Transposing Concentration-Discharge Curves onto Unmonitored Catchments to Estimate Seasonal Nutrient Loads
Abstract
Many lakes and reservoirs in Europe suffer from severe eutrophication. Accurate quantification of nutrient loads are critical for effective mitigation measures, but this information is often unknown. For example, in France, only 50 out of 481 lakes and reservoirs have national monitoring allowing estimation of interannual nitrogen and phosphorus loads, and even these loads are computed from low-frequency data. To address this lack of data, we developed a straightforward method to predict seasonal loads in lake tributaries. First, we analyzed concentration-discharge (C-Q) curves in monitored catchments and identified slopes, intercepts, and coefficient of variation of the log(C)-log(Q) regressions determined for both low and high flows, separated by the median daily flow [Moatar et al., 2017]. Then, we used stepwise multiple linear regression models to empirically link the characteristics of C-Q curves with a set of catchment descriptors such as land use, lithology, morphology indices, climate, and hydrological indicators. Modeled C-Q relationships were then used to estimate annual and seasonal nutrient loads in nearby and similar unmonitored catchments. We implemented this approach on a large dataset from France where stream flow was surveyed daily and water quality (suspended solids, nitrate, total phosphorus, and orthophosphate concentrations) was measured on a monthly basis at 233 stations over the past 20 years in catchments from 10 to 3000 km². The concentration at the median daily flow (seen here as a metric of the general level of contamination in a catchment) was predicted with uncertainty ranging between 30 and 100 %, depending on the variable. C-Q slopes were predicted with large errors, but a sensitivity analysis was conducted to determine the impact of C-Q slopes uncertainties on computed annual and seasonal loads. This approach allows estimation of seasonal and annual nutrient loads and could be potentially implemented to improve protection and restoration of aquatic ecosystems. Moatar, F., B. W. Abbott, C. Minaudo, F. Curie, and G. Pinay. 2017. Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment, and major ions. Water Resources Research 53:1270-1287.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.H41F1513M
- Keywords:
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- 0470 Nutrients and nutrient cycling;
- BIOGEOSCIENCES;
- 0496 Water quality;
- BIOGEOSCIENCES;
- 1871 Surface water quality;
- HYDROLOGY;
- 1880 Water management;
- HYDROLOGY