Causes and Consequences of Clogging in the Santa Cruz River Natalie Case and Julie Stromberg School of Life Sciences, Arizona State University March 29, 2011
Research Problem In waterways used for groundwater recharge or riparian ecosystem support, effluent-induced clogging and reduced infiltration may impair these ecosystem services The clogging process is not well studied in flowing streams - the mechanisms behind it need to be determined and understood before it can be managed. Ina Rd
Riparian tree die-off along Santa Cruz River at Rio Rico, 2005 Aerial photo courtesy of Friends of the Santa Cruz River
Clogging Refers to a processes where the flow path of water through a porous medium is reduced Can be through Physical, Chemical, and Biological processes Time dependent -Clays -Gas -Precipitates Biological -Biofilms -Gas -Precipitates -Fine silts -Suspended solids -Organic matter Chemical Physical
Focus on Bio- Clogging SEM of bacteria attached to sand grains Seifert and Engesgaard 2007
Research Plan Determine the causes of riverbed clogging in an effluent-dominated system, identifying the mechanism and variables that affect the process We hypothesize that in an effluent-dominated system, with constant, warm and high-nutrient waters, microorganisms significantly contribute to reduced infiltration.
Study Area 3 sites near Tucson 3 sites near Nogales 3 sites near Sierra Vista Phoenix Effluent study river Tucson Sierra Vista Nogales Non-effluent control river
Experimental Design Compare the microbiology, hydrology, and sediment traits of two effluent-dominated river reaches to one not greatly impacted by effluent. Methods Surface and pore water samples analyzed for dissolved O 2, temp, ph, NO 3 /NO 2, NH 4, PO 4, Organic Carbon (NPOC) Sediment samples analyzed for particle size composition and microbiology (plate counts, functional groups)
Hydraulic conductivity measured using falling head test with piezometers; at pool, thalweg, and bank Field data collected April, August, October, November 2010
Observations
Results 25 20 Surface water quality varies among reaches and sites Surface Water Thalweg Bank Pool Average Ammonia April, 2010 October, 2010 25 20 SW T B P NH 4 (mg/l) 15 10 NH 4 + (mg N/L) 15 10 5 5 0 Lewis Fairbanks Rio Santa Chaves Ina Hardin Sasco Control SCR-Nogales SCR-Tucson 0 Control Charelston Fairbanks SCR-Nogales SCR-Tucson Rio Rico, 3km Palo Parado, 11km Santa Gertrudis, 16km Ina, 0.5km Avra Valley, 11km Trico, 25km
Results Average Nitrate+Nitrite April, 2010 October, 2010 N as NO 3 /NO 2 14 12 10 8 6 4 Surface Water Thalweg Bank Pool NO 3 - /NO2 - (mg N/L) 14 12 10 8 6 4 SW T B P 2 2 0 Lewis Fairbanks Rio Santa Chaves Ina Hardin Sasco Control SCR-Nogales SCR-Tucson 0 Control Charelston Fairbanks SCR-Nogales SCR-Tucson Rio Rico, 3km Palo Parado, 11km Santa Gertrudis, 16km Ina, 0.5km Avra Valley, 11km Trico, 25km
Results Average Total Phosphorous Total Phosphorous (mg/l) 14 12 10 8 6 4 Surface Water Thalweg Bank Pool April, 2010 October, 2010 TP (mg/l) 14 12 10 8 6 4 SW T B P 2 2 0 Lewis Fairbanks Rio Santa Chaves Ina Hardin Sasco Control SCR-Nogales SCR-Tucson 0 Control SCR-Nogales SCR-Tucson Charelston Fairbanks Rio Rico, 3km Palo Parado, 11km Santa Gertrudis, 16km Ina, 0.5km Avra Valley, 11km Trico, 25km
Results 20 15 Surface Water Thalweg Bank Pool Average Total Organic Carbon April, 2010 October, 2010 20 SW T B P 15 NPOC (mg/l) 10 NPOC (mg/l) 10 5 5 0 Lewis Fairbanks Rio Santa Chaves Ina Hardin Sasco Control SCR-Nogales SCR-Tucson 0 Control SCR-Nogales SCR-Tucson Charelston Fairbanks Rio Rico, 3km Palo Parado, 11km Santa Gertrudis, 16km Ina, 0.5km Avra Valley, 11km Trico, 25km
Hydraulic conductivity varies among river reaches, sites, and fluvial surfaces 0.4 0.3 Site-averaged Hydraulic Conductivity April 2010 October 2010 Thalweg Bank Pool 0.4 0.3 Thalweg Bank Pool K (cm/s) 0.2 K (cm/s) 0.2 0.1 0.1 0.0 Control SCR-Nogales SCR-Tucson Lewis Fairbanks Rio Rico, 3km Santa Gertrudis, 16km Chavez Siding, 24km Ina, 0.5km Hardin, 29km Sasco, 39km 0.0 SCR-Nogales Rio Rico, 3km Palo Parado, 11km Santa Gertrudis, 16km Ina, 0.5km SCR-Tucson Avra Valley, 11km Trico, 25km
Sediment particle size is strongly related to hydraulic conductivity Hydraulic conductivity, 20-cm depth (K) 0.5 0.4 0.3 0.2 0.1 0.0 Clogging threshold? October, 2010 Santa Cruz River 0 10 20 30 40 50 60 70 Clay, silt, very fine sand, and fine sand (%) Pearson correlation between sediment class and K Clay Silt VF-Sand F-Sand M-Sand C-Sand VC-Sand Gravel (%) (%) (%) (%) (%) (%) (%) (%) r value -0.46-0.51-0.53-0.65-0.35 0.34 0.35 0.22 p value 0.03 0.01 0.01 0.00 0.44 0.57 0.52 1.00
50 Bacterial abundance correlates strongly with fine-textured sediments Clay, silt, very fine sand, fine sand (%) 40 30 20 10 0 0 2e+8 4e+8 6e+8 8e+8 1e+9 Sediment heterotrophic plate counts: colony forming units
Bacterial abundance is related to hydraulic conductivity Hydraulic conductivity (K) 0.5 0.4 0.3 0.2 0.1 0.0 Clogging threshold? October, 2010 Santa Cruz River 0 2e+8 4e+8 6e+8 8e+8 1e+9 Sediment heterotrophic plate counts: colony forming units Numbers of CFUs also increase with certain water quality parameters (NH4, TP, NPOC)
Conclusions Hydraulic conductivity tends to be reduced in fine sediments, in both the control and effluent river Reduced hydraulic conductivity is also associated with higher bacterial numbers Bacteria are more abundant in fine sediments and in areas with higher levels of organic carbon and ammonia Additional analysis underway to tease apart connections between water quality, bacterial abundance, sediment texture, and hydraulic conductivity, and to identify thresholds for reduced conductivity
Future Work Pre-monsoon trip planned to collect during a longer flood-free sequence Ecolog plates to assess microbial community physiological profile Participation in Enhanced Recharge Project, funded by ABOR Additional Studies Laboratory manipulation of clogging conditions Sequencing of microbial DNA for functional groups
Acknowledgements Funding source: Southwest Consortium for Environmental Research and Policy Advisors and assistants: Dr. Julie Stromberg, Dr. Channah Rock and Kelley Riley for microbial methods, Dr. Tom Meixner for hydrological methods, Meg White, Danika Setaro, Amanda Suchy, Lane Butler, Joe Monfeli, Michelle Perry, Tristan Dunton, Evan Balbona, Nick McLamb, Ray Leimkuehler, John Shasky, Kellie Elliott, and Chelsey Hull for field assistance