scholarly journals External Groundwater Alleviates the Degradation of Closed Lakes in Semi-Arid Regions of China

2019 ◽  
Vol 12 (1) ◽  
pp. 45 ◽  
Author(s):  
Jiaqi Chen ◽  
Jiming Lv ◽  
Ning Li ◽  
Qingwei Wang ◽  
Jian Wang
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Isotopic Signature ◽  
Water Infiltration ◽  
Agricultural Irrigation ◽  
Lake Area ◽  
Daihai Lake ◽  
Huangqihai Lake ◽  
The Impact ◽  
Semi Arid

There are a large number of lakes with beaded distribution in the semi-arid areas of the Inner Mongolian Plateau, and some of them have degraded or even disappeared during the past three decades. We studied the reasons of the disappearance of these lakes by determining the way of replenishment of these lakes and the impact of the natural-social environment of the basin, with the aim of saving these gradually disappearing lakes. Based on remote sensing image and hydrological analysis, this paper studied the recharge of Daihai Lake and Huangqihai Lake. The deep learning method was used to establish the time-series of lake evolution. The same method was combined with the innovative woodland and farmland extraction method to set up the time-series of ground classification composition in the basins. Using relevant survey data, combined with soil water infiltration test, water chemical, and isotopic signature analysis of various water bodies, we found that the Daihai Lake area is the largest in dry season and the smallest in rainy season and the other lake is not satisfied with this phenomenon. In addition, we calculated the specific recharge and consumption of the study basin. These experiments indicated that the exogenous groundwater is recharged directly through the faults at the bottom of Daihai Lake, while the exogenous groundwater is recharged in Huangqihai Lake through rivers indirectly. Large-scale exploitation of groundwater for agricultural irrigation and industrial production is the main cause of lake degradation. Reducing the extraction of groundwater for agricultural irrigation is an important measure to restore lake ecology.

scholarly journals InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3207
Author(s):  
Ioannis Papoutsis ◽  
Charalampos Kontoes ◽  
Stavroula Alatza ◽  
Alexis Apostolakis ◽  
Constantinos Loupasakis
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Large Scale ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Processing Chain ◽  
Persistent Scatterer ◽  
Basin Scale ◽  
Wide Range ◽  
The Impact

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

scholarly journals 501 Years of Spring Precipitation History for the Semi-Arid Northern Iran Derived from Tree-Ring δ18O Data

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 889
Author(s):  
Zeynab Foroozan ◽  
Jussi Grießinger ◽  
Kambiz Pourtahmasi ◽  
Achim Bräuning
Keyword(s):  
Tree Ring ◽  
Large Scale ◽  
Major Influence ◽  
World Knowledge ◽  
Spring Precipitation ◽  
Northern Iran ◽  
Climate Conditions ◽  
Atmospheric Circulation Indices ◽  
The Impact ◽  
Semi Arid

In semi-arid regions of the world, knowledge about the long-term hydroclimate variability is essential to analyze and evaluate the impact of current climate change on ecosystems. We present the first tree-ring δ18O based hydroclimatic reconstruction for northern semi-arid Iran spanning the period 1515–2015. A highly significant correlation between tree-ring δ18O variations of juniper trees and spring (April–June) precipitation reveals a major influence of spring water availability during the early growing season. The driest period of the past 501 years occurred in the 16th century while the 18th century was the wettest, during which the overall highest frequency of wet year events occurred. A gradual decline in spring precipitation is evident from the beginning of the 19th century, pointing to even drier climate conditions. The analysis of dry/wet events indicates that the frequency of years with relatively dry spring increased over the last three centuries, while the number of wet events decreased. Our findings are in accordance with historical Persian disaster records (e.g., the severe droughts of 1870–1872, 1917–1919; severe flooding of 1867, the 1930s, and 1950). Correlation analyses between the reconstruction and different atmospheric circulation indices revealed no significant influence of large-scale drivers on spring precipitation in northern Iran.

