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Few methods directly transfer streamflow measurements for continuous prediction of ungauged catchments. Top-kriging has been used mainly to predict the statistical properties of runoff but has been shown to outperform traditional regionalization approaches of rainfall-runoff models. We applied the Top-kriging approach across the Loire River basin and compared predictions to a geomorphology-based approach. Whereas Top-kriging uses spatial correlation, the other approach has the advantage of being more physically based by using a well-known geomorphology-based hydrological model (WFIUH) and its inversion. Both approaches require an equal degree of calibration and provide similar performances. We also demonstrate that the Ghosh distance, which considers the nested nature of catchments, can be used efficiently to calculate weights and to identify the suitability of gauged catchments for use as donor catchments. This result is particularly relevant for catchments with Strahler orders above five, i.e., where donor catchments are more strongly nested. [ABSTRACT FROM AUTHOR]

In water quality monitoring programs, standard sampling frequency schemes tend to be applied throughout entire regions or states. Ideally, the common standard among monitoring stations ought not to be the sampling frequency but instead the level of uncertainty of the estimated water quality indicators. Until now, there was no obvious way of doing this. This article proposes, for the first time, guidelines to select appropriate sampling frequencies to harmonize the level of uncertainty in the case of yearly nitrate indicators for the regional river water quality monitoring network in Brittany, France. A database of 50 watershed-year datasets (nine watersheds of 4 to 252 km² in size) was used for which high temporal resolution data (hourly and daily) were available for flow and nitrate concentrations. For each dataset, the uncertainty levels were calculated by numerically simulating sampling intervals varying from 2 to 60 days. The precision limits of the uncertainties were successfully correlated to a hydrological reactivity index. The correlations were used to derive sampling frequency charts. These charts cat: be used by watershed managers to optimize the sampling frequency scheme for any watershed for a desired uncertainty level, provided that the dimensionless local hydrological reactivity can be calculated from previous records of continuous flow rates. The sampling frequency charts also suggest that, depending on the hydrological reactivity, expected uncertainties generated by monthly sampling range between ±6% and ±14% for the annual load and between -5% and +2.5% to +7.2% for the annual concentration average. [ABSTRACT FROM AUTHOR]

The objectives of this study are to evaluate the uncertainty in annual nitrate loads and concentrations (such as annual average and median concentrations) as induced by infrequent sampling and by the algorithms used to compute fluxes. A total of 50 watershed-years of hourly to daily flow and concentration data gathered from nine watersheds (5 to 252 km²) in Brittany, France, were analyzed. Original (high frequency) nitrate concentration and flow data were numerically sampled to simulate common sampling frequencies. Annual fluxes and concentration indicators calculated from the simulated samples were compared to the reference values calculated from the high-frequency data. The uncertainties contributed by, several algorithms used to calculate annual fluxes were also quantified. In all cases, uncertainty increased as sampling intervals increased. Results showed that all the tested algorithms that do not use continuous flow data to compute nitrate fluxes introduced considerable uncertainty. The flow-weighted average concentration ratio method was found to perform best across the 50 annual datasets. Analysis of the bias values suggests that the 90th and 95th percentiles and the maximum concentration values tend to be systematically underestimated in the long term, but the load estimates (using the chosen algorithm) and the average and median concentrations were relatively unbiased. Great variability in the precision of the load estimation algorithms was observed, both between watersheds of different sizes and between years for a particular watershed. This has prevented definitive uncertainty predictions for nitrate loads and concentrations in this preliminary work, but suggests that hydrologic factors, such as the watershed hydrological reactivity, could be a key factor in predicting uncertainty levels. [ABSTRACT FROM AUTHOR]