Our study area is the San Juan Hydrologic Unit (SJHU, Figure 1) located in Southern California, United States that covers an area of 496 mi² throughout Riverside (∼18%), Orange (∼52%) and San Diego (∼30%) Counties. SJHU comprises several major watersheds that drain directly to the Pacific Ocean, including San Juan Creek, Aliso Creek, Laguna Canyon Creek, Salt Creek, and Segunda Deshecha Creek. Most of the upper tributaries of the major watersheds are undeveloped, while the lower coastal portions of the watersheds are highly urbanized (Figure 2). The major land uses in the region include developed pervious land, single- and multi-family residential homes, transportation, and open space with low vegetation. The region is characterized by a Mediterranean climate with wet winters and dry summers. This region, also known as The South Orange County Watershed Management Area (South OC WMA) has a designated Water Quality Improvement Plan (WQIP) instigated by the San Diego Regional Water Quality Control Board. Altered hydrology and channel erosion from increasing urbanization in the area has been identified as a high priority water quality condition. The identification and elimination of nuisance dry weather flows and restoration of 23,000 linear feet of degraded stream habitat are key implementation strategies to achieve the goals of the WQIP. ¹

For modeling purposes, SJHU was divided into 73 sub basins and stream segments, including areas that are heavily urbanized and areas that remain mostly natural. However, as some subbasins in the SJHU have existing flow management (n = 13), they were not included in the analysis. The subbasins were delineated following Taniguchi-Quan (this issue).

The process we provide observes the following criteria:

5. Aims to protect multiple biological assemblages

Acknowledging these criteria, we explored the implications of various choices outlined above through the following process: Bioassessment data collected from wadeable streams across southern California were modeled with Functional Flow Metrics (FFM) calculated from regional flow models developed for southern California (Sengupta et al., 2018) to create regional flow-ecology curves. Ecologically meaningful relationships were determined through a FFM filtering process that included boosted regression tree analysis and generalized linear models. Three thresholds relating to the index score, i.e. that defines a reference or altered biological condition, and three thresholds of probability, i.e. defining the point in the curve at which the Delta H limit is determined, were combined to create nine threshold combinations for each index (e.g., 0.79 index threshold and 0.5 probability threshold represents one combination, Table 2). All threshold combinations were compared for each chosen FFM. To estimate where altered flow impacts biological condition (hereafter referred to as biological alteration) in the study area, FFM calculated from a hydrological model were used to predict the probability of achieving a healthy bioassessment index score for each subbasin. These scores were used to define biological alteration for each threshold combination using specific criteria. The threshold combinations were compared and tested for appropriate discriminatory power for use in prioritization decision making. The final combinations were used to prioritize subbasins for management actions for both bioassessment indices separately as well as a synthesized combination of both indices. Our recommendations are based on the outcome of this approach.

Each index had three identified thresholds of alteration that correspond to 1) Likely altered (ASCI: 0.75, CSCI: 0.63), 2) possibly altered (ASCI: 0.86, CSCI: 0.79), and 3) likely intact (ASCI: 0.94, CSCI: 0.92), which correspond to the first, 10th and 30th percentile value of the index based on the distribution of reference scores (Mazor et al., 2016; Theroux et al., 2020). The index threshold is the score in which to transform the continuous index score to binary (1: reference condition, 0: altered condition) as the response input for the GLMs. Following the FFM filtering process, GLMs were performed, as above, on the chosen FFM and bioassessment indices formatted with each index threshold. Here, the AIC values from each model are reported, but not used for comparison. The probability threshold is the point in the curve where we determine the Delta H limits and ranges from 0 to 1. For simplicity and to include a full range of values, we tested the probability thresholds of 0.25, 0.50 and 0.75. This resulted in 9 threshold combinations for each FFM (Table 2).

Flow-ecology analysis (as described in Section 2.3.2) was conducted for each index threshold and Delta H limits were extracted for each bioassessment index, chosen FFM and threshold combination.

Delta FFM in the SJHI study area was calculated through a continuous Loading Simulation Program in C++ (LSPC) following Taniguchi-Quan et al. (this issue), which resulted in one Delta H value per subbasin (n = 60) per year (1994–2018).

