Modeling Functional Flows in California’s Rivers

Abstract

Environmental flows are critical to the recovery and conservation of freshwater ecosystems worldwide. However, estimating the flows needed to sustain ecosystem health across large, diverse landscapes is challenging. To advance protections of environmental flows for streams in California, United States, we developed a statewide modeling approach focused on functional components of the natural flow regime. Functional flow components in California streams—fall pulse flows, wet season peak flows and base flows, spring recession flows, and dry season baseflows—support essential physical and ecological processes in riverine ecosystems. These functional flow components can be represented by functional flow metrics (FFMs) and quantified by their magnitude, timing, frequency, duration, and rate-of-change from daily streamflow records. After calculating FFMs at reference-quality streamflow gages in California, we used machine-learning methods to estimate their natural range of values for all stream reaches in the state based on physical watershed characteristics, and climatic factors. We found that the models performed well in predicting FFMs in streams across a diversity of landscape and climate contexts, according to a suite of model performance criteria. Using the predicted FFM values, we established initial estimates of ecological flows that are expected to support critical ecosystem functions and be broadly protective of ecosystem health. Modeling functional flows at large regional scales offers a pathway for increasing the pace and scale of environmental flow protections in California and beyond.

Increasing stability of a native freshwater fish assemblage following flow rehabilitation

“…The functional flows concept (a conceptual extension of the natural flow regime; Poff et al, 1997) is an important conservation management tool for declining freshwater taxa in regulated rivers, especially in the western USA (Grantham et al, 2022; Yarnell et al, 2022). Augmenting the flow of cold water from dams specifically is increasingly common and effective for recovering native fish (Poff et al, 1997; Richter & Thomas, 2007; Watts et al, 2011; Willis et al, 2021).…”

Perennial and Non‐Perennial Streamflow Regime Shifts Across California, USA

“…Compared to traditional statistical methods, random forests have greater flexibility, fewer data restrictions, and combine many decision trees to create a more accurate classification (Baudron et al., 2013). We used random forests in this approach since they have been successfully used to characterize low flow metrics (e.g., Grantham et al., 2022; Hammond et al., 2021; Zipper et al., 2021) and spatial patterns of flow intermittency in California watersheds (e.g., Moidu et al., 2021). Additionally, our approach builds off of previous modeling work, analyzing minimally disturbed streams in California (Grantham et al., 2022; Zimmerman et al., 2018).…”

“…We used random forests in this approach since they have been successfully used to characterize low flow metrics (e.g., Grantham et al., 2022; Hammond et al., 2021; Zipper et al., 2021) and spatial patterns of flow intermittency in California watersheds (e.g., Moidu et al., 2021). Additionally, our approach builds off of previous modeling work, analyzing minimally disturbed streams in California (Grantham et al., 2022; Zimmerman et al., 2018). In this analysis, we developed a model with a binary annual response variable where a one indicated an non‐perennial year (i.e., greater than 5 consecutive zero flow days during the dry season) and 0 a perennially flowing year.…”

When good signatures go bad: Applying hydrologic signatures in large sample studies

“…This expansion is part of a wider move towards large sample studies that develop datasets and draw conclusions across scales, hydro climates and ecosystems (Addor et al, 2020; Kratzert et al, 2023). Studies that calculate signature values over 100s of gauged watersheds include evaluating national models (Almagro et al, 2021; Coxon et al, 2019; Donnelly et al, 2016; Massmann, 2020; McMillan et al, 2016), selecting model structures (David et al, 2022), interpreting machine learning models (Botterill & McMillan, 2022; Kratzert et al, 2019), predicting signatures from watershed attributes (Addor et al, 2018; Beck et al, 2015; Grantham et al, 2022; Janssen & Ameli, 2021) and classifying watersheds (Kuentz et al, 2017). However, signature use is challenged by large datasets, as it becomes impractical to check whether the signature quantifies the intended hydrograph property in each watershed.…”