Artificial artificial intelligence for urban drainage systems?

A major challenge in data-driven urban drainage management and research is determining whether sensors are functioning properly. The Holy Grail for urban drainage research could be a machine learning model, trained like large language models but on vast, public and annotated urban drainage datasets—if only we had them.

Everybody knows it: wastewater smells, it is infectious, it is bad. Sewers are even worse, buried in the ground, extremely harsh environments for us and our machines. But, believe it or not, the worst are monitoring data from sewer systems. Although basic data checks (Becouze-Lareure et al., 2012; DWA, 2011) have been suggested, they are not routinely applied. Consequently, data of unknown quality levels pile up and weaken the horrible reputation even more. Sceptical? An in-depth analysis of data submitted for compliance assessment of urban drainage systems in Germany found that about a third of the ca. 300 datasets failed basic plausibility checks (Dittmer et al., 2015).


As we were facing similar data quality issues in our real-world lab “Urban Water Observatory” (UWO) we called recent machine learning methods to the rescue (Figure 1, left) (Disch and Blumensaat, 2019). However, methods such as Support vector machines and Autoencoders, did not really outperform much simpler ARIMA approaches in detecting anomalies in the time series data. And this although these methods usually excel in finding anomalies in large datasets, even invisible watermarks in images (Zhao et al., 2023). In our case, unless the UWO data were heavily preprocessed, denoised, smoothed and imputed with expert knowledge, all methods performed surprisingly bad. But how can one get such high-quality, nicely annotated monitoring data from sewers?


For every-day images of cats, busses and dogs, crowdsourcing is the go-to method to annotate thousands and thousands of images which can then be used to train and optimize classifiers. This also seems to work well for special tasks, such as plants/botany (Goëau et al., 2011), text snippets, etc. and it is no surprise that by typing captchas, you are actually creating ground truth data to help train machine learning models (Yennhi95zz, 2023).
 

Figure 1, left: Potential anomalies detected show the benefit of using multiple sensors. On the left, variability is captured by all sensors, while on the right, it's only detected by one. Right: F1 scores from Disch and Blumensaat (2019) for different pre-processing methods (A-D) show medium quality between 0.5-0.8, good at 0.8-0.9, and excellent above 0.9. For real data (A-C), performance improves with more pre-processing, with ARIMA performing best. The Autoencoder struggles to match synthetic data performance (D).

 

But for sewers? Would it even be possible to use crowdsourcing methods, the so-called artificial artificial intelligence, on such special data as sewer water levels and flows? To tackle this question, we initiated a small internal annotation project within our research group (Rieckermann and Disch, 2024). Using the same dataset from our machine learning trials, we shifted the task from machine-detected synthetic anomalies to relying solely on our expertise as sewer researchers. And yes, while we're not spending all our time scanning for anomalies like the Nebuchadnezzar crew monitors the Matrix’s streaming data, we can easily spot a frozen water level sensor when a time series flattens. We've also developed a sharp eye for detecting abnormal fluctuations. It’s as good as it gets—the best game in town.


We gathered 7 time series of sewer flows from various locations in the UWO, refined the machine learning trial scripts, and quickly developed some Python scripts to allow everyone to upload their annotated data into a shared database. To evaluate our annotation skills, we chose the F1 criterion, as in the previous study, which combines the hit rate (precision) and accuracy (recall) of the analyst.
 


First lesson: before we got any results, we had to go back to start and re-design the tool we used for annotation. We included the ability to visualize multiple time series of nearby sensors at the same time, because we found that it was much more difficult to interpret a single time series alone (Fig 1, left). Also, analysts wanted to see the natural variability during several days while annotating.
Second lesson: our results were sobering. On average, we got mediocre scores in the range of 0.6 -0.7 (Fig. 2, right). On the one hand, this is rather mediocre regarding the best scores of the machine learning tools mentioned above (ARIMA: 0.9-0.8). On the other hand, the good performance of the machine learning models was only possible using pre-processed and annotated data. On raw data the score also dropped to around 0.4-0.5.
Third, we believe that the performance is rather low because, i) flows in small sewers vary a lot, not only during wet periods, but also during dry weather, ii) the raw data were very noisy, iii) the annotation tool was limiting and, most interestingly, iv) we analysts had very different mental models regarding anomalies and how to label the data.
 


 

Figure 2, left: Screenshot of our custom annotation tool. Advantages are that the analyst can see related times and at a glance assess the typical variability. Right: F1 scores from our annotation exercise, which are rather mediocre. We believe that the performance is rather low because, i) flows in small sewers vary a lot, not only during wet periods, but also during dry weather, ii) the raw data were very noisy, iii) we analysts had very different mental models regarding anomalies and how to label the data, iv) the annotation tool was limiting.

 

In summary, while we see a lot of potential for data-driven modeling, or machine learning, in urban drainage research, we agree with others (Eggimann et al., 2017; Fu et al., 2024) that one challenge in our field is missing training datasets. Unfortunately, our results suggest that, unlike images of cats or busses, urban drainage data are not particularly suited to obtain annotations from crowdsourcing. One way forward might be standards for annotations, community actions on artificial artifical intelligence, similar to the “battle of the water networks” (Marchi et al., 2014), and, most of all, role model utilities, such as the one in Fehraltorf, Switzerland, which provide open access to their routine wastewater datasets from sewer systems. Even if they are not perfect.


Oh, and just one more thing… Do you know a good labelling tool for urban drainage time series data? Are you the role model utility which shares their data? Get in touch with us or the CoUDLabs project.
 

