scholarly journals Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signatures of snow and complex terrain

2008 ◽  
Vol 5 (4) ◽  
pp. 1927-1966 ◽  
Author(s):  
C. J. Williams ◽  
J. P. McNamara ◽  
D. G. Chandler
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Soil Depth ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Spatial Correlations ◽  
Static Properties ◽  
Semi Arid

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and consequent soil moisture, and that the interaction of dynamic (timing of water input) and static properties propagate that relative constant spatial variability through the hydrologic year. The results demonstrate snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments throughout the year and infer that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

scholarly journals Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signature of snow and complex terrain

2009 ◽  
Vol 13 (7) ◽  
pp. 1325-1336 ◽  
Author(s):  
C. J. Williams ◽  
J. P. McNamara ◽  
D. G. Chandler
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Soil Depth ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Spatial Correlations ◽  
Static Properties ◽  
Semi Arid

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and resulting soil moisture patterns, and that the interaction of dynamic (timing of water input) and static influences propagate that relative constant spatial variability through most of the hydrologic year. The results demonstrate that snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments and suggest that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

scholarly journals Effects of topographic domain and land use on spatial variability of deep soil moisture in the semi-arid Loess Plateau of China

2019 ◽  
Vol 50 (5) ◽  
pp. 1281-1292 ◽  
Author(s):  
Bowei Yu ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Chong Huang ◽  
He Li
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Spatial Variability ◽  
Loess Plateau ◽  
Hydrological Cycle ◽  
Loess Soil ◽  
Slope Aspect ◽  
Deep Soil ◽  
Positive Correlations ◽  
Semi Arid

Abstract Deep soil moisture is fundamental to hydrological cycle and ecosystem sustainability in arid and semi-arid regions. This study examined the combined effects of topographic domain and land use on the spatial variability of deep soil moisture (0–5 m) on the semi-arid Loess Plateau of China. Our results showed that deep soil moisture was generally temporally stable due to the thick loess soil in the plateau region. The depth-averaged soil moisture was slightly lower in the gully domain compared to the hillslope domain but was dependent on the soil depths. Soil moisture variability was clearly larger in the gully domain when compared with that in the hillslope domain in the 0–5 m profile. The mean soil moisture contents in comparable soil depths were lower in forestland than in grassland (and farmland), particularly in the hillslope domain. Land uses had similar vertical distribution characteristics of deep soil moisture for each topographic domain. Soil moisture showed highly significant positive correlations with slope aspect in the hillslope domain and with profile curvature in the gully domain.

Spatial Dynamics of Soil Moisture in a Complex Terrain in the Semi-Arid Loess Plateau Region, China1

2008 ◽  
Vol 44 (5) ◽  
pp. 1121-1131 ◽  
Author(s):  
Huaxing Bi ◽  
Jianjun Zhang ◽  
Jinzhao Zhu ◽  
Liangliang Lin ◽  
Chaoying Guo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Complex Terrain ◽  
Spatial Dynamics ◽  
Plateau Region ◽  
Semi Arid

scholarly journals Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

2014 ◽  
Vol 11 (9) ◽  
pp. 10635-10681 ◽  
Author(s):  
C. Alvarez-Garreton ◽  
D. Ryu ◽  
A. W. Western ◽  
C.-H. Su ◽  
W. T. Crow ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Distributed Model ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Lumped Model ◽  
Model Soil ◽  
Main Driver ◽  
Semi Arid

Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.

Spatial variability of snow isotopic composition and accumulation rate at the stake farm of Vostok station (Сentral Antarctica)

2019 ◽  
Vol 65 (1) ◽  
pp. 46-62 ◽  
Author(s):  
A. A. Ekaykin ◽  
D. O. Vladimirova ◽  
N. A. Tebenkova ◽  
E. V. Brovkov ◽  
A. N. Veres ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Isotopic Composition ◽  
Accumulation Rate ◽  
Ice Sheet ◽  
Ice Cores ◽  
Snow Accumulation ◽  
Geochemical Data ◽  
Snow Surface ◽  
Vostok Station