Biological alteration for each threshold combination was annually classified using the following criteria:

• Biologically Altered: if change in FFM falls outside of Delta FFM limits

The percentage of subbasins biologically altered for each threshold combination for both bioassessment index and single FFM were plotted over time. Any threshold combinations that had percentage altered subbasins between 25 and 75% were kept for further analysis. In cases where more than three combinations were determined, the limits were reduced to 40–60%. This process was repeated for each FFM and the combination that appeared consistently in all FFM for each bioassessment index was chosen for the final analysis.

Biologically altered years based on the final threshold combination for each index were summarized as a percentage of the modelled period (1994–2018), which were used to synthesize alteration across all chosen FFM within each subbasin. The subbasin was classified as “likely altered” if at least two of the three chosen FFM were altered for >50 percent of years. This resulted in an alteration map for both CSCI and ASCI.

To determine priority subbasins, we synthesized biological alteration across bioassessment indices the following criteria was applied:

• High priority: Both indices indicate biologically altered flow

Biological alteration was mapped by subbasin for each index separately and prioritization was mapped using the synthesized combination of indices. All maps were created in R statistical programming version 4.0.2 (R Core Team, 2020), using packages “sf” (Pebesma, 2018) and ggmap (Kahle and Wickham, 2013) with spatial projection of NAD83, California zone 6 (ft US).

The FFM filtering process resulted in three ecologically meaningful flow-ecology relationships for each bioassessment index.

Applying the different combinations of index thresholds on the GLMs and probability thresholds produced highly varied Delta H limits (Supplementary Table S4; Figures 4, 5). For example, the most conservative combination for CSCI and DS_Dur_WS (index: 0.92, probability: 0.75, Delta H limits: −26.17 to 1.95 days) and the most liberal combination (index: 0.63, probability: 0.25, Delta H limits: −146.5 to 148.4 days) showed a percentage difference of 165.8% (266 days). This difference comprises 71.7% of the entire range of Delta H values (372 days) in the study area across all subbasins and years.

The delta H limits extracted from the flow-ecology analysis for all threshold combinations applied to the 60 subbasins of the study area produced biological alteration that ranged widely (Figure 6; Supplementary Table S6). The most lenient threshold combination (CSCI = 0.63, ASCI = 0.75, Probability = 0.25) in each panel, generally shows the lowest level of alteration of all combinations. The most stringent combinations (CSCI = 0.92, ASCI = 0.94, Probability = 0.75) show a much higher level of alteration for all FFM. The yellow color throughout the subbasins for Q99 (Figure 6 top panel) indicates that both indices show low alteration throughout the study area for most threshold combinations. The remaining FFM for both CSCI and ASCI show higher and more varied levels of alteration throughout the study area and for the threshold combinations indicated by the varied blue (high alteration), green (moderate alteration) and yellow (low alteration) subbasins. The percentage of biological alteration over subbasins and years ranged between 0 and 100 for both CSCI and ASCI across all chosen metrics and subbasins. Threshold combinations applied on CSCI models initially deduced several combination options for DS_Dur_WS and SP_Tim (Figure 7). However, only one combination within the % biological alteration limits for Q99 (index: 0.92, Probability: 0.25), which corresponded with combination determined for DS_Dur_WS and SP_Tim. Threshold combinations applied on ASCI models deduced several combination options with one combination consistently determined for each FFM (index: 0.94, Probability: 0.50). Delta H limits for the final threshold combinations, together with overall biological alteration are outlined in Table 3.

Biological alteration for each index (Figure 8) showed fewer subbasins likely to be altered (n = 20) for CSCI than ASCI (n = 29). The synthesized prioritization map (Figure 8) shows varied levels of biological alteration throughout the study area (High priority; n subbasins = 16, Medium priority; n subbasins = 17, Low priority; n subbasins = 27). The subbasins determined as medium priority consisted of 4 subbasins deemed likely altered for CSCI and 13 subbasins deemed likely altered for ASCI. In most cases of alteration, the FFM show higher values than the delta H limits for each bioassessment indices, i.e., Q99 (n = 16), SP_Tim (n = 16) and DS_Dur_WS (n = 15) in subbasins determined as high priority. In contrast, SP_Dur showed lower values than the delta H limits for the majority of high priority subbasins (n = 13).

