Representative locations from time series of soil water content using time stability and wavelet analysis

The concept of time stability has been widely used in the design and assessment of monitoring networks of soil moisture, as well as in hydrological studies, because it is as a technique that allows identifying of particular locations having the property of representing mean values of soil moisture in the field. In this work, we assess the effect of time stability calculations as new information is added and how time stability calculations are affected at shorter periods, subsampled from the original time series, containing different amounts of precipitation. In doing so, we defined two experiments to explore the time stability behavior. The first experiment sequentially adds new data to the previous time series to investigate the long-term influence of new data in the results. The second experiment applies a windowing approach, taking sequential subsamples from the entire time series to investigate the influence of short-term changes associated with the precipitation in each window. Our results from an operating network (seven monitoring points equipped with four sensors each in a 2-ha blueberry field) show that as information is added to the time series, there are changes in the location of the most stable point (MSP), and that taking the moving 21-day windows, it is clear that most of the variability of soil water content changes is associated with both the amount and intensity of rainfall. The changes of the MSP over each window depend on the amount of water entering the soil and the previous state of the soil water content. For our case study, the upper strata are proxies for hourly to daily changes in soil water content, while the deeper strata are proxies for medium-range stored water. Thus, different locations and depths are representative of processes at different time scales. This situation must be taken into account when water management depends on soil water content values from fixed locations.     

Measurement instability and temporal bias in chemical soil monitoring: sources and control measures

Concern that human impacts on the environment may be harmful to natural resources such as soils as well as to living conditions is the major motivation for long-term environmental monitoring. However, the evidence that measurement bias is not constant through time affects time series as an artifact; this also holds true for chemical soil monitoring. Measurement instabilities occur along the whole measurement chain, from soil sampling to the expression of results. The first step in controlling measurement instability is to identify its relevant sources, and the second is to control it by stabilizing, minimizing, or quantifying measurement instability. For all five steps in the measurement process, from soil sampling to the expression of the analytical results, sources of measurement instability are identified and measures of control discussed, leading to the main conclusion concerning the requirement to continuously control the relevant environmental and measurement boundary conditions that may affect measurement instability. The innovative aspect of this paper consists in explicitly addressing measurement instability in chemical soil monitoring and tracking it along the whole measurement chain. The paper is also a plea for a change of paradigm in long-term environmental monitoring, namely to consider temporal measurements as unstable unless their degree of stability is traceably demonstrated, adequately quantified, and included in interpretation.     

Moisture monitoring in waste disposal surface barriers

Surface barriers for waste disposal sites should preventwaste water and gas emission into the environment. It isnecessary to assess their proper operation by monitoring thewater regime of the containment. A set of three new water contentmeasuring devices has been developed that provide an economicalsolution for monitoring the moisture distribution and waterdynamic. They will give an early warning service if the barriersystem is at risk of being damaged. The cryo soil moisture sensor`LUMBRICUS' is an in situ self-calibrating absolute water contentmeasuring device. It measures moisture profiles at spot locationsdown to 2.5 m depth with an accuracy of better than 1.5% and adepth resolution of 0.03 m. The sensor inherently measuresdensity changes and initial cracks of shrinking materials likeclay minerals. The large area soil moisture sensor `TAUPE' is amoisture sensitive electric cable network to be buried in themineral barrier material of the cover. A report will be givenwith results and experiences on an exemplary installation at theWaste Disposal Facility Karlsruhe-West. 800 m² of the barrierconstruction have been continuously monitored since December1997. Volumetric water content differences of 1.5% have beendetected and localised within 4 m. This device is alreadyinstalled in two other waste disposal sites. A modified `TAUPE'was constructed for the control of tunnels and river dams aswell. Thin sheet moisture sensor `FORMI' is specifically designedfor moisture measurements in liners like bentonite, textile andplastic. Due to its flexibility it follows the curvature of theliner. The sensor measures independently from neighbouringmaterials and can be matched to a wide range of differentthickness of the material. The sensors are patented in several countries.     

Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks

Diel dissolved oxygen (DO) time series measured continuously using proximal sensors in situ for a temperate lake were denoised using discrete wavelet transform (DWT) with the orthogonal wavelet families of coiflet, daubechies, and symmlet with order of 10. Diel DO time series denoised were modeled using nine temporal artificial neural networks (ANNs) as a function of water level, water temperature, electrical conductivity, pH, day of year, and hour. Our results showed that time-lag recurrent network (TLRN) using denoised data emulated diel DO dynamics better than the best-performing TLRN using the original data, time-delay neural network (TDNN), and recurrent network (RNN). Daubechies basis dealt with diel DO data slightly better than the other bases given its coefficient of determination (r ²?=?87.1 %), while symmlet performed slightly better than the other bases in terms of root mean square error (RMSE?=?1.2 ppm) and mean absolute error (MAE?=?0.9 ppm).     

Antifouling strategies for marine and riverine sensors

Traditionally, water quality has been monitored by sampling and lab based analysis. However, there are disadvantages associated with this method, for example, deterioration of samples with time, limited sampling points, limited temporal monitoring. This has provided impetus for the development of sensors which can be deployed from remote locations over extended deployment periods. However, a major limitation of these systems is their vulnerability to biofouling. This review outlines the research that has been carried out on strategies for the protection of marine and riverine sensors against fouling.     

The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi Mountain and its surroundings (Istanbul)

The effect of land cover change, from natural to anthropogenic, on physical geography conditions has been studied in Kayisdagi Mountain. Land degradation is the most important environmental issue involved in this study. Most forms of land degradation are natural processes accelerated by human activity. Land degradation is a human induced or natural process that negatively affects the ability of land to function effectively within an ecosystem. Environmental degradation from human pressure and land use has become a major problem in the study area because of high population growth, urbanization rate, and the associated rapid depletion of natural resources. When studying the cost of land degradation, it is not possible to ignore the role of urbanization. In particular, a major cause of deforestation is conversion to urban land. The paper reviews the principles of current remote sensing techniques considered particularly suitable for monitoring Kayisdagi Mountain and its surrounding land cover changes and their effects on physical geography conditions. In addition, this paper addresses the problem of how spatially explicit information about degradation processes in the study area rangelands can be derived from different time series of satellite data. The monitoring approach comprises the time period between 1990 and 2005. Satellite remote sensing techniques have proven to be cost effective in widespread land cover changes. Physical geography and particularly natural geomorphologic processes like erosion, mass movement, physical weathering, and chemical weathering features etc. have faced significant unnatural variation.     

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Tailoring groundwater quality monitoring to vulnerability: a GIS procedure for network design

Monitoring networks aiming to assess the state of groundwater quality and detect or predict changes could increase in efficiency when fitted to vulnerability and pollution risk assessment. The main purpose of this paper is to describe a methodology aiming at integrating aquifers vulnerability and actual levels of groundwater pollution in the monitoring network design. In this study carried out in a pilot area in central Italy, several factors such as hydrogeological setting, groundwater vulnerability, and natural and anthropogenic contamination levels were analyzed and used in designing a network tailored to the monitoring objectives, namely, surveying the evolution of groundwater quality relating to natural conditions as well as to polluting processes active in the area. Due to the absence of an aquifer vulnerability map for the whole area, a proxi evaluation of it was performed through a geographic information system (GIS) methodology, leading to the so called “susceptibility to groundwater quality degradation”. The latter was used as a basis for the network density assessment, while water points were ranked by several factors including discharge, actual contamination levels, maintenance conditions, and accessibility for periodical sampling in order to select the most appropriate to the network. Two different GIS procedures were implemented which combine vulnerability conditions and water points suitability, producing two slightly different networks of 50 monitoring points selected out of the 121 candidate wells and springs. The results are compared with a “manual” selection of the points. The applied GIS procedures resulted capable to select the requested number of water points from the initial set, evaluating the most confident ones and an appropriate density. Moreover, it is worth underlining that the second procedure (point distance analysis [PDA]) is technically faster and simpler to be performed than the first one (GRID?+?PDA).     

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T _s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T _s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.     

