. This lets the model learn optimal parameterizations directly from observed data while still obeying mass-balance and storage constraints encoded in HBV -- combining interpretability and physical consistency of process-based hydrology with the flexibility of deep learning.
