油污染土壤中微生物量──碳的测定方法研究

用熏蒸-培养法测定油污染土壤中的微生物量─—碳。对某油田距采油井1、15、30m的土壤测定结果表明，受原油污染的土壤微生物量的大小随土壤含油量变化而变化。熏蒸-培养法受土壤水分状况影响较大，不适用于强酸性土壤及刚施用过大量有机肥的土壤，在测定性质差异很大的土壤时，必须分别测定转换系数Kc，才能得到可靠的结果。     

盐生植物单叶蔓荆对盐碱地的修复效应研究

以盐生植物单叶蔓荆为研究对象，在威海高区海水浴场的沙滩边缘提取实验组与对照组土样，然后分别测定几组能够描述土壤质量的数据，如土壤颗粒中空气的体积、贮水能力、有机质含量以及pH值等，最后通过比较实验组和对照组中土壤颗粒的空气体积、贮水能力、有机质含量以及pH值的不同，从所得实验结果中分析得出盐生植物单叶蔓荆对盐碱土具有修复效应的结论，并结合威海的实际情况讨论了在海边盐碱地上种植单叶蔓荆的意义。     

晋西北黄土丘陵区植被类型林地土壤有效水和持水能力刍议

以晋西北黄土丘陵区岚县不同人工林地土壤水分为研究对象,分析了杨树、杏树、核桃3种不同树种的土壤水含量在垂直剖面的变化特征。研究表明,在0～600 cm深度的土壤中,随着土壤深度的增加,3种林地土壤水分含量呈现迅速减少然后缓慢增加再减少的波动趋势,土壤水分含量总体具有核桃林地杏树林地杨树林地;杨树林地土壤水分缺失度较大,核桃林地水分缺失度相对最小;干层发育严重程度表现为杨树林地杏树林地核桃林地。从3种植被对土壤水分的利用和破坏方面来看,核桃树更适宜作为该区商品林地的种植树种。     

柽柳对滨海盐渍土的改良作用及其应用

以山东省威海市高区环海路上的柽柳为研究对象,在其根系附近采集实验组土样并在其周围的空白地面上采集对照组土样,测定几组能够说明其土质的数据,如土壤中生物的分类与计数、有机质的含量、土壤的pH值、土壤中空气的体积以及土壤颗粒的贮水能力.通过将实验组与对照组的比较,分析得出在滨海盐渍土上栽植柽柳不但可增加土壤中微生物的含量,而且可增加土壤中的空气和有机质的含量,提高土壤颗粒的贮水能力,还可降低土壤的pH值,增加观赏性.因此,根据所测的各项指标得出柽柳对滨海盐渍土具有改良作用的结论,为推动与利用柽柳在滨海盐渍土上的种植提供理论依据.     

阿克苏地区春季土壤水分垂直向运动

土壤中毛管水是传递压力的，毛管水运动的方向，受土壤上层温度变化和土面蒸发制约，与土壤中汽态水的运动方向（由热至冷）一致。下午和晚上因地面辐射而冷却，闭塞空气收缩，潜水向上通过毛细管吸入闭塞空气中，潜水位下降；上午和白天因地面日晒，土温升高，闭塞空气膨胀，排出较粗毛管水，成为重力水而下渗，抬升潜水位。春天大风，土温升高，晚上使潜水位升高，白天土面蒸发加大，又使潜水位下降。用潜水蒸渗仪，可测到大风天改变土壤水分运行的方向。     

不同修复剂对重金属污染土壤修复效果研究

本试验自制重金属污染土作为研究样本,试验采用室内盆栽试验,试验设置6个不同处理:未添加化学修复剂(CK)、2%骨炭(A)、2%活性炭(B)、2%磷矿粉(C)、2%土壤修复剂Ⅰ(D)和2%土壤修复剂Ⅱ(E),与已筛选出的富集乡土植物油菜、马铃薯、狼尾草、小麦、刺儿菜、巴天酸模相配合,对重金属污染土壤进行修复,测定土壤中重金属含量。结果表明"2%土壤修复剂Ⅱ(E)"修复效果最佳,尤其是与巴天酸模、刺儿菜植物搭配为最佳。因此对重金属污染土壤可采用植物和化学修复相结合的修复方法,修复效果明显。提出了生物修复和化学修复技术结合的污染土壤的修复技术措施,旨在为推动本省污染土壤的修复储备技术和积累经验,为有效合理利用土地环境资源,保持土地资源利用可持续性和延长土地资源使用周期提供了科学依据。     

土壤的改良与土壤化学化

主观改良旨在改善土壤性质与土壤形成和发育条件，提高土填肥力，它是利用人工方法，通过灌溉、排水、淋洗、清理土壤等方法以及施加化学与有机肥料调节水、热、盐、生物化学、物理化学等状况来实现的．因此，土壤改良是一项重要的农业技术措施，对于水热状况与通气性不良的土壤以及盐碱化地区尤为重要。1区域性土壤的改良区域性土壤改良可分为土地灌溉与排水两种类型。为提高作物水分保证程度，提供水资源而进行的人工浸润土壤，灌溉条件起着积极作用．土地排水的目的在于从根系层范围排去多余水分达到对植物生长有利的水热条件，并改善土…     

