BALTRAD tailored ender-user product: Risk assessment map for urban drainage management

Transcription

1 BALTRAD tailored ender-user product: Risk assessment map for urban drainage management Authors: Jesper E. Nielsen, Michael R. Rasmussen (AAU) Date: December 2012 BALTRAD Document: BALTRAD+ W4-Risk assessment map Part-financed by the European Union (European Regional Development Fund and European Neighbourhood Partnership Instrument 1

2 Contents 1. Motivation and introduction Observation data and data infrastructure Data processing concept Advection based interpolation Radar rain gauge adjustment The Risk Assessment Map web interface August 26th event Flooding of Lystrup and Djursland highway Motivation and introduction Water utility companies in charge and responsible for urban drainage systems can benefit greatly from having easy access to high quality precipitation data. Precipitation is the biggest load on urban drainage system, causing vast majority of system failures. However, the urban drainage systems are to some extend designed to fail, as the systems are not designed to handle the most extreme rainfall. This choice is an economic balance between damage cost of rare extreme events and the construction cost insuring that the system are able to handle these events. Consequently, when system failure happens it is not necessarily the same as the system is not functioning correctly. In a situation of urban flooding a series of questions quickly arises: Which areas are flooded? How severe is the flooding? Where will the emergency response be needed and is the response most effective? What causes the flooding extreme rainfall or was the system not working properly? Should action be taken to prevent this situation to happen again in the future? Etc. Most of these questions require fast decisions and relies mainly or partly on detailed knowledge about the urban rainfall. Nevertheless, most water utility companies rely on rain gauge observations from sparse gauge networks making these decisions. This is the main motivation for developing the radar based Risk Assessment Map (RAM) as a BALTRAD tailored end-user product. The idea behind the application is that the RAM should support the daily routines in urban drainage management by facilitating an easy access to radar based observations. The properties and functionally of the Risk Assessment Map product was determined in a dialogue with the BALTRAD partner Aarhus Water Utility, where following paradigm was established with the intention to make the product as useful as possible: - The application should be intuitive and easy to use. In a busy working day it is important that the benefits from using the product it reached easily. - The radar based product should be corrected and validated against ground observations. Data credibility is important; as the product will only be useful if it is trustworthy. - Data quality in terms of performance and/or accuracy should be provided with the radar estimates. It is important data quality is transparent to the user; if the radar for some reason has a poor performance this should be visible. - The application should have both a real-time and historical dimension; the current precipitation situation should be just as accessible as precipitation information from last week or month. Real-time data is highly valuable during the flooding for the emergency response and damage control, while historical data have its value when the flooding event is evaluated and analysed. Based on this paradigm the risk assessment map was developed for Aarhus urban catchment. 2

3 2. Observation data and data infrastructure The RAM is based on meteorological weather radar observation from DMI s Virring C-band radar located approximately 20 km south-east of Aarhus. The rain gauge network in the area consists of eight gauges, where three (Trankær, Viby and Egå) is part of the national rain gauge network managed by the Danish Water Pollution Committee (DWPC). The rest of the rain gauges are operated solely by Aarhus Water Utility and the combined gauge network is used for their daily management of the urban drainage system and waste water treatment plants. Figure 1 presents the location of the radar and rain gauges in the area. Spørring Gauge Gauge Egå WWT Gauge Tilst Gauge Åby WWT Gauge Marselisborg WWT gauge Gauge Harlev WWT Gauge Viby WWT Gauge Trankær Gauge DMI C-band radar Figure 1: Observation data setup radar and rain gauges. (WWT is acronym for Waste Water Treatment plant) (Google maps, 2012) The RAM product is physically produced on the Aarhus UD BALTRAD server hosted at Aalborg University in Aalborg. The data infrastructure is based on two pipelines of data sources. The radar data is received from the DMI BALTRAD node via BALTRAD data exchange to the Aarhus Water BALTRAD UD node, whereas the rain gauge data source is send via FTP from Aarhus Water (in Aarhus) to the Aarhus Water BALTRAD UD node (in Aalborg). 3

