Discussion

Developing general flow-ecology relationships in freshwater ecosystems is a global priority (ELOHA framework; Poff et al. 2010). Spatial hydrologic gradient analyses have the potential to address this need, however, studies that substitute space-for-time come with certain limitations. We have proposed and tested a framework (Figure 2.1) to address three limitations of using spatial gradient analysis in ecohydrology studies: (1) availability of flow data, (2) temporal trends confounding spatial gradients, and (3) statistical power to extract gradients amongst the many ecologically relevant flow indices. This framework supports Step 1 & 3 in the ELOHA framework of establishing the hydrologic data foundation and identifying flow gradients that can then feed into Step 4 that relates those gradients to ecological parameters of management interest. We applied this framework for a set of catchments and found that only a subset of flow indices were estimable given data constraints, that temporal trends were unlikely to heavily bias a spatial analysis, and that there were three primary hydrologic gradients.

2.5.1

Can flow indices be estimated robustly?

Spatial hydrologic gradient analyses are often constrained by sample size at a regional scale due to sparse placement of instrumentation or gauging stations with a short time period of data records. Our index uncertainty analysis confirmed that five years was too short a period to estimate indices robustly. This is consistent with the analysis that Kennard et al. (2010) conducted on six 75-year flow time series in Australia, and with other studies demonstrating better index estimation with longer time series (e.g. Richter et al. 1996; Cunderlik, Ouarda & Bobée 2004; Huh et al. 2005). Many indices that captured dispersion, timing and frequency of flow events were poorly estimated even with 10 years of flow data, supporting the view that indices capturing extreme events often require longer time series, perhaps as long as 15-30 years (Kennard et al. 2010).

We found representative indices from most flow categories were retained after robustness analysis in our system using the framework, demonstrating good coverage of potential ecologically- relevant aspects of flow regime. The one exception was timing indices which were poorly estimated, even with 10 or 15 years of data. Additionally, the frequency of high- and low-flow events calculated using the flow duration curve to define thresholds were uncertain using a 10-year time series, but when thresholds were defined using median flows as a reference (as in the commonly used FRE3 index; Clausen & Biggs 2000), indices were well estimated while still capturing the desired hydrologic processes (flood and drought events). Thus, our framework and subsequent analyses enabled us to

30 target indices that captured most of the ecologically-relevant aspects of flow regime, while also being robustly estimable.

Our findings suggest, for our system, that the 15 year minimum flow time series rule-of- thumb (Kennard et al. 2010) is true for some indices but not others. And, importantly, our

framework allowed for the determination of an appropriate common period length on a system and index specific basis. This can be applied to any system with similar data. The decision points in the framework strategically balance which indices can be estimated with the available data and whether the non-robust indices can be ejected without compromising the goals of the study. Or in the case that indices of interest cannot be estimated, that a longer common period should be adopted, which decreases the regional sample size but improves the confidence in the gradient analysis study. While our specific dataset faced limitations regarding the estimation of especially timing variables, it highlighted the utility of our framework in assessing these limitations, and resulted in a set of defensible indices that captured flow processes that were in line with the goals of the follow on ecological studies.

2.5.2

Potential for temporal trends to confound spatial gradients

One of the primary limitations of gradient analysis is the potential for unmeasured processes (such as non-stationary indices) to confound spatial gradients (Fukami & Wardle 2005). Non-stationary flows can arise through direct human influence (such as progressive water abstraction), or indirect effects such as shifts in land-use that affect surface run off or climate change. The affected indices will show poor robustness and a common period estimate will fail to estimate a long term average; this temporal trend can confound inferences on spatial hydrologic variability driven by differences among catchments alone. Thus, it is critical to evaluate evidence for temporal trends where spatial gradient analyses are being considered.

Detection of temporal trends can be problematic, as the statistical tools can be

underpowered if flow records are short. Our framework requires only a subset of representative gauging stations from the regional set to contain longer records, and then uses those periods of longer record to determine the likelihood of confounding temporal trends amongst all flow records. The time series analysis showed only one statistically-supported trend, suggesting that temporal trends will have a low probability of influencing inferences from a hydrologic gradient analysis in our system. Although we cannot conclude that flow regimes are not changing over time, we do provide evidence that inter-annual variability in flow metrics is greater than systematic temporal changes in flow regimes in our system, suggesting that any observed effects of flow patterns on the ecology of

31 the system will more likely be attributable to the spatial hydrologic gradient than to any temporal changes in flow.

The power of these tests will always be dependent on both sample size (Yue & Pilon 2004) and on how representative stations with longer flow records are of hydrological flow patterns in the region generally. The primary advance of our framework is that it enables quantification of trends across a whole system, by inferring the probability of non-stationarity from a subset of recorded flow characteristics from rivers in a region. We recognize that the proposed framework only

captures monotonic trends, and could fail to capture step changes (e.g. due to engineering works or cyclical weather patterns). However, information on engineering works is generally more reliable and easily obtainable (in contrast to abstraction which in our system is not well monitored), and could be used to evaluate the range over which indices should be estimated.

2.5.3

Extracting hydrologic gradients

The many hydrologic indices that capture elements of flow regime increase the complexity of gradient analyses and thus require data reduction methods to derive statistically-supported hydrologic gradients. This enables clear inferences to emerge even when sample sizes are limited. Our use of factor analysis is not new in hydrologic studies (e.g. Belmar et al. 2013); however, it is not generally paired with parallel analysis that tests the statistical power of any underlying gradients. Our analysis strengthens the ability to define major hydrologic gradients, because the robustness of factor extraction is a function of regional sample size (de Winter, Dodou & Wieringa 2009).

We found three statistically-supported gradients, suggesting that even though regional sample size was relatively low, we can address questions related to flow magnitude (Poff & Zimmerman 2010), flow variability (Stewardson & Gippel 2003; Samuelson & Rood 2004), and the frequency and duration of low flow events (Rolls, Leigh & Sheldon 2012). That one of the extracted factors was related to low flow hydrology, which is most likely to be affected by future water abstraction, was promising because we can now address primary water management concerns. Indices that are highly correlated with the extracted factors, or the factor scores themselves, can be taken forward into ecological studies, with a high degree of confidence as they were derived from robustly estimated flow indices.

2.5.4

Conclusions

We believe this structured, defensible method for characterising flow gradients at a regional scale will lead to robust studies of ecological responses to flow regime that can inform general flow-

32 ecology relationships that fit within the broader Ecological Limits of Hydrologic Alteration

Framework (Poff et al. 2010) and can be used for setting environmental flows (Davies et al. 2014). By addressing sources of uncertainty before the ecological studies are carried out, we can invest our time and resources into the most fruitful model systems for improving our fundamental

understanding of river ecology.