Simplified direct water footprint model to support urban water management

Water resources conservation corresponding to urban growth is an increasing challenge for European policy makers. Water footprint (WF) is one of the methods to address this challenge. The objective of this study was to develop a simplified model to assess the WF of direct domestic and non-domestic water use within an urban area and to demonstrate its effectiveness in supporting new urban water management strategies and solutions. The new model was tested on three Central European urban areas with different characteristics i.e., Wroclaw (Poland), Innsbruck (Austria), and Vicenza (Italy). Obtained WFs varied from 291 dm3/(day∙capita) in Wroclaw, 551 dm3/(day∙capita) in Vicezna to 714 dm3/(day∙capita) in Innsbruck. In addition, WF obtained with the proposed model for the city of Vicenza was compared with a more complex approach. The results proved the model to be robust in providing reasonable results using a small amount of data

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Water demand forecasting is a crucial task in the efficient management of the water supply system. This paper compares classical and adapted machine learning algorithms used for water usage predictions including ARIMA, support vector regression, random forests and extremely randomized trees. These models were enriched with human mobility data to improve the predictive power of water demand forecasting. Furthermore, a framework for processing mobility data into time-series correlated with water usage data is proposed. This study uses 51 days of water consumption readings and over 7 million geolocated mobility records from urban areas. Results show that using human mobility data improves water demand prediction. The best forecasting algorithm employing a random forest method achieved 90.4% accuracy (measured by the mean absolute percentage error) and is better by 1% than the same algorithm using only water data, while classic ARIMA approach achieved 90.0%. The Blind (copying) prediction achieved 85.1% of accuracy

Intercomparison of Flow and Transport Models Applied to Vertical Drainage in Cropped Lysimeters

International audienceThe vertical water flow, heat flow and transport of the herbicide methabenzthiazuron were monitored for 627 days in lysimeters sampled at a field site close to the research centre Jülich, Germany. During this period the lysimeters were cropped with winter wheat, winter barley and oat. The models TRACE, MARTHE, ANSWERS and MACRO were applied to the lysimeter data with the scope of upscaling local scale process understanding for regional scale. MARTHE and TRACE solve the 3-d Richards' equation for variably saturated water flow. MACRO is a 1-d model based on the Richards' Equation and accounting for preferential flow in the unsaturated zone, while ANSWERS is a regional scale capacity based watershed model. Measurements of soil moisture, evapotranspiration, drainage, soil temperature, pesticide residues and leaching are used for comparison with model results. Although the adopted models differ in terms of model concepts, the use of model performance indices proved a proper simulation of water flow for all models. The heat flow is also well described with ANSWERS, MARTHE and MACRO. Larger deviations were found between model results and measured pesticide transport. An inadequate reproduction of the measured MBT degradation was found for the available model input parameters. A very small amount of MBT leaching, observed in the measurements, was only reproduced with MACRO after strong calibration. In other respects only plant parameters were calibrated. Calibration of the crop conversion factor used for scaling of the potential evapotranspiration was found to be a crucial parameter for the adequate description of the water balance by the models