References

  1. Becouze-Lareure, C., Bazin, C., Namour, P., Breil, P., Perrodin, Y., 2012. Multi-Level Approach of the Ecotoxicological Impact of a Combined Sewer Overflow on a Peri-Urban Stream. J. Water Resour. Prot. 4, 984–992. doi.org/10.4236/jwarp.2012.411114
  2. Disch, A., Blumensaat, F., 2019. Messfehler oder Prozessanomalie? – Echtzeit-Datenvalidierung für eine zuverlässige Prozessüberwachung in Kanalnetzen. Presented at the Aqua Urbanica 2019: Regenwasser weiterdenken - Bemessen trifft Gestalten, Rigi Kaltbad.
  3. Dittmer, U., Alber, P., Seller, C., Lieb, W., 2015. Kenngrössen für die Bewertung des Betriebes von Regenüberlaufbecken. Presented at the Jahrestagung der Lehrer und Obleute der Kläranlagen- und Kanal-Nachbarschaften des DWA-Landesverbands Baden-Württemberg am 25./26. März 2015.
  4. DWA, 2011. DWA-M 181 -Messung von Wasserstand und Durchfluss in Entwässerungssystemen.
  5. Eggimann, S., Mutzner, L., Wani, O., Schneider, M.Y., Spuhler, D., Moy de Vitry, M., Beutler, P., Maurer, M., 2017. The Potential of Knowing More: A Review of Data-Driven Urban Water Management. Environ. Sci. Technol. 51, 2538–2553. doi.org/10.1021/acs.est.6b04267
  6. Fu, G., Savic, D., Butler, D., 2024. Making Waves: Towards data-centric water engineering. Water Res. 256, 121585. doi.org/10.1016/j.watres.2024.121585
  7. Goëau, H., Joly, A., Selmi, S., Bonnet, P., Mouysset, E., Joyeux, L., Molino, J.-F., Birnbaum, P., Bathelemy, D., Boujemaa, N., 2011. Visual-based plant species identification from crowdsourced data, in: Proceedings of the 19th ACM International Conference on Multimedia, MM ’11. Association for Computing Machinery, New York, NY, USA, pp. 813–814. doi.org/10.1145/2072298.2072472
  8. Marchi, A., Salomons, E., Ostfeld, A., Kapelan, Z., Simpson, A.R., Zecchin, A.C., Maier, H.R., Wu, Z.Y., Elsayed, S.M., Song, Y., Walski, T., Stokes, C., Wu, W., Dandy, G.C., Alvisi, S., Creaco, E., Franchini, M., Saldarriaga, J., Páez, D., Hernández, D., Bohórquez, J., Bent, R., Coffrin, C., Judi, D., McPherson, T., van Hentenryck, P., Matos, J.P., Monteiro, A.J., Matias, N., Yoo, D.G., Lee, H.M., Kim, J.H., Iglesias-Rey, P.L., Martínez-Solano, F.J., Mora-Meliá, D., Ribelles-Aguilar, J.V., Guidolin, M., Fu, G., Reed, P., Wang, Q., Liu, H., McClymont, K., Johns, M., Keedwell, E., Kandiah, V., Jasper, M.N., Drake, K., Shafiee, E., Barandouzi, M.A., Berglund, A.D., Brill, D., Mahinthakumar, G., Ranjithan, R., Zechman, E.M., Morley, M.S., Tricarico, C., de Marinis, G., Tolson, B.A., Khedr, A., Asadzadeh, M., 2014. Battle of the Water Networks II. J. Water Resour. Plan. Manag. 140, 04014009. doi.org/10.1061/(ASCE)WR.1943-5452.0000378
  9. Rieckermann, J., Disch, A., 2024. Challenges and Prospects in Anomaly Detection of Sewer Monitoring Data: Annotating Synthetic Sewer Data with Known Sensor Failures. https://doi.org/10.31224/3520
  10. Yennhi95zz, 2023. How Google Trains AI with Your Help through CAPTCHA. Medium. URL https://medium.com/@yennhi95zz/how-google-trains-ai-with-your-help-through-captcha-876cb4eb4d01 (accessed 10.24.24).
  11. Zhao, X., Zhang, K., Su, Z., Vasan, S., Grishchenko, I., Kruegel, C., Vigna, G., Wang, Y.-X., Li, L., 2023. Invisible Image Watermarks Are Provably Removable Using Generative AI. doi.org/10.48550/arXiv.2306.01953

 

