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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts

Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short–term and long–term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. In conclusion, the issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real–world perspective and needs a suitable institutional environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

Continental-scale river forecasting platforms forecast streamflow at reaches that can be used as boundary conditions to drive a local-scale flood inundation model. Uncertainty accumulated during this process stems not only from any part of the forecasting chain but can also be caused by the daily variations in weather forcing that keeps evolving as the event advances. This work aims to examine the influence of the evolving forecast streamflow on predicting the maximum inundation for extreme floods. A diagnostic case study was made on the basis of a hindcast of Hurricane Matthew striking the eastern U.S. in 2016. The U.S. National Water Model was one-way coupled to a hydrodynamic inundation model through a developed automated workflow. Although the river forcing has significantly mismatched hydrographs versus observations, the simulated peak water surface elevations and maximum extents were validated to be comparable with the observations, which indicates that the inundation model may not be sensitive to the inherited uncertainty from the weather forcing. Moreover, the uncertainty of the forecast streamflow time series caused only one order of magnitude fewer variations in inundation prediction; this dampening effect may become clearer for extreme events with large areas inundated. In addition, the forecast total volume of stream discharge appears to be an important metric for assessing the performance of river forcing for inundation mapping, as a linear correlation between the total volume and the accuracy of the predicted peak water surface elevation and maximum extent was found, with the coefficients of determination all above 0.8. Extra best-practice experience of running similar operational tasks demonstrated the tradeoff between the cost and accuracy gain.

flood, national water model, RIFT↗
DOE OSTI2022

Machine learning for postprocessing ensemble streamflow forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗
NASA NTRS2012

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Koster, Randal↗
DOE OSTI2025

Novel Deep Learning Transformer Model for Short to Sub‐Seasonal Streamflow Forecast

Accurate short-to-subseasonal streamflow forecasts are becoming crucial for effective water management in an increasingly variable climate. However, streamflow forecast remains challenging over extended lead times, uncertainty in meteorological inputs, and increased frequency and variability in extreme weather and climate events. We implemented a Future Time Series Transformer (FutureTST) model for streamflow forecasting that separately integrates past meteorological and streamflow data while incorporating future weather conditions. FutureTST achieves a mean Nash-Sutcliffe Efficiency (NSE) of 0.82 to 0.67 for 1- to 30-day streamflow forecasts. Incorporating upstream streamflow information improved forecast accuracy by up to 10%. During real-time forecast, FutureTST maintains higher forecast skills of 9.03 for 1-day and 5.74 for 14-day forecasts. In contrast, calibrated process-based hydrological model forecasts become unreliable beyond a 4-day lead time. Our findings demonstrate the potential of FutureTST as a reliable streamflow forecasting tool that offers a valuable addition to operational flood monitoring systems and climate-resilient decision-making.

Ambika, Anukesh Krishnankutty [Oak Ridge National ↗
NASA NTRS2013

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Streamflow Prediction↗
DOE OSTI2021

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km 2 ) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km 2 . The space-time scale, or reference time t r ) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for t r > 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

FFTSF: Revisiting Sub-Seasonal Streamflow Forecasting with Simple Feedforward Network

Accurate short-to-subseasonal streamflow forecasts are vital for water management, including flood preparedness, drought mitigation, hydropower scheduling, and ecosystem protection. However, extending a forecast beyond a few days remains challenging due to complexity of hydrological processes. While recent self-attention based transformer architectures such as iTransformer have gained traction in time-series forecasting, these models suffer from several critical limitations: (1) significant computational overhead that scales quadratically with sequence length, (2) vulnerability to overfitting on limited hydrological datasets, (3) degraded performance on long-horizon forecasts due to attention decay, and (4) excessive architectural complexity that hampers interpretability and operational deployment. In this study, we propose a simple Feedforward Time Series Forecasting (FFTSF) network that directly addresses these limitations through its lightweight architecture and long-range forecasting capabilities. We evaluate FFTSF across 178 USGS stream gauges spanning diverse climate regimes by forecasting lead times of 1-, 7-, 14-, and 30-days. Our results demonstrate that FFTSF achieves competitive performance at short lead times (NSE of 0.778 for 1-day forecasts) while substantially outperforming complex baselines at longer forecast period, achieving the highest NSE (0.271) at 30-day forecasts with greater robustness and stability. For 30-day forecasts, FFTSF achieves a 71% improvement over NLinear, 57% improvement over DLinear and 12% improvement over the computationally intensive iTransformer while requiring fewer computational resources. Our findings reveal that architectural complexity is not necessary for hydrological forecasting, demonstrating that well-designed simple models can outperform attention mechanisms for subseasonal streamflow forecasting. The computational efficiency and consistent long-range performance of FFTSF make it suitable for water management applications where reliable extended forecasts are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2021

Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Remotely sensed snow cover observations give an opportunity to improve operational snow melt and stream flow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study,the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1(MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations haveimproved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations.The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations.Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCAdata in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snow pack and therefore provides more robust simulations,which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

42 ENGINEERING↗
NASA NTRS2020

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite-based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6-month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE-DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE-DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water-related disaster warning systems for Africa.

Augusto Getirana↗
NASA NTRS1975

A study of application of remote sensing to river forecasting. Volume 2: Detailed technical report, NASA-IBM streamflow forecast model user's guide

The Model is described along with data preparation, determining model parameters, initializing and optimizing parameters (calibration) selecting control options and interpreting results. Some background information is included, and appendices contain a dictionary of variables, a source program listing, and flow charts. The model was operated on an IBM System/360 Model 44, using a model 2250 keyboard/graphics terminal for interactive operation. The model can be set up and operated in a batch processing mode on any System/360 or 370 that has the memory capacity. The model requires 210K bytes of core storage, and the optimization program, OPSET (which was used previous to but not in this study), requires 240K bytes. The data band for one small watershed requires approximately 32 tracks of disk storage.

Source record↗