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Lakes are important water resources in Inner Mongolia and play essential roles in flood storage, water source maintenance, aquaculture, water volume regulation, and the regional ecological balance. However, most lakes in Inner Mongolia have undergone significant shrinkage over the past few decades. In order to quantify the lake changes in Inner Mongolia and analyze the factors associated with these changes, information about 546 lakes in seven years (1990, 1995, 2000, 2005, 2010, 2015, and 2018) was retrieved using 30 m resolution Landsat images taken of the entire region over 29 years (1989–2018). In addition, water census data from 2010 and 1:250,000 geological maps were used as references. The analysis revealed that the lakes in Inner Mongolia exhibited rapidly decreasing trends during the past three decades, with both the area and the number of lakes decreasing to a minimum by 2010. The number of lakes with areas of >1 km2 decreased from 384 in 1990 to 301 in 2018; the total area of lakes with individual areas of >1 km2 decreased from 4905.74 km2 in 1990 to 4187.45 km2 in 2018. With respect to the lake distribution among different geomorphological units, the analysis revealed that the lake shrinkage was most pronounced on the West Liaohe Plain, followed by the northern Inner Mongolian Plateau. Furthermore, in relation to different climatic zones, lake shrinkage primarily occurred in the mid-temperate semi-arid zone, wherein the lake area decreased by 776.6 km2. We hypothesize that the changes in the lake number and area in Inner Mongolia resulted from the combined effects of natural conditions and anthropogenic disturbances; possibly, lake shrinkage was mainly driven by the rising temperature and decreasing precipitation, along with water regulation projects, agricultural irrigation, mining development, and population growth that also had non-negligible effects on the lakes.