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                  ies on computationally heavy MCMC routines for parameter inferences. These p
                  roperties make it more suitable for off-line applications. The IND can help
                  in diagnosing the causes of output errors and is computationally inexpensive
                  . It produces best results on short forecast horizons that are typical for o
                  nline applications.
' (1463 chars) serialnumber => protected'0043-1397' (9 chars) doi => protected'10.1002/2014WR016678' (20 chars) uid => protected8243 (integer) _localizedUid => protected8243 (integer)modified _languageUid => protectedNULL _versionedUid => protected8243 (integer)modified pid => protected124 (integer)
000000004a9cb2a50000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=8087, pid=124) originalId => protected8087 (integer) authors => protected'Fencl,&nbsp;M.; Rieckermann,&nbsp;J.; Sýkora,&nbsp;P.; Stránský,&nbsp;D.;
                   Bareš,&nbsp;V.
' (92 chars) title => protected'Commercial microwave links instead of rain gauges: fiction or reality?' (70 chars) journal => protected'Water Science and Technology' (28 chars) year => protected2015 (integer) volume => protected71 (integer) issue => protected'1' (1 chars) startpage => protected'31' (2 chars) otherpage => protected'37' (2 chars) categories => protected'areal rainfall; rainfall monitoring; rainfall spatial variability; telecommu
                  nication microwave links; QPE; urban hydrology
' (122 chars) description => protected'Commercial microwave links (MWLs) were suggested about a decade ago as a new
                   source for quantitative precipitation estimates (QPEs). Meanwhile, the theo
                  ry is well understood and rainfall monitoring with MWLs is on its way to bei
                  ng a mature technology, with several well-documented case studies, which inv
                  estigate QPEs from multiple MWLs on the mesoscale. However, the potential of
                   MWLs to observe microscale rainfall variability, which is important for urb
                  an hydrology, has not been investigated yet. In this paper, we assess the po
                  tential of MWLs to capture the spatio-temporal rainfall dynamics over small
                  catchments of a few square kilometres. Specifically, we investigate the infl
                  uence of different MWL topologies on areal rainfall estimation, which is imp
                  ortant for experimental design or to a priori check the feasibility of using
                   MWLs. In a dedicated case study in Prague, Czech Republic, we collected a u
                  nique dataset of 14 MWL signals with a temporal resolution of a few seconds
                  and compared the QPEs from the MWLs to reference rainfall from multiple rain
                   gauges. Our results show that, although QPEs from most MWLs are probably po
                  sitively biased, they capture spatio-temporal rainfall variability on the mi
                  croscale very well. Thus, they have great potential to improve runoff predic
                  tions. This is especially beneficial for heavy rainfall, which is usually de
                  cisive for urban drainage design.
' (1401 chars) serialnumber => protected'0273-1223' (9 chars) doi => protected'10.2166/wst.2014.466' (20 chars) uid => protected8087 (integer) _localizedUid => protected8087 (integer)modified _languageUid => protectedNULL _versionedUid => protected8087 (integer)modified pid => protected124 (integer)
000000004a9cb2ae0000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=8371, pid=124) originalId => protected8371 (integer) authors => protected'Sikorska,&nbsp;A.&nbsp;E.; Del Giudice,&nbsp;D.; Banasik,&nbsp;K.; Rieckerma
                  nn,&nbsp;J.
' (87 chars) title => protected'The value of streamflow data in improving TSS predictions - Bayesian multi-o
                  bjective calibration
' (96 chars) journal => protected'Journal of Hydrology' (20 chars) year => protected2015 (integer) volume => protected530 (integer) issue => protected'' (0 chars) startpage => protected'241' (3 chars) otherpage => protected'254' (3 chars) categories => protected'TSS; uncertainty analysis; Bayesian inference; multivariate calibration; mod
                  el bias; autocorrelated errors
' (106 chars) description => protected'The concentration of total suspended solids (TSS) in surface waters is a com
                  monly used indicator of water quality impairments. Its accurate prediction r
                  emains, however, problematic because: i) TSS build-up, erosion, and wash-off
                   are not easily identifiable; ii) calibrating a TSS model requires observati
                  ons of sediment loads, which are rare, and streamflow observations to calcul
                  ate concentrations; iii) predicted TSS usually deviate systematically from o
                  bservations, an effect which is commonly neglected. Ignoring systematic erro
                  rs during calibration can lead to overconfident (i.e. unreliable) uncertaint
                  y estimates during predictions. In this paper, we therefore investigate whet
                  her a statistical description of systematic model errors makes it possible t
                  o generate reliable predictions for TSS. In addition, we explore how the rel
                  iability of TSS predictions increases when streamflow data are additionally
                  used in model calibration. A key aspect of our study is that we use a Bayesi
                  an multi-output calibration and a novel autoregressive error model, which de
                  scribes the model predictive error as a sum of independent random noise and
                  autocorrelated bias. Our results show that using a statistical description o
                  f model bias provides more reliable uncertainty estimates of TSS than before
                   and including streamflow data into calibration makes TSS predictions more p
                  recise. For a case study of a small ungauged catchment, this improvement was
                   as much as 15%. Our approach can be easily implemented for other water qual
                  ity variables which are dependent on streamflow.
' (1568 chars) serialnumber => protected'0022-1694' (9 chars) doi => protected'10.1016/j.jhydrol.2015.09.051' (29 chars) uid => protected8371 (integer) _localizedUid => protected8371 (integer)modified _languageUid => protectedNULL _versionedUid => protected8371 (integer)modified pid => protected124 (integer)
000000004a9cb2a00000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=8011, pid=124) originalId => protected8011 (integer) authors => protected'Del Giudice,&nbsp;D.; Reichert,&nbsp;P.; Bareš,&nbsp;V.; Albert,&nbsp;C.; R
                  ieckermann,&nbsp;J.
' (95 chars) title => protected'Model bias and complexity - understanding the effects of structural deficits
                   and input errors on runoff predictions
' (115 chars) journal => protected'Environmental Modelling and Software' (36 chars) year => protected2015 (integer) volume => protected64 (integer) issue => protected'' (0 chars) startpage => protected'205' (3 chars) otherpage => protected'214' (3 chars) categories => protected'model structural deficits; rainfall errors; stochastic uncertainty analysis;
                   Bayesian bias description; hydrodynamic simulations; model comparison
' (146 chars) description => protected'Oversimplified models and erroneous inputs play a significant role in impair
                  ing environmental predictions. To assess the contribution of these errors to
                   model uncertainties is still challenging. Our objective is to understand th
                  e effect of model complexity on systematic modeling errors. Our method consi
                  sts of formulating alternative models with increasing detail and flexibility
                   and describing their systematic deviations by an autoregressive bias proces
                  s. We test the approach in an urban catchment with five drainage models. Our
                   results show that a single bias description produces reliable predictions f
                  or all models. The bias decreases with increasing model complexity and then
                  stabilizes. The bias decline can be associated with reduced structural defic
                  its, while the remaining bias is probably dominated by input errors. Combini
                  ng a bias description with a multimodel comparison is an effective way to as
                  sess the influence of structural and rainfall errors on flow forecasts.
' (983 chars) serialnumber => protected'1364-8152' (9 chars) doi => protected'10.1016/j.envsoft.2014.11.006' (29 chars) uid => protected8011 (integer) _localizedUid => protected8011 (integer)modified _languageUid => protectedNULL _versionedUid => protected8011 (integer)modified pid => protected124 (integer)
000000004a9cb2a20000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7397, pid=124) originalId => protected7397 (integer) authors => protected'Dürrenmatt,&nbsp;D.&nbsp;J.; Del Giudice,&nbsp;D.; Rieckermann,&nbsp;J.' (72 chars) title => protected'Dynamic time warping improves sewer flow monitoring' (51 chars) journal => protected'Water Research' (14 chars) year => protected2013 (integer) volume => protected47 (integer) issue => protected'11' (2 chars) startpage => protected'3803' (4 chars) otherpage => protected'3816' (4 chars) categories => protected'dynamic time warping; sensor diagnosis; sewer flow monitoring; signal proces
                  sing
' (80 chars) description => protected'Successful management and control of wastewater and storm water systems requ
                  ires accurate sewer flow measurements. Unfortunately, the harsh sewer enviro
