Estimating dominant runoff modes across the conterminous United States

Effective natural resource planning depends on understanding the prevalence of runoff generating processes. Within a specific area of interest, this demands reproducible, straightforward information that can complement available local data and can orient and guide stakeholders with diverse training and backgrounds. To address this demand within the contiguous United States (CONUS), we characterized and mapped the predominance of two primary runoff generating processes: infiltration‐excess and saturation‐excess runoff (IE vs. SE, respectively). Specifically, we constructed a gap‐filled grid of surficial saturated hydraulic conductivity using the Soil Survey Geographic and State Soil Geographic soils databases. We then compared surficial saturated hydraulic conductivity values with 1‐hr rainfall‐frequency estimates across a range of return intervals derived from CONUS‐scale random forest models. This assessment of the prevalence of IE versus SE runoff also incorporated a simple uncertainty analysis, as well as a case study of how the approach could be used to evaluate future alterations in runoff processes resulting from climate change. We found a low likelihood of IE runoff on undisturbed soils over much of CONUS for 1‐hr storms with return intervals \u3c5 years. Conversely, IE runoff is most likely in the Central United States (i.e., Texas, Louisiana, Kansas, Missouri, Iowa, Nebraska, and Western South Dakota), and the relative predominance of runoff types is highly sensitive to the accuracy of the estimated soil properties. Leveraging publicly available data sets and reproducible workflows, our approach offers greater understanding of predominant runoff generating processes over a continental extent and expands the technical resources available to environmental planners, regulators, and modellers

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century

Evaluating alternative weather inputs for hydrologic modeling in the tropics: an application to Puerto Rico

<p>This R script and data are associated with a paper in <i>Hydrological Processes</i>.</p><p>http://onlinelibrary.wiley.com/doi/10.1002/hyp.10860/abstract</p><p>Correctly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi-tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, <em>n</em> = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, <em>n</em> = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were <0 using GHCN weather station records (8 of 11) and CFSR reanalysis data (9 of 11). Autocalibration of individual SWAT models for each of the 11 basins against each of the available weather datasets yielded NSE values > 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations <10 km from the basin centroid. In addition to demonstrating the need to evaluate available weather inputs, this research reinforces the value of CFSR data as a means to supplement ground records and consistently determine a baseline for hydrologic model performance. </p

Shifting species ranges & changing phenology: A new approach to mining social media for ecosystems observations

<p>Presented at the American Geophysical Union Fall 2013 Meeting in San Francisco, California, Dec. 8-13.</p> <p>Geoscientists & ecologists are increasingly using social media to solicit 'citizen scientists' to participate in the data collection process. However, social media users are also a largely untapped resource of spontaneous, unsolicited observations of the natural world. Of particular interest are observations of species phenology & range to better develop a predictive understanding of how ecosystems are affected by a changing climate and human-mediated influences. Social media users' observations include information on phenological & biological phenomena such as flowers blooming, native & invasive species sightings, unusual behaviors, animal tracks, droppings, damage, feeding, nesting, etc. Our AGU2011 pilot study on the North American armadillo suggests that useful observational data can be extracted from Twitter to map current species ranges to compare with past ranges. We have expanded that work by mining Twitter for a number of North American species and ecosystem observations to determine usefulness for environmental applications such as: 1) supplementing existing databases, 2) identifying outlier phenomena, 3) guiding additional crowd-sourced studies and data collection efforts, 4) recruiting citizen scientists, 5) gauging sentiment about the observations and 6) informing ecosystems policy-making and education.</p> <p>We present the results for our evaluation of a representative sample from a list of 200+ species for which we've collected data since August 2011. Our results include frequency of reports and sightings by day, week and month, where the number of observations range from a few per month to ten or more per day. We discuss challenges, best practices and tools for distilling information from crowd-sourced observations gathered via Twitter in the form of 140-character 'tweets'. For example, geolocation is a critical issue. Despite the prevalence of smart phones, specific latitudinal and longitudinal coordinates are included in fewer than 10% of the observations. This number can be substantially increased at both local & regional scales by using user profile and contextual geolocation algorithms. We identify potential outlier observations, map ranges, and evaluate the usefulness of citizen sentiment conveyed in the observations as a potential metric for policy makers and managers. Based on these results we draw conclusions on best applications for use of crowd-sourced social media observations: Identifying outliers, front-tracking, guiding traditional data collection efforts and informing policy- and decision-makers about citizen sentiment toward resources.</p

