Fate of nitrate in seepage from a restored wetland receiving agricultural tailwater

Constructed and restored wetlands are a common practice to filter agricultural runoff, which often contains high levels of pollutants, including nitrate. Seepage waters from wetlands have potential to contaminate groundwater. This study used soil and water monitoring and hydrologic and nitrogen mass balances to document the fate and transport of nitrate in seepage and surface waters from a restored flow-through wetland adjacent to the San Joaquin River, California. A 39% reduction in NO3-N concentration was observed between wetland surface water inflows (12.87±6.43mgL-1; mean±SD) and outflows (7.87±4.69mgL-1). Redox potentials were consistently below the nitrate reduction threshold (~250mV) at most sites throughout the irrigation season. In the upper 10cm of the main flowpath, denitrification potential (DNP) for soil incubations significantly increased from 151 to 2437mgNO3-Nm-2d-1 when nitrate was added, but showed no response to carbon additions indicating that denitrification was primarily limited by nitrate. Approximately 72% of the water entering the wetland became deep seepage, water that percolated beyond 1-m depth. The wetland was highly effective at removing nitrate (3866kgNO3-N) with an estimated 75% NO3-N removal efficiency calculated from a combined water and nitrate mass balance. The mass balance results were consistent with estimates of NO3-N removed (5085kgNO3-N) via denitrification potential. Results indicate that allowing seepage from wetlands does not necessarily pose an appreciable risk for groundwater nitrate contamination and seepage can facilitate greater nitrate removal via denitrification in soil compared to surface water transport alone

Regional differences in the response of California’s rangeland production to climate and future projection

Rangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching communities to cope with future changes. Herein, we examined how climate regulates the interannual variability of GPP in California’s diverse annual rangeland, based on the contemporary records of satellite derived GPP at 500 m resolution since 2001. We built Gradient Boosted Regression Tree models for 23 ecoregion subsections, relating annual GPP with 30 climatic variables, to disentangle the partial dependence of GPP on each climate variable. The machine learning results showed that GPP was most sensitive to growing season (GS) precipitation, with a reduction in GPP up to 200 g cm ^−2 yr ^−1 when GS precipitation decreased from 400 to 100 mm yr ^−1 in one of the driest subsections. We also found that years with more evenly distributed GS precipitation had higher GPP. Warmer winter minimum air temperature enhanced GPP in approximately two-thirds of the subsections. In contrast, average GS air temperatures showed a negative relationship with annual GPP. When the pre-trained models were forced by downscaled future climate projections, changes in the predicted rangeland productivity by mid- and end of century were more remarkable at the ecoregion subsection scale than at the state level. Our machine learning-based analysis highlights key regional differences in GPP vulnerability to climate and provides insights on the intertwining and potentially counteracting effects of seasonal temperature and precipitation regimes. This work demonstrates the potential of using remote sensing to enhance field-based rangeland monitoring and, combined with machine learning, to inform adaptive management and conservation within the context of weather extremes and climate change

Regional differences in the response of California’s rangeland production to climate and future projection

Rangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching communities to cope with future changes. Herein, we examined how climate regulates the interannual variability of GPP in California’s diverse annual rangeland, based on the contemporary records of satellite derived GPP at 500 m resolution since 2001. We built Gradient Boosted Regression Tree models for 23 ecoregion subsections, relating annual GPP with 30 climatic variables, to disentangle the partial dependence of GPP on each climate variable. The machine learning results showed that GPP was most sensitive to growing season (GS) precipitation, with a reduction in GPP up to 200 g cm−2 yr−1 when GS precipitation decreased from 400 to 100 mm yr−1 in one of the driest subsections. We also found that years with more evenly distributed GS precipitation had higher GPP. Warmer winter minimum air temperature enhanced GPP in approximately two-thirds of the subsections. In contrast, average GS air temperatures showed a negative relationship with annual GPP. When the pre-trained models were forced by downscaled future climate projections, changes in the predicted rangeland productivity by mid- and end of century were more remarkable at the ecoregion subsection scale than at the state level. Our machine learning-based analysis highlights key regional differences in GPP vulnerability to climate and provides insights on the intertwining and potentially counteracting effects of seasonal temperature and precipitation regimes. This work demonstrates the potential of using remote sensing to enhance field-based rangeland monitoring and, combined with machine learning, to inform adaptive management and conservation within the context of weather extremes and climate change

Multiple ecosystem service outcomes across vegetation states.

<p>Displays the provisioning of multiple ecosystem services across vegetation states. Values were relativized by maximum observed levels across all three vegetation states.</p

Landscape vegetation state composition management scenarios.

<p>Landscape vegetation state composition management scenarios.</p

State and transition model for Sierra Nevada Gravelly Loam Foothill ecological site.

<p>A simplified expert state and transition model (STM) for the Sierra Nevada Sierra Nevada Gravelly Loam Foothill ecological site. Boxes represent vegetation states and the arrows represent thresholds between states. The underlying orthoimagery (USDA-NAIP imagery 2014) illustrates the gradient of oak woodland management across the study area. The images within the boxes provide an example visualization of the respective vegetative states. Photo credit: Danny Eastburn.</p

Soil health.

<p>Soil health.</p

California’s blue oak woodland range.

<p>California’s endemic blue oak (<i>Quercus douglasii</i>) woodlands and savannas span approximately 4 million hectares. Our analysis focused on a managed oak woodland landscape at the University of California Sierra Foothill Research and Extension Center (SFREC) in Yuba County, California, US. Map was created using data from California Department of Forestry and Fire Protection FRAP 2006. The digital orthoimagery was acquired from United States Department of Agriculture National Geospatial Data Asset NAIP Imagery.</p

Mechanisms controlling the impact of multi-year drought on mountain hydrology

Abstract Mountain runoff ultimately reflects the difference between precipitation (P) and evapotranspiration (ET), as modulated by biogeophysical mechanisms that intensify or alleviate drought impacts. These modulating mechanisms are seldom measured and not fully understood. The impact of the warm 2012–15 California drought on the heavily instrumented Kings River basin provides an extraordinary opportunity to enumerate four mechanisms that controlled the impact of drought on mountain hydrology. Two mechanisms intensified the impact: (i) evaporative processes have first access to local precipitation, which decreased the fractional allocation of P to runoff in 2012–15 and reduced P-ET by 30% relative to previous years, and (ii) 2012–15 was 1 °C warmer than the previous decade, which increased ET relative to previous years and reduced P-ET by 5%. The other two mechanisms alleviated the impact: (iii) spatial heterogeneity and the continuing supply of runoff from higher elevations increased 2012–15 P-ET by 10% relative to that expected for a homogenous basin, and iv) drought-associated dieback and wildfire thinned the forest and decreased ET, which increased 2016 P-ET by 15%. These mechanisms are all important and may offset each other; analyses that neglect one or more will over or underestimate the impact of drought and warming on mountain runoff

Ecological datasets in the Southern Sierra Critical Zone Observatory.xlsx

The ecological datasets were collected in the Southern Sierra Critical Zone Observatory (SSCZO) bioclimatic gradient is located on the western slope of southern Sierra Nevada in California, spanning from 405 to 2700 m in elevation.  More detailed site characteristics and sampling methods can be found at the SSCZO website (http://criticalzone.org/sierra/infrastructure/field-areas-sierra/)  </p