A runoff risk model based on topographic wetness indices and probability distributions of rainfall and soil moisture for central New York agricultural fields

Hofmeister, K.; Georgakakos, Christine; Walter, M. Todd

doi:10.2489/jswc.71.4.289

Cited by 7 publications

(6 citation statements)

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“…Buchanan et al, 2014) or frequency of soil saturation (e.g. Hofmeister et al, 2016). It is important to note that over a period of 1 year Anderson et al (2015) made in situ denitrification measurements from groundwater at the riparian, ephemeral spring and forested hilltop sites, at two to six points across an area of~100 m 2 , whereas during this study samples were collected from the top 10 cm of soil at one time point from a 1 m 2 area around one of Anderson et al's (2015) sample sites.…”

Section: Study Sitementioning

confidence: 99%

“…Because these factors do not vary intra-annually, STI serves as a useful indicator of soil moisture conditions over relatively long time frames. Average denitrification rates at the HTRC sites had a strong positive correlation with STI, with larger STI values indicating generally wetter conditions, and therefore presumably lower O 2 conditions (Buchanan et al, 2014;Hofmeister et al, 2016). Average denitrification rates at the HTRC sites had a strong positive correlation with STI, with larger STI values indicating generally wetter conditions, and therefore presumably lower O 2 conditions (Buchanan et al, 2014;Hofmeister et al, 2016).…”

Section: Introductionmentioning

confidence: 98%

“…Anderson et al (2015) measured denitrification over the course of a year at three sites along a STI-defined hydrological gradient located at the Harford Teaching and Research Center (HTRC) near Harford, NY using the isotopically based pushpull method (Addy et al, 2002). Average denitrification rates at the HTRC sites had a strong positive correlation with STI, with larger STI values indicating generally wetter conditions, and therefore presumably lower O 2 conditions (Buchanan et al, 2014;Hofmeister et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients

Nadeau

Roco

Debenport

et al. 2019

Environmental Microbiology

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Summary This study coupled a landscape‐scale metagenomic survey of denitrification gene abundance in soils with in situ denitrification measurements to show how environmental factors shape distinct denitrification communities that exhibit varying denitrification activity. Across a hydrologic gradient, the distribution of total denitrification genes (nap/nar + nirK/nirS + cNor/qNor + nosZ) inferred from metagenomic read abundance exhibited no consistent patterns. However, when genes were considered independently, nirS, cNor and nosZ read abundance was positively associated with areas of higher soil moisture, higher nitrate and higher annual denitrification rates, whereas nirK and qNor read abundance was negatively associated with these factors. These results suggest that environmental conditions, in particular soil moisture and nitrate, select for distinct denitrification communities that are characterized by differential abundance of genes encoding apparently functionally redundant proteins. In contrast, taxonomic analysis did not identify notable variability in denitrifying community composition across sites. While the capacity to denitrify was ubiquitous across sites, denitrification genes with higher energetic costs, such as nirS and cNor, appear to confer a selective advantage in microbial communities experiencing more frequent soil saturation and greater nitrate inputs. This study suggests metagenomics can help identify denitrification hotspots that could be protected or enhanced to treat non‐point source nitrogen pollution.

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Section: Study Sitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients

Nadeau

Roco

Debenport

et al. 2019

Environmental Microbiology

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“…Extreme precipitation characteristics

f_{P} (|, p | P > p_{c})

and λ p could also be defined with other data sets such as remotely sensed products, climate reanalysis products (Auerbach et al, ; Fuka et al, ), or paleodata (Steinschneider et al, ). Similarly,

f_{S} (|, s)

could be estimated from long records of observed watershed hydrologic state variables, which affect runoff risk (e.g., soil moisture, snowpack; e.g., Hofmeister et al, ). Finally, if correlations between

f_{P} (|, p | P > p_{c})

and

f_{S} (|, s)

are found to be significant within a catchment, this dependency could be modeled explicitly with probabilistic approaches such as copula models (e.g., Genest & Favre, ).…”

Section: Methodology Extensionsmentioning

confidence: 99%

A Vulnerability‐Based, Bottom‐up Assessment of Future Riverine Flood Risk Using a Modified Peaks‐Over‐Threshold Approach and a Physically Based Hydrologic Model