Mining social media for ecosystem observations: methods and examples from aquatic and terrestrial systems

<p>Presented at the Western Division of the American Fisheries Society Annual Meeting in Mazatlan, Mexico, Apr. 6-11.</p> <p> </p> <p>Scientists are increasingly using social media to solicit 'citizen scientists' to participate in the data collection process. However, social media users are also a largely untapped resource of spontaneous, unsolicited observations of the natural world. Of particular interest are observations of species biology, range and phenology to better develop a predictive understanding of how ecosystems are affected by a changing climate and human-mediated influences. Social media users' observations include information on biological and phenological phenomena such as flowers blooming, animal tracks and droppings, damage, feeding, nesting, unusual behaviors, native and invasive species sightings, fisheries catch locations and size, etc. Here we mine Twitter for a number of North American species and ecosystem observations to determine usefulness for environmental applications such as: 1) supplementing existing databases, 2) identifying outlier phenomena, 3) guiding additional crowd-sourced studies and data collection efforts, 4) recruiting citizen scientists, 5) gauging sentiment about the observations and 6) informing ecosystems policy-making and education. We present the results for our evaluation of a representative sample from a list of 200+ species – both aquatic and terrestrial – for which we've collected data since February 2014 and August 2011, respectively. We discuss challenges, best practices and tools for distilling information from crowd-sourced observations gathered via Twitter. We identify potential outlier observations, map ranges, and evaluate the usefulness of citizen sentiment conveyed in the observations as a potential metric for policy makers and managers and draw conclusions on best applications for use of crowd-sourced social media observations: identifying outliers, front-tracking, guiding traditional data collection efforts and informing policy- and decision-makers about citizen sentiment toward resources.</p

TopoSWAT Source

Topography exerts critical control on many hydrologic, geomorphologic, and environmental biophysical processes. In order to properly model such dynamics in the Soil and Water Assessment Tool (SWAT), we explicitly integrate topography into the initialization procedure with a purpose-built extension. This ArcMap® toolbox (TopoSWAT) interfaces directly with ArcSWAT, to create multiple SWAT data layers, update the SWAT databases and generate the lookup tables required by the model. User defined data layers are processed in a single-step toolbox and include, aspect, elevation, and topographic index (TI), which are then intersected with the vector FAO Global Soils dataset. The toolbox then builds a soil raster layer at the resolution of the project’s base Digital Elevation Model and creates the ArcSWAT required ‘usersoil’ database table along with the corresponding soil lookup table required to map the specific raster soil values to the soil parameters in the ‘usersoil’ database table. This toolbox effectively creates a new soil dataset that incorporates topographic features. This standardized method and toolset allows SWAT modelers to easily incorporate topographic features they believe are important for their catchments without requiring any changes to the current ArcSWAT initialization system.  Some of the topographic features may be necessary for process-based routines that one may want to incorporate into SWAT, e.g., energy-budget snowmelt modeling.  However, such routines will need to be added to the SWAT model source code. This SWAT toolbox adds flexibility to SWAT modeling with little extra effort on the part of modelers

