/ Residential development of farmland is one of the primary driving forces of land degradation in both rural and urban fringe areas throughout the world. The loss of prime agricultural land is of ...great concern to planning offices and organizations seeking to preserve open space. The objective of this study is to demonstrate the use of clusteranalysis as a possible tool for the identification of farms prone to residential development. Eighty-four farms in Sterling, Massachusetts, were separated into two groups by k-means nonhierarchical cluster analysis using farm size, slope, and distance to the nearest city center and highway as surrogates of farmland conversion. Discriminant analysis showed that the two groups derived from the cluster analysis were 98.8% accurate (P < 0.0000). Results from the statistical analysis may serve as a starting point for the identification of individual farms prone to residential development. To explain the driving forces of farmland conversion to residential uses, interviews should be conducted with farmers, landowners, and land buyers. The use of multivariate statistical techniques to identify farms in jeopardy of residential development, in conjunction with qualitative assessments that explain the probability of development of individual farms, may prove a useful strategy to understand and predict farmland conversion.
The aim of this study was to assess the factors that account for the geographical variation in soil organic carbon stocks at the 0–30‐cm depth (
SOC
30
) of forests in
J
apan. Boosted regression tree ...analysis was applied to 2157 points throughout
J
apan and to four regional geographical subdivisions with 16 environmental variables. The rank of predictor variables was different for
J
apan as a whole and among the regions. For
J
apan as a whole, soil group, air temperature, slope inclination, altitude and organic carbon stocks of litter were the most important factors that affected
SOC
30
stocks. Overall,
SOC
30
stocks decreased with air temperature, which was attributed to the decomposability of organic carbon. In addition,
SOC
30
stocks decreased with slope inclination because of instability of the topsoil on slopes, which, in turn, is related to the increase in rock fragment content and decrease in soil bulk density. The distribution of volcanic soil resulted in larger
SOC
30
stocks than was expected from climatic conditions. Precipitation was not important because of conflicting effects between the increase in soil organic carbon content with increasing net primary production and the decrease in mineral soil mass by the loss in topsoil. The regional analyses provide insight into the factors that cause variation in
SOC
30
stocks, which were obscured by the macroscale analysis of Japan as a whole, thereby illustrating the power of regional geographical analyses. Our results provide an improved basis for soil, forestry and biogeochemical models that require accurate estimates of
SOC
30
stocks.
Highlights
We assessed factors that account for geographic variation in soil organic carbon (
SOC
) stocks.
Effect and dependence of factors were estimated by a machine learning approach.
SOC
stocks were affected by soil type, climate, site‐specific location and organic matter input.
Volcanic soil distribution, climate, slope steepness and historical overuse of forest affected
SOC
stocks.
Synoptic typing is a method of classifying atmospheric conditions (i.e., pressure, temperature, wind direction) and can be used to relate those conditions to terrestrial processes. In this study, a ...daily synoptic calendar was developed for the northeastern United States to assess the atmospheric controls on variations in stream chemistry across three forested watersheds in Vermont, Rhode Island, and Maryland. Stream discharge along with dissolved organic carbon and nitrate concentrations were monitored in situ from March to November for 3 years (2014–2016) and then compared to the regional daily synoptic calendar to assess (a) which atmospheric patterns exported the greatest flux of water and solutes, (b) whether all three watersheds responded similarly to the regional atmospheric patterns, and (c) how these fluxes changed seasonally. Seven broad categories of atmospheric patterns were identified. In general, low pressure systems situated over the Great Lakes region produced the most rainfall which resulted in the highest peak streamflow by event along with daily fluxes of DOC and NO3−‐N across the watersheds. In contrast, southwest flow regimes and Northwest Flow regimes occurred the most frequently (36% and 14% of study days, respectively) and resulted in the largest overall export (+50% of total) of water and solutes from the three watersheds. Regardless of watershed size or location, streamflow dynamics were similar when classified using synoptic typing. As such, valuable insight into the meteorological mechanisms behind temporal variation in carbon and nitrate stream export in forested watersheds can be gained by employing regional synoptic weather analyses.
