Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches -especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R 2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) lidar-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved correlations.

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Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWI) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches in the US. We calculated TWIs using over 400 unique formulations that considered different: Digital Elevation Model (DEM) resolution (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA; each field was visited 5–8 times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) LiDAR-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved the correlations.

Quantitative flow-ecology relationships are needed to evaluate how water withdrawals for unconventional natural gas development may impact aquatic ecosystems. Addressing this need, we studied current patterns of hydrologic alteration in the Marcellus Shale region and related the estimated flow alteration to fish community measures. We then used these empirical flow-ecology relationships to evaluate alternative surface water withdrawals and environmental flow rules. Reduced high-flow magnitude, dampened rates of change, and increased low-flow magnitudes were apparent regionally, but changes in many of the flow metrics likely to be sensitive to withdrawals also showed substantial regional variation. Fish community measures were significantly related to flow alteration, including declines in species richness with diminished annual runoff, winter low-flow, and summer median-flow. In addition, the relative abundance of intolerant taxa decreased with reduced winter high-flow and increased flow constancy, while fluvial specialist species decreased with reduced winter and annual flows. Stream size strongly mediated both the impact of withdrawal scenarios and the protection afforded by environmental flow standards. Under the most intense withdrawal scenario, 75% of reference headwaters and creeks (drainage areas <99 km ) experienced at least 78% reduction in summer flow, whereas little change was predicted for larger rivers. Moreover, the least intense withdrawal scenario still reduced summer flows by at least 21% for 50% of headwaters and creeks. The observed 90th quantile flow-ecology relationships indicate that such alteration could reduce species richness by 23% or more. Seasonally varying environmental flow standards and high fixed minimum flows protected the most streams from hydrologic alteration, but common minimum flow standards left numerous locations vulnerable to substantial flow alteration. This study clarifies how additional water demands in the region may adversely affect freshwater biological integrity. The results make clear that policies to limit or prevent water withdrawals from smaller streams can reduce the risk of ecosystem impairment.

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 <5 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.

It is well established that wet environment potential evapotranspiration (PET) can be reliably estimated using the energy budget at the canopy or land surface. However, in most cases the necessary radiation measurements are not available and, thus, empirical temperature‐based PET models are still widely used, especially in watershed models. Here we question the presumption that empirical PET models require fewer input data than more physically based models. Specifically, we test whether the energy‐budget‐based Priestley‐Taylor (P‐T) model can reliably predict daily PET using primarily air temperature to estimate the radiation fluxes and associated parameters. This method of calculating PET requires only daily minimum and maximum temperature, day of the year, and latitude. We compared PET estimates using directly measured radiation fluxes to PET calculated from temperature‐based radiation estimates at four humid AmeriFlux sites. We found good agreement between P‐T PET calculated from measured radiation fluxes and P‐T PET determined via air temperature. In addition, in three of the four sites, the temperature‐based radiation approximations had a stronger correlation with measured evapotranspiration (ET) during periods of maximal ET than fully empirical Hargreaves, Hamon and Oudin methods. Of the three fully empirical models, the Hargreaves performed the best. Overall, the results suggest that daily PET estimates can be made using a physically based approach even when radiation measurements are unavailable.

Riparian buffers are commonly promoted to protect stream water quality. A common conceptual assumption is that buffers "intercept" and treat upland runoff. As a shift in paradigm, it is proposed instead that riparian buffers should be recognized as the parts of the landscape that most frequently generate storm runoff. Thus, water quality can be protected from contaminated storm runoff by disassociating riparian buffers from potentially polluting activities. This paper reviews and synthesizes some simple engineering approaches that can be used to delineate riparian buffers for rural watersheds based on risk of generating runoff. Although reference is made to specific future research that may improve the proposed methods for delineating riparian buffers, the approaches described here provide planners and engineers with a set of currently available scientifically defensible tools. It is recommended that planners and engineers use available rainfall and stream discharge data to parameterize the buffer-sizing equations and use variable-width buffers, based on a topographic index, to achieve a realistic representation of runoff generating areas.