Assessing and enhancing national water model streamflow predictions for montane catchments in the northeastern United States

Abstract

This study evaluates National Water Model (NWM) performance in low-order montane catchments across the northeastern United States by comparing retrospective simulations to measured observations. To address deficiencies, we develop a machine learning (ML) correction model for selected sites using LightGBM, a different approach from conventional bias correction methods. Montane, low-order streams play a crucial role in water quality and flood generation but pose challenges for streamflow prediction and are under-represented in the national streamgaging network. NWM provides streamflow forecasts across the United States; yet a focused assessment of its performance in these settings has not been comprehensively undertaken. Results indicate NWM performance varied seasonally, with the best performance during the fall and particularly poor performance during snowmelt, spring runoff, and high flow events, with a tendency towards flow underestimation. The ML correction model markedly improved hourly streamflow prediction accuracy based on continuous time series and runoff event-based metrics. Including antecedent water level measurements as input, even from distant sites, greatly improved model performance, demonstrating the potential to improve predictions by deploying supplemental low-cost water level sensors. We demonstrate that NWM performance can be improved in these complex watersheds using ML tools. This approach could be implemented elsewhere to improve NWM streamflow predictions.