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ONGOING PROJECTS
ONGOING PROJECTS
Data-Driven Optimization for Storm Sewer Sensing and Control from Sparse Measurements
Data-Driven Optimization for Storm Sewer Sensing and Control from Sparse Measurements
Increased urbanization is major driver of urban stream degradation, not only by altering streamflow regimes but also by degrading water quality. These altered streamflow regimes – marked by more frequent, “flashy”, and erosive stormflows and decreased baseflows – are ubiquitous. To address flashy stormflows, green infrastructure (GI) has been implemented to reduce peak flows, recover natural hydrological processes, and remove contaminants. Particularly, active control techniques have been increasingly used on GI to manage its operations in real time and potentially enhance its performance. However, various challenges exist. For example, current active control technologies and practices are often constrained by limited capacity to manage both surface and subsurface flows – namely, flashy stormflows and baseflows – simultaneously. In addition, as systems grow in scale (e.g., from one basin to a network), coordination becomes computationally intensive and more complex to manage, particularly compounded by finite resources and budget for monitoring and control. To address these challenges, this science plan offers a data-model integrated framework to discern the embedding dynamics in urban storm sewer systems, optimize placement of the sensor networks, forecast system response, and realize active control of green infrastructure to balance quick stormflows and baseflows, thereby restoring natural river flow regimes.
NSF-ERI: Effects of urban water infrastructure and proximate soil profiles on coupled surface-subsurface hydrology
NSF-ERI: Effects of urban water infrastructure and proximate soil profiles on coupled surface-subsurface hydrology
The sustainability and resilience of urban water systems is limited by poor understanding of subsurface hydrologic processes that control flood generation and how they are impacted by urban soil profiles and water distribution and collection infrastructure. This project will integrate soil profile surveys with hydrologic model simulations to understand 1) how do soil hydraulic properties vary spatially due to urbanization processes that “replumb” the subsurface (i.e., trenching and backfilling)? 2) how do the spatial structure and deterioration of water distribution and collection systems relative to soil profiles affect the coupling of surface and subsurface hydrologic processes in urban watersheds? And 3) what are the implications of infrastructure rehabilitation and transition from gray to green infrastructure for flood response and long-term water balances?
Md Azizul Islam, I., Kumar, A., Zhang, K. Variations of Urban Water Balances Considering Subsurface Sewer Fluxes: A Hydrologic Modeling Study (Under review in Journal of Hydrology).
Storm Sewer Optimization and Forecasting from Limited Data: A Data-Model Integrated Approach
Storm Sewer Optimization and Forecasting from Limited Data: A Data-Model Integrated Approach
Increasing resilience to floods under climate change has become one of the major challenges for urban areas. To obtain a complete understanding of the flooding events and achieve accurate, timely prediction, hydrologists and water managers are challenged by the desire to measure and model at high spatio-temporal resolution. However, time, budget, and technology constraints limit the resolution that can be achieved in practice. Process-based models are limited by high computational and calibration requirements; Machine learning models also suffer from challenges in physical interpretability and transparency and high requirement of training data. This project will integrate linear-algebra-based data-driven analytics with urban hydrologic models to identify the optimal placements of sensors in urban sewer networks and forecast sewer system responses based on sparse measurements.
Monitoring and modeling the thermal impact and contaminant removal performance of bio-infiltration systems on trout streams
Monitoring and modeling the thermal impact and contaminant removal performance of bio-infiltration systems on trout streams
Bio-infiltration practices have been found to be effective in managing stormwater runoff and mitigating the impact of nutrients, pathogens, and water temperature on receiving water bodies. To enhance the stormwater runoff control performance, bio-infiltration practices are increasingly aligned as a treatment train to provide a synergetic effect on runoff retention, thermal impact, and contaminant removal. This study, in collaboration with The City of Duluth, St. Louis County Public Works, and MNDNR, will conduct field monitoring and modeling in multiple bio-infiltration basins in Tischer Creek watershed to study 1) how does each bio-infiltration practice within a treatment train perform in reducing runoff temperature and contaminant? 2) how many bio-infiltration units are required for a reasonable removal performance? 3) what are the optimal size of bio-infiltration practices?
Zhang, K., Huss, D., & Merten, G.H., 2025. A Field Evaluation on the Thermal Mitigation Effect of an Iron Sand–Enhanced Bioinfiltration Basin. Journal of Sustainable Water in the Built Environment, 11(4), p.05025005.
