@article{smitomb/internal_phosphorus_loading_model: characterizing and projecting internal phosphorus loading through bayesian mass-balance modeling_2023, DOI={10.5281/zenodo.10428197}, journal={Zenodo}, year={2023}, month={Dec} }
 @article{smitomb/internal_phosphorus_loading_model: characterizing and projecting internal phosphorus loading through bayesian mass-balance modeling_2023, DOI={10.5281/zenodo.10428198}, journal={Zenodo}, year={2023}, month={Dec} }
 @article{katin_del giudice_obenour_2022, title={Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling}, volume={26}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-26-1131-2022}, DOI={10.5194/hess-26-1131-2022}, abstractNote={Abstract. Low bottom water dissolved oxygen conditions (hypoxia) occur almost every summer in the northern Gulf of Mexico due to a combination of nutrient loadings and water column stratification. Several statistical and mechanistic models have been used to forecast the midsummer hypoxic area, based on spring nitrogen loading from major rivers. However, sub-seasonal forecasts are needed to fully characterize the dynamics of hypoxia over the summer season, which is important for informing fisheries and ecosystem management. Here, we present an approach to forecasting hypoxic conditions at a daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydrometeorological model inputs. We find that May precipitation over the Mississippi River basin is a key predictor of summer discharge and loading that substantially improves forecast performance. Accounting for spring wind conditions also improves
forecast performance, though to a lesser extent. The proposed approach generates forecasts for two different sections of the Louisiana–Texas shelf (east and west), and it explains about 50 % of the variability in the total hypoxic area when tested against historical observations (1985–2016). Results also show how forecast uncertainties build over the summer season, with longer lead times from the nominal forecast release date of 1 June, due to increasing stochasticity in riverine and meteorological inputs. Consequently, the portion of overall forecast variance associated with uncertainties in data inputs increases from 26 % to 41 % from June–July to August–September, respectively. Overall, the study demonstrates a unique approach to assessing and reducing uncertainties in temporally resolved hypoxia forecasting.
}, number={4}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, publisher={Copernicus GmbH}, author={Katin, Alexey and Del Giudice, Dario and Obenour, Daniel R.}, year={2022}, month={Feb}, pages={1131–1143} }
 @article{del giudice_fang_scavia_davis_evans_obenour_2021, title={Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling}, volume={755}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2020.142487}, abstractNote={The adverse impacts of harmful algal blooms (HABs) are increasing worldwide. Lake Erie is a North American Great Lake highly affected by cultural eutrophication and summer cyanobacterial HABs. While phosphorus loading is a known driver of bloom size, more nuanced yet crucial questions remain. For example, it is unclear what mechanisms are primarily responsible for initiating cyanobacterial dominance and subsequent biomass accumulation. To address these questions, we develop a mechanistic model describing June–October dynamics of chlorophyll a, nitrogen, and phosphorus near the Maumee River outlet, where blooms typically initiate and are most severe. We calibrate the model to a new, geostatistically-derived dataset of daily water quality spanning 2008–2017. A Bayesian framework enables us to embed prior knowledge on system characteristics and test alternative model formulations. Overall, the best model formulation explains 42% of the variability in chlorophyll a and 83% of nitrogen, and better captures bloom timing than previous models. Our results, supported by cross validation, show that onset of the major midsummer bloom is associated with about a month of water temperatures above 20 °C (occurring 19 July to 6 August), consistent with when cyanobacteria dominance is usually reported. Decreased phytoplankton loss rate is the main factor enabling biomass accumulation, consistent with reduced zooplankton grazing on cyanobacteria. The model also shows that phosphorus limitation is most severe in August, and nitrogen limitation tends to occur in early autumn. Our results highlight the role of temperature in regulating bloom initiation and subsequent loss rates, and suggest that a 2 °C increase could lead to blooms that start about 10 days earlier and grow 23% more intense.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Del Giudice, Dario and Fang, Shiqi and Scavia, Donald and Davis, Timothy W. and Evans, Mary Anne and Obenour, Daniel R.}, year={2021}, month={Feb} }