不同浸提条件对土壤氨氮提取数量的影响

土壤溶解性有机氮（DON）的推算是通过测试土壤全氮、氨氮和硝态氮的数量,利用全氮减去氨氮和硝态氮获得。不同区域或不同学者对DON的定义不同,在测试过程中选取多种浸提方法测定土壤氨氮,结果导致测试结果存在较大的差异。但这种差异受到何种因素控制,目前还没有形成统一的认识。基于此,研究应用正交试验方法,通过统计分析了振荡时间、浸提剂、温度以及土水比对土壤氨氮浸提数量的影响。结果表明：不同因素对氨氮浸提数量的影响程度为土水比>浸提剂>振荡时间>温度;对氨氮浸提数量分别表现为随土水比与浸提剂浓度增大而升高;在浸提温度低于50℃时,土壤氨氮的浸提数量随温度升高而升高;振荡时间在60 min内,土壤氨氮的提取数量随着振荡时间增加而增高。     

页岩气钻屑固化填埋池监测与评价研究

为了探讨钻屑固化填埋池对周围土壤及水体环境的影响,以西南某页岩气田为例,分别选取了具有代表性的水基钻屑固化填埋池和油基灰渣固化填埋池,先对钻屑固化处置方式和效果进行了评价,再对钻屑固化填埋池土壤径流液及土壤进行了监测,分析了固化填埋池对周边土壤环境的影响。结果表明:钻屑固化体浸出液达到GB8978-1996《污水综合排放标准》一级排放标准,钻屑固化填埋池土壤径流液满足GB5084-2005《农田灌溉水质标准》旱作标准;水基钻屑固化填埋池和油基灰渣固化填埋池上覆土壤重金属综合污染指数分别是0.42、0.45,周边土壤重金属综合污染指数分别是0.45、0.54。钻屑固化填埋池各项监测指标均未超标,土壤重金属综合污染指数均小于0.7,钻屑固化填埋暂时未对周边土壤造成影响,属于清洁水平。短期内钻屑固化填埋效果较好,对周围土壤环境影响较小。     

水基钻屑固化填埋对周边土壤环境的影响

为了探讨水基钻屑固化填埋对周边土壤环境的影响,以西南某页岩气田为例,选取了3处具有代表性的井场,先对水基钻屑固化处置方式和效果进行了评价,然后再对水基钻屑固化填埋池周边土壤进行监测,分析了固化填埋池对周边土壤环境的影响。结果表明,井场的水基钻屑固化填埋池周边土壤重金属的综合污染指数分别是0.45,0.56,0.29,三个井场的水基钻屑固化填埋池周边土壤重金属的综合污染指数均小于0.7,与相关标准相比,说明水基钻屑固化填埋暂时未对周边土壤造成影响,属于清洁水平。短期内水基钻屑的固化填埋效果较好,对周围土壤的环境影响较小。     

Comparison of Field Performance of Multiple Soil Moisture Sensors in a Semi‐Arid Rangeland1

Abstract:  Automated electronic soil moisture sensors, such as time domain reflectometry (TDR) and capacitance probes are being used extensively to monitor and measure soil moisture in a variety of scientific and land management applications. These sensors are often used for a wide range of soil moisture applications such as drought forage prediction or validation of large‐scale remote sensing instruments. The convergence of three different research projects facilitated the evaluation and comparison of three commercially available electronic soil moisture probes under field application conditions. The sensors are all installed in shallow soil profiles in a well instrumented small semi‐arid shrub covered subwatershed in Southeastern Arizona. The sensors use either a TDR or a capacitance technique; both of which indirectly measure the soil dielectric constant to determine the soil moisture content. Sensors are evaluated over a range of conditions during three seasons comparing responses to natural wetting and drying sequences and using water balance and infiltration simulation models. Each of the sensors responded to the majority of precipitation events; however, they varied greatly in response time and magnitude from each other. Measured profile soil moisture storage compared better to water balance estimates when soil moisture in deeper layers was accounted for in the calculations. No distinct or consistent trend was detected when comparing the responses from the sensors or the infiltration model to individual precipitation events. The results underscore the need to understand how the sensors respond under field application and recognize the limitations of soil moisture sensors and the factors that can affect their accuracy in predicting soil moisture in situ.     