Past projects
Past projects
Streamflow and water-quality time series reconstruction using sparse sensing
Streamflow and water-quality time series reconstruction using sparse sensing
High-dimensional states can often leverage a latent low-dimensional representation. This inherent compressibility enables those high-dimensional states to be reconstructed or predicted from sparse measurements through sparse sensing. In this project, we utilized sparse sensing to reconstruct and/or predict streamflow and water quality (e.g., nitrate and phosphorus concentrations) time series across watersheds. These works focused on exploring the applicability of sparse sensing on environmental signals and pursuing effective strategies to reduce the required sampling efforts. Other potential applications of sparse sensing include sensor location optimization, gap filling, and making predictions.
Bin Mamoon, W., Zhang, K., Luhar, M., & Parolari, A.J. (2025). Stream nutrient load and concentration estimation from minimal measurements, Geophysical Research Letters, 52(8), e2025GL114935
Zhang, K., Bin Mammoon, W., Schwartz, E, & Parolari, A.J. (2023). Reconstruction of sparse stream flow and concentration time-series through compressed sensing. Geophysical Research Letters, 50, e2022GL101177.
Zhang, K., Luhar, M., Brunner, M.I., Parolari, A.J. (2023). Streamflow prediction in ungauged watersheds in the United States through data-driven sparse sensing, Water Resources Research, 59, e2022WR034092.
Prioritization of green stormwater infrastructure in urban drainage systems with multi-factorial consideration
Prioritization of green stormwater infrastructure in urban drainage systems with multi-factorial consideration
Green infrastructure (GI) has gained traction as a preferred stormwater management practice for its benefits in mimicking pre-development hydrology, removing non-point source pollution, and improving water quality. GI has been adopted as a key component of the Metropolitan Sewerage District’s (MMSD’s) strategic plan. There is a strong and urgent need to quantify the system-scale impact of existing and future GI implementations and prioritize GI investments. However, this is not a simple task because the GI impact can be diluted by uncertainties from the climate and those associated with the complex environment such as the changing land cover. In this project, we integrated data-based analysis with hydrologic & hydraulic models to determine 1) the system response and priority sewersheds for GI considering recent decadal and future forecasted precipitation, 2) the system response and priority sewersheds for GI in the whole MMSD service area, and 3) the priority sewersheds for GI considering various climatic, hydrogeologic, hydraulic, and socio-economic factors.
Analyzing the impact of subsurface urban drainage on inflow & infiltration and watershed hydrology
Analyzing the impact of subsurface urban drainage on inflow & infiltration and watershed hydrology
Urbanization increases impervious cover and involves urban drainage infrastructure which dissects the subsurface and induces artificial controls on streamflow by draining water from the subsurface soils, e.g., inflow and infiltration (I&I). With this effect, sometimes referred to as “urban karst”, the slow subsurface flows can be redistributed to fast sewer flow, and the baseflow recession of streamflow can be altered. Therefore, understanding the volume and dynamics of I&I and its impact on urban hydrology is a key in urban hydrology characterization. In this project, we utilized data analytics and numerical models to study 1) what fraction of the urban water balance is discharged through I&I? 2) how does subsurface drainage such as I&I affect baseflow recession in urban streams? and 3) considering I&I, how does enhanced infiltration by green infrastructure affect the stormwater distribution?
Zhang, K., Sebo, S., McDonald, W., Bhaskar, A., Shuster, W., Stewart, R., Parolari, A.J. (2023). The role of inflow and infiltration (I/I) in urban water balances and streamflow regimes: A hydrograph analysis along the sewershed-watershed continuum, Water Resources Research, 59, e2022WR032529.
Zhang, K., Parolari, A. (2022). Impact of stormwater infiltration on rainfall-derived inflow and infiltration at watershed scale: A physically based surface-subsurface urban hydrologic model. Journal of Hydrology, 610, 127938.