Our prioritization analysis identified locations of basins that are high, medium, and low priority for management actions. Importantly, 17 subbasins were determined as medium priority. These subbasins outline the locations where biological alteration was determined as altered for one, but not both, bioassessment indices. By using only one index, these locations would have been mis-classified as high or low priority. This miscalculation would have resulted in several subbasins not being designated for management actions, effectively being ignored, as well as several subbasins requiring management that could compromise other aspects of the stream ecosystem (Tonkin et al., 2021). This result adheres to two items of our process criteria 1) Aiming to protect multiple biological assemblages and 2) Reduces misclassification of priority areas (i.e., error of omission).

In this study, ASCI and CSCI were impacted mostly through augmented flow metrics where the values were higher than the associated upper Delta H limits. That is, three flow metrics, magnitude of largest storm (96%), dry season duration (86–89%) and spring timing (100%) were frequently too high or too long, respectively, to support healthy algae and macroinvertebrate communities. These exceedances could be a result of increased flows in the region due to urban run-off, including irrigation overspray which can augment baseflows and flashier storm events from an increase in impervious surfaces also impacting peak flows (Hawley and Bledsoe 2011). Spring recession duration varies in direction of alteration, that is, some subbasins have flows that are too long, while others are too short. It is challenging to discuss the source of depletion without further modeling or investigation, however the high variability in the direction of the spring recession duration (overall 31 subbasins showed a duration that was too short) could be caused by the rain dominated, highly impervious system promoting flashy flow that reduces the prominence of the spring recession compared to natural conditions or those observed in snow dominated systems (Yarnell et al., 2010).

The locations classed as high priority are generally characterized by a high level of imperviousness from urbanization (Figures 2, 8). However, several subbasins, i.e., in Trabuco Creek consist of lower imperviousness than the surrounding subbasins yet are still classified as high priority. These subbasins may be impacted by development in the adjacent subbasins from which urban run-off flows into the creek through multiple outflows in the Trabuco Creek area. ³ The seven high priority subbasins are hydrologically connected, therefore impacts downstream may be the result of accumulative effect of augmented flows upstream, which is potentially also the situation in Lower Aliso Creek. The high priority subbasins, located in urbanized areas, are the places that need the most attention from flow managers. There are substantial socioeconomic considerations associated with restoration and flow management within urban streams. To reduce augmented flows from urban run-off, management will need to focus on specific source-control techniques in conjunction with community commitment (Fletcher et al., 2013). Under these circumstances it is vital to maintain support of the local community, however balancing perspectives from cultural, political, and ecological viewpoints can create critical challenges (Wohl et al., 2015).

The locations classed as low priority are generally characterized by low imperviousness (Figures 2, 8) and hence, are less impacted by urbanization and exhibit more natural conditions, resulting in less degradation. Exceptions to this rule, are the subbasins located in Oso Creek, including the confluence with Trabuco Creek. Interestingly, nine subbasins were identified as low priority in highly impervious areas (Figures 2, 8). Three clusters of two subbasins are hydrologically connected and comprise much of Oso Creek and tributaries of San Juan creek as well as the smaller watershed of Salt Creek. The remaining four subbasins are not hydrologically connected but contain sections of Aliso Creek. These subbasins may show some form of resilience to flow alteration dependent on several biological and physical factors likely acting on different scales (see Mazor et al., 2018), such as water abstraction reducing the volume of urban run-off. Further investigation of potential influencing factors will be necessary to fully understand the resilience to flow alteration in these subbasins.

Our comparison of index and probability combination thresholds adheres to our process criteria item: demonstrating an appropriate level of sensitivity to discriminate locations to inform decision making. Committing to specific thresholds is not strictly a scientific distinction, however, it is a decision that could impact flow management outcomes.

In most applications of CSCI and ASCI, the 10th percentile value (possibly altered, CSCI: 0.79, ASCI: 0.86) is applied (Stein et al., 2017; Mazor et al., 2018; Beck et al., 2019a; Beck et al., 2019b). We show that for determining prioritization the most useful threshold was the 30th percentile for both indices (likely intact, CSCI: 0.92, ASCI: 0.94) due to both sensitivity and discriminatory power.