 @article{han_aziz_del giudice_hall_obenour_2021, title={Exploring nutrient and light limitation of algal production in a shallow turbid reservoir}, volume={269}, ISSN={["1873-6424"]}, DOI={10.1016/j.envpol.2020.116210}, abstractNote={Harmful algal blooms are increasingly recognized as a threat to the integrity of freshwater reservoirs, which serve as water supplies, wildlife habitats, and recreational attractions. While algal growth and accumulation is controlled by many environmental factors, the relative importance of these factors is unclear, particularly for turbid eutrophic systems. Here we develop and compare two models that test the relative importance of vertical mixing, light, and nutrients for explaining chlorophyll-a variability in shallow (2–3 m) embayments of a eutrophic reservoir, Jordan Lake, North Carolina. One is a multiple linear regression (statistical) model and the other is a process-based (mechanistic) model. Both models are calibrated using a 15-year data record of chlorophyll-a concentration (2003–2018) for the seasonal period of cyanobacteria dominance (June–October). The mechanistic model includes a novel representation of vertical mixing and is calibrated in a Bayesian framework, which allows for data-driven inference of important process rates. Both models show that chlorophyll-a concentration is much more responsive to nutrient variability than mixing, light, or temperature. While both models explain approximately 60% of the variability in chlorophyll-a, the mechanistic model is more robust in cross-validation and provides a more comprehensive assessment of algal drivers. Overall, these models indicate that nutrient reductions, rather than changes in mixing or background turbidity, are critical to controlling cyanobacteria in a shallow eutrophic freshwater system.}, journal={ENVIRONMENTAL POLLUTION}, author={Han, Yue and Aziz, Tarek N. and Del Giudice, Dario and Hall, Nathan S. and Obenour, Daniel R.}, year={2021}, month={Jan} }
 @article{katin_del giudice_hall_paerl_obenour_2021, title={Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity}, volume={447}, ISSN={["1872-7026"]}, DOI={10.1016/j.ecolmodel.2021.109497}, abstractNote={The Neuse River Estuary (North Carolina, USA) is a valuable ecosystem that has been affected by the expansion of agricultural and urban watershed activities over the last several decades. Eutrophication, as a consequence of enhanced anthropogenic nutrient loadings, has promoted high phytoplankton biomass, hypoxia, and fish kills. This study compares and contrasts three models to better understand how nutrient loading and other environmental factors control phytoplankton biomass, as chl-a, over time. The first model is purely statistical, while the second model mechanistically simulates both chl-a and nitrogen dynamics, and the third additionally simulates phosphorus. The models are calibrated to a multi-decadal dataset (1997–2018) within a Bayesian framework, which systematically incorporates prior information and accounts for uncertainties. All three models explain over one third of log-transformed chl-a variability, with the mechanistic models additionally explaining the majority of the variability in bioavailable nutrients (R2 > 0.5). By disentangling the influences of riverine nutrient concentrations, flows, and loadings on estuary productivity we find that concentration reductions, rather than total loading reductions, are the key to controlling estuary chl-a levels. The third model indicates that the estuary, even in its upstream portion, is rarely phosphorus limited, and will continue to be mostly nitrogen limited even under a 30% phosphorus reduction scenario. This model also predicts that a 10% change in nitrogen loading (flow held constant) will produce an approximate 4.3% change in estuary chl-a concentration, while the statistical model suggests a larger (10%) effect. Overall, by including a more detailed representation of environmental factors controlling algal growth, the mechanistic models generate chl-a forecasts with less uncertainty across a range of nutrient loading scenarios. Methodologically, this study advances the use of Bayesian methods for modeling the eutrophication dynamics of an estuarine system over a multi-decadal period.}, journal={ECOLOGICAL MODELLING}, author={Katin, Alexey and Del Giudice, Dario and Hall, Nathan S. and Paerl, Hans W. and Obenour, Daniel R.}, year={2021}, month={May} }
 @article{villez_del giudice_neumann_rieckermann_2020, title={Accounting for erroneous model structures in biokinetic process models}, volume={203}, ISSN={["1879-0836"]}, DOI={10.1016/j.ress.2020.107075}, abstractNote={In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. In this work, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Villez, Kris and Del Giudice, Dario and Neumann, Marc B. and Rieckermann, Jorg}, year={2020}, month={Nov} }
 @article{del giudice_matli_obenour_2020, title={Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios}, volume={30}, ISSN={["1939-5582"]}, DOI={10.1002/eap.2032}, abstractNote={Abstract}, number={2}, journal={ECOLOGICAL APPLICATIONS}, author={Del Giudice, Dario and Matli, V. R. R. and Obenour, Daniel R.}, year={2020}, month={Mar} }
 @article{fang_giudice_scavia_binding_bridgeman_chaffin_evans_guinness_johengen_obenour_2019, title={A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent}, volume={8}, DOI={10.1016/j.scitotenv.2019.133776}, abstractNote={Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June–October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.}, journal={Science of The Total Environment}, publisher={Elsevier BV}, author={Fang, Shiqi and Giudice, Dario Del and Scavia, Donald and Binding, Caren E. and Bridgeman, Thomas B. and Chaffin, Justin D. and Evans, Mary Anne and Guinness, Joseph and Johengen, Thomas H. and Obenour, Daniel R.}, year={2019}, month={Aug}, pages={133776} }
