Improvement of Lake and Reservoir Parameterization in the NOAA National Water Model

Khazaei, Bahram; Read, Laura; Casali, Matthew; Sampson, K. M.; Yates, David

doi:10.1061/9780784483466.050

Cited by 4 publications

(3 citation statements)

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“…This highlights potential issues regarding the impact of dams on NWM model output and the specific drivers of streamflow peaks. It must be noted, however, that the NWM retrospective data set does not include detailed lake/reservoir information such as storage, release, or other operational parameters, but rather a simplified fill and spill technique, implemented for about 5000 reservoirs across the continental US (Cosgrove et al, 2021; Khazaei et al, 2021; Mattern et al, 2021; Rezaeianzadeh et al, 2021; Viterbo, Mahoney, et al, 2020; Viterbo, Read, et al, 2020). On a short time‐scale (hourly to daily) this could severely impact results; however, the monthly and annual aggregations used in this study allow for the treatment of lakes and reservoirs as flow‐through units, such that the mass balance (input minus output) is considered zero.…”

Section: Summary and Discussionmentioning

confidence: 99%

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

Raczyński,

Dyer

2023

Hydrological Processes

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Utilizing simulated daily flow values for 61 948 stream nodes from the National Water Model (NWM) v.2.1 retrospective data set, which covers the period 1979–2020, streamflow peaks (SFP) were determined using an objective threshold method based on the Fisher‐Jenks breakpoint algorithm. This approach provides estimates of low probability, high in‐channel water level conditions. Strong spatial relations in temporal streamflow peak distribution were observed, with four main spatial clusters covering: (1) the central parts of Mississippi to South Carolina; (2) the south and north parts of Mississippi to South Carolina; (3) Florida, the Gulf Coast, and parts of North Carolina and Virginia; and (4) the Atlantic Coast and parts of the Gulf Coast. The first area is characterized by high multiannual SFP occurrence with long total durations, high contribution to total discharge, and occurrence throughout the year with peaks in February and March. Increasing annual peak trends are present locally. The second area is characterized by similar parameters with increased flash flooding potential. In the third area, lower occurrence and share in total discharge are observed, with two peaks of occurrence in the September–October and March periods. In the fourth region, the lowest occurrences and shortest durations are present, along with strong seasonality manifested in September–October occurrences that constitute over 80% of all SFP volumes. Decreasing trends in maximal monthly flows for the June–October period are also observed here. The results provide valuable regionalized information about historical high flow characteristics across the southeast United States, which can be used as a basis for improved long‐term flood prediction based on seasonality models and constitute a baseline for defining regional changes in high flow frequency and distribution.

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Section: Summary and Discussionmentioning

confidence: 99%

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

Raczyński,

Dyer

2023

Hydrological Processes

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“…Compared to natural hydrologic processes that can be expressed by physical relationships, it remains unclear how reservoirs are operated to regulate streamflow, as observations on reservoir operation (e.g., reservoir water level and release) are very limited due to the complex ownership and regulations. For example, the National Water Model is able to predict streamflow for over 2 million reaches in the US, while a limited number of reservoirs are simulated by a simple level pool routing scheme (Gochis et al., 2018; Khazaei et al., 2021) where reservoir releases are passively determined by reservoir water level and spillway characteristics based on hydraulic laws (e.g., weir flow equations). However, the releases from actively managed reservoirs, which are crucial infrastructures involving multiple stakeholders and with significant downstream impacts, are regulated by gates and determined by reservoir managers based on a range of real‐world constraints and trade‐offs.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Temporal Scale Framework for Data‐Driven Reservoir Release Modeling

Longyang

Zeng

2023

Water Resources Research

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have become increasingly important or even dominant driving forces of hydrologic processes in many watersheds over the world. In these watersheds, the streamflow observed at gauging stations represents the interaction between hydrologic and anthropogenic driving forces, rather than the "natural" or "unregulated" flows simulated in hydrologic models (Blair & Buytaert, 2016;Clark et al., 2015). Reservoirs are one of the key water infrastructures that directly regulate the streamflow timing and variability to fulfill various purposes including flood control, water supply, hydroelectricity generation, navigation, and fluvial ecosystem services (

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“…For example, the National Water Model is able to predict streamflow for over two million reaches in US, while a limited number of reservoirs are simulated by a simple level pool routing scheme (Gochis et al, 2018;Khazaei et al, 2021) where reservoir releases are passively determined by reservoir water level and spillway characteristics based on hydraulic laws (e.g., weir flow equations). However, for the actively managed reservoirs, which are a key infrastructure that involves various stakeholders and has significant impact on downstream flow, the releases are regulated by gates and determined by reservoir managers depending on various real-world constraints and tradeoffs.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Temporal Scale Data-driven Reservoir Operation Modeling

Longyang

Zeng

2022

Preprint

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As an important anthropogenic interference on the water cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data-driven models provide a promising approach to represent reservoir operation rules by capturing relationships embedded in historical records. Similar to hydrologic processes vary across temporal scales, reservoir operations manifest themselves at different timescales, prioritizing different targets to mitigate streamflow variability at a given time scale. To capture interactions of reservoir operations across time scales, we proposed a hierarchical temporal scale framework to investigate the behaviors of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions. Machine learning models were constructed to simulate reservoir operation at daily, weekly, and monthly scales, where decisions at short-term scales interact with long-term decisions. We found that the hierarchical temporal scale configuration better captures reservoir releases than models constructed at a single time scale, especially for reservoirs with multiple operation targets. Model-based sensitivity analysis shows that for more than one third of the studied reservoirs, the release schemes, as a function of decision variables, vary at different time scales, suggesting that operators are commonly faced with complicated trade-offs to serve multiple purposes. The proposed hierarchical temporal scale approach is flexible to incorporate various data-driven models and decision variables to derive reservoir operation rule, providing a robust framework to understand the feedbacks between natural streamflow variability and human interferences across time scales.

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Improvement of Lake and Reservoir Parameterization in the NOAA National Water Model

Cited by 4 publications

References 0 publications

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

A Hierarchical Temporal Scale Framework for Data‐Driven Reservoir Release Modeling

Hierarchical Temporal Scale Data-driven Reservoir Operation Modeling

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