                  nment and insufficient flow meter calibration often lead to inaccurate and b
                  iased data. In this paper, we improve sewer flow monitoring by creating redu
                  ndant information on sewer velocity from natural wastewater tracers. Continu
                  ous water quality measurements upstream and downstream of a sewer section ar
                  e used to estimate the travel time based on i) cross-correlation (XCORR) and
                   ii) dynamic time warping (DTW). DTW is a modern data mining technique that
                  warps two measured time series non-linearly in the time domain so that the d
                  issimilarity between the two is minimized. It has not been applied in this c
                  ontext before. From numerical experiments we can show that DTW outperforms X
                  CORR, because it provides more accurate velocity estimates, with an error of
                   about 7% under typical conditions, at a higher temporal resolution. In addi
                  tion, we can show that pre-processing of the data is important and that trac
                  er reaction in the sewer reach is critical. As dispersion is generally small
                  , the distance between the sensors is less influential if it is known precis
                  ely. Considering these findings, we tested the methods on a real-world sewer
                   to check the performance of two different sewer flow meters based on temper
                  ature measurements. Here, we were able to detect that one of two flow meters
                   was not performing satisfactorily under a variety of flow conditions. Altho
                  ugh theoretical analyses show that XCORR and DTW velocity estimates contain
                  systematic errors due to dispersion and reaction processes, these are usuall
                  y small and do not limit the applicability of the approach.
' (1731 chars) serialnumber => protected'0043-1354' (9 chars) doi => protected'10.1016/j.watres.2013.03.051' (28 chars) uid => protected7397 (integer) _localizedUid => protected7397 (integer)modified _languageUid => protectedNULL _versionedUid => protected7397 (integer)modified pid => protected124 (integer)
000000004a9cb2b50000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=8991, pid=124) originalId => protected8991 (integer) authors => protected'Del Giudice,&nbsp;D.; Honti,&nbsp;M.; Scheidegger,&nbsp;A.; Albert,&nbsp;C.;
                   Reichert,&nbsp;P.; Rieckermann,&nbsp;J.
' (116 chars) title => protected'Improving uncertainty estimation in urban hydrological modeling by statistic
                  ally describing bias
' (96 chars) journal => protected'Hydrology and Earth System Sciences' (35 chars) year => protected2013 (integer) volume => protected17 (integer) issue => protected'10' (2 chars) startpage => protected'4209' (4 chars) otherpage => protected'4225' (4 chars) categories => protected'' (0 chars) description => protected'Hydrodynamic models are useful tools for urban water management. Unfortunate
                  ly, it is still challenging to obtain accurate results and plausible uncerta
                  inty estimates when using these models. In particular, with the currently ap
                  plied statistical techniques, flow predictions are usually overconfident and
                   biased. In this study, we present a flexible and relatively efficient metho
                  dology (i) to obtain more reliable hydrological simulations in terms of cove
                  rage of validation data by the uncertainty bands and (ii) to separate predic
                  tion uncertainty into its components. Our approach acknowledges that urban d
                  rainage predictions are biased. This is mostly due to input errors and struc
                  tural deficits of the model. We address this issue by describing model bias
                  in a Bayesian framework. The bias becomes an autoregressive term additional
                  to white measurement noise, the only error type accounted for in traditional
                   uncertainty analysis. To allow for bigger discrepancies during wet weather,
                   we make the variance of bias dependent on the input (rainfall) or/and outpu
                  t (runoff) of the system. Specifically, we present a structured approach to
                  select, among five variants, the optimal bias description for a given urban
                  or natural case study. We tested the methodology in a small monitored stormw
                  ater system described with a parsimonious model. Our results clearly show th
                  at flow simulations are much more reliable when bias is accounted for than w
                  hen it is neglected. Furthermore, our probabilistic predictions can discrimi
                  nate between three uncertainty contributions: parametric uncertainty, bias,
                  and measurement errors. In our case study, the best performing bias descript
                  ion is the output-dependent bias using a log-sinh transformation of data and
                   model results. The limitations of the framework presented are some ambiguit
                  y due to the subjective choice of priors for bias parameters and its inabili
                  ty to address the causes of model discrepancies. Further research should foc
                  us on quantifying and re...
' (2093 chars) serialnumber => protected'1027-5606' (9 chars) doi => protected'10.5194/hess-17-4209-2013' (25 chars) uid => protected8991 (integer) _localizedUid => protected8991 (integer)modified _languageUid => protectedNULL _versionedUid => protected8991 (integer)modified pid => protected124 (integer)
000000004a9cb2b00000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7508, pid=124) originalId => protected7508 (integer) authors => protected'Schleiss,&nbsp;M.; Rieckermann,&nbsp;J.; Berne,&nbsp;A.' (55 chars) title => protected'Quantification and modeling of wet-antenna attenuation for commercial microw
                  ave links
' (85 chars) journal => protected'IEEE Geoscience and Remote Sensing Letters' (42 chars) year => protected2013 (integer) volume => protected10 (integer) issue => protected'5' (1 chars) startpage => protected'1195' (4 chars) otherpage => protected'1199' (4 chars) categories => protected'attenuation; microwave links; rain; wet antenna' (47 chars) description => protected'Data from a new experimental setup deployed in Dübendorf (Switzerland) are
                   used to quantify and model the magnitude and dynamics of the wet-antenna at
                  tenuation (WAA) affecting a 1.85-km commercial microwave link at 38 GHz. The
                   results show that the WAA exhibits the following properties: 1) It is bound
                  ed by a maximum value of about 2.3 dB; 2) it increases exponentially toward
                  2.3 dB during the first 5-20 min of rainfall; and 3) it decreases exponentia
                  lly as soon as the rain stops. A new dynamic WAA model that reproduces these
                   three features and can be calibrated using solely link measurements is prop
                  osed. Its performance is evaluated at different temporal resolutions and com
                  pared with other wet-antenna models from the literature. The results show th
                  at the dynamic model outperforms all other models and significantly reduces
                  the uncertainty of the retrieved path-averaged rain rates.
' (894 chars) serialnumber => protected'1545-598X' (9 chars) doi => protected'10.1109/LGRS.2012.2236074' (25 chars) uid => protected7508 (integer) _localizedUid => protected7508 (integer)modified _languageUid => protectedNULL _versionedUid => protected7508 (integer)modified pid => protected124 (integer)
000000004a9cb2bb0000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7311, pid=124) originalId => protected7311 (integer) authors => protected'Bianchi,&nbsp;B.; Rieckermann,&nbsp;J.; Berne,&nbsp;A.' (54 chars) title => protected'Quality control of rain gauge measurements using telecommunication microwave
                   links
' (82 chars) journal => protected'Journal of Hydrology' (20 chars) year => protected2013 (integer) volume => protected492 (integer) issue => protected'' (0 chars) startpage => protected'15' (2 chars) otherpage => protected'23' (2 chars) categories => protected'rainfall monitoring; rain gauge; microwave link; quality control' (64 chars) description => protected'Accurate rain rate measurements are essential for many hydrological applicat
                  ions. Although rain gauge remains the reference instrument for the measureme
                  nt of rain rate, the strong spatial and temporal variability of rainfall mak
                  es it difficult to spot faulty rain gauges. Due to the poor spatial represen
                  tativeness of the point rainfall measurements, this is particularly difficul
                  t where their density is low. Taking advantage of the high density of teleco
                  mmunication microwave links in urban areas, a consistency check is proposed
                  to identify faulty rain gauges using nearby microwave links. The methodology
                   is tested on a data set from operational rain gauges and microwave links, i
                  n Zürich (Switzerland). The malfunctioning of rain gauges leading to errors
                   in the occurrence of dry/rainy periods are well identified. In addition, th
                  e gross errors affecting quantitative rain gauge measurements during rainy p
                  eriods, such as blocking at a constant value, random noise and systematic bi
                  as, can be detected. The proposed approach can be implemented in real time.