La sécheresse agricole est l'un des risques agricoles les plus dommageables dans le monde qui peut entraîner des pertes agricoles importantes et une pénurie d'eau. L'utilisation d'images satellites pour surveiller la sécheresse agricole a fait l'objet d'une attention croissante de la part des chercheurs et a également été appliquée à l'échelle régionale et mondiale. Dans cet article, la température de surface de la terre (LST) et les produits d'éclat du nouveau Sentinel-3A SLSTR (radiomètre de température de surface de la mer et de la terre) lancé par l'Agence spatiale européenne (ESA) sont utilisés pour la première fois pour estimer l'indice de condition de température de la végétation (VTCI), qui à son tour est utilisé pour surveiller la sécheresse agricole dans la plaine de Hetao en Mongolie intérieure, en Chine. Cet article analyse initialement la corrélation entre le LST et l'indice de végétation à différence normalisée (NDVI) en utilisant des séries temporelles MODIS LST et NDVI dans différentes conditions de croissance de la végétation. Les résultats révèlent que VTCI ne peut être utilisé que pendant les saisons chaudes (fin du printemps et période estivale) lorsque des corrélations négatives entre LST et NDVI sont observées. Par conséquent, les images VTCI sont capturées dans la zone d'étude entre juillet et août 2017 à l'aide de Sentinel-3A SLSTR LST et NDVI et sont utilisées pour l'enquête sur la sécheresse. Ces images révèlent que le VTCI moyen des pixels des terres cultivées dans la zone d'étude est passé de 0,4511 le 28 juillet à 0,5229 le 12 août avant de diminuer à 0,4710 le 18 août en raison des précipitations de la première période, indiquant ainsi que VTCI a une réponse opportune aux précipitations. Pendant ce temps, la comparaison croisée des valeurs VTCI de Sentinel-3A SLSTR montre une grande cohérence en termes de distribution spatiale avec celle estimée à partir des produits EOS MODIS. La différence entre ces indices variait de −0,1 à 0,1 pour la plupart des points, en particulier dans la couverture terrestre cultivée. Dans l'ensemble, les résultats appuient l'utilisation des produits LST et NDVI de Sentinel-3A SLSTR dans la surveillance de la sécheresse agricole. La sequía agrícola es uno de los peligros agrícolas más dañinos en todo el mundo que puede provocar importantes pérdidas agrícolas y escasez de agua. El uso de imágenes satelitales para monitorear la sequía agrícola ha recibido cada vez más atención de la investigación y también se ha aplicado a escala regional y mundial. En este documento, la temperatura de la superficie terrestre (LST) y los productos de radiancia del nuevo Sentinel-3A SLSTR (radiómetro de temperatura de la superficie del mar y la tierra) lanzado por la Agencia Espacial Europea (esa) se utilizan por primera vez para estimar el índice de condición de temperatura de la vegetación (VTCI), que a su vez se utiliza para monitorear la sequía agrícola en la llanura de Hetao de Mongolia Interior, China. Este documento analiza inicialmente la correlación entre LST y el índice de vegetación de diferencia normalizada (NDVI) mediante el uso de productos MODIS LST y NDVI de series de tiempo en diferentes condiciones de crecimiento de la vegetación. Los hallazgos revelan que el VTCI solo se puede usar en estaciones cálidas (finales de los períodos de primavera y verano) cuando se observan correlaciones negativas entre LST y NDVI. Por lo tanto, las imágenes de VTCI se capturan en el área de estudio entre julio y agosto de 2017 mediante el uso de Sentinel-3A SLSTR LST y NDVI y se utilizan para la investigación de la sequía. Estas imágenes revelan que el VTCI promedio de los píxeles de tierra cultivada en el área de estudio ha aumentado de 0.4511 el 28 de julio a 0.5229 el 12 de agosto antes de disminuir a 0.4710 el 18 de agosto debido a las precipitaciones en el primer período, lo que indica que el VTCI tiene una respuesta oportuna a las precipitaciones. Mientras tanto, la comparación cruzada de los valores de VTCI de Sentinel-3A SLSTR muestra una alta consistencia en términos de distribución espacial con la estimada de los productos EOS MODIS. La diferencia entre estos índices osciló entre -0,1 y 0,1 para la mayoría de los puntos, especialmente en la cubierta vegetal cultivada. En general, los hallazgos respaldan el uso de los productos LST y NDVI de Sentinel-3A SLSTR en el monitoreo de la sequía agrícola. Agricultural drought is one of most damaging agricultural hazards worldwide that can bring significant agricultural losses and water scarcity. The use of satellite images for monitoring agricultural drought has received increasing research attention and has also been applied at both the regional and global scales. In this paper, the land surface temperature (LST) and radiance products of the new Sentinel-3A SLSTR (sea and land surface temperature radiometer) launched by European Space Agency (ESA) are used for the first time for estimating the vegetation temperature condition index (VTCI), which in turn is used for monitoring agricultural drought in the Hetao Plain of Inner Mongolia, China. This paper initially analyzes the correlation between LST and normalized difference vegetation index (NDVI) by using time series time MODIS LST and NDVI products under different vegetation growth conditions. The findings reveal that VTCI can only be used in warm seasons (late spring and summer periods) when negative correlations between LST and NDVI are observed. Therefore, VTCI images are captured in the study area between July and August 2017 by using Sentinel-3A SLSTR LST and NDVI and are utilized for drought investigation. These images reveal that the average VTCI of the cultivated land pixels in the study area has increased from 0.4511 on July 28 to 0.5229 on August 12 before declining to 0.4710 on August 18 due to the rainfall in the first period, thereby indicating that VTCI has a timely response to rainfall. Meanwhile, cross-comparison of VTCI values from Sentinel-3A SLSTR shows high consistency in terms of spatial distribution with that estimated from EOS MODIS products. The difference between these indices ranged from −0.1 to 0.1 for most points, especially in the cultivated land cover. Overall, the findings support the use of the LST and NDVI products of Sentinel-3A SLSTR in monitoring agricultural drought. يعد الجفاف الزراعي أحد أكثر المخاطر الزراعية ضررًا في جميع أنحاء العالم والذي يمكن أن يؤدي إلى خسائر زراعية كبيرة وندرة المياه. حظي استخدام صور الأقمار الصناعية لرصد الجفاف الزراعي باهتمام بحثي متزايد وتم تطبيقه أيضًا على المستويين الإقليمي والعالمي. في هذه الورقة، يتم استخدام درجة حرارة سطح الأرض (LST) ومنتجات الإشعاع الخاصة بالمقياس الإشعاعي الجديد Sentinel -3A SLSTR (مقياس درجة حرارة سطح البحر والأرض) الذي أطلقته وكالة الفضاء الأوروبية (ESA) لأول مرة لتقدير مؤشر حالة درجة حرارة الغطاء النباتي (VTCI)، والذي يستخدم بدوره لرصد الجفاف الزراعي في سهل هيتاو في منغوليا الداخلية، الصين. تحلل هذه الورقة في البداية العلاقة بين LST ومؤشر الاختلاف الطبيعي للغطاء النباتي (NDVI) باستخدام السلاسل الزمنية لمنتجات MODIS LST و NDVI في ظل ظروف نمو مختلفة للغطاء النباتي. تكشف النتائج أنه لا يمكن استخدام VTCI إلا في المواسم الدافئة (أواخر فصلي الربيع والصيف) عند ملاحظة الارتباطات السلبية بين LST و NDVI. لذلك، يتم التقاط صور VTCI في منطقة الدراسة بين يوليو وأغسطس 2017 باستخدام Sentinel -3A SLSTR LST و NDVI وتستخدم للتحقيق في الجفاف. تكشف هذه الصور أن متوسط VTCI لبكسلات الأراضي المزروعة في منطقة الدراسة قد ارتفع من 0.4511 في 28 يوليو إلى 0.5229 في 12 أغسطس قبل أن ينخفض إلى 0.4710 في 18 أغسطس بسبب هطول الأمطار في الفترة الأولى، مما يشير إلى أن VTCI لديها استجابة في الوقت المناسب لهطول الأمطار. وفي الوقت نفسه، تُظهر المقارنة المتقاطعة لقيم VTCI من Sentinel -3A SLSTR اتساقًا عاليًا من حيث التوزيع المكاني مع تلك المقدرة من منتجات EOS MODIS. تراوح الفرق بين هذه المؤشرات من -0.1 إلى 0.1 لمعظم النقاط، خاصة في الغطاء الأرضي المزروع. بشكل عام، تدعم النتائج استخدام منتجات LST و NDVI من Sentinel -3A SLSTR في مراقبة الجفاف الزراعي.