中国土壤环境调查、评价与监测

介绍了近半个多世纪以来中国土壤环境调查情况,对20世纪50年代、80年代全国土壤调查情况进行了描述,阐述了2006年以来全国土壤污染状况调查情况。结合土壤环境现状、土壤监测与评价存在的问题,对未来中国土壤监测规划进行了展望。提出了确定土壤环境监测国控点,构建国家土壤环境监测网,落实土壤环境例行监测的监测思路,以进一步推进土壤环境监测和保护。     

海南省省控土壤环境监测基础点位布设思路与方法

为满足新时期土壤污染防治管理要求,掌握海南省土壤环境质量状况及变化趋势,海南省亟需加强省控土壤环境监测基础点位布设,完善土壤环境监测网。通过梳理海南省土壤环境监测工作基础和监测网络构建情况,形成了省控基础点位布设思路、原则和方法,并应用GIS技术完成点位布设。海南省省控土壤环境监测基础点位基本覆盖了海南省农用地主要土壤类型和成土母岩,监测结果可以反映海南省农用地土壤环境质量整体状况。     

Retrospective assessment of dryland soil stability in relation to grazing and climate change

Accelerated soil erosion is an aspect of dryland degradation that is affected by repeated intense drought events and land management activities such as commercial livestock grazing. A soil stability index (SSI) that detects the erosion status and susceptibility of a landscape at the pixel level, i.e., stable, erosional, or depositional pixels, was derived from the spectral properties of an archived time series (from 1972 to 1997) of Landsat satellite data of a commercial ranch in northeastern Utah. The SSI was retrospectively validated with contemporary field measures of soil organic matter and erosion status that was surveyed by US federal land management agencies. Catastrophe theory provided the conceptual framework for retrospective assessment of the impact of commercial grazing and soil water availability on the SSI. The overall SSI trend was from an eroding landscape in the early drier 1970s towards stable conditions in the wetter mid-1980s and late 1990s. The landscape catastrophically shifted towards an extreme eroding state that was coincident with the “The Great North American Drought of 1988”. Periods of landscape stability and trajectories toward stability were coincident with extremely wet El Niño events. Commercial grazing had less correlation with soil stability than drought conditions. However, the landscape became more susceptible to erosion events under multiple droughts and grazing. Land managers now have nearly a year warning of El Niño and La Niña events and can adjust their management decisions according to predicted landscape erosion conditions.     

农业面源污染防治的监测问题分析

近年来,农业面源污染已成为许多国家和地区水环境质量改善的主要影响因素,开展农业面源污染监测将为深入打好污染防治攻坚战提供重要支撑。该文系统分析当前我国农业面源污染监测存在的主要问题,综合考虑国内外经验,提出如下建议:采取空间嵌套式的布局模式优化地表水环境监测点位,充分发挥环境监测的预测预报和风险评估功能;建立包括污染源、产排污系数和空间传输过程的农业面源污染全过程监测网络;定期开展土壤氮、磷养分含量评估和地下水硝酸盐氮测定和评估;建立完善数据整合与共享机制。     

遥感监测土壤湿度综述

遥感技术具有大面积同步观测，时效性、经济性强等特点，为大面积动态监测土壤湿度提供了可能。本文对近年来国内外遥感监测土壤湿度的理论、方法的发展和应用进行了回顾，重点介绍了目前已经比较成熟和广泛应用的基于可见光与热红外波段的植被指数方法以及在干旱、半干旱地区的应用，通过对比分析了各种遥感监测方法的优缺点，指出了土壤湿度遥感监测方法存在的不足，展望了土壤湿度遥感监测方法的发展趋势。     

Spatial and temporal variation of moisture content in the soil profiles of two different agricultural fields of semi-arid region