' (1063 chars) serialnumber => protected'0022-1694' (9 chars) doi => protected'10.1016/j.jhydrol.2013.03.042' (29 chars) uid => protected7311 (integer) _localizedUid => protected7311 (integer)modified _languageUid => protectedNULL _versionedUid => protected7311 (integer)modified pid => protected124 (integer)
000000004a9cb2b80000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7457, pid=124) originalId => protected7457 (integer) authors => protected'Blumensaat,&nbsp;F.; Staufer,&nbsp;P.; Heusch,&nbsp;S.; Reußner,&nbsp;F.; S
                  chütze,&nbsp;M.; Seiffert,&nbsp;S.; Gruber,&nbsp;G.; Zawilski,&nbsp;M.; Rie
                  ckermann,&nbsp;J.
' (169 chars) title => protected'Water quality-based assessment of urban drainage impacts in Europe - where d
                  o we stand today?
' (93 chars) journal => protected'Water Science and Technology' (28 chars) year => protected2012 (integer) volume => protected66 (integer) issue => protected'2' (1 chars) startpage => protected'304' (3 chars) otherpage => protected'313' (3 chars) categories => protected'decision making; integrated urban water management; receiving water quality;
                   uncertainty analysis; water quality-based impact assessment
' (136 chars) description => protected'Traditionally, design and optimisation of urban drainage systems was mainly
                  driven by cost efficiency, surface flood prevention, and later by emission r
                  eduction. More recent procedures explicitly include ecological conditions of
                   the receiving water in the definition of acceptable pollutant discharges vi
                  a sewer system and treatment plant outlets. An ambient Water Quality based i
                  mpact Assessment (WQA) principle therefore requires an integrative system op
                  timisation. However, a broad range of mostly national WQA protocols exist ac
                  ross Europe varying in structure and complexity, assessment concept, spatial
                   and temporal scope and handling of uncertainty. This variety inherently imp
                  lies a considerable risk of subjectivity in the impact assessment with highl
                  y variable outcomes. The present review identifies differences and similarit
                  ies of WQA protocols in use and discusses their strengths and weaknesses thr
                  ough: (i) a systematic comparison of WQA protocols by selected attributes, (
                  ii) a review of real-life cases reported in the literature and expert interv
                  iews, and (iii) an illustration of our main findings by applying selected WQ
                  A in an instructive example. The review discusses differences in structure a
                  nd concept, which are mainly identified for simplistic WQA protocols. The ap
                  plication of selected protocols to an example case shows a wide variety of n
                  umerical results and conclusive decisions. It is found that existing protoco
                  ls target different questions within the decision making process, which user
                  s should be more aware of. Generally, to make assessments more reliable, fur
                  ther fundamental research is required to fully understand the relationship b
                  etween stressors and stream ecosystem responses which will make assessments
                  more reliable. Technically, tools suggested in WQA protocols show severe def
                  iciencies and an uncertainty assessment should be mandatory.
' (1884 chars) serialnumber => protected'0273-1223' (9 chars) doi => protected'10.2166/wst.2012.178' (20 chars) uid => protected7457 (integer) _localizedUid => protected7457 (integer)modified _languageUid => protectedNULL _versionedUid => protected7457 (integer)modified pid => protected124 (integer)
000000004a9cb2bd0000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=9017, pid=124) originalId => protected9017 (integer) authors => protected'Wang,&nbsp;Z.; Schleiss,&nbsp;M.; Jaffrain,&nbsp;J.; Berne,&nbsp;A.; Riecker
                  mann,&nbsp;J.
' (89 chars) title => protected'Using Markov switching models to infer dry and rainy periods from telecommun
                  ication microwave link signals
' (106 chars) journal => protected'Atmospheric Measurement Techniques' (34 chars) year => protected2012 (integer) volume => protected5 (integer) issue => protected'7' (1 chars) startpage => protected'1847' (4 chars) otherpage => protected'1859' (4 chars) categories => protected'' (0 chars) description => protected'A Markov switching algorithm is introduced to classify attenuation measureme
                  nts from telecommunication microwave links into dry and rainy periods. It is
                   based on a simple state-space model and has the advantage of not relying on
                   empirically estimated threshold parameters. The algorithm is applied to dat
                  a collected using a new and original experimental set-up in the vicinity of
                  Zürich, Switzerland. The false dry and false rain detection rates of the al
                  gorithm are evaluated and compared to 3 other algorithms from the literature
                  . The results show that, on average, the Markov switching model outperforms
                  the other algorithms. It is also shown that the classification performance c
                  an be further improved if redundant information from multiple channels is us
                  ed.
' (763 chars) serialnumber => protected'1867-1381' (9 chars) doi => protected'10.5194/amt-5-1847-2012' (23 chars) uid => protected9017 (integer) _localizedUid => protected9017 (integer)modified _languageUid => protectedNULL _versionedUid => protected9017 (integer)modified pid => protected124 (integer)
000000004a9cbd420000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=8869, pid=124) originalId => protected8869 (integer) authors => protected'Sikorska,&nbsp;A.&nbsp;E.; Scheidegger,&nbsp;A.; Banasik,&nbsp;K.; Rieckerma
                  nn,&nbsp;J.
' (87 chars) title => protected'Bayesian uncertainty assessment of flood predictions in ungauged urban basin
                  s for conceptual rainfall-runoff models
' (115 chars) journal => protected'Hydrology and Earth System Sciences' (35 chars) year => protected2012 (integer) volume => protected16 (integer) issue => protected'4' (1 chars) startpage => protected'1221' (4 chars) otherpage => protected'1236' (4 chars) categories => protected'' (0 chars) description => protected'Urbanization and the resulting land-use change strongly affect the water cyc
                  le and runoff-processes in watersheds. Unfortunately, small urban watersheds
                  , which are most affected by urban sprawl, are mostly ungauged. This makes i
                  t intrinsically difficult to assess the consequences of urbanization. Most o
                  f all, it is unclear how to reliably assess the predictive uncertainty given
                   the structural deficits of the applied models. In this study, we therefore
                  investigate the uncertainty of flood predictions in ungauged urban basins fr
                  om structurally uncertain rainfall-runoff models. To this end, we suggest a
                  procedure to explicitly account for input uncertainty and model structure de
                  ficits using Bayesian statistics with a continuous-time autoregressive error
                   model. In addition, we propose a concise procedure to derive prior paramete
                  r distributions from base data and successfully apply the methodology to an
                  urban catchment in Warsaw, Poland. Based on our results, we are able to demo
                  nstrate that the autoregressive error model greatly helps to meet the statis
                  tical assumptions and to compute reliable prediction intervals. In our study
                  , we found that predicted peak flows were up to 7 times higher than observat
                  ions. This was reduced to 5 times with Bayesian updating, using only few dis
                  charge measurements. In addition, our analysis suggests that imprecise rainf
                  all information and model structure deficits contribute mostly to the total
                  prediction uncertainty. In the future, flood predictions in ungauged basins
                  will become more important due to ongoing urbanization as well as anthropoge
                  nic and climatic changes. Thus, providing reliable measures of uncertainty i
                  s crucial to support decision making.
' (1709 chars) serialnumber => protected'1027-5606' (9 chars) doi => protected'10.5194/hess-16-1221-2012' (25 chars) uid => protected8869 (integer) _localizedUid => protected8869 (integer)modified _languageUid => protectedNULL _versionedUid => protected8869 (integer)modified pid => protected124 (integer)
000000004a9cbd470000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7024, pid=124) originalId => protected7024 (integer) authors => protected'Thomas,&nbsp;K.&nbsp;V.; Bijlsma,&nbsp;L.; Castiglioni,&nbsp;S.; Covaci,&nbs
                  p;A.; Emke,&nbsp;E.; Grabic,&nbsp;R.; Hernández,&nbsp;F.; Karolak,&nbsp;S.;
                   Kasprzyk-Hordern,&nbsp;B.; Lindberg,&nbsp;R.&nbsp;H.; Lopez de Alda,&nbsp;M
                  .; Meierjohann,&nbsp;A.; Ort,&nbsp;C.; Pico,&nbsp;Y.; Quintana,&nbsp;J.&nbsp
                  ;B.; Reid,&nbsp;M.; Rieckermann,&nbsp;J.; Terzic,&nbsp;S.; van Nuijs,&nbsp;A
                  .&nbsp;L.&nbsp;N.; de Voogt,&nbsp;P.
' (416 chars) title => protected'Comparing illicit drug use in 19 European cities through sewage analysis' (72 chars) journal => protected'Science of the Total Environment' (32 chars) year => protected2012 (integer) volume => protected432 (integer) issue => protected'' (0 chars) startpage => protected'432' (3 chars) otherpage => protected'439' (3 chars) categories => protected'sewage biomarker analysis; cocaine; methamphetamine; amphetamine; MDMA; cann