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 9 (1) ◽  
pp. 175-214
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale ◽  
The Impact

Abstract. An important aspect to assess the impact of climate change on water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimize the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of naturalised runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behavior of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evaporation and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber also gives good results.

scholarly journals Impact of solar variability on the frequency variability of the Indian summer monsoon

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 67-82
Author(s):  
J. R. KULKARNI ◽  
M. MUJUMDAR ◽  
S. P. GHARGE ◽  
V. SATYAN ◽  
G. B. PANT
Keyword(s):  
Time Series ◽  
Summer Monsoon ◽  
Solar Irradiance ◽  
General Circulation ◽  
Large Scale ◽  
Monsoon Season ◽  
Circulation Model ◽  
Solar Constant ◽  
Good Correspondence ◽  
The Impact

Earlier investigations into the epochal behavior of fluctuations in All India Summer Monsoon Rainfall (AISMR) have indicated the existence of a Low Frequency Mode (LFM) in the 60-70 years range. One of the probable sources of this variability may be due to changes in solar irradiance. To investigate this, time series of 128-year solar irradiance data from 1871-1998 has been examined. The Wavelet Transform (WT) method is applied to extract the LFM from these time series, which show a very good correspondence. A case study has been carried out to test the sensitivity of AISMR to solar irradiance. The General Circulation Model (GCM) of the Center of Ocean-Land-Atmosphere (COLA) has been integrated in the control run (using the climatological value of solar constant i.e., 1365 Wm-2) and in the enhanced solar constant condition (enhanced by 10 Wm-2) for summer monsoon season of 1986. The study shows that the large scale atmospheric circulation over the Indian region, in the enhanced solar constant scenario is favorable to good monsoon activity. A conceptual model for the impact of solar irradiance on the AISMR at LFM is also suggested.

scholarly journals Causal networks of biosphere–atmosphere interactions

2019 ◽  
Author(s):  
Christopher Krich ◽  
Jakob Runge ◽  
Diego G. Miralles ◽  
Mirco Migliavacca ◽  
Oscar Perez-Priego ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Large Scale ◽  
Terrestrial Ecosystem ◽  
Causal Discovery ◽  
Mediterranean Ecosystem ◽  
Interaction Patterns ◽  
Climate Data ◽  
Detection Rates ◽  
The Impact

Abstract. Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models.

scholarly journals Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

2019 ◽  
Vol 11 (14) ◽  
pp. 1665 ◽  
Author(s):  
Tianle He ◽  
Chuanjie Xie ◽  
Qingsheng Liu ◽  
Shiying Guan ◽  
Gaohuan Liu
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Winter Wheat ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Training Sample ◽  
Central China ◽  
Surface Reflectance ◽  
The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

scholarly journals Impacts of Large-Scale Open-Pit Coal Base on the Landscape Ecological Health of Semi-Arid Grasslands

2019 ◽  
Vol 11 (15) ◽  
pp. 1820 ◽  
Author(s):  
Zhenhua Wu ◽  
Shaogang Lei ◽  
Qingqing Lu ◽  
Zhengfu Bian
Keyword(s):  
Large Scale ◽  
Landscape Planning ◽  
Open Pit ◽  
Ecological Health ◽  
Gradual Decline ◽  
Temporal And Spatial ◽  
Arid Grasslands ◽  
Landscape Ecological ◽  
The Impact ◽  
Semi Arid

