ABSTRACT
Due to the current extent of arid regions, the pressure on available water resources is increasing. A suitable measure for water availability and dynamics in dry soil is the relative humidity of the soil air. Due to the heterogeneity of soil, water inputs, and root water uptake, the humidity of soil air will vary in space. Therefore, area-representative measurement methods are needed to find a representative measure of the soil water status. Existing sensors for the direct determination of relative humidity only represent a single location with a spatial extent of up to several cm. We introduce a new measuring principle that averages over a spatially heterogeneously distributed relative humidity. It is based on the selective diffusion of water vapor pressure through a tubular semipermeable membrane to/from a closed measurement chamber. A measured pressure change is sensitive to the water vapor pressure and enables, without any external calibration, to estimate an average of the relative humidity. The comparison of our first laboratory prototype of the new sensor with calibrated reference sensors for relative humidity in a range of approx. 4 to 100% proves the linearity of the measuring method and its high accuracy. For further optimization improved reference measurement techniques are necessary. A potential application is the improvement of water use efficiency in irrigated agriculture.
ABSTRACT
For the evaluation of action programs to reduce surface water pollution, water authorities invest heavily in water quality monitoring. However, sampling frequencies are generally insufficient to capture the dynamical behavior of solute concentrations. For this study, we used on-site equipment that performed semicontinuous (15 min interval) NO(3) and P concentration measurements from June 2007 to July 2008. We recorded the concentration responses to rainfall events with a wide range in antecedent conditions and rainfall durations and intensities. Through sequential linear multiple regression analysis, we successfully related the NO(3) and P event responses to high-frequency records of precipitation, discharge, and groundwater levels. We applied the regression models to reconstruct concentration patterns between low-frequency water quality measurements. This new approach significantly improved load estimates from a 20% to a 1% bias for NO(3) and from a 63% to a 5% bias for P. These results demonstrate the value of commonly available precipitation, discharge, and groundwater level data for the interpretation of water quality measurements. Improving load estimates from low-frequency concentration data just requires a period of high-frequency concentration measurements and a conceptual, statistical, or physical model for relating the rainfall event response of solute concentrations to quantitative hydrological changes.