Funded by Metropolitan Milwaukee Sewer District through the Water Environment & Policy I/UCRC Research Center (2020-2021)
Funded by Metropolitan Milwaukee Sewer District through the Water Environment & Policy I/UCRC Research Center (2020-2021)
Evaluating the optimal design and spatial allocation of green stormwater infrastructure in shallow groundwater environments via surface-subsurface hydrologic modeling
Evaluating the optimal design and spatial allocation of green stormwater infrastructure in shallow groundwater environments via surface-subsurface hydrologic modeling
Well-designed and implemented green stormwater infrastructure (GSI) can help to recover the natural hydrologic regime of urban areas. A large-scale GI planning requires a good understanding of the impact of GI spatial allocation on surface-subsurface hydrologic dynamics. This study developed and utilized a coupled surface-subsurface hydrological model (SWMM-MODFLOW) and other variably saturated hydrologic models to simulate two-way interactions between GI and subsurface hydrology at lot and catchment scales. In this project, I utilized multiple hydrologic models to study 1) how does GSI perform in shallow groundwater environments? 2) what are the appropriate designs for GSI in such environments? and 3) how does the spatial allocation of GSI affect the local and regional groundwater table fluctuation?
Zhang, K., Chui, T.F.M. (2024). Spatial allocation of bioretention cells considering interaction with shallow groundwater: A simulation-optimization approach. Science of the Total Environment, 935(20), 173369.
Zhang, K., Chui, T.F.M. (2020). Assessing the impact of spatial allocation of bioretention cells on shallow groundwater - an integrated surface-subsurface catchment-scale analysis with SWMM-MODFLOW. Journal of Hydrology, 586, 124910.
Zhang, K., Chui, T.F.M. (2020). Design measures to mitigate the impact of shallow groundwater on hydrologic performance of permeable pavements. Hydrological Processes, 34, 5146-5166. (IF: 3.784).
Zhang, K., Chui, T.F.M. (2019). A review on implementing infiltration-based green infrastructure in shallow groundwater environments: Challenges, approaches, and progress. Journal of Hydrology, 579, 124089. (IF: 6.708)
Zhang, K., Chui, T.F.M, Yang, Y. (2018). Simulating the hydrologic performance of low impact development practices in shallow groundwater via a modified SWMM. Journal of Hydrology, 566, 313-331.
Zhang, K., Chui, T.F.M. (2018). Interactions between shallow groundwater and LID underdrain flow at different temporal scales. Hydrological Processes, 32(23), 3495-3512. (IF: 3.784)
Zhang, K., Chui, T.F.M. (2017). Evaluating hydrologic performance of bioretention cells in shallow groundwater. Hydrological Processes, 31, 4122-4135.
Funded by University of Hong Kong (2016-2020)
Hydrologic performance of various permeable pavements via field monitoring
Hydrologic performance of various permeable pavements via field monitoring
As part of the further expansion of Shek Wu Hui Sewage Treatment Works (SWHSTW) undertaken by AECOM Asia Co Ltd., a porous pavement study (RD1108) was initiated. Pavement trial panels were designed and constructed at SWHSTW and Stonecutter Island Sewage Treatment Works (SCISTW). The porous pavement trials include three pedestrian trial panels (3m x 1m each) and four vehicular trial panels (30m x 3m each), three at SWHSTW one at SCISTW. The surface pavers of the pedestrian ones were porous blocks, grass cover and resin bound surfacing material, while those of the vehicular ones are open cell pavers and two types of porous blocks at SWHSTW, and permeable interlocking concrete paver at SCISTW. Construction of trial panels commenced in June 2016 and completed in January 2017. A series of hydrologic testing and monitoring was conducted, which included the in-situ permeability tests, the seven-month monitoring under natural rainfall during the rainy season, the 12-month serviceability trial (vehicular traffic count and topographic survey) and the two sets of artificial rainfall experiments before and after the 12-month serviceability trial (in March 2017 and February 2018 respectively). The hydrologic performance was quantified by runoff peak reduction, volume reduction, peak delay, and was compared among panels and between the artificial rainfall experiments in 2017 and 2018.
Peng, H., Zhang, K., & Chui, T.F.M. (2025). Hydrologic performance of permeable pavements under extreme and regular rainfall conditions. Journal of Hydrology, 652, 132653.
Zhang, K., Huang, P., Chui, T.F. (2022). Runoff mitigation by underdrained permeable pavement in shallow groundwater environments: A field investigation? Journal of Hydrologic Engineering, 27(7), 04022011.
MAILING ADDRESS
MAILING ADDRESS
Kun Zhang
Kun Zhang
258 Swenson Civil Engineering (SCiv)
258 Swenson Civil Engineering (SCiv)
1405 University Dr, Duluth, MN 55812
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