  Machine Learning is beginning to provide state-of-the-art performance in a
range of environmental applications such as streamflow prediction in a
hydrologic basin. However, building accurate broad-scale models for streamflow
remains challenging in practice due to the variability in the dominant
hydrologic processes, which are best captured by sets of process-related basin
characteristics. Existing basin characteristics suffer from noise and
uncertainty, among many other things, which adversely impact model performance.
To tackle the above challenges, in this paper, we propose a novel
Knowledge-guided Self-Supervised Learning (KGSSL) inverse framework to extract
system characteristics from driver and response data. This first-of-its-kind
framework achieves robust performance even when characteristics are corrupted.
We show that KGSSL achieves state-of-the-art results for streamflow modeling
for CAMELS (Catchment Attributes and MEteorology for Large-sample Studies)
which is a widely used hydrology benchmark dataset. Specifically, KGSSL
outperforms other methods by up to 16 \% in reconstructing characteristics.
Furthermore, we show that KGSSL is relatively more robust to distortion than
baseline methods, and outperforms the baseline model by 35\% when plugging in
KGSSL inferred characteristics.
