scholarly journals Measuring diel and spatial variation in biogenic N2delivery, production, and loss with natural tracers: Application to watershed-scale estimation of denitrification

2018 ◽  
Vol 16 (9) ◽  
pp. 556-575 ◽  
Author(s):  
Karen L. Knee ◽  
John Gardner ◽  
Dana C. Brenner ◽  
Rebecca J. Fox ◽  
Joshua Thompson ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Watershed Scale ◽  
Natural Tracers ◽  
Scale Estimation

scholarly journals Spatial variations of deep soil moisture and the influencing factors in the Loess Plateau, China

2016 ◽  
Author(s):  
X. N. Fang ◽  
W. W. Zhao ◽  
L. X. Wang ◽  
Q. Feng ◽  
J. Y. Ding ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variation ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Rapid Change ◽  
Spatial Variations ◽  
Native Vegetation ◽  
Watershed Scale ◽  
Deep Soil ◽  
Soil Layers

Abstract. Soil moisture in deep soil layers is a relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the spatial variations of deep soil moisture and its influencing factors at a moderate watershed scale is important to ensure the sustainability of vegetation restoration efforts. In this study, we focused on analyzing the spatial variation and factors influencing soil moisture content (SMC) in (0–500 cm) soil layers based on a soil moisture survey of the Ansai watershed, Yanan, Shannxi province. Our results can be divided into four main findings. (1) At the watershed scale, the higher spatial variation of deep SMC occurred at 0–20 cm, 120–140 cm and 480–500 cm in the vertical direction. At a comparable depth but in the horizontal direction, the spatial variation of deep SMC under native vegetation was much lower than that in human-managed vegetation and introduced vegetation. (2) The deep SMC in native vegetation and human-managed vegetation was significantly higher than that of introduced vegetation, and different degrees of soil desiccation occurred under all introduced vegetation types. (3) Taking the SMC condition of native vegetation as a reference for local control, soil could be divided into four layers: I) shallow rapid change layer (0–60 cm); II) main rainfall infiltration layer (60–220 cm); III) transition layer (220–400 cm); and IV) stable layer (400–500 cm). Positive and significant correlations existed between SMC at layers II, III and IV, and the correlations of the neighboring layer ranges were clearly stronger than that of nonadjacent depth ranges, although the SMC at shallow rapid change layer I showed a disconnect (i.e., no correlations) with those at the three other soil depth layers. (4) The influencing factors of deep SMC at the watershed scale varied with land management types. The main local controls of SMC variation were soil particle composition and annual average rainfall; human agricultural management measures can alter soil buck density, which contributes to higher deep SMC. In introduced vegetation, plant growth conditions, planting density, and litter water holding traits showed significant relationships with deep SMC. The results of this study are of practical significance for vegetation restoration strategies and the sustainability of restored ecosystems.

scholarly journals Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3567
Author(s):  
Xinyao Xie ◽  
Ainong Li ◽  
Huaan Jin ◽  
Jinhu Bian ◽  
Zhengjian Zhang ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Gross Primary Productivity ◽  
Process Models ◽  
Watershed Scale ◽  
Mountainous Areas ◽  
Boreal Ecosystem ◽  
Mean Square Errors ◽  
Process Based Models

Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.

The Methow Beaver Project: the Challenges of an Ecosystem Services Experiment

2019 ◽  
Vol 3 (1) ◽  
pp. 1-14
Author(s):  
Philip Brick ◽  
Kent Woodruff
Keyword(s):  
Ecosystem Services ◽  
Climate Adaptation ◽  
Castor Canadensis ◽  
Water Storage ◽  
Climate Projections ◽  
High Mountain ◽  
Natural Resource Policy ◽  
Watershed Scale ◽  
Positive Role ◽  
Climate Conditions

This case explores the Methow Beaver Project (MBP), an ambitious experiment to restore beaver (Castor canadensis) to a high mountain watershed in Washington State, USA. The Pacific Northwest is already experiencing weather regimes consistent with longer term climate projections, which predict longer and drier summers and stronger and wetter winter storms. Ironically, this combination makes imperative more water storage in one of the most heavily dammed regions in the nation. Although the positive role that beaver can play in watershed enhancement has been well known for decades, no project has previously attempted to re-introduce beaver on a watershed scale with a rigorous monitoring protocol designed to document improved water storage and temperature conditions needed for human uses and aquatic species. While the MBP has demonstrated that beaver can be re-introduced on a watershed scale, it has been much more difficult to scientifically demonstrate positive changes in water retention and stream temperature, given hydrologic complexity, unprecedented fire and floods, and the fact that beaver are highly mobile. This case study can help environmental studies students and natural resource policy professionals think about the broader challenges of diffuse, ecosystem services approaches to climate adaptation. Beaver-produced watershed improvements will remain difficult to quantify and verify, and thus will likely remain less attractive to water planners than conventional storage dams. But as climate conditions put additional pressure on such infrastructure, it is worth considering how beaver might be employed to augment watershed storage capacity, even if this capacity is likely to remain at least in part inscrutable.

Digital representation of spatial variation of multivariate landscape data

2006 ◽  
Vol 7 (2) ◽  
pp. 181-188 ◽  
Author(s):  
A. Altobelli ◽  
E. Bressan ◽  
E. Feoli ◽  
P. Ganis ◽  
F. Martini
Keyword(s):  
Spatial Variation ◽  
Digital Representation

Function of coprophagous beetles (Coleoptera: Scarabaeidae, Geotrupidae, Hydrophilidae) in cattle pastures inferred from pitfall trapping data

2010 ◽  
Vol 27 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Martin Šlachta ◽  
Jan Frelich ◽  
Tomáš Tonka
Keyword(s):  
Seasonal Variation ◽  
Spatial Variation ◽  
Dry Weight ◽  
Seasonal Distribution ◽  
The Other ◽  
Pitfall Trapping ◽  
Other Hand ◽  
Nested Anova ◽  
Species Groups ◽  
Dung Decomposition

Function of coprophagous beetles (Coleoptera: Scarabaeidae, Geotrupidae, Hydrophilidae) in cattle pastures inferred from pitfall trapping dataAn analysis of data on the dry weight biomass of coprophagous beetles in standardized dung (4.5 l) was conducted in order to characterize the spatial and the seasonal distribution of the beetles' biomass in cattle pastures and to elucidate their function in dung decomposition. Nested Anova with factors of farm, site (nested in farm), seasonal period and year was used to evaluate the effect of these factors on the biomass of four functional species groups: the dung dwellers ofScarabaeidae(subfamilyAphodiinae), the dung dwellers ofHydrophilidae, the small tunnellers ofScarabaeidae(subfamilyCoprinae) and the large tunnellers ofGeotrupidae. The spatial variation of biomass (between the sites and the farms) was insignificant (P>0.05) in the two dung-dweller groups and in the large-tunnellers group. On the other hand, a significant (P<0.05) seasonal variation of biomass was found in all but the large tunneller group. In dung dwellers, the spring biomass was formed mainly by two species,Aphodius prodromusandA. sphacelatus. In summer, most of the biomass was accounted for bySphaeridium lunatum, S. scarabaeoidesandA. rufipes. In the two tunneller groups,Onthophagus fracticornis, Geotrupes stercorariusandG. spinigerformed a majority of the biomass in dung.

A watershed-scale monitoring protocol for bull trout

2009 ◽  
Author(s):  
Dan Isaak ◽  
Bruce Rieman ◽  
Dona Horan
Keyword(s):  
Bull Trout ◽  
Watershed Scale ◽  
Monitoring Protocol
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