Ensemble Streamflow Forecasting Using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations

In many river basins across the world, snowmelt is an important source of streamflow. However, detailed snowmelt modeling is hampered by limited input data and uncertainty arising from inadequate model structure and parametrization. Data assimilation that updates model states based on observations, reduces uncertainty and improves streamflow forecasts. In this study, we evaluated the Utah Energy Balance (UEB) snowmelt model coupled to the Sacramento Soil Moisture Accounting (SAC‐SMA) and rutpix7 stream routing models, integrated within the Research Distributed Hydrologic Model (RDHM) framework for streamflow forecasting. We implemented an ensemble Kalman filter for assimilation of snow water equivalent (SWE) observations in UEB and a particle filter for assimilation of streamflow to update the SAC‐SMA and rutpix7 states. Using leave one out validation, it was shown that the modeled SWE at a location where observations were excluded from data assimilation was improved through assimilation of data from other stations, suggesting that assimilation of sparse observations of SWE has the potential to improve the distributed modeling of SWE over watershed grid cells. In addition, the spatially distributed snow data assimilation improved streamflow forecasts and the forecast volume error was reduced. On the other hand, the assimilation of streamflow observations did not provide additional forecast improvement over that achieved by the SWE assimilation for seasonal forecast volume likely due to there being little information content in streamflow at the forecast date prior to its rising during the melt period and this application of particle filter being better suited for shorter timescales

HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models

Physically based distributed hydrologic models require geospatial and time-series data that take considerable time and effort in processing them into model inputs. Tools that automate and speed up input processing facilitate the application of these models. In this study, we developed a set of web-based data services called HydroDS to provide hydrologic data processing ‘software as a service.’ HydroDS provides functions for processing watershed, terrain, canopy, climate, and soil data. The services are accessed through a Python client library that facilitates developing simple but effective data processing workflows with Python. Evaluations of HydroDS by setting up the Utah Energy Balance and TOPNET models for multiple headwater watersheds in the Colorado River basin show that HydroDS reduces the input preparation time compared to manual processing. It also removes the requirements for software installation and maintenance by the user, and the Python workflows enhance reproducibility of hydrologic data processing and tracking of provenance

Advancing Streamflow Forecasts Through the Application of a Physically Based Energy Balance Snowmelt Model With Data Assimilation and Cyberinfrastructure Resources

The Colorado Basin River Forecast Center (CBRFC) provides forecasts of streamflow for purposes such as flood warning and water supply. Much of the water in these basins comes from spring snowmelt, and the forecasters at CBRFC currently employ a suite of models that include a temperature-index snowmelt model. While the temperature-index snowmelt model works well for weather and land cover conditions that do not deviate from those historically observed, the changing climate and alterations in land use necessitate the use of models that do not depend on calibrations based on past data. This dissertation reports work done to overcome these limitations through using a snowmelt model based on physically invariant principles that depends less on calibration and can directly accommodate weather and land use changes. The first part of the work developed an ability to update the conditions represented in the model based on observations, a process referred to as data assimilation, and evaluated resulting improvements to the snowmelt driven streamflow forecasts. The second part of the research was the development of web services that enable automated and efficient access to and processing of input data to the hydrological models as well as parallel processing methods that speed up model executions. These tasks enable the more detailed models and data assimilation methods to be more efficiently used for streamflow forecasts