A framework for downscaling precipitation from RCM projections to the high resolutions in time and space required in the urban hydrological climate change impact assessment is outlined and demonstrated. The basic approach is that of Delta Change, developed for both continuous and event-based applications. In both cases, Delta Change Factors (DCFs) are calculated which represent the expected future change of some key precipitation statistics. In the continuous case, short-term precipitation from climate projections are analysed in order to estimate DCFs associated with different percentiles in the frequency distribution of non-zero intensities. The DCFs may then be applied to an observed time series, producing a realisation of a future time series. The event-based case involves downscaling of Intensity-Duration-Frequency (IDF) curves based on extreme value analysis of annual maxima using the Gumbel distribution. The resulting DCFs are expressed as a function of duration and frequency (i.e., return period) and may be used to estimate future design storms. The applications are demonstrated in case studies focusing on the expected changes in short-term precipitation statistics until 2100 in the cities of Linz
OPEN ACCESSSustainability 2012, 4 867 (Austria) and Wuppertal (Germany). The downscaling framework is implemented in the climate service developed within the EU-project SUDPLAN.
Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.
Discharges of untreated wastewater from combined sewer overflows (CSOs) can affect hydraulic stress and have significant environmental impacts on receiving water bodies. Common flow rate and water level sensors for monitoring of CSO events are expensive in terms of investment costs, installation, operation and maintenance. This paper presents a novel surrogate method to detect CSO events by using two low-cost temperature sensors. The novelty is the experimental setup for installation of temperature sensors in CSO structures and an algorithm developed to automatically calculate the duration of CSO events considering the response time of the system. The occurrence and duration of CSO events is computed based on the convergence of the two temperature signals. The method was tested under field conditions in a CSO structure, and the results were compared to the information gathered from a parallel installed flow sensor. The application of two temperature sensors installed inside a CSO structure was proven to be robust and accurate for the automatic detection of the occurrence and duration of CSO events. Within the 7-month test phase, 100% of the 20 CSO events could be detected without false detections. The accuracy of detecting the start and end of the CSO events was 2 min in comparison to the flow sensor.Electronic supplementary materialThe online version of this article (10.1007/s10661-018-6589-3) contains supplementary material, which is available to authorized users.
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