Modelling the Mixing Function to Constrain Coseismic Hydrochemical Effects: An Example from the French Pyrénées

Groundwater coseismic transient anomalies are evidenced and characterized by modelling the mixing function F characteristic of the groundwater dynamics in the Ogeu (western French Pyrénées) seismic context. Investigations of water-rock interactions at Ogeu indicate that these mineral waters from sedimentary environments result from the mixing of deep waters with evaporitic signature with surficial karstic waters. A 3-year hydrochemical monitoring of Ogeu springwater evidences that using arbitrary thresholds constituted by the mean ± 1 or 2σ, as often performed in such studies, is not a suitable approach to characterize transient anomalies. Instead, we have used a mixing function F calculated with chemical elements, which display a conservative behavior not controlled by the precipitation of a mineral phase. F is processed with seismic energy release (E_s) and effective rainfalls (R). Linear impulse responses of F to E_s and R have been calculated. Rapid responses (10 days) to rainwater inputs are evidenced, consisting in the recharge of the shallow karstic reservoir by fresh water. Complex impulse response of F to microseismic activity is also evidenced. It consists in a 2-phase hydrologic signal, with an inflow of saline water in the shallow reservoir with a response delay of 10 days, followed by an inflow of karstic water with a response delay of 70 days, the amount being higher than the saline inflow. Such a process probably results from changes in volumetric strain with subsequent microfracturation transient episodes allowing short inflow of deep salted water in the aquifer. This study demonstrates that groundwater systems in such environments are unstable systems that are highly sensitive to both rainfall inputs and microseismic activity. Impulse responses calculation of F to E_s is shown to be a powerful tool to identify transient anomalies. Similar processing is suggested to be potentially efficient to detect precursors of earthquakes when long time-series (5 years at least) are available in areas with high seismicity.     

尼泊尔M8.1地震震前卫星重力场时变特征

利用多年GRACE卫星重力变化资料,给出尼泊尔及周缘地区的重力场时空变化,以探索2015-04-25尼泊尔M8.1地震震前重力变化特征;并基于球形位错理论,利用断层模型数据模拟同震重力场变化。研究表明,区域重力场变化以2008年为界存在明显的分时段特征,2003~2008年以重力增加为主,而2009~2014年以重力减少为主;且存在明显的分区性,区域重力场在尼泊尔地震震中四周呈正负相间分布;震前局部重力场变化在压缩区重力增加,拉张区重力减少,与理论同震重力变化结果一致。总结研究区域近年来M7.0以上地震震前重力变化特征发现,地震基本发生在重力变化高梯度带的过渡带及重力变化零值线附近。     

Linking land cover dynamics with driving forces in mountain landscape of the Northwestern Iberian Peninsula

The mountainous areas of the northwestern Iberian Peninsula have undergone intense land abandonment. In this work, we wanted to determine if the abandonment of the rural areas was the main driver of landscape dynamics in Gerês–Xurés Transboundary Biosphere Reserve (NW Iberian Peninsula), or if other factors, such as wildfires and the land management were also directly affecting these spatio-temporal dynamics. For this purpose, we used earth observation data acquired from Landsat TM and ETM + satellite sensors, complemented by ancillary data and prior field knowledge, to evaluate the land use/land cover changes in our study region over a 10-year period (2000–2010). The images were radiometrically calibrated using a digital elevation model to avoid cast- and self-shadows and different illumination effects caused by the intense topographic variations in the study area. We applied a maximum likelihood classifier, as well as other five approaches that provided insights into the comparison of thematic maps. To describe the land cover changes we addressed the analysis from a multilevel approach in three areas with different regimes of environmental protection. The possible impact of wildfires was assessed from statistical and spatially explicit fire data. Our findings suggest that land abandonment and forestry activities are the main factors causing the changes in landscape patterns. Specifically, we found a strong decrease of the ‘meadows and crops’ and ‘sparse vegetation areas’ in favor of woodlands and scrublands. In addition, the huge impact of wildfires on the Portuguese side have generated new ‘rocky areas’, while on the Spanish side its impact does not seem to have been a decisive factor on the landscape dynamics in recent years. We conclude rural exodus of the last century, differences in land management and fire suppression policies between the two countries and the different protection schemes could partly explain the different patterns of changes recorded in these covers.     