 @article{katin_giudice_obenour_2019, title={Modeling biophysical controls on hypoxia in a shallow estuary using a Bayesian mechanistic approach}, volume={7}, DOI={10.1016/j.envsoft.2019.07.016}, abstractNote={This study describes development of a mechanistically parsimonious model to dynamically simulate bottom layer (subpycnocline) dissolved oxygen (BLDO) concentration in the Neuse River Estuary, USA (1997–2015). The approach embeds differential equations controlling May–October BLDO within a Bayesian framework, enabling rigorous uncertainty quantification considering prior knowledge and calibration to historical data. Model simulations explain 62% of variability in bimonthly mean BLDO observations. Results indicate that during July–August, 36% of BLDO is consumed meeting oxygen demand associated with seasonal primary production, while the rest is depleted meeting long-term oxygen demand (LTOD), associated with storage of organic matter in estuary sediments. Interannual LTOD variation is associated with November–April longitudinal velocities, suggesting elevated flushing in winter decreases oxygen demands in summer. Results also indicate that the system is more responsive to nutrient loading reductions than previously thought, though it may take multiple years to produce measurable declines in hypoxia due to hydro-meteorological variability.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Katin, Alexey and Giudice, Dario Del and Obenour, Daniel R.}, year={2019}, month={Jul} }
 @article{machac_reichert_rieckermann_giudice_albert_2018, title={Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator}, volume={7}, DOI={10.1016/j.envsoft.2018.07.016}, abstractNote={As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Machac, David and Reichert, Peter and Rieckermann, Jörg and Giudice, Dario Del and Albert, Carlo}, year={2018}, month={Jul} }
 @article{giudice_zhou_sinha_michalak_2018, title={Long-term phosphorus loading and springtime temperatures explain interannual variability of hypoxia in a large temperate lake}, volume={1}, DOI={10.1021/acs.est.7b04730}, abstractNote={Anthropogenic eutrophication has led to the increased occurrence of hypoxia in inland and coastal waters around the globe. While low dissolved oxygen conditions are known to be driven primarily by nutrient loading and water column stratification, the relative importance of these factors and their associated time scales are not well understood. Here, we explore these questions for Lake Erie, a large temperate lake that experiences widespread annual summertime hypoxia. We leverage a three-decade data set of summertime hypoxic extent (1985-2015) and examine the role of seasonal and long-term nutrient loading, as well as hydrometeorological conditions. We find that a linear combination of decadal total phosphorus loading from tributaries and springtime air temperatures explains a high proportion of the interannual variability in average summertime hypoxic extent (R2 = 0.71). This result suggests that the lake responds primarily to long-term variations in phosphorus inputs, rather than springtime or annual loading as previously assumed, which is consistent with internal phosphorus loading from lake sediments likely being an important contributing mechanism. This result also demonstrates that springtime temperatures have a substantial impact on summertime hypoxia, likely by impacting the timing of onset of thermal stratification. These findings imply that management strategies based on reducing tributary phosphorus loading would take several years to reap full benefits, and that projected future increases in temperatures are likely to exacerbate hypoxia in Lake Erie and other temperate lakes.}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Giudice, Dario Del and Zhou, Yuntao and Sinha, Eva and Michalak, Anna M}, year={2018}, month={Jan} }
 @article{del giudice_muenich_kalcic_bosch_scavia_michalak_2018, title={On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions}, volume={105}, ISSN={["1873-6726"]}, DOI={10.1016/j.envsoft.2018.03.009}, abstractNote={Sophisticated methods for uncertainty quantification have been proposed for overcoming the pitfalls of simple statistical inference in hydrology. The implementation of such methods is conceptually and computationally challenging, however, especially for large-scale models. Here, we explore whether there are circumstances in which simple approaches, such as least squares, produce comparably accurate and reliable predictions. We do so using three case studies, with two involving a small sewer catchment with limited calibration data, and one an agricultural river basin with rich calibration data. We also review additional published case studies. We find that least squares performs similarly to more sophisticated approaches such as a Bayesian autoregressive error model in terms of both accuracy and reliability if calibration periods are long or if the input data and the model have minimal bias. Overall, we find that, when mindfully applied, simple statistical methods such as least squares can still be useful for uncertainty quantification.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, publisher={Elsevier BV}, author={Del Giudice, D. and Muenich, R. L. and Kalcic, M. M. and Bosch, N. S. and Scavia, D. and Michalak, A. M.}, year={2018}, month={Jul}, pages={286–295} }
 @article{del giudice_davies_2017, title={Communication: A few words can make a big impact}, volume={541}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85016156962&partnerID=MN8TOARS}, DOI={10.1038/541030e}, number={7635}, journal={Nature}, author={Del Giudice, D. and Davies, A.B.}, year={2017} }
 @article{giudice_albert_rieckermann_reichert_2016, title={Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation}, volume={3}, DOI={10.1002/2015wr017871}, abstractNote={Abstract}, journal={Water Resour. Res.}, publisher={Wiley-Blackwell}, author={Giudice, Dario Del and Albert, Carlo and Rieckermann, Jörg and Reichert, Peter}, year={2016}, month={Mar}, pages={n/a-n/a} }