                  abis
' (80 chars) description => protected'The analysis of sewage for urinary biomarkers of illicit drugs is a promisin
                  g and complementary approach for estimating the use of these substances in t
                  he general population. For the first time, this approach was simultaneously
                  applied in 19 European cities, making it possible to directly compare illici
                  t drug loads in Europe over a 1-week period. An inter-laboratory comparison
                  study was performed to evaluate the analytical performance of the participat
                  ing laboratories. Raw 24-hour composite sewage samples were collected from 1
                  9 European cities during a single week in March 2011 and analyzed for the ur
                  inary biomarkers of cocaine, amphetamine, ecstasy, methamphetamine and canna
                  bis using in-house optimized and validated analytical methods. The load of e
                  ach substance used in each city was back-calculated from the measured concen
                  trations. The data show distinct temporal and spatial patterns in drug use a
                  cross Europe. Cocaine use was higher in Western and Central Europe and lower
                   in Northern and Eastern Europe. The extrapolated total daily use of cocaine
                   in Europe during the study period was equivalent to 356. kg/day. High per c
                  apita ecstasy loads were observed in Dutch cities, as well as in Antwerp and
                   London. In general, cocaine and ecstasy loads were significantly elevated d
                  uring the weekend compared to weekdays. Per-capita loads of methamphetamine
                  were highest in Helsinki and Turku, Oslo and Budweis, while the per capita l
                  oads of cannabis were similar throughout Europe. This study shows that a sta
                  ndardized analysis for illicit drug urinary biomarkers in sewage can be appl
                  ied to estimate and compare the use of these substances at local and interna
                  tional scales. This approach has the potential to deliver important informat
                  ion on drug markets (supply indicator).
' (1787 chars) serialnumber => protected'0048-9697' (9 chars) doi => protected'10.1016/j.scitotenv.2012.06.069' (31 chars) uid => protected7024 (integer) _localizedUid => protected7024 (integer)modified _languageUid => protectedNULL _versionedUid => protected7024 (integer)modified pid => protected124 (integer)
000000004a9cbd440000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7022, pid=124) originalId => protected7022 (integer) authors => protected'Staufer,&nbsp;P.; Scheidegger,&nbsp;A.; Rieckermann,&nbsp;J.' (60 chars) title => protected'Assessing the performance of sewer rehabilitation on the reduction of infilt
                  ration and inflow
' (93 chars) journal => protected'Water Research' (14 chars) year => protected2012 (integer) volume => protected46 (integer) issue => protected'16' (2 chars) startpage => protected'5185' (4 chars) otherpage => protected'5196' (4 chars) categories => protected'sewer infiltration; rainfall induced infiltration; performance assessment; v
                  ariability; uncertainty; regression analysis; experimental design
' (141 chars) description => protected'Inflow and Infiltration (<I>I</I>/<I>I</I>) into sewer systems is generally
                  unwanted, because, among other things, it decreases the performance of waste
                  water treatment plants and increases combined sewage overflows. As sewer reh
                  abilitation to reduce <I>I</I>/<I>I</I> is very expensive, water managers no
                  t only need methods to accurately measure <I>I</I>/<I>I</I>, but also they n
                  eed sound approaches to assess the actual performance of implemented rehabil
                  itation measures. However, such performance assessment is rarely performed.
                  On the one hand, it is challenging to adequately take into account the varia
                  bility of influential factors, such as hydro-meteorological conditions. On t
                  he other hand, it is currently not clear how experimental data can indeed su
                  pport robust evidence for reduced <I>I</I>/<I>I</I>. In this paper, we there
                  fore statistically assess the performance of rehabilitation measures to redu
                  ce <I>I</I>/<I>I</I>. This is possible by using observations in a suitable r
                  eference catchment as a control group and assessing the significance of the
                  observed effect by regression analysis, which is well established in other d
                  isciplines. We successfully demonstrate the usefulness of the approach in a
                  case study, where rehabilitation reduced groundwater infiltration by 23.9%.
                  A reduction of stormwater inflow of 35.7%, however, was not statistically si
                  gnificant. Investigations into the experimental design of monitoring campaig
                  ns confirmed that the variability of the data as well as the number of obser
                  vations collected before the rehabilitation impact the detection limit of th
                  e effect. This implies that it is difficult to improve the data quality afte
                  r the rehabilitation has been implemented. Therefore, future practical appli
                  cations should consider a careful experimental design. Further developments
                  could employ more sophisticated monitoring methods, such as stable environme
                  ntal isotopes, to directly observe the individual infiltration components. I
                  n addition, water manage...
' (2140 chars) serialnumber => protected'0043-1354' (9 chars) doi => protected'10.1016/j.watres.2012.07.001' (28 chars) uid => protected7022 (integer) _localizedUid => protected7022 (integer)modified _languageUid => protectedNULL _versionedUid => protected7022 (integer)modified pid => protected124 (integer)
000000004a9cbd490000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=6849, pid=124) originalId => protected6849 (integer) authors => protected'Rieckermann,&nbsp;J.; Anta,&nbsp;J.; Scheidegger,&nbsp;A.; Ort,&nbsp;C.' (71 chars) title => protected'Assessing wastewater micropollutant loads with Approximate Bayesian Computat
                  ions
' (80 chars) journal => protected'Environmental Science and Technology' (36 chars) year => protected2011 (integer) volume => protected45 (integer) issue => protected'10' (2 chars) startpage => protected'4399' (4 chars) otherpage => protected'4406' (4 chars) categories => protected'' (0 chars) description => protected'Wastewater production, like many other engineered and environmental processe
                  s, is inherent stochastic in nature and requires the use of complex stochast
                  ic models, for example, to predict realistic patterns of down-the-drain chem
                  icals or pharmaceuticals and personal care products. Up until now, a formal
                  method of statistical inference has been lacking for many of those models, w
                  here explicit likelihood functions were intractable. In this Article, we inv
                  estigate Approximate Bayesian Computation (ABC) methods to infer important p
                  arameters of stochastic environmental models. ABC methods have been recently
                   suggested to perform model-based inference in a Bayesian setting when model
                   likelihoods are analytically or computationally intractable and have not be
                  en applied to environmental systems analysis or water quality modeling befor
                  e. In a case study, we investigate the performance of three different algori
                  thms to infer the number of wastewater pulses contained in three high-resolu
                  tion data series of benzotriazole and total nitrogen loads in sewers. We fin
                  d that all algorithms perform well and that the uncertainty in the inferred
                  number of corresponding wastewater pulses varies between 6% and 28%. In our
                  case, the results are more sensitive to substance characteristics than to ca
                  tchment properties. Although the application of ABC methods requires careful
                   tuning and attention to detail, they have a great general potential to upda
                  te stochastic model parameters with monitoring data and improve their predic
                  tive capabilities.
' (1538 chars) serialnumber => protected'0013-936X' (9 chars) doi => protected'10.1021/es1030432' (17 chars) uid => protected6849 (integer) _localizedUid => protected6849 (integer)modified _languageUid => protectedNULL _versionedUid => protected6849 (integer)modified pid => protected124 (integer)
000000004a9cbd4e0000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=6798, pid=124) originalId => protected6798 (integer) authors => protected'Mathieu,&nbsp;C.; Rieckermann,&nbsp;J.; Berset,&nbsp;J.-D.; Schürch,&nbsp;S
                  .; Brenneisen,&nbsp;R.
' (98 chars) title => protected'Assessment of total uncertainty in cocaine and benzoylecgonine wastewater lo
                  ad measurements
' (91 chars) journal => protected'Water Research' (14 chars) year => protected2011 (integer) volume => protected45 (integer) issue => protected'20' (2 chars) startpage => protected'6650' (4 chars) otherpage => protected'6660' (4 chars) categories => protected'sewage treatment plant; wastewater; cocaine and benzoylecgonine loads; analy
                  tical uncertainty
