Influence of groundwater level change on vegetation coverage and their spatial variation in arid regions

Sampling and testing are conducted on groundwater depth and vegetation coverage in the 670 km2 of the Sangong River Basin and semi-variance function analysis is made afterwards on the data obtained by the application of geo-statistics. Results showed that the variance curve of the groundwater depth and vegetation coverage displays an exponential model. Analysis of sampling data in 2003 indicates that the groundwater depth and vegetation coverage change similarly in space in this area. The Sangong River Basin is composed of upper oasis, middle ecotone and lower sand dune. In oasis and ecotone, influenced by irrigation of the adjoining oasis, groundwater level has been raised and soil water content also increased compared with sand dune nearby, vegetation developed well. But in the lower reaches of the Sangong River Basin, because of descending of groundwater level, soil water content decreased and vegetation degenerated. From oasis to abandoned land and desert grassland, vegetation coverage and groundwater level changed greatly with significant difference respectively in spatial variation. Distinct but similar spatial variability exists among the groundwater depth and vegetation coverage in the study area, namely, the vegetation coverage decreasing (increasing) as the groundwater depth increases (decreases). This illustrates the great dependence of vegetation coverage on groundwater depth in arid regions and further implies that among the great number of factors affecting vegetation coverage in arid regions, groundwater depth turns out to be the most determinant one.     

塔城市一次暴雪天气的分析

2003年2月22日塔城市出现一次暴雪天气过程。文章着重分析了此次天气过程的高空环流形势、地面形势、T—Td等，指出暖湿气流是造成暴雪的主要原因。     

Curie-point depth from spectral analysis of magnetic data in central-southern Europe

The basal depth of the outer layer with internal magnetic sources was calculated from magnetic data available within a roughly 500 km wide and 1200 km long area, running from central Germany to southern Italy. The dataset, deriving from different aeromagnetic surveys, is reduced to the reference altitude of 3000 m a.s.l. and a reference year of 1980.0. The adopted method, which transforms the spatial data into the frequency domain, provides a relationship between the two-dimensional spectrum of the magnetic anomalies and the top and centroid depths of the magnetic sources. The magnetic layer bottom depth (MLBD) thus obtained is 29-33 km deep in the stable areas (central Europe Variscan units, Corsica-Sardinia Variscan block) and corresponds to the Moho, having an average temperature of 560 °C. From the Alps to the Apennines, MLBD ranges between 22 and 28 km and is clearly shallower than the Moho. In these units, the wide variation of MLBD appears to be compatible with the presence of shallow magnetised bodies, consisting of lower crustal rocks (Ivrea-Verbano zone), ophiolitic units (Penninic zone and Voltri Massif) and intrasedimentary basic volcanic bodies (Po Basin). An average value of 25 km can be attributed to MLBD, which corresponds to a temperature of 550 °C. In the peri-Tyrrhenian zone and the Ligurian Sea, MLBD is below the Moho, which ranges from 17 to 20 km depth, and it has a temperature matching approximately to the Curie temperature of magnetite (580 °C).     

Hydrogeochemistry of karst underground waters at shallow depth in Guiyang City, Guizhou Province

The aim of this study is to shed light on the hydrogeochemical characteristics of karst underground waters at shallow depth in Guiyang City, Guizhou Province with an emphasis on the geochemistry of major elements. Guiyang City bears abundant underground waters and it is also an important representative of the karst areas throughout the world. Ca^2 and Mg^2 are the dominant cations, accounting for 81% -99.7% of the total, and HCO3^- and SO4^2- are the dominant anions. Weathering of limestones and dolostones is the most important factor controlling the hydrogeochemistry of underground waters, and weathering of sulfate and evaporite rocks is less important. Moreover, the precipitation and human activities also have a definite influence on the hydrogeoehemistry of underground waters in the region studied.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Varying water utilization of Haloxylon ammodendron plantations in a desert‐oasis ecotone

Haloxylon ammodendron is a desert shrub used extensively in China for restoring degraded dry lands. An understanding of the water source used by H. ammodendron plantations is critical achieving sustainable vegetation restoration. We measured mortality, shoot size, and rooting depth in 5‐, 10‐, 20‐, and 40‐year‐old H. ammodendron plantations. We examined stable isotopic ratios of oxygen (δ¹⁸O) in precipitation, groundwater, and soil water in different soil layers and seasons, and in plant stem water to determine water sources at different shrub ages. We found that water acquisition patterns in H. ammodendron plantations differed with plantation age and season. Thus, the main water source for 5‐year‐old shrubs was shallow soil water. Water sources of 10‐year‐old shrubs shifted depending on the soil water conditions during the season. Although their tap roots could absorb deep soil water, the plantation main water sources were from soil water, and about 50% of water originated from shallow and mid soil. This pattern might occur because main water sources in these plantations were changeable over time. The 20‐ and 40‐year‐old shrubs acquired water mainly from permanent groundwater. We conclude that the main water source of a young H. ammodendron plantation was soil water recharged by precipitation. However, when roots reached sufficient depth, water originated mainly from the deep soil water, especially in the dry season. The deeply rooted 20‐ and 40‐year‐old shrubs have the ability to exploit a deep and reliable water source. To achieve sustainability in these plantations, we recommend a reduction in the initial density of H. ammodendron in the desert‐oasis ecotone to decelerate the consumption of shallow soil water during plantation establishment.     

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.