Sulfur dioxide emissions and degassing behavior of Erebus volcano,Antarctica

Emission rates of sulfur dioxide (SO₂) were measured at Erebus volcano, Antarctica in December between 1992 and 2005. Since 1992 SO₂ emissions rates are normally distributed with a mean of 61 ± 27 Mg d^− 1 (0.7 ± 0.3 kg s^− 1) (n = 8064). The emission rates vary over minutes, hours, days and years. Hourly and daily variations often show systematic and cyclic trends. Long-wavelength, large amplitude trends appear related to lava lake area and both are likely controlled by processes occurring at depth. Time series analysis of continuous sequences of measurements obtained over periods of several hours reveals periodicity in SO₂ output ranging from 10 to 360 min, with a 10 min cycle being the most dominant. Closed and open-system degassing models are considered to explain observed variable degassing rates. Closed-system degassing is possible as rheological stiffening and stick/slip may occur within the system. However, the timescales represented in these models do not fit observations made on Erebus. Open-system degassing and convection fits the observations collected as the presented models were developed for a system similar to Erebus in terms of degassing, eruptive activity and process repose time. We show that with the observed emission rate (0.71 kg s^− 1) and a crystal content of 30%, magma will cool 65 °C to match observed heat fluxes; this cooling is sufficient enough to drive convection.     

Biome-level relationships between vegetation indices and climate variables using time-series analysis of remotely-sensed data

ABSTRACT

Climatic factors such as rainfall and temperature play a vital role in the growth characteristics of vegetation. While the relationship between climate and vegetation growth can be accurately predicted in instances where vegetation is homogenous, this becomes complex to determine in heterogeneous vegetation environments. The aim of this paper was to study the relationship between remotely-sensed monthly vegetation indices (i.e. Normalized Difference Vegetation Index and Enhanced Vegetation Index) and climatic variables (temperature and precipitation) using time-series analysis at the biome-level. Specifically, the autoregressive distributed lag model (ARDL1 and ARDL2, corresponding respectively to one month and two month lags) and the Koyck-transformed distributed lag model were used to build regression models. All three models estimated NDVI and EVI fairly accurately in all biomes (Relative Root-Mean-Squared-Error (RMSE): 12.0–26.4%). Biomes characterized by relative homogeneity (Grassland, Savanna, Indian Ocean Coastal Belt and Forest Biomes) achieved the most accurate estimates due to the dominance of a few species. Comparisons of lag size (one month compared to two months) generally showed similarities (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood) with quite high comparability in certain biomes – this indicates the utility of the ARDL1 and ARDL2 model, depending on the availability of appropriate data. These findings demonstrate the variation in estimation linked to the biome, and thus the validity of biome-level correlation of climatic data and vegetation indices.     

Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data

Understanding forest biomass dynamics is crucial for carbon and environmental monitoring, especially in the context of climate change. In this study, we propose a robust approach for monitoring aboveground forest biomass (AGB) dynamics by combining Landsat time-series with single-date inventory data. We developed a Random Forest (RF) based kNN model to produce annual maps of AGB from 1988 to 2017 over 7.2 million ha of forests in Victoria, Australia. The model was internally evaluated using a bootstrapping technique. Predictions of AGB and its change were then independently evaluated using multi-temporal Lidar data (2008 and 2016). To understand how natural and anthropogenic processes impact forest AGB, we analysed trends in relation to the history of disturbance and recovery. Specifically, change metrics (e.g., AGB loss and gain, Years to Recovery - Y2R) were calculated at the pixel level to characterise the patterns of AGB change resulting from forest dynamics. The imputation model achieved a RMSE value of 132.9 Mg ha⁻¹ (RMSE% = 46.3%) and R² value of 0.56. Independent assessments of prediction maps in 2008 and 2016 using Lidar-based AGB data achieved relatively high accuracies, with a RMSE of 108.6 Mg ha⁻¹ and 135.9 Mg ha⁻¹ for 2008 and 2016, respectively. Annual validations of AGB maps using un-changed, homogenous Lidar plots suggest that our model is transferable through time (RMSE ranging from 109.65 Mg ha⁻¹ to 112.27 Mg ha⁻¹ and RMSE% ranging from 25.38% to 25.99%). In addition, changes in AGB values associated with forest disturbance and recovery (decrease and increase, respectively) were captured by predicted maps. AGB change metrics indicate that AGB loss and Y2R varied across bioregions and were highly dependent on levels of disturbance severity (i.e., a greater loss and longer recovery time were associated with a higher severity disturbance). On average, high severity fire burnt from 200 Mg ha⁻¹ to 550 Mg ha⁻¹ of AGB and required up to 15 years to recover while clear-fell logging caused a reduction in 250 Mg ha⁻¹ to 600 Mg ha⁻¹ of AGB and required nearly 20 years to recover. In addition, AGB within un-disturbed forests showed statistically significant but monotonic trends, suggesting a mild gradual drop over time across most bioregions. Our methods are designed to support forest managers and researchers in developing forest monitoring systems, especially in developing regions, where only a single date forestry inventory exists.     