Coal is an important energy resource in the world, especially in China. Extensive coal exploitation seriously damaged the grassland and its fragile ecosystem. However, temporal and spatial impact laws of open-pit coal exploitation on Landscape Ecological Health (LEH) of semi-arid grasslands are still not clear. Therefore, the main objective of this paper is to study impact of Large-scale Open-pit Coal Base (LOCB) on the LEH of semi-arid grasslands from the perspectives of temporal and spatial. Taking Shengli LOCB of Xilinguole grassland in Inner Mongolia as an example, we demonstrate a conceptual model of LOCB impact on LEH of semi-arid grasslands, and establish a research system called landscape Index-pattern Evolution-Driving force-Spatial statistics (IEDS). A complete process integrated from investigation, monitoring, and evaluation to the analysis of impact laws was developed. Result indicated that coal mining causes gradual increase of landscape patches, landscape fragmentation, gradual decline of landscape connectivity, complexity and irregularity of landscape shape, enhancement of landscape heterogeneity and complexity, gradual decline of landscape stability, gradual decrease of grassland landscape and annual increase of unhealthy grassland landscape. The LEH of grassland basically belongs to the state of slight deterioration. In the past 15 years, the spatial and temporal distribution characteristics of LEH in the study area are similar. This study provides scientific reference for ecological disturbance research, environmental protection, landscape planning, restoration and renovation of ecological environment in mining areas. At the same time, future research should integrate geological, hydrological, soil, vegetation, microorganisms, animals, climate, and other perspectives to study the impact of mining on landscape ecology deeply.

Four years of InSAR time series analysis reveals an unprecedent inventory of active DSGSD in the Western Alps

2021 ◽  
Author(s):  
Pauline André ◽  
Marie-Pierre Doin ◽  
Marguerite Mathey ◽  
Swann Zerathe ◽  
Riccardo Vassallo ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Western Alps ◽  
Spatial Extent ◽  
European Alps ◽  
Series Analysis ◽  
Gravitational Deformation ◽  
Deformation Patterns ◽  
The Impact

<p>Based on geomorphological criteria, large-scale slow gravitational deformation affecting entire mountain flank, often being referred as Deep-Seated Gravitational Slope Deformation (DSGSD), have been shown to affect most of the reliefs worldwide. For instance in the European Alps, these deformation patterns were identified in several areas such as the Aosta Valley (Martinotti et al., 2011) or the Mercantour massif (Jomard, 2006). DSGSD inventories based on visual interpretation of scarps and field mapping were then compiled (e.g. Crosta et al., 2013) revealing the widespread occurrence of DSGSD. However, many aspects of these large-scale gravitational processes remain unclear and in particular their present-day activity and temporal evolution remain largely unknown.</p><p>The present study aims at characterizing the spatial extent of DSGSD, and their velocity, at the scale of Western Alps through InSAR time series analysis using NSBAS processing chain (Doin et al., 2001). We used the whole SAR Sentinel-1 archive, between 2014 and 2018, with an acquisition every 6 days, on an ascending track. The processing was adapted to fit the specific conditions of the Alps (seasonal snow cover, strong local relief, vegetation and strong atmospheric heterogeneities). In particular we implemented a correction using the ERA 5 weather model and we used snow masks in winter allowing to select long temporal baseline interferograms with as little snow as possible. As we specifically aim to study deformation patterns at the scale of valley flanks, an average high-pass filter on moving subwindows has been applied to the interferograms prior to the implementation of time-serie inversions. This step strongly reduced the impact of residual atmospheric delays.</p><p>The resulting velocity map in the line of sight (LOS) of the satellite reveals ubiquitous gravitational deformation patterns over the whole Western Alps, with localized patches of moving slopes showing sharp discontinuities with stable surrounding areas. We used radar geometry and InSAR measurement quality factors as indicators to identify the most trusted areas and to extract an inventory of potential DSGSD with their spatial extent. Doing so, we identified more than two thousands slowly deforming areas characterized by LOS velocities from 4 to 20 mm/year. We then compared the geometries of our “InSAR-detected-deforming-slopes” with previously published DSGSD inventories. Good agreements were found for example in the Aosta valley where most of the deforming areas from our velocity map are falling into the DSGSD outlines of Crosta et al. (2013). Currently, we continue to investigate the potential of this large-scale velocity map for DSGSD understanding and we plan to use artificial intelligence to search for possible generic properties between the detected sites.</p>

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