Key Points
Synoptic‐scale weather patterns are associated with terrestrial stream dynamics at the regional scale
Regardless of watershed size or location within the study region, water and solute fluxes were similarly classified using synoptic typing
Storms from more frequent synoptic types dominated water and solute fluxes compared to storms from larger, infrequent synoptic types
Canopy interception of incident precipitation is a critical component of the forest water balance during each of the four seasons. Models have been developed to predict precipitation interception ...from standard meteorological variables because of acknowledged difficulty in extrapolating direct measurements of interception loss from forest to forest. No known study has compared and validated canopy interception models for a leafless deciduous forest stand in the eastern United States. Interception measurements from an experimental plot in a leafless deciduous forest in northeastern Maryland (39°42′N, 75°50′W) for 11 rainstorms in winter and early spring 2004/05 were compared to predictions from three models. The Mulder model maintains a moist canopy between storms. The Gash model requires few input variables and is formulated for a sparse canopy. The WiMo model optimizes the canopy storage capacity for the maximum wind speed during each storm. All models showed marked underestimates and overestimates for individual storms when the measured ratio of interception to gross precipitation was far more or less, respectively, than the specified fraction of canopy cover. The models predicted the percentage of total gross precipitation (P
G) intercepted to within the probable standard error (8.1%) of the measured value: the Mulder model overestimated the measured value by 0.1% ofP
G; the WiMo model underestimated by 0.6% ofP
G; and the Gash model underestimated by 1.1% ofP
G. The WiMo model’s advantage over the Gash model indicates that the canopy storage capacity increases logarithmically with the maximum wind speed. This study has demonstrated that dormant-season precipitation interception in a leafless deciduous forest may be satisfactorily predicted by existing canopy interception models.
Using synoptic classification techniques, synoptic‐scale weather types associated with large exports of dissolved organic carbon and nitrate‐nitrogen in three forested watersheds in the northeastern ...United States are analyzed. In contrast to Siegert et al. (2021, https://doi.org/10.1029/2020jd033413; Part 1), which details the general synoptic conditions associated with stream chemistry variations, this study focuses on the long‐term frequency, trends, and global‐scale forcing mechanisms of individual, chemistry‐relevant, synoptic types. Nine individual types are identified as the most important to stream chemistry exports in northeastern watersheds during a 2.5‐year period of high‐frequency stream chemistry observation (2014–2016). Streamflow chemistry was the most influenced by Northwest Flow synoptic weather types in Vermont, cold front passages associated with Weak Westerly Flow types in Rhode Island, and Southwest Flow (SWF) types in Maryland. Each type provides unique atmospheric conditions that result in variations in precipitation, and thus chemical signals. From 1948 to 2017, the Arctic and North Atlantic Oscillations greatly influenced the interannual frequency of the analyzed synoptic types. When the indices were positively phased, SWF types were more frequent, while Northwest Flow and Weak Westerly Flow types were less frequent. Long‐term trends in synoptic type frequency are, inpart, due to documented changes in these teleconnection indices toward more positively phased configurations. As the climate continues to change into the 21st century, such associations may continue to drive large changes in synoptic type frequency, and thus watershed hydrology and biogeochemistry in the Northeast region.
Key Points
Individual synoptic weather types uniquely influence the export of dissolved organic carbon (DOC) and NO3 solutes in watersheds of the Northeast United States
The interannual frequency and precipitation signals of multiple chemistry‐relevant synoptic types exhibit long‐term linear trends
Variations in synoptic type frequency, and thus stream chemistry, are partially forced by teleconnection indices, such as the North Atlantic Oscillation (NAO)
Interception of precipitation by fruit litter is a poorly understood component of the hydrologic cycle in forested ecosystems. Even less well understood is the effect of meteorological conditions on ...the evaporation of precipitation intercepted by forest litter. This study sought to examine the influence of meteorological conditions on the evaporation of intercepted precipitation by fruit litter from Liquidambar styraciflua L. (sweetgum) by deriving and calibrating a regression model to estimate evaporation from the fruit litter that may be of potential use to forest and watershed managers. Data on evaporative losses from the fruit litter used to derive and calibrate the statistical model were acquired through a larger field experiment conducted from mid November 2002 through April 2003. Results from the forward stepwise least squares multiple regression model demonstrated that evaporative losses from the fruit litter were estimated with a high degree of accuracy based on the amount of water stored, solar radiation inputs, and vapor pressure deficit (adjusted R2=0.836, F=82.28, P<0.00001). The amount of water stored in the fruit litter explained the highest proportion of variance in the regression model. Storm to storm comparisons also highlighted the importance of solar radiation and wind speed in determining evaporation from the fruit litter. The regression model potentially may be used in conjunction with a canopy interception model to predict interception losses from L. styraciflua dominated forests and plantations.
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