厦门湾上屿附近海域悬浮颗粒物中若干元素的时空分布特征

本文测定了在厦门湾上的屿附近海域定点站６个航次所采集悬浮颗粒物中Ｆｅ,Ｍｎ,Ａｌ,Ｔｉ,Ｂａ和Ｃａ６种金属元素的含量,并结合相关要素,探讨了该海域悬浮颗粒物的化学组成及其生物地球化学特性。对悬浮颗粒物中Ｆｅ,Ｍｎ,Ａｌ,Ｔｉ,Ｂａ和Ｃａ等元素的垂直分布与时间变异研究发现,它们多数与悬浮颗粒物关系密切,沉积物再悬浮作用是控制水体尤其是底层水悬浮颗粒物时空变化的主要因素。     

Dynamic modeling of long-term sedimentation in the Yadkin River basin

Modeling of sediment transport in relation to changing land-surface conditions against a background of considerable natural variability is a challenging area in hydrology. Bayesian dynamic linear models (DLMs) however, offer opportunities to account for non-stationarity in relationships between hydrologic input and basin response variables. Hydrologic data are from a 40 years long record (1951–1990) from the 5905 km² Yadkin River basin in North Carolina, USA. DLM regressions were estimated between log-transformed volume-weighted sediment concentration as a response and log-transformed rainfall erosivity and river flow, respectively, as input variables. A similar regression between log-transformed river flow and log-transformed basin averaged rainfall was also analyzed. The dynamic regression coefficient which reflects the erodibility of the basin decreased significantly between 1951 and 1970, followed by a slowly rising trend. These trends are consistent with observed land-use shifts in the basin. Bayesian DLMs represent a substantial improvement over traditional monotonic trend analysis. Extensions to incorporate multiple regression and seasonality are recommended for future applications in hydrology.     

B) Organisations internationales

Abstract

A modification of Svanidze's fragments method was applied to generate synthetic samples of monthly inflows to the La Angostura and Malpaso dams, located on the Grijalva River, in the state of Chiapas, Mexico. This modification allows generation of the monthly runoff of several dams in a series and preservation of their cross-correlations. Comparing statistics of generated and historical series, a good agreement between them was observed, especially for the autocorrelations between consecutive months and for cross-correlations between both dams, except for slight differences in the standard deviation and the skewness during the dry period.     

基于卡尔曼滤波重建的GPS点位修正

由于单点定位的结果受卫星星历误差、卫星钟误差以及卫星信号传播过程中大气延迟误差的影响较为显著,因此解算出的定位结果在真值附近上下浮动。文中采用ARMA模型建立卡尔曼滤波的观测方程和状态方程,并对定位结果进行滤波;采用一次滤波后的坐标值作为初值,建立ARMA模型并二次滤波。实验表明,滤波有效防止了定位结果偏差过大情况的发生,使滤波收敛值与准确值最大偏差不超过3cm,表明采用一次滤波后的坐标值建立的模型更为合理,从而为单点定位结果的时间序列模型的建立提供一种新方法。