Modelling Hydrological Indices at Ungauged Stream Segments to Classify Flow Regime

ABSTRACT Hydrologic variability is predicted to increase across much of the world in response to changing conditions and increased groundwater and surface water extraction. Because flow regime is a primary control on habitat viability for aquatic species, including non‐native, native, threatened and endangered species, ecologists often need access to hydrological data to support sampling efforts, including at smaller scales that span ungauged sites. However, predicting flow regime is difficult at ungauged sites and models that predict flow parameters (e.g., mean annual flow, flow predictability and annual coefficient of flow) are often designed for use at large scales. Here, we demonstrate a workflow for deriving hydrologic indices in small‐sample, data‐limited contexts to support ecological research. We used USGS streamflow data to calculate 10 hydrological indices at gauged sites in a relatively small central Texas, USA, watershed using the Indicators of Hydrological Alteration software. Values at gauged sites were then used in conjunction with environmental parameters (climate, geology, land use, etc.) to model the same hydrological indices at ungauged sites within the watershed. Models were validated using bootstrap resampling (final average R 2 of 0.76, range of 0.62–0.88). Cluster analysis was used to group gauged and ungauged sites into two clusters of ‘wet’ and ‘dry’ sites. ‘Dry’ sites experience systematic declines or complete cessation in discharge because of anthropogenic effects such as river water withdrawal and groundwater extraction. Regression‐based regionalization for flow regime prediction improves our ability to model hydrological regimes in rivers and, specifically, in river segments lacking continuous gauge data and to derive model variables that are key predictors required for many ecological models.