Modeling spatio-temporal variation of soil moisture with depth in the soil profile plays an important role for semi-arid crop production from an agro-hydrological perspective. This study was performed in Guvenc Catchment. Two soil series that were called Tabyabayir (TaS) and Kervanpinari (KeS) and classified as Leptosol and Vertisol Soil Groups were used in this research. The TeS has a much shallower (0–34 cm) than the KeS (0–134 cm). At every sampling time, a total of geo-referenced 100 soil moisture samples were taken based on horizon depths. The results indicated that soil moisture content changed spatially and temporally with soil texture and profile depth significantly. In addition, land use was to be important factor when soil was shallow. When the soil conditions were towards to dry, higher values for the coefficient of variation (CV) were observed for TaS (58 and 43 % for A and C horizons, respectively); however, the profile CV values were rather stable at the KeS. Spatial variability range of TaS was always higher at both dry and wet soil conditions when compared to that of KeS. Excessive drying of soil prevented to describe any spatial model for surface horizon, additionally resulting in a high nugget variance in the subsurface horizon for the TaS. On the contrary to TaS, distribution maps were formed all horizons for the KeS at any measurement times. These maps, depicting both dry and wet soil conditions through the profile depth, are highly expected to reduce the uncertainty associated with spatially and temporally determining the hydraulic responses of the catchment soils.     

Time-Varying Parameter Analysis of the Baltic Sea Freshwater Runoffs

The Baltic Sea runoff events during 1970–2000 were analysed with non-linear estimation in order to find potential stability and time-varying (non-constancy) properties of time series parameters. In the Baltic Sea area, the wintertime rainfall is expected to increase, especially in the northern parts of the watershed; furthermore, seasonality is expected to decrease, i.e. the runoffs will become more evenly distributed over seasons, mainly by decreasing springtime peaks. We found a general increase in runoffs over the time period studied that consisted of a level shift upwards during the early 1980s. The runoffs considerably resulted from inputs in the months of April to June (which points to the significance of winter precipitation) and are biased to northern parts of the catchment. Our results suggest that this is connected with snow and ice melting in the northern areas of the watershed, especially in the subarctic catchment area of the Neva River (the area includes, for example, three of the four biggest lakes in Europe, Lake Ladoga, Lake Onega and Lake Saimaa, which are annually covered by ice). The yearly variation in runoff is significantly stabilised by large N–S extension of the watersheds of these lakes. The results are generally in accordance with the presented climatic expectations; however, decreasing seasonality was not obvious in the series yet. However, we found potential implications of system change towards decreased seasonality. By better understanding of ongoing processes in the sea, the results can give us an opportunity to better evaluate the system control, predicted areal consequences and ecosystem effects of the climate change. The analysis proved to be a sensitive tool for studying climatic variation in a relatively small area and over a short time span.     

Early-season agricultural drought: detection, assessment and monitoring using Shortwave Angle and Slope Index (SASI) data

Early season or crop-planting-period (ES/CPP) drought conditions have become a recurrent phenomenon in tropical countries like India, due to fluctuations in the time of onset and progression of monsoon rains. ES/CPP agricultural drought assessment is a major challenge because of the difficulties in the generation of operational products on soil moisture at larger scales. The present study analyzed the Shortwave Angle Slope Index (SASI) derived from Near Infrared and Shortwave Infrared data of Moderate Resolution Imaging Spectroradiometer, for tracking surface moisture changes and assessing the agricultural drought conditions during ES/CPP, over Andhra Pradesh state, India. It was found that in-season progression of SASI was well correlated with rainfall and crop planting patterns in different districts of the study area state in both drought and normal years. Rainfall occurrence, increase in crop planted area, and decrease in SASI were in chronological synchronization in the season. Change in SASI from positive to negative values is a unique indication of dryness to wetness shift in the season. Duration of positive SASI values indicated the persistence of agricultural drought in the crop planting period. Mean SASI values were able to discriminate an area which was planted in normal year and unplanted in drought year. SASI thresholds provide an approximate and rapid estimate of the crop planting favorable area in a region which is useful to assess the impact of drought. Thus, SASI is a potential index to strengthen the existing operational drought monitoring systems. Further work needs to be on the integration of multiple parameters—SASI, soil texture, soil depth, rainfall and cropping pattern, to evolve a geospatial product on crop planting favorable areas. Such products pave the way for quantification of drought impact on agriculture in the early part of the season, which is a major inadequacy in the current drought monitoring system.