 @article{del giudice_2016, title={Health}, volume={354}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84990924181&partnerID=MN8TOARS}, DOI={10.1126/science.354.6308.46}, abstractNote={Andrew Allcock has news of three practical Industry 4.0-style developments, in machine tool maintenance, automotive part grinding/honing and rotor grinding and milling}, number={6308}, journal={Science}, author={Del Giudice, D.}, year={2016} }
 @article{giudice_löwe_madsen_mikkelsen_rieckermann_2015, title={Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors}, volume={5}, DOI={10.1002/2014wr016678}, abstractNote={Abstract}, journal={Water Resour. Res.}, publisher={Wiley-Blackwell}, author={Giudice, Dario Del and Löwe, Roland and Madsen, Henrik and Mikkelsen, Peter Steen and Rieckermann, Jörg}, year={2015}, month={Jul}, pages={n/a-n/a} }
 @article{giudice_reichert_bareš_albert_rieckermann_2015, title={Model bias and complexity – Understanding the effects of structural deficits and input errors on runoff predictions}, volume={64}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84937157028&partnerID=MN8TOARS}, DOI={10.1016/j.envsoft.2014.11.006}, abstractNote={Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts.}, number={1}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Giudice, D. Del and Reichert, P. and Bareš, V. and Albert, C. and Rieckermann, J.}, year={2015}, month={Feb}, pages={205–214} }
 @article{sikorska_giudice_banasik_rieckermann_2015, title={The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration}, volume={9}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84943800958&partnerID=MN8TOARS}, DOI={10.1016/j.jhydrol.2015.09.051}, abstractNote={The concentration of total suspended solids (TSS) in surface waters is a commonly used indicator of water quality impairments. Its accurate prediction remains, however, problematic because: (i) TSS build-up, erosion, and wash-off are not easily identifiable; (ii) calibrating a TSS model requires observations of sediment loads, which are rare, and streamflow observations to calculate concentrations; and (iii) predicted TSS usually deviate systematically from observations, an effect which is commonly neglected. Ignoring systematic errors during calibration can lead to overconfident (i.e. unreliable) uncertainty estimates during predictions. In this paper, we therefore investigate whether a statistical description of systematic model errors makes it possible to generate reliable predictions for TSS. In addition, we explore how the reliability of TSS predictions increases when streamflow data are additionally used in model calibration. A key aspect of our study is that we use a Bayesian multi-output calibration and a novel autoregressive error model, which describes the model predictive error as a sum of independent random noise and autocorrelated bias. Our results show that using a statistical description of model bias provides more reliable uncertainty estimates of TSS than before and including streamflow data into calibration makes TSS predictions more precise. 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 quality variables which are dependent on streamflow.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Sikorska, A.E. and Giudice, D. Del and Banasik, K. and Rieckermann, J.}, year={2015}, month={Sep}, pages={241–254} }
 @article{dürrenmatt_del giudice_rieckermann_2013, title={Dynamic time warping improves sewer flow monitoring}, volume={47}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84878500847&partnerID=MN8TOARS}, DOI={10.1016/j.watres.2013.03.051}, abstractNote={Successful management and control of wastewater and storm water systems requires accurate sewer flow measurements. Unfortunately, the harsh sewer environment and insufficient flow meter calibration often lead to inaccurate and biased data. In this paper, we improve sewer flow monitoring by creating redundant information on sewer velocity from natural wastewater tracers. Continuous water quality measurements upstream and downstream of a sewer section are 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 dissimilarity between the two is minimized. It has not been applied in this context before. From numerical experiments we can show that DTW outperforms XCORR, because it provides more accurate velocity estimates, with an error of about 7% under typical conditions, at a higher temporal resolution. In addition, we can show that pre-processing of the data is important and that tracer reaction in the sewer reach is critical. As dispersion is generally small, the distance between the sensors is less influential if it is known precisely. Considering these findings, we tested the methods on a real-world sewer to check the performance of two different sewer flow meters based on temperature measurements. Here, we were able to detect that one of two flow meters was not performing satisfactorily under a variety of flow conditions. Although theoretical analyses show that XCORR and DTW velocity estimates contain systematic errors due to dispersion and reaction processes, these are usually small and do not limit the applicability of the approach.}, number={11}, journal={Water Research}, author={Dürrenmatt, D.J. and Del Giudice, D. and Rieckermann, J.}, year={2013}, pages={3803–3816} }
 @article{del giudice_honti_scheidegger_albert_reichert_rieckermann_2013, title={Improving uncertainty estimation in urban hydrological modeling by statistically describing bias}, volume={17}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84887060034&partnerID=MN8TOARS}, DOI={10.5194/hess-17-4209-2013}, abstractNote={Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural 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 output (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 stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.