' (93 chars) description => protected'To check the effectiveness of campaigns preventing drug abuse or indicating
                  local effects of efforts against drug trafficking, it is beneficial to know
                  consumed amounts of substances in a high spatial and temporal resolution. Th
                  e analysis of drugs of abuse in wastewater (WW) has the potential to provide
                   this information. In this study, the reliability of WW drug consumption est
                  imates is assessed and a novel method presented to calculate the total uncer
                  tainty in observed WW cocaine (COC) and benzoylecgonine (BE) loads. Specific
                  ally, uncertainties resulting from discharge measurements, chemical analysis
                   and the applied sampling scheme were addressed and three approaches present
                  ed. These consist of (i) a generic model-based procedure to investigate the
                  influence of the sampling scheme on the uncertainty of observed or expected
                  drug loads, (ii) a comparative analysis of two analytical methods (high perf
                  ormance liquid chromatography–tandem mass spectrometry and gas chromatogra
                  phy–mass spectrometry), including an extended cross-validation by influent
                   profiling over several days, and (iii) monitoring COC and BE concentrations
                   in WW of the largest Swiss sewage treatment plants. In addition, the COC an
                  d BE loads observed in the sewage treatment plant of the city of Berne were
                  used to back-calculate the COC consumption. The estimated mean daily consume
                  d amount was 107 ± 21 g of pure COC, corresponding to 321 g of street-grade
                   COC.
' (1449 chars) serialnumber => protected'0043-1354' (9 chars) doi => protected'10.1016/j.watres.2011.09.049' (28 chars) uid => protected6798 (integer) _localizedUid => protected6798 (integer)modified _languageUid => protectedNULL _versionedUid => protected6798 (integer)modified pid => protected124 (integer)
000000004a9cbd530000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=6060, pid=124) originalId => protected6060 (integer) authors => protected'Rutsch,&nbsp;M.; Rieckermann,&nbsp;J.; Cullmann,&nbsp;J.; Ellis,&nbsp;J.&nbs
                  p;B.; Vollertsen,&nbsp;J.; Krebs,&nbsp;P.
' (117 chars) title => protected'Towards a better understanding of sewer exfiltration' (52 chars) journal => protected'Water Research' (14 chars) year => protected2008 (integer) volume => protected42 (integer) issue => protected'10–11' (7 chars) startpage => protected'2385' (4 chars) otherpage => protected'2394' (4 chars) categories => protected'sewer leakage; exfiltration; groundwater recharge; groundwater contamination
                  ; modelling
' (87 chars) description => protected'This paper gives a full review of the importance of sewer leakage, which has
                   received increased attention throughout the last decades. Despite the inten
                  sive interdisciplinary research that has been invested, its magnitude is sti
                  ll unclear and a comprehensive solution for the assessment of sewer exfiltra
                  tion does not seem to be at hand. However, given that mechanisms of exfiltra
                  tion and the factors influencing its extent are similar all over the world,
                  it seems possible to develop a generic leakage approach. Several methods for
                   modelling sewer leakage are reviewed and the available measuring techniques
                   are critically evaluated. Based on this evaluation, we suggest a unifying f
                  ramework to facilitate focused model building. Specifically, we identify ope
                  n research questions and propose to (i) standardise measurement results to e
                  nable better understanding, (ii) perform more long-term experiments under re
                  alistic field conditions, and (iii) assess the uncertainty of measurement an
                  d model results so that findings are not over-interpreted.
' (1046 chars) serialnumber => protected'0043-1354' (9 chars) doi => protected'10.1016/j.watres.2008.01.019' (28 chars) uid => protected6060 (integer) _localizedUid => protected6060 (integer)modified _languageUid => protectedNULL _versionedUid => protected6060 (integer)modified pid => protected124 (integer)
000000004a9cbd500000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=5489, pid=124) originalId => protected5489 (integer) authors => protected'Rieckermann,&nbsp;J.; Neumann,&nbsp;M.; Ort,&nbsp;C.; Huisman,&nbsp;J.&nbsp;
                  L.; Gujer,&nbsp;W.
' (94 chars) title => protected'Dispersion coefficients of sewers from tracer experiments' (57 chars) journal => protected'Water Science and Technology' (28 chars) year => protected2005 (integer) volume => protected52 (integer) issue => protected'5' (1 chars) startpage => protected'123' (3 chars) otherpage => protected'133' (3 chars) categories => protected'dispersion; mixing; modelling; sewer; tracer; transport' (55 chars) description => protected'In this paper, 60 tracer experiments in 37 different sewer reaches have been
                   analyzed for longitudinal dispersion under dry weather flow conditions. It
                  was found that dispersion coefficients of sewers are two to three orders of
                  magnitude smaller than those measured in rivers and do not differ much from
                  system to system. Suitable equations were identified to predict reasonable d
                  ispersion coefficients in sewer reaches with uniform geometry and stable flo
                  w conditions, For engineering applications that require a high degree of acc
                  uracy the performance of tracer measurements is recommended.
' (592 chars) serialnumber => protected'0273-1223' (9 chars) doi => protected'10.2166/wst.2005.0124' (21 chars) uid => protected5489 (integer) _localizedUid => protected5489 (integer)modified _languageUid => protectedNULL _versionedUid => protected5489 (integer)modified pid => protected124 (integer)
000000004a9cbd550000000059256c12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=5081, pid=124) originalId => protected5081 (integer) authors => protected'Rieckermann,&nbsp;J.; Borsuk,&nbsp;M.; Reichert,&nbsp;P.; Gujer,&nbsp;W.' (72 chars) title => protected'A novel tracer method for estimating sewer exfiltration' (55 chars) journal => protected'Water Resources Research' (24 chars) year => protected2005 (integer) volume => protected41 (integer) issue => protected'5' (1 chars) startpage => protected'W05013 (11 pp.)' (15 chars) otherpage => protected'' (0 chars) categories => protected'' (0 chars) description => protected'A novel method is presented to estimate exfiltration from sewer systems usin
                  g artificial tracers. The method relies upon use of an upstream indicator si
                  gnal and a downstream reference signal to eliminate the dependence of exfilt
                  ration estimates on the accuracy of discharge measurement. An experimental d
                  esign, a data analysis procedure, and an uncertainty assessment process are
                  described and illustrated by a case study. In a 2-km reach of unknown condit
                  ion, exfiltration was estimated at 9.9 ± 2.7%. Uncertainty in this estimate
                   was primarily due to the use of sodium chloride (NaCl) as the tracer substa
                  nce. NaCl is measured using conductivity, which is present at nonnegligible
                  levels in wastewater, thus confounding accurate identification of tracer pea
                  ks. As estimates of exfiltration should have as low a measurement error as p
                  ossible, future development of the method will concentrate on improved exper
                  imental design and tracer selection. Although the method is not intended to
                  replace traditional CCTV inspections, it can provide additional information
                  to urban water managers for rational rehabilitation planning.
' (1125 chars) serialnumber => protected'0043-1397' (9 chars) doi => protected'10.1029/2004WR003699' (20 chars) uid => protected5081 (integer) _localizedUid => protected5081 (integer)modified _languageUid => protectedNULL _versionedUid => protected5081 (integer)modified pid => protected124 (integer)
