Differentiable, learnable, regionalized process-based models with
physical outputs can approach state-of-the-art hydrologic prediction accuracy
- URL: http://arxiv.org/abs/2203.14827v1
- Date: Mon, 28 Mar 2022 15:06:53 GMT
- Title: Differentiable, learnable, regionalized process-based models with
physical outputs can approach state-of-the-art hydrologic prediction accuracy
- Authors: Dapeng Feng, Jiangtao Liu, Kathryn Lawson, and Chaopeng Shen
- Abstract summary: We show that differentiable, learnable, process-based models (called delta models here) can approach the performance level of LSTM for the intensively-observed variable (streamflow) with regionalized parameterization.
We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework.
- Score: 1.181206257787103
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predictions of hydrologic variables across the entire water cycle have
significant value for water resource management as well as downstream
applications such as ecosystem and water quality modeling. Recently, purely
data-driven deep learning models like long short-term memory (LSTM) showed
seemingly-insurmountable performance in modeling rainfall-runoff and other
geoscientific variables, yet they cannot predict unobserved physical variables
and remain challenging to interpret. Here we show that differentiable,
learnable, process-based models (called {\delta} models here) can approach the
performance level of LSTM for the intensively-observed variable (streamflow)
with regionalized parameterization. We use a simple hydrologic model HBV as the
backbone and use embedded neural networks, which can only be trained in a
differentiable programming framework, to parameterize, replace, or enhance the
process-based model modules. Without using an ensemble or post-processor,
{\delta} models can obtain a median Nash Sutcliffe efficiency of 0.715 for 671
basins across the USA for a particular forcing data, compared to 0.72 from a
state-of-the-art LSTM model with the same setup. Meanwhile, the resulting
learnable process-based models can be evaluated (and later, to be trained) by
multiple sources of observations, e.g., groundwater storage,
evapotranspiration, surface runoff, and baseflow. Both simulated
evapotranspiration and fraction of discharge from baseflow agreed decently with
alternative estimates. The general framework can work with models with various
process complexity and opens up the path for learning physics from big data.
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