ALBERT

Description: Understanding large-scale patterns in flow intermittence is important for effective river management. The duration and frequency of zero-flow periods are associated with the ecological characteristics of rivers and have important implications for water resources management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero flow) in each year of record (FREQ; yr−1), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr−1), and the mean duration of zero-flow periods (mDUR; days). Approximately 20% of stations had recorded at least one zero-flow period in their record. Dissimilarities between pairs of gauges calculated from the annual times series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used random forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller than catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero flows. While 20% of gauges were classified as intermittent, the flow-regime model predicted 39% of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary predictions of the effects of climate change on flow intermittence.

Description: Hydrological classification has emerged as a suitable procedure to disentangle the inherent hydrological complexity of river networks. This practice has contributed to determining key biophysical relations in fluvial ecosystems and the effects of flow modification. Thus, a plethora of classification approaches, which agreed in general concepts and methods but differed largely in specific procedures, have emerged in the last decades. However, few studies have compared the implication of applying contrasting approaches and specifications over the same hydrological data. In this work, using cluster analysis and modelling approaches, we classify the entire river network covering the northern third of the Iberian Peninsula. Specifically, we developed classifications of increasing level of detail, ranging from 2 to 20 class levels, either based on raw and normalized daily flow series and using two contrasting approaches to determine class membership: classify-then-predict (ClasF) and predict-then-classify (PredF). Classifications were compared in terms of their statistical strength, the hydrological interpretation, the ability to reduce the bias associated with underrepresented parts of the hydrological space and their spatial correspondnece. The results highlighted that both the data processing and the classification strategy largely influenced the classification outcomes and properties, although differences among procedures were not always statistically significant. The normalization of flow data removed the influence of flow magnitude and generated more complex classifications in which a wider range of hydrologic characteristics were considered. The application of the PredF strategy produced, in most of the cases, classifications with higher discrimination ability and presented greater ability to deal with the presence of distinctive gauges in the data set than using the ClasF strategy.

Description: Hydrological classification has emerged as a suitable procedure to disentangle the inherent hydrological complexity of river networks. This practice has contributed to determine key biophysical relations in fluvial ecosystems and the effects of flow modification. Thus, a plethora of classification approaches, which agreed in general concepts and methods but differed largely in specific procedures, have emerged in the last decades. However, few studies have compared the implication of applying contrasting approaches over the same hydrological data. In this work, using cluster analysis and modelling approaches, we classify the entire river network covering the northern third of the Iberian Peninsula. Specifically, we developed classifications of increasing level of detail, ranging from 2 to 20-class levels, either based on raw and normalized daily flow series and using two contrasting approaches to determine class membership: Classify-Then-Predict (ClasF) and Predict-Then-Classify (PredF). Classifications were compared in terms of their statistical strength, the hydrological interpretation, the ability to reduce the bias associated to the underrepresented parts of the hydrological space and the spatial correspondence. The results highlighted that both the data processing and the classification strategy largely influenced the classification outcomes and properties, although differences among procedures were not always statistically significant. The normalization of flow data removed the effect of flow size and generated more complex classifications in which a wider range of hydrologic characteristics were considered. The application of the PredF strategy produced, in most of the cases, classifications with higher discrimination ability, greater ability to address the bias associated with the presence of distinctive gauges and classifications in which classes were more evenly distributed than using the ClasF strategy.

Description: Understanding large-scale patterns in flow intermittence is important for effective water resource management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times-series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero-flow) in each year of record (FREQ; yr−1), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr−1), and the mean duration of zero-flow periods (mDUR; days). Approximately 20% of stations had recorded at least one zero-flow period. Dissimilarities between pairs of gauges calculated from the annual times-series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero-flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used Random Forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller than catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero-flows. While 20% of gauges were classified as intermittent, the flow-regime model predicted 39% of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow-setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary estimates of the effects of climate change on flow intermittence.