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chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected893 (integer) _localizedUid => protected893 (integer)modified _languageUid => protectedNULL _versionedUid => protected893 (integer)modified pid => protected21 (integer) 000000004a9cbd3f0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=894, pid=21) title => protected'ACL_Archive_RainSeriesCH_archive_ALL_rw' (39 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected894 (integer) _localizedUid => protected894 (integer)modified _languageUid => protectedNULL _versionedUid => protected894 (integer)modified pid => protected21 (integer) 000000004a9cb07a0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=915, pid=21) title 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TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=969, pid=21) title => protected'eaw-cms1-blog' (13 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected969 (integer) _localizedUid => protected969 (integer)modified _languageUid => protectedNULL _versionedUid => protected969 (integer)modified pid => protected21 (integer) 000000004a9cbdc80000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1005, pid=21) title => protected'cifs-homedirs-old@eawag.ch' (26 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected1005 (integer) _localizedUid => protected1005 (integer)modified _languageUid => protectedNULL _versionedUid => protected1005 (integer)modified pid 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protectedNULL _versionedUid => protected1051 (integer)modified pid => protected21 (integer) 000000004a9cbdd60000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1069, pid=21) title => protected'ACL_Archive_MeteoSuisse_Precip_Radar_archive_ALL_r' (50 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected1069 (integer) _localizedUid => protected1069 (integer)modified _languageUid => protectedNULL _versionedUid => protected1069 (integer)modified pid => protected21 (integer) 000000004a9cbdd90000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1070, pid=21) title => protected'ACL_Archive_MeteoSuisse_Precip_Forecast_archive_AL' (50 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected1070 (integer) _localizedUid => protected1070 (integer)modified _languageUid => protectedNULL _versionedUid => protected1070 (integer)modified pid => protected21 (integer) 000000004a9cb4520000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1092, pid=21) title => protected'GPO_NXClient_Users' (18 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected1092 (integer) _localizedUid => protected1092 (integer)modified _languageUid => protectedNULL _versionedUid => protected1092 (integer)modified pid => protected21 (integer) 000000004a9cb4550000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1145, pid=21) title => protected'adobe.users@eawag.ch' (20 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) uid => protected1145 (integer) _localizedUid => protected1145 (integer)modified _languageUid => protectedNULL _versionedUid => protected1145 (integer)modified pid => protected21 (integer) 000000004a9cb4580000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1153, pid=21) title => protected'natelgo.subscribers@eawag.ch' (28 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1153 (integer) _localizedUid => protected1153 (integer)modified _languageUid => protectedNULL _versionedUid => protected1153 (integer)modified pid => protected21 (integer) 000000004a9cbddc0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1170, pid=21) title => protected'climate-impacts' (15 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1170 (integer) _localizedUid => protected1170 (integer)modified _languageUid => protectedNULL _versionedUid => protected1170 (integer)modified pid => protected21 (integer) 000000004a9cbde70000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1188, pid=21) title => protected'ACL_Project_Voices_ALL_rw' (25 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1188 (integer) _localizedUid => protected1188 (integer)modified _languageUid => protectedNULL _versionedUid => protected1188 (integer)modified pid => protected21 (integer) 000000004a9cb01f0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1191, pid=21) title => protected'MBX_Eawag_La-Bu-Users_Calendar_only' (35 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1191 (integer) _localizedUid => protected1191 (integer)modified _languageUid => protectedNULL _versionedUid => protected1191 (integer)modified pid => protected21 (integer) 000000004a9cbde40000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1236, pid=21) title => protected'Sensors' (7 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1236 (integer) _localizedUid => protected1236 (integer)modified _languageUid => protectedNULL _versionedUid => protected1236 (integer)modified pid => protected21 (integer) 000000004a9cb3e30000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1239, pid=21) title => protected'MAZ_APP_Teams_Users' (19 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1239 (integer) _localizedUid => protected1239 (integer)modified _languageUid => protectedNULL _versionedUid => protected1239 (integer)modified pid => protected21 (integer) 000000004a9cbde90000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1265, pid=21) title => protected'ACL_Project_Sensorlab_ALL_rw' (28 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1265 (integer) _localizedUid => protected1265 (integer)modified _languageUid => protectedNULL _versionedUid => protected1265 (integer)modified pid => protected21 (integer) 000000004a9cbdee0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1311, pid=21) title => protected'g-leiter-sww' (12 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1311 (integer) _localizedUid => protected1311 (integer)modified _languageUid => protectedNULL _versionedUid => protected1311 (integer)modified pid => protected21 (integer) 000000004a9cbdf30000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1578, pid=21) title => protected'ACL_Various_DM-Finanzberichte_SDIR_EU-INFRAIA-Co-U' (50 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1578 (integer) _localizedUid => protected1578 (integer)modified _languageUid => protectedNULL _versionedUid => protected1578 (integer)modified pid => protected21 (integer) 000000004a9cbdf00000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1594, pid=21) title => protected'ACL_Various_DM-Finanzberichte_SDIR_Innosuisse_rw' (48 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1594 (integer) _localizedUid => protected1594 (integer)modified _languageUid => protectedNULL _versionedUid => protected1594 (integer)modified pid => protected21 (integer) 000000004a9cbdf50000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1604, pid=21) title => protected'ACL_Various_DM-Finanzberichte_SDIR_Innosuisse-Riec' (50 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1604 (integer) _localizedUid => protected1604 (integer)modified _languageUid => protectedNULL _versionedUid => protected1604 (integer)modified pid => protected21 (integer) 000000004a9cbdfa0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1605, pid=21) title => protected'Teams Users' (11 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1605 (integer) _localizedUid => protected1605 (integer)modified _languageUid => protectedNULL _versionedUid => protected1605 (integer)modified pid => protected21 (integer) 000000004a9cb0240000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1614, pid=21) title => protected'ACL_Project_PhD_Supervision_ALL_rw' (34 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1614 (integer) _localizedUid => protected1614 (integer)modified _languageUid => protectedNULL _versionedUid => protected1614 (integer)modified pid => protected21 (integer) 000000004a9cbdff0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1618, pid=21) title => protected'ACL_Archive_SmartWaterPipes_archive_ALL_rw' (42 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1618 (integer) _localizedUid => protected1618 (integer)modified _languageUid => protectedNULL _versionedUid => protected1618 (integer)modified pid => protected21 (integer) 000000004a9cb4650000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1624, pid=21) title => protected'MAZ_APP_M365_All_Users@eawag.ch' (31 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1624 (integer) _localizedUid => protected1624 (integer)modified _languageUid => protectedNULL _versionedUid => protected1624 (integer)modified pid => protected21 (integer) 000000004a9cbdfc0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1630, pid=21) title => protected'MAZ_WFL_M365_approver_sww@eawag.ch' (34 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1630 (integer) _localizedUid => protected1630 (integer)modified _languageUid => protectedNULL _versionedUid => protected1630 (integer)modified pid => protected21 (integer) 000000004a9cb46a0000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1687, pid=21) title => protected'MAZ_LIC_Eawag_M365_A5_F_Default' (31 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1687 (integer) _localizedUid => protected1687 (integer)modified _languageUid => protectedNULL _versionedUid => protected1687 (integer)modified pid => protected21 (integer) 000000004a9cbd810000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1692, pid=21) title => protected'ACL_Data_HyperSpec2023_data_ALL_rw' (34 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1692 (integer) _localizedUid => protected1692 (integer)modified _languageUid => protectedNULL _versionedUid => protected1692 (integer)modified pid => protected21 (integer) 000000004a9cbd860000000059256c12 => TYPO3\CMS\Extbase\Domain\Model\FrontendUserGroupprototypepersistent entity (uid=1724, pid=21) title => protected'ACL_Data_HyperSpec2023_data_ALL_mgmt' (36 chars) lockToDomain => protected'' (0 chars) description => protected'' (0 chars) subgroup => protectedNULL uid => protected1724 (integer) _localizedUid => protected1724 (integer)modified _languageUid => protectedNULL _versionedUid => protected1724 (integer)modified pid => protected21 (integer) name => protected'Jörg Rieckermann' (17 chars) firstName => protected'Jörg' (5 chars) middleName => protected'' (0 chars) lastName => protected'Rieckermann' (11 chars) address => protected'Überlandstrasse 133' (20 chars) telephone => protected'+41 58 765 5397' (15 chars) fax => protected'+41 58 765 5802' (15 chars) email => protected'joerg.rieckermann@eawag.ch' (26 chars) lockToDomain => protected'' (0 chars) title => protected'Dr.' (3 chars) zip => protected'8600' (4 chars) city => protected'Dübendorf' (10 chars) country => protected'Schweiz' (7 chars) www => protected'' (0 chars) company => protected'Eawag' (5 chars) image => protectedTYPO3\CMS\Extbase\Persistence\ObjectStorageprototypeobject (empty) lastlogin => protectedDateTimeprototypeobject (2000-01-01T00:00:00+01:00, 946681200)modified uid => protected289 (integer) _localizedUid => protected289 (integer)modified _languageUid => protectedNULL _versionedUid => protected289 (integer)modified pid => protected21 (integer)
profileImage => 'fileadmin/user_upload/tx_userprofiles/profileImages/rieckejo.jpg' (64 chars) uid => '289' (3 chars)

Author

Andy Disch

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Jörg Rieckermann
Group Leader
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