Statistical Behaviour of Load Estimators Based on Routine Monthly Data Series

M.A Lorenzo-Gonzalez, D Quilez, D Isidoro
2012 21st Century Watershed Technology: Improving Water Quality and Environment Conference Proceedings, May 27-June 1, 2012, Bari, Italy   unpublished
Irrigation contributes to the pollution of water bodies through the pollutant loads in the irrigation return flows. Establishing the relationship between changing irrigation and agricultural practices and pollutant loads over long periods may help to identify the irrigation-related factors that most affect water quality. This paper aims to ascertain the statistical performance of 5 salt and nitrate load estimators based on the long-term monthly records of the surface water quality monitoring
more » ... ality monitoring network (SWQ) of the Ebro Basin Authority (CHE). These estimates were compared with daily estimates in the Arba River Three estimation methods used grab-samples monthly TDS i and NO 3i from the SWQ network (multiplied by instant, Q i , mean daily, Q d , or monthly, Q m , flows), whilst the other two were the product of the regression estimates of TDS and NO 3 from Q d by Q d or Q m . The instant concentrationbased models were also tested with daily data from the R-E network, with more complete records. The regression estimators performed better than the models based on instant samples for salt loads. But for nitrogen loads, the estimators based on NO 3i and Q d or Q m also performed well when drawing data from the more complete R-E data series. Although the biases for the 5 methods were not significant; only these estimators presented errors low enough to allow their use in generating reliable load time series.
doi:10.13031/2013.41430 fatcat:7fj2o4d6hjczxkdpudzyd6jpry