ESTIMATION OF STREAMFLOW,BASE FLOW,AND NITRATE‐NITROGEN LOADS IN IOWA USING MULTIPLE LINEAR REGRESSION MODELS1

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest.     

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.     

Geoelectrical methods for monitoring geological CO2 storage: First results from cross-hole and surface–downhole measurements from the CO2SINK test site at Ketzin (Germany)

The feasibility of monitoring CO₂ migration in a saline aquifer at a depth of about 650 m with cross-hole and surface–downhole electrical resistivity tomography (ERT) is investigated at the CO₂SINK test site close to Ketzin (Germany). The permanent vertical electrical resistivity array (VERA) consists of 45 electrodes (15 in the injection well Ktzi201 and 15 in each of the two observation wells Ktzi200 and Ktzi202), successfully placed on the electrically insulated casings, in the depth range of about 590–740 m with a spacing of about 10 m. The three Ketzin wells are arranged as perpendicular triangle with distances of 50 and 100 m.First synthetic modelling studies indicate an increase of the electrical resistivity of about 200% caused by CO₂ injection, corresponding to a bulk CO₂ saturation of 50%, which is in good agreement with laboratory studies. Finite difference inversion of field data delivers three-dimensional resistivity distributions between the wells which are consistent with the reservoir modelling studies.To increase the limited observation area provided by the cross-hole measurements, additional surface–downhole measurements were deployed. A main CO₂ migration in SE–NW direction is deduced from surface to downhole resistivity experiments.The first cross-hole time-lapse results show that the resolution and the coverage of the electrode array in the Ketzin setting are sufficient to resolve the expected resistivity changes on the characteristic length scale of the electrode array. Significant resistivity changes could be measured, however, detailed information on the CO₂ plume could not be resolved yet by VERA under the existing geological circumstances.