4 3. Data processing concept The RAM is based on a combination of radar and rain gauge data radar. This data processing can conceptually be described by two overall processing steps before the data is presented on the RAM web interface: 1. Advection based interpolation of the radar data 2. Radar rain gauge adjustments 3.1 Advection based interpolation The purpose of advection based interpolation is to increase the temporal radar data resolution in order to obtain more accurate accumulations and accurate rain intensity time series. The Radar data has a temporal resolution of 10 minutes, which in reality means that the precipitation can travel several kilometres between two radar scans. In relation to urban drainage applications this property is rather undesirable, because the radar observed precipitation may jump over the catchment of interest. The interpolation method applied is based on advection. The movement of the precipitation is estimated from the radar images, and used to predict the location of the rainfall in between the radar scans. Figure 2 presents the effect of the radar data interpolation on a rain intensity time series. As illustrated, the interpolated radar data matches the gauge observation better, as more of the short time scale becomes visible in the data. Gauge Observation Interpolated Radar data Original Radar data Figure 2: Example of advection based interpolation of the radar data. 3.2 Radar rain gauge adjustment The second step in the data processing is the Radar rain gauge adjustment. This is done regularly (once every hour) by means of bias adjustment based on the last 25 mm of precipitation. This insures that enough rainfall is used for the adjustment but also that the adjustment is based on the most recent rainfall. Therefore, the length of data period varies according the amount of precipitation prior to the RAM production. 4

5 The RAM presented is only adjusted based on data prior to the RAM production. Thus, the data presented has not been used for the adjustment. This makes the data processing robust to delays in the gauge observation data. The bias adjustment factor is based on one hour accumulation comparisons between the Radar estimate and the rain gauge observation. Moreover, the rain gauges were equally weighted in the process. 4. The Risk Assessment Map web interface The web interface of the RAM in presented in figure 3.When the user enters the RAM web interface the start page will show the most resent RAM. However, if the user is interested in data back in time a quick an easy selection of year, month, day and time handles this and navigates the user to the desired RAM. The RAM consists of three overall elements: - The RAM showing the one hour accumulated precipitation. - A scatter (1:1) plot illustrating the performance prior to the displayed data, with NSE performance (Nash Sutcliffe Efficiency index) and the applied bias factor. - Rain intensity time series of the rain gauge observations compared with the processed radar data in the corresponding gauge pixel. Year selection Month selection Date selection Rain intensity time series of the rain gauge observations is compared with the processed radar data in the corresponding gauge pixel. The displayed time series correspond to the accumulated image displayed to the left. Date and time of displayed data One hour accumulation based on advection interpolated and gauge adjusted radar data Time selection Evaluation of the radar performance prior to the displayed data Figure 3: The web interface of the Risk Assessment Map BALTRAD end-user product. 5

6 The purpose of these elements is first of all to present the data in a useable way for risk assessment. However, just as important is the transparency of the data quality. Based on the presented information the user is quickly able to make a confidence judgement of the data. Form the illustrated example in figure 3, it is seen that the radar has performed well prior to the RAM (NSE=0.84) and there is currently a fairly good correlation between the gauge and radar time series. All in all this shows great confidence in this particular RAM. 5. August 26 th event Flooding of Lystrup and Djursland highway To demonstrate the capability of risk assessment map, a case in Aarhus is used. The background is that a highway was established (Djurslandsmotorvejen) that connects the Aarhus area with the Djursland area. As illustrated in figure 4, both the highway and a residential area were flooded as a consequence of severe rain. The question was: Why is only this area affected? There are no official rain gauges in the area. The Egå raingauge (se figure 1) was closest to the event. Although it registered significant rain, it was not in the centre of the rain cell which caused the flooding. Figure 4: Picture documentation of the August 26 th Background: (Google maps, 2012) event. Foto: Dagø Jan/Polfoto and EB.dk. As the flooding seemed very local, it would have been very useful to early in the emergency response to know if this could be a problem with the drainage system or simply that excessive rain had fallen over the catchment. 6

7 The risk assessment map (if this was available at the time) could have supplied the emergency responders with information if similar events were under development other places in the city or they could allocate all available manpower and equipment for remove as much water as possible to reduce costs. This event is still debated in Aarhus and has been subject for several news reports during the last 6 months. It is still undecided how large a role the new highway played in the flooding of the area. Figure 5 and 6 illustrates the RAM for this particular event from 5AM to 7AM Figure 5: Risk Assessment Map from the 26 th of August 05:00 06:00 In figure 5, the rain cell approaches the affected area and during this hour almost 30 mm of rain has fallen. In figure 6, the cell continuous towards North-East and the maximum accumulated rains is also around 30 mm. In the following 2 hour, the accumulated rain was around 10 mm. The total rain depth was 70 mm with 60 mm within 2 hours. 7

8 Figure 6: Risk Assessment Map from the 26 th of August 06:00 07:00 Should a similar event occur in 2013, the RAM could help Aarhus Vand A/S in their support af the emergency response. This example also illustrates that rain gauge in these intense cell situations are of less use unless they are place in high numbers and with close proximity. Evaluations with AAU nowcast model also reveal that a warning could have been issued with approximately 1 hour lead time before the event occurred. A combination of the risk assessment map with a nowcast system could potentially have made it possible to start the mitigation efforts earlier then was the case. 8