Estimating dominant runoff modes across the conterminous United States

Effective natural resource planning depends on understanding the prevalence of runoff generating processes. Within a specific area of interest, this demands reproducible, straightforward information that can complement available local data and can orient and guide stakeholders with diverse training and backgrounds. To address this demand within the contiguous United States (CONUS), we characterized and mapped the predominance of two primary runoff generating processes: infiltration‐excess and saturation‐excess runoff (IE vs. SE, respectively). Specifically, we constructed a gap‐filled grid of surficial saturated hydraulic conductivity using the Soil Survey Geographic and State Soil Geographic soils databases. We then compared surficial saturated hydraulic conductivity values with 1‐hr rainfall‐frequency estimates across a range of return intervals derived from CONUS‐scale random forest models. This assessment of the prevalence of IE versus SE runoff also incorporated a simple uncertainty analysis, as well as a case study of how the approach could be used to evaluate future alterations in runoff processes resulting from climate change. We found a low likelihood of IE runoff on undisturbed soils over much of CONUS for 1‐hr storms with return intervals \u3c5 years. Conversely, IE runoff is most likely in the Central United States (i.e., Texas, Louisiana, Kansas, Missouri, Iowa, Nebraska, and Western South Dakota), and the relative predominance of runoff types is highly sensitive to the accuracy of the estimated soil properties. Leveraging publicly available data sets and reproducible workflows, our approach offers greater understanding of predominant runoff generating processes over a continental extent and expands the technical resources available to environmental planners, regulators, and modellers

I3. Hydrological Modeling Where No Meteorological Stations Exist

An important characteristic of hydrological is the need for accurate forcing data, such as precipitation and temperature. Acquiring precipitation and temperature gauge data poses a variety of chal¬lenges, not least the fact that gauges are often located outside of target watersheds and may not accurately represent local conditions. Over the last decade, there has been a drive to archive global atmospheric data from which our daily and hourly weather forecasts originate, primarily for the purpose of weather forecast improvement. We are investigating ways to utilize these products for hydrological modeling purposes and to address some of the inherent problems associated with the use conventional gauge data. In this study, we compare calibrations of a watershed model using derived statistical representations of precipitation forecasts from a “poor-man’s” ensemble of raw gridded atmospheric models interpolated to the center of the model subbasin, versus, calibration to the closest precipitation gauge measurement. In addition, we investigate at what scale and radii the use of direct gridded model outputs may introduce less or equal error to watershed modeling projects using the closest gauge station

Real-Time Forecast of Hydrologically Sensitive Areas in the Salmon Creek Watershed, New York State, Using an Online Prediction Tool

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Predicting phosphorus dynamics in complex terrains using a variable source area hydrology model

Phosphorus (P) loss from agricultural watersheds has long been a critical water quality problem, the control of which has been the focus of considerable research and investment. Preventing P loss depends on accurately representing the hydrological and chemical processes governing P mobilization and transport. The Soil and Water Assessment Tool (SWAT) is a watershed model commonly used to predict run-off and non-point source pollution transport. SWAT simulates run-off employing either the curve number (CN) or the Green and Ampt methods, both assume infiltration-excess run-off, although shallow soils underlain by a restricting layer commonly generate saturation-excess run-off from variable source areas (VSA). In this study, we compared traditional SWAT with a re-conceptualized version, SWAT-VSA, that represents VSA hydrology, in a complex agricultural watershed in east central Pennsylvania. The objectives of this research were to provide further evidence of SWAT-VSA’s integrated and distributed predictive capabilities against measured surface run-off and stream P loads and to highlight the model’s ability to drive sub-field management of P. Thus, we relied on a detailed field management database to parameterize the models. SWAT and SWAT-VSA predicted discharge similarly well (daily Nash–Sutcliffe efficiencies of 0.61 and 0.66, respectively), but SWAT-VSA outperformed SWAT in predicting P export from the watershed. SWAT estimated lower P loss (0.0–0.25 kg ha^-1) from agricultural fields than SWAT-VSA (0.0–1.0+ kg ha^-1), which also identified critical source areas – those areas generating large run-off and P losses at the sub-field level. These results support the use of SWAT-VSA in predicting watershed-scale P losses and identifying critical source areas of P loss in landscapes with VSA hydrology