CSCI relationships to FFM were variable across different combinations of thresholds and probabilities (e.g., Figure 7, Dry Season Duration). This variation may stem from their diverse life histories and hence direct responses to variations in streamflow (Konrad et al., 2008; Rehn 2009). ASCI relationships to FFM were also variable (Figure 7 right panel), although, with the chosen threshold combination we see a higher amount of biological alteration overall for ASCI (Figure 8) than CSCI. The combination able to discriminate algal response effectively was the second most stringent combinations in the analysis, which suggests that extreme flow alteration is needed to drastically impact algal communities. Previous studies indicate that flow influences algal communities (Kirkwood et al., 2009; Miller et al., 2009; Schneider and Petrin 2017; Cheng et al., 2019). However confounding factors (i.e., water quality) mediated by altered flow regimes (Allan 2004; Nilsson and Renöfält 2008; Lange et al., 2016), are likely also driving algal condition in these subbasins. Further investigation would be needed understand the impact of flow-mediated water quality on algal communities to ensure a successful response to flow management and restoration (Suren et al., 2003).

Through our BRT and GLM analysis we deduced the most important flow components for our study, thus abiding by our criteria item to represent the most relevant components of the annual hydrograph. By applying the functional flows approach we can deduce a more mechanistic understanding of the relationship between flow and the bioassessment indices. The flow metric describing the magnitude of the largest storm (Q99) was the most important metric for both CSCI and ASCI. This is not surprising, as peak flows support many physical (e.g., maintenance and rejuvenation of habitat), biogeochemical (e.g., increase nutrient cycling and exchange) and biological (e.g., limit nonnative species through natural disturbance) functions (Supplementary Table S1). This relationship agrees with several studies that have determined peak flow metrics as important influences on macroinvertebrates (Buchanan et al., 2013; Stein et al., 2017; Mazor et al., 2018; Yarnell et al., 2020; Bower et al., 2022) and algae (Lake 2000; Tsai et al., 2013; Townsend et al., 2017).

It is challenging to compare the relationship between the remaining FFM and bioassessment indices due to limitations in the flow-ecology literature. Specifically, few studies focus flow-ecology analysis directly on primary producers (Poff and Zimmerman 2010; Yarnell et al., 2020) and efforts to model season-specific metrics such as spring recession have only recently been undertaken (Yarnell et al., 2016). Nonetheless, recent studies provide insights into the FFM and index relationships identified in this study.

The selected FFM for evaluating biological condition using CSCI and ASCI scores describe duration of dry season (Dry_Dur_WS) as well as the timing and duration of spring recession flow (SP_Tim & SP_Dur), respectively. Steel et al. (2018) similarly found spring recession to have a high influence on macroinvertebrate community diversity possibly through increasing habitat heterogeneity which in turn increases species diversity and decreases water temperature, promoting algal productivity and macroinvertebrate diversity (Supplementary Table S1). Additionally, spring recession timing as well as seasonal predictability was found to be important for biological communities statewide across a range of stream types (Peek et al., this issue). In our study, the timing of the spring recession was consistently later in all subbasins, with the duration of the spring recession being both too long and too short. Spring duration is dependent on the timing of both the spring recession and the dry season, therefore a late spring recession, coupled with an early dry season may drive the short duration, with a late dry season driving the longer spring duration. This lack of seasonal predictability may be influencing biological alteration in our study area as many species are adapted to specific flow regimes and/or rely on timing cues for reproduction or developmental aspects of their life cycle (Poff and Ward 1989; Kennen et al., 2010; de la Fuente et al., 2018).

Dry season flows are known to maintain habitat availability for a broad range of aquatic species as well as support algal growth and primary productivity by, e.g., maintaining water temperature and dissolved oxygen (Supplementary Table S1). The duration of these low flows in our study area are consistently longer, which could explain the importance of this flow metric in augmented subbasins. The deviations from reference condition can alter hydraulic condition favoring certain types of algae, or creating unsuitable conditions for algal growth (Townsend and Padovan 2009) as well as favoring macroinvertebrate species less sensitive to changes in water quality (Leigh 2013). For example, low flows tend to favor filamentous algal mats due to increases in water temperature, nutrient concentration and reductions in velocity, and high flows can favor scour-tolerant diatoms (McIntire 1966; Dewson et al., 2007; Tang et al., 2013), which will ultimately impact algal community assemblages.