SOIL MOISTURE SENSORS FOR CONTINUOUS MONITORING1

ABSTRACT: Certain physical and chemical properties of soil vary with soil water content. The relationship between these properties and water content is complex and involves both the pore structure and constituents of the soil solution. One of the most economical techniques to quantify soil water content involves the measurement of electrical resistance of a dielectric medium that is in equilibrium with the soil water content. The objective of this research was to test the reliability and accuracy of fiberglass soil-moisture electrical resistance sensors (ERS) as compared to gravimetric sampling and Time Domain Reflectometry (TDR). The response of the ERS was compared to gravimetric measurements at eight locations on the USDA-ABS Walnut Gulch Experimental Watershed. The comparisons with TDR sensors were made at three additional locations on the same watershed. The high soil rock content (≥45 percent) at seven locations resulted in consistent overestimation of soil water content by the ERS method. Where rock content was less than 10 percent, estimation of soil water was within 5 percent of the gravimetric soil water content. New methodology to calibrate the ERS sensors for rocky soils will need to be developed before soil water content values can be determined with these sensors.     

USE OF SOIL MOISTURE PROBES TO ESTIMATE GROUND WATER RECHARGE AT AN OIL SPILL SITE1

Soil moisture data collected using an automated data logging system were used to estimate ground water recharge at a crude oil spill research site near Bemidji, Minnesota. Three different soil moisture probes were tested in the laboratory as well as the field conditions of limited power supply and extreme weather typical of northern Minnesota: a self‐contained reflectometer probe, and two time domain reflectometry (TDR) probes, 30 and 50 cm long. Recharge was estimated using an unsaturated zone water balance method. Recharge estimates for 1999 using the laboratory calibrations were 13 to 30 percent greater than estimates based on the factory calibrations. Recharge indicated by the self‐contained probes was 170 percent to 210 percent greater than the estimates for the TDR probes regardless of calibration method. Results indicate that the anomalously large recharge estimates for the self‐contained probes are not the result of inaccurate measurements of volumetric moisture content, but result from the presence of crude oil, or borehole leakage. Of the probes tested, the 50 cm long TDR probe yielded recharge estimates that compared most favorably to estimates based on a method utilizing water table fluctuations. Recharge rates for this probe represented 24 to 27 percent of 1999 precipitation. Recharge based on the 30 cm long horizontal TDR probes was 29 to 37 percent of 1999 precipitation. By comparison, recharge based on the water table fluctuation method represented about 29 percent of precipitation.     

Enhancing the Ability of a Soil Moisture‐based Index for Agricultural Drought Monitoring by Incorporating Root Distribution

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices (e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture‐based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang‐Huai‐Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index (CMI), and the improved root‐weighted soil moisture index (RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution.     

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.     

ELEVATED ATMOSPHERIC CO2 INCREASES WATER AVAILABILITY IN A WATER-LIMITED GRASSLAND ECOSYSTEM1

ABSTRACT: Californian annual grassland on sandstone (moderately fertile) and serpentine (very infertile) soils at the Jasper Ridge Biological Preserve, Stanford, California, were exposed to ambient or elevated (ambient + 36 Pa CO₂) atmospheric CO₂ in open-top chambers since December 1991. We measured ecosystem evapotranspiration with open gas-exchange systems, and soil moisture with time-domain reflectometry (TDR) over 0–15 cm (serpentine) and 0–30 cm (sandstone) depths, at times of peak above ground physiological activity. Evapotranspiration decreased by 12 to 63 percent under elevated CO₂ in three consecutive years in the sandstone ecosystem (p = 0.053, p = 0.162, p = 0.082 in 1992, 1993, and 1994, respectively). In correspondence with decreased evapotranspiration, late-season soil moisture reserves in the sandstone were extended temporally by 10 ± 3 days in 1993 and by 28 ± 11 days in 1994. The effect of elevated CO₂ on soil moisture was greater in the drier spring of 1994 (419 mm annual rainfall) than in 1993 (905 mm annual rainfall). In the serpentine ecosystem, evapotranspiration and soil moisture reserves were not clearly affected by elevated CO₂. Soil water may be conserved in drought-affected ecosystems exposed to elevated CO₂, but the amount of conservation appears to depend on the relative importance of transpiration and soil evaporation in controlling water flux.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies1

Anderson, SallyRose, Glenn Tootle, and Henri Grissino‐Mayer, 2012. Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies. Journal of the American Water Resources Association (JAWRA) 48(4): 849‐858. DOI: 10.1111/j.1752‐1688.2012.00651.x Abstract: Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. We used tree‐ring chronologies to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k‐nearest neighbor techniques. We correlated moisture sensitive tree‐ring chronologies in and adjacent to the UCRB with regional soil moisture and tested the relationships for temporal stability. Chronologies that were positively correlated and stable for the calibration period were retained. We used stepwise linear regression to identify the best predictor combinations for each soil moisture region. The regressions explained 42‐78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained more variance in the datasets. Reconstructed soil moisture data was standardized and compared with standardized reconstructed streamflow and snow water equivalent data from the same region. Soil moisture and other hydrologic variables were highly correlated, indicating reconstructions of soil moisture in the UCRB using tree‐ring chronologies successfully represent hydrologic trends.