Using spot gauging data to interpolate low flow characteristics
A general framework for improving the use of sporadic measurement data to estimate low flow characteristics is presented. The variable studied is QMNA5, the annual minimum monthly flow with a return period of 5 years. The dataset consists of 133 gauging stations of the Seine-Normandy basin. The method using the sporadic measurement data provides estimates of QMNA5 at a poorly gauged site by multiplying QMNA5 computed at a nearby gauging station by a scale coefficient k. The value of k is obtained through synchronous measurements recorded at the poorly-gauged site and at the nearby station. A sensitivity analysis of the data collection strategy and of the similarity of the two sites indicated a weak sensitivity to the number of sporadic measurement data. However, the risk of a large bias is obviously higher when few measurements are involved. In addition, we recommend collecting measurements during the low flow period, choosing the donor site upstream or downstream from the poorly gauged site, and computing k as the ratio of the average of the synchronous discharges. This method was compared to three regionalization procedures: regression analysis and two variants of kriging. Their efficiency was measured by cross validation. The procedure considered in turn each station from the dataset as a poorly gauged site. Regression analysis performed worse than the two kriging techniques, the efficiencies of which were comparable. The method using sporadic measurement data yielded the best predictive performance even when the collected data were few. This method was introduced into a geostatistical framework to extrapolate the values obtained at the poorly gauged sites. A cross validation was carried out and demonstrated that there was a significant gain in accuracy in the interpolated QMNA5 values, even when using only a few sporadic measurements to supplement regional information.