Browsing by Autor "David Lambl"

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Forecasting Seasonal Streamflow Using a Stacked Recurrent Neural Network
(2020) David Lambl; Daniel S. Katz; E. Hale; Alden Keefe Sampson
&lt;p&gt;Providing accurate seasonal (1-6 months) forecasts of streamflow is critical for applications ranging from optimizing water management to hydropower generation. In this study we evaluate the performance of stacked Long Short Term Memory (LSTM) neural networks, which maintain an internal set of states and are therefore well-suited to modeling dynamical processes.&lt;/p&gt;&lt;p&gt;Existing LSTM models applied to hydrological modeling use all available historical information to forecast contemporaneous output. This modeling approach breaks down for long-term forecasts because some of the observations used as input are not available in the future (e.g., from remote sensing and in situ sensors). To solve this deficiency we train a stacked LSTM model where the first network encodes the historical information in its hidden states and cells. These states and cells are then used to initialize the second LSTM which uses meteorological forecasts to create streamflow forecasts at various horizons. This method allows the model to learn general hydrological relationships in the temporal domain across different catchment types and project them into the future up to 6 months ahead.&lt;/p&gt;&lt;p&gt;Using meteorological time series from NOAA&amp;#8217;s Climate Forecast System (CFS), remote sensing data including snow cover, vegetation and surface temperature from NASA&amp;#8217;s MODIS sensors, SNOTEL sensor data, static catchment attributes, and streamflow data from USGS we train a stacked LSTM model on 100 basins, and evaluate predictions on out-of-sample periods from these same basins. We perform sensitivity analysis on the effects of remote sensing data, in-situ sensors, and static catchment attributes to understand the informational content of these various inputs under various model architectures. Finally, we benchmark our model to forecasts derived from simple climatological averages and to forecasts created by a single LSTM that excludes all inputs without forecasts.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;
Improving Data-Driven Flow Forecasting in Large Basins using Machine Learning to Route Flows
(2023) David Lambl; Mostafa Elkurdy; Phil Butcher; Laura Read; Alden Keefe Sampson
Producing accurate hourly streamflow forecasts in large basins is difficult without a distributed model to represent both streamflow routing through the river network and the spatial heterogeneity of land and weather conditions. HydroForecast is a theory-guided deep learning flow forecasting product that consists of short-term (hourly predictions out to 10 days), seasonal (10 day predictions out to a year), and daily reanalysis models. This work focuses primarily on the short-term model which has award winning accuracy across a wide range of basins.In this work, we discuss the implementation of a novel distributed flow forecasting capability of HydroForecast, which splits basins into smaller sub-basins and routes flows from each subbasin to the downstream forecast points of interest. The entire model is implemented as a deep neural network allowing end-to-end training of both sub-basin runoff prediction and flow routing. The model's routing component predicts a unit hydrograph of flow travel time at each river reach and timestep allowing us to inspect and interpret the learned river routing and to seamlessly incorporate any upstream gauge data.&#160;We compare the accuracy of this distributed model to our original flow forecasting model at selected sites and discuss future improvements that will be made to this model.
Increasing the Accuracy and Resilience of Streamflow Forecasts through Data Augmentation and High Resolution Weather Inputs
(2025) David Lambl; Simon Topp; Phil Butcher; Mostafa Elkurdy; Laura Reed; Alden Keefe Sampson
Accurately forecasting streamflow is essential for effectively managing water resources. High-quality operational forecasts allow us to prepare for extreme weather events, optimize hydropower generation, and minimize the impact of human development on the natural environment. However, streamflow forecasts are inherently limited by the quality and availability of upstream weather sources. The weather forecasts that drive hydrological modeling vary in their temporal resolutions and are prone to outages, such as the ECMWF data outage in November of 2023.&#160;Here, we present HydroForecast Short Term 3 (ST-3), a state-of-the-art probabilistic deep learning model for medium-term (10-day) streamflow forecasts. ST-3 combines long short-term memory architecture with Boolean tensors representing data availability and dense embeddings for processing of the information in these tensors. This architecture allows for a training routine that implements data augmentation to synthesize varying amounts of availability of weather inputs. The result is a model that 1) makes accurate forecasts even in the case of an upstream data outage, 2) achieves higher accuracy by leveraging data of varying temporal resolutions including regional weather inputs with shorter lead times than the most common medium term weather inputs, and 3) generates individual forecast traces for each individual weather source, facilitating inference across regions where weather data availability is limited.&#160;Initial results across CAMELS sites in North America indicate that the incorporation of near-term high resolution weather data increases early horizon forecast KGE by nearly 0.25 with meaningful improvements in metrics seen across our customers&#8217; operational sites. Validation metrics across individual weather sources, as well as model interrogation through integrated gradients highlights a high level of fidelity in the model&#8217;s learned physical relationships across forecast scenarios.