The use of permeable block pavement has been acknowledged as one of the promising Low Impact Development (LID) strategies to mitigate the harmful effects of depletion of natural surfaces, due to the uncontrollable development of infrastructure and buildings. Numerous studies, associated with drainage properties and long-term performance of this traditional pavement alternative, have been conducted in the past 30 years. Nevertheless, standardized equipment and methodologies are still limited, specifically for small-scale laboratory models. This paper suggests equipment that is capable of evaluating the hydraulic performance of permeable pavement materials in a laboratory set-up, by monitoring permeability and simulating the physical clogging process. Constant head permeability tests with systematic application of fine clogging particles were conducted on three identical permeable block systems (PBS), composed of four stone pavers. Each test system received an equivalent amount of eight years’ particle loading of silica sand, with different size distributions. The experimental results revealed that all the models showed permeability degradation trends similar to those presented in other literature.

Transverse and longitudinal dispersion in gravity stable, favourable viscosity ratio flows are investigated and compared with earlier data obtained for miscible fluids and for tracer flow. Data from laboratory measurements of longitudinal dispersion in low viscosity ratio (8.63×10(-)(4)) and high density contrast (471 kg m(-3)) displacements are compared with literature data for more modest viscosity ratios and density differences and with earlier theoretical analysis. The longitudinal dispersivity was reduced by a factor of 2 for flows influenced by gravity. This reduction was relatively insensitive to the magnitude of the density contrast and the flow rate, for Peclet numbers less than 100 and found to be consistent with earlier theoretical predictions. Additional transverse dispersion data was obtained for fluids with a density contrast of 225 kg m(-3) and a matched viscosity ratio over a range of Peclet numbers (1

According to the concerted efforts to reduce global greenhouse gas emissions (GHGE), the Information and Communication Industry (ICT) has received little attention as a significant contributor to GHG. ICT has a very significant role to play in reducing greenhouse gas emissions in Canada. While climate challenges increase, there is a growing need to reduce Greenhouse gas emissions. The ICT sector has an important role in enabling significant reductions in those emissions and costs. This current study aims to examine the effects of GHGE on climate change in Canada with a focus on the ICT sector. This paper will provide a review of ICT definitions and Greenhouse gases and how GHGE can be reduced. This paper presents an approach to investigating the impact of information and communication technologies (ICTs) on Greenhouse gases emission and its effect on climate change in Canada.

<p>La disponibilