Predicting the natural flow regime: models for assessing hydrological alteration in streams

Abstract

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national-and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites.

Multi-variate flood damage assessment: a tree-based data-mining approach

“…In these cases, tree-based models have been applied to multivariate data sets where interactions between parameters and threshold processes played a role. Similarly, Carlisle et al (2010) applied tree-based models to predict the value for 13 metrics of the magnitude, frequency, duration, timing and rate of change of streamflow given watershed characteristics, and Mototch et al (2005) investigated the relationship between the spatial distribution of snow water equivalent and landscape properties in an alpine catchment. Grunwald et al (2009) modeled successfully the relationship between phosphor load and environmental predictor variables for ten farms.…”

Flow regime change in an endorheic basin in southern Ethiopia

“…Key hydrological variables can be used to characterise the natural flow regime (NFR) of a river. These can be assessed statistically to understand the extent of change to that flow regime and for support of hydrological and ecological assessment for ecosystem conservation (Carlisle et al, 2010).…”

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“…De uma maneira geral, as dificuldades na relação entre a hidrologia e ecologia ainda são grandes (Carlisle et al, 2009), a recente ênfase de estudos relacionando os recursos hídricos e o meio ambiente tem mudado as prioridades de estimativas seguras do regime natural de vazões. Nos últimos anos, órgãos financiadores estão fomentando pesquisas com objetivo de subsidiar estudos sobre o referido tema compatibilizando os aspectos hidrológicos, limnológicos, ecológicos e socioeconômicos dos regimes de vazões (CNPq, 2006).…”