Fall pulse flows were of minimal importance in our study for both CSCI and ASCI, however Peek et al. (this issue) found a prominent influence statewide and has been found elsewhere to be important for benthic macroinvertebrates (Kennen et al., 2010; Yarnell et al., 2020) as functionally, fall pulse flows reactivate the system (Supplementary Table S1) after summer low flows. Our metrics describing fall pulse flow however, included multiple missing values (i.e., NA), which may be due to the hardcoded fall time window that the functional flow calculator uses (Patterson et al., 2020). Fall pulse flows, which represent the first flushing flows of the water year, may occur outside of the defined time window for fall flows, i.e., 1st October to 15th December (Patterson et al., 2020). This temporal mismatch may lead the calculator to discard genuine fall pulse flows that fall outside the specific time window.

We focused our study on prioritizing areas for restoration and flow management, however our process can be used for other applications such as deriving flow targets, assessing current flow alteration effects on biological condition and changes under different management and climate change scenarios (e.g., Buchanan et al., 2013; Cartwright et al., 2017; Stein et al., 2017; Kakouei et al., 2018; Mazor et al., 2018; Maloney et al., 2021). Although the data used in this study was specific to southern California, the data types are common to many other regions. The two primary sources of data needed to run the analysis are hydrologic model outputs and a bioassessment index. The CSCI and ASCI are specific to California, but Indices of Biotic Integrity (IBIs) are common. The approach described can be applied in any area where these two common data sources can be generated or compiled. The process used here is a great complement to established environmental flows frameworks such as the Ecological Limits of Hydrologic Alteration (Poff et al., 2010) where flow-ecology relationships are developed to assess the response of stream ecology, e.g., fish, vegetation and invertebrates, to the alteration of flow. The recently developed California Environmental Flow Framework (CEFF) is a generalized management approach for “determining ecosystem water needs that can be used to inform the development of environmental flow recommendations statewide” (Stein et al., 2021). The framework outlines guidance for identifying and developing ecological flow needs through flow-ecology analysis to ultimately provide environmental flow recommendations to aid management decisions. By providing a process to evaluate prioritization through flow-ecology analysis, our study has provided an important foundation for the application of CEFF. The next step in CEFF is to develop ecological flow needs that consider altered physical habitat including the evaluation of non-flow related influences (Stein et al., 2021). To implement flow management actions in the high priority areas, further investigation is needed the fully understand the source of the alteration. The direction of alteration in our study differed throughout the study area; therefore, implementation of management actions would need to be tailored to individual subbasins accordingly. For example, in cases where, e.g., magnitude of largest storm are augmented, flow could be managed through outflow diversions and/or retention areas with slow-release of storm flow. In areas where flows are depleted, management related to groundwater pumping or channel morphology could be implemented.

Application of CEFF involves function-based flow metrics, that account for ecological functions that occur across the entire active floodplain at a seasonal scale. Targets therefore would be considered based on the five functional flow components that relate to seasons, however implementation programs to meet these targets could be at daily, monthly or seasonal scales depending on the variability of the system and how finely the discharges can be managed. Taniguchi-Quan et al. (this issue), investigated the high priority subbasins identified from our study and illustrated the use of CEFF to develop ecological flow needs supportive of focal species of management concern.

Our study focused on macroinvertebrate and algae response to altered flow. However, flow is not the only variable affecting the biological condition of streams. Local scale conditions such as water temperature, dissolved oxygen availability, nutrient concentration, substrate, and habitat complexity, also play a key role in the structure of biological communities. Many of these factors are strongly linked to flow, therefore it is possible that flow management and restoration efforts may be able to address multiple issues related to biological degradation. However, for flow management and restoration to be fully successful, it is important to consider the effects of other influencing factors at various spatial scales (King et al., 2016; Verdonschot et al., 2016). Such a task is complicated by interactive effects that modify the impact of confounding factors (Folt et al., 1999), such as habitat complexity dampening the strength of temperature effects (Scrine et al., 2017) or a combined effect of flow velocity and increased nutrient concentrations being smaller than the individual impact (Bækkelie et al., 2017).