Brief Summary of Minutes of Annual Meeting

Research highlights:

Zhuping Sheng (Texas A&M) – urban stormwater management; community gardens; permeable surfaces; roof greenhouses. 

• Challenge – salinity. Looking at potential reaction models.  Looking to link GW models with SWAT (w/MODFLOW). 
• Recommendations – uncertainty of data, incorporate RS via UAVs; use UAVs to assess salinity; multispectral imaging to assess salinity??

 Rabin Bhattarai (UIUC) –

• challenges of assessing impact of NPS, specifically nutrients (nitrates, phosphates) on water quality; $4M project to collect data to assess farm BMPs and field response to different nutrient inputs vs. topography/soil/weather/drained & undrained fields, etc. Farm-to-farm monitoring.
• Looking at denitrification and phosphate removal media (ie. divert water for denitrification and phosphate removal media). Developing a web tool and enter their farming information, brings up wx, SSURGO info, to see implications and implement change in nutrient practices. Project to look at how drainage impacts yield and nutrient practices to optimize drainage system.
• Side comments – scaling is important. We need to be able to look at large & small-scale models (can we effectively up-scale small scale models)?  Larger scale models give us regional assessments of environmental hydrologic issues.  Smaller scale models are necessary for BMPs due to localities of water management. 

Sunday (Iowa) –

• Using techniques to input field strips (roadside ditches) into models and evaluate their effectiveness – looking at where they are in landscape, adding to maps, and using them to model hydrology w/roadside ditches.
• Attempting for a comprehensive database of structural and non-structural BMPs. Question – 1) how do we extend the BMPs that are normally structured to small (field) scale, but when using watershed scale models to evaluate their effectiveness? Do we scale down models or scale up BMPs?  2) how do we integrate BMPs into models? 

 Cibin Raj (Penn State)

• manure management & “manure-sheds”; phosphorous index; built geospatial tool to look at landscape, proximity to streams, and various other indicators, and assess how suitable locations are for manure management.
• Adaptability of BMPs and influence on riparian areas – looking at flexible buffer systems, what is grown & how it is managed in the riparian buffers. Using SWAT and ANAGNPS for buffer-specific nutrient loading.
• Using SWAT for Chesapeake Bay modeling (vice Ches Bay model).
• Interested in uncertainty of BMPs. Exploring landscape optimization, nutrient management, and scalability.  Looking at slow-release fertilizers.
• Also looking at urban BMPs – identification, monitoring, quantification & effectiveness.

 Art Gold (URI)  

• UV vis sensors to understand contaminant fluxes during baseflow and storm events. Focus/challenge – concentration vs. flow (C vs. Q) – doesn’t scale well & see very different relationships in large vs. small catchments.
• Algae/cynoabacteria blooms – increasing # of lakes closed due to algal blooms: what’s the role of the timing and extent of nutrients on these algal blooms? EPA wants the right to measure nutrient outputs & take action.

 David Sample (Virginia Tech)

• Temperature is not well-accounted for in ag or urban models (one motivator is toxicity to trout);
• uses SWMM. R-SWMM optimizes. Monitoring:  monitoring ponds as nutrient retention locations – is phosphorous getting bound in sediments, is it mobile, are they exporting nitrogen? 
• Looking at bioretention in ponds & wetlands as a real-time control system for nutrient loading and flooding control.
• Process based model

  Soni (URI)

• riparian buffer zones, modeling of N & P loading (Marzia);
• mitigation plan for reservoir flooding (SWAT integrated with OASIS for reservoir release operations) (Supria);
• urban hydrology modeling & effectiveness of roadside best-management practices & impact of soil amendment via SWMM (Luna);
• saltwater intrusion monitoring (GPR, electro resistivity, UAVs), MODFLOW coupled SEAWAT, & requires climate input for storm surges (Jeeban & Mamoon);
• downscaling of climate data for RI & machine-learning to assess high frequency signals (Shiva).
• Precursors of disinfection by products (Maxwell and Hichem)
• PRMS modeling for runoff prediction/climate change k-nn clustering, fourier transformation to understand baseflow and stream temperature (Kyle)