                    }, number={10}, journal={Hydrology and Earth System Sciences}, author={Del Giudice, D. and Honti, M. and Scheidegger, A. and Albert, C. and Reichert, P. and Rieckermann, J.}, year={2013}, pages={4209–4225} }
 @article{coutu_del giudice_rossi_barry_2012, title={Modeling of facade leaching in urban catchments}, volume={48}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84870950923&partnerID=MN8TOARS}, DOI={10.1029/2012WR012359}, abstractNote={Building facades are protected from microbial attack by incorporation of biocides within them. Flow over facades leaches these biocides and transports them to the urban environment. A parsimonious water quantity/quality model applicable for engineered urban watersheds was developed to compute biocide release from facades and their transport at the urban basin scale. The model couples two lumped submodels applicable at the basin scale, and a local model of biocide leaching at the facade scale. For the facade leaching, an existing model applicable at the individual wall scale was utilized. The two lumped models describe urban hydrodynamics and leachate transport. The integrated model allows prediction of biocide concentrations in urban rivers. It was applied to a 15 km2urban hydrosystem in western Switzerland, the Vuachère river basin, to study three facade biocides (terbutryn, carbendazim, diuron). The water quality simulated by the model matched well most of the pollutographs at the outlet of the Vuachère watershed. The model was then used to estimate possible ecotoxicological impacts of facade leachates. To this end, exceedance probabilities and cumulative pollutant loads from the catchment were estimated. Results showed that the considered biocides rarely exceeded the relevant predicted no‐effect concentrations for the riverine system. Despite the heterogeneities and complexity of (engineered) urban catchments, the model application demonstrated that a computationally “light” model can be employed to simulate the hydrograph and pollutograph response within them. It thus allows catchment‐scale assessment of the potential ecotoxicological impact of biocides on receiving waters.}, number={12}, journal={Water Resources Research}, author={Coutu, S. and Del Giudice, D. and Rossi, L. and Barry, D.A.}, year={2012} }
 @article{coutu_del giudice_rossi_barry_2012, title={Parsimonious hydrological modeling of urban sewer and river catchments}, volume={464-465}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84866158883&partnerID=MN8TOARS}, DOI={10.1016/j.jhydrol.2012.07.039}, abstractNote={A parsimonious model of flow capable of simulating flow in natural/engineered catchments and at WWTP (Wastewater Treatment Plant) inlets was developed. The model considers three interacting, dynamic storages that account for transfer of water within the system. One storage describes the “flashy” response of impervious surfaces, another pervious areas and finally one storage describes subsurface flow. The sewerage pipe network is considered as an impervious surface and is thus included in the impervious surface storage. In addition, the model assumes that water discharged from several CSOs (combined sewer overflows) can be accounted for using a single, characteristic CSO. The model was calibrated on, and validated for, the Vidy Bay WWTP, which receives effluent from Lausanne, Switzerland (population about 200,000), as well as for an overlapping urban river basin. The results indicate that a relatively simple approach is suitable for predicting the responses of interacting engineered and natural hydrosystems.}, journal={Journal of Hydrology}, author={Coutu, S. and Del Giudice, D. and Rossi, L. and Barry, D.A.}, year={2012}, pages={477–484} }