The knowledge of the spatial distribution of the snow accumulation rate and isotopic composition in different scales, from local to continental, over the Antarctic Ice Sheet is critically important for the interpretation of the paleoclimate data obtained from deep ice cores, for correct assessment of the ice sheet mass balance, etc. With this in mind, we have synthesized geodetic, glaciological and geochemical data collected in the vicinity of central Antarctic Vostok station in 1970–2017 in order to shed light on the processes governing the spatial distribution of snow isotopic composition and accumulation rate in the spatial scale from 100 to 1000 m. First, we have discovered that snow surface height and snow accumulation rate field are strongly affected by the influence of the logistic convoy route annually operating between Russian Antarctic stations Vostok and Progress. This influence is detectable up to 1 km leeward from the route. At the same time the isotopic composition of the upper 10 cm of the snow does not show any anomalies in the vicinity of the route. This is an unexpected result, because large anomalies of the ice sheet surface (e.g., megadunes) are known to affect the snow isotopic composition. Second, in the undisturbed part of the snow surface near Vostok station we have discovered quasi-periodic (with the wavelength of about 400 m) low-amplitude variations of the surface height that are covariant with the corresponding waves in snow accumulation and isotopic composition. We suggest that spatial variability of the snow isotopic composition is due to the different ratio of summer and winter precipitation deposited in different locations, as evident from a strong negative correlation between δD and dxs parameters. The results of this study may explain the nature of the low-frequency noise (with the time-scale from decades to centuries) observed in the climate records obtained from shallow and deep ice cores in central Antarctica.

Spatial variability of soil organic carbon in the West Liao River Basin

2018 ◽  
Vol 64 (1-4) ◽  
pp. 25-34
Author(s):  
Yong-hua Zhu ◽  
Sheng Zhang ◽  
Biao Sun ◽  
Xiao-kang Xi ◽  
Yu Liu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Soil Depth ◽  
Soil Layer ◽  
Kriging Interpolation ◽  
Spherical Models ◽  
Basin Edge ◽  
Depth Increase

Quantification of the pattern and spatial distribution of soil organic carbon (SOC) is essential to comprehending many eco-hydrological processes. To obtain a better understanding of the spatial variability of SOC in a typical farming-pastoral zone, 270 soil samples were collected at 45 sampling sites from every 20 cm soil layer. Semi-variance function theory and ordinary Kriging interpolation were applied to identify the spatial variability of SOC. The results showed that SOC in the area was relatively low and decreased with depth and from the basin edge to the centre with a measured mean content of 0.07–0.65 g/kg. The strongest variability in the zone in the top soil layer (0–40 cm) was in the centre part of the zone, which was supposed to be the most concentrated area of human activities in the zone. As soil depth increase, the degree of variation of SOC decreased. Gaussian, exponential, and spherical models were suggested to successfully simulate SOC in different soil depth zones. The spatial distribution of SOC showed strong variability in the same soil depth zone, with a nugget to sill ratio of less than 14% and a range of 30–160 km.

Impacts of fire on soil organic carbon stocks in a grazed semi-arid tropical Australian savanna: accounting for landscape variability

2014 ◽  
Vol 36 (4) ◽  
pp. 359 ◽  
Author(s):  
D. E. Allen ◽  
P. M. Bloesch ◽  
R. A. Cowley ◽  
T. G. Orton ◽  
J. E. Payne ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Management Practices ◽  
Soil Depth ◽  
Carbon Stocks ◽  
Soil Organic Carbon Stocks ◽  
Semi Arid ◽  
Soc Stocks

Fire and grazing are commonplace in Australian tropical savannas and the effects of these management practices on soil organic carbon stocks (SOC) is not well understood. A long-term (20 years) experiment studying the effects of fire on a grazed semi-arid tropical savanna was used to increase this understanding. Treatments, including frequency of fire (every 2, 4 and 6 years), season of fire [early (June) vs late (October) dry season] and unburnt control plots, were imposed on Vertosol grassland and Calcarosol woodland sites, which were grazed. Additionally long-term enclosures [unburnt (except the Calcarosol in 2001) and ungrazed since 1973] on each soil type adjacent to each site were sampled, although not included in statistical analyses. SOC stocks were measured to a soil depth of 0.3 m using a wet oxidation method (to avoid interference by carbonates) and compared on an equivalent soil mass basis. Significant treatment differences in SOC stocks were tested for, while accounting for spatial background variation within each site. SOC stocks (0–0.3 m soil depth) ranged between 10.1 and 28.9 t ha–1 (Vertosol site) and 20.7 and 54.9 t ha–1 (Calcarosol site). There were no consistent effects of frequency or season of fire on SOC stocks, possibly reflecting the limited statistical power of the study and inherent spatial variability observed. Differences in the response to frequency and season of fire observed between these soils may have been due to differences in clay type, plant species composition and/or preferential grazing activity associated with fire management. There may also have been differences in C input between treatments and sites due to differences in the herbage mass and post-fire grazing activity on both sites and changed pasture composition, higher herbage fuel load, and a reduction in woody cover on the Vertosol site. This study demonstrated the importance of accounting for background spatial variability and treatment replication (in the absence of baseline values) when assessing SOC stocks in relation to management practices. Given the absence of baseline SOC values and the potentially long period required to obtain changes in SOC in rangelands, modelling of turnover of SOC in relation to background spatial variability would enable management scenarios to be considered in relation to landscape variation that may be unrelated to management. These considerations are important for reducing uncertainty in C-flux accounting and to provide accurate and cost-effective methods for land managers considering participation in the C economy.

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