Knighton

Walter

2017

Water Resources Research

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There is a chronic disconnection among purely probabilistic flood frequency analysis of flood hazards, flood risks, and hydrological flood mechanisms, which hamper our ability to assess future flood impacts. We present a vulnerability‐based approach to estimating riverine flood risk that accommodates a more direct linkage between decision‐relevant metrics of risk and the dominant mechanisms that cause riverine flooding. We adapt the conventional peaks‐over‐threshold (POT) framework to be used with extreme precipitation from different climate processes and rainfall‐runoff‐based model output. We quantify the probability that at least one adverse hydrologic threshold, potentially defined by stakeholders, will be exceeded within the next N years. This approach allows us to consider flood risk as the summation of risk from separate atmospheric mechanisms, and supports a more direct mapping between hazards and societal outcomes. We perform this analysis within a bottom‐up framework to consider the relevance and consequences of information, with varying levels of credibility, on changes to atmospheric patterns driving extreme precipitation events. We demonstrate our proposed approach using a case study for Fall Creek in Ithaca, NY, USA, where we estimate the risk of stakeholder‐defined flood metrics from three dominant mechanisms: summer convection, tropical cyclones, and spring rain and snowmelt. Using downscaled climate projections, we determine how flood risk associated with a subset of mechanisms may change in the future, and the resultant shift to annual flood risk. The flood risk approach we propose can provide powerful new insights into future flood threats.

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“…Estimating fertilizer runoff from agricultural areas is usually a complex exercise, since it involves a large number of sampling sites distributed over a large area with different terrain characteristics, which can range from inaccessible mountainous areas to coastal plains (He and DeMarchi, 2014;Hofmeister et al, 2016;López-Vicente et al, 2016). As a result, certain areas are left unsampled.…”

Section: Introductionmentioning

confidence: 99%

Estimating Nitrogen Runoff From Agriculture to Coastal Zones by a Rapid GIS and Remote Sensing-Based Method for a Case Study From the Irrigation District Río Mayo, Gulf of California, México

González-Rivas¹,

Tapia-Silva

Bustillos-Guzmán³

et al. 2020

Front. Mar. Sci.

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Coastal eutrophication due to agricultural runoff is one of the main ecological problems for coastal zones around the world. The increase in nutrients has created multiple consequences on marine ecosystems, such as harmful algal blooms, zones of hypoxia or anoxia, and loss of biodiversity. The coastal areas of México are no exception. In the scientific community, there has been a great effort to estimate the amount of nutrients that enter marine ecosystems. However, a rapid method to make this estimation without field data is still required. In this study, the cultivated area and the entrance of nutrients to the coastal marine environment were determined for one of the main irrigation districts located in the state of Sonora, which drains into the Gulf of California, Mexico. We used an elevation model combined with the digitization of drain canal maps to delimitate sub-basins and allocate the coastal runoff entry points. Landsat 8 satellite data were classified and validated for the period of two agrarian cycles (2015-2016) to obtain the agriculture parcels by sub-basins. The quantity of the nutrient runoff was estimated using the cultivation area and nitrogen runoff percentage, according to values found in published reports. As a result, we identified eight sub-basins, two of which stand out for the amount of nitrogen that drains both into a coastal lagoon and directly to the sea. We confirmed this information by correlation analysis using Moderate Resolution Imaging Spectroradiometer (MODIS) of the diffuse attenuation coefficient at 490 nm K d (490) detected around the coastal runoff entry points. We concluded that there is a high input in the coastal zones and lagoons of nutrients and other pollutants from the agricultural zones of the Río Mayo irrigation district. We also concluded that the performance of the method applied to estimate the volume of the nitrogen runoff is useful, rapid, and can be improved with in situ data.

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A runoff risk model based on topographic wetness indices and probability distributions of rainfall and soil moisture for central New York agricultural fields

Cited by 7 publications

References 57 publications

Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients

Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients

A Vulnerability‐Based, Bottom‐up Assessment of Future Riverine Flood Risk Using a Modified Peaks‐Over‐Threshold Approach and a Physically Based Hydrologic Model

Estimating Nitrogen Runoff From Agriculture to Coastal Zones by a Rapid GIS and Remote Sensing-Based Method for a Case Study From the Irrigation District Río Mayo, Gulf of California, México

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