Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data
Abstract
We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.H53F1540F
- Keywords:
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- 1805 Computational hydrology;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY;
- 1860 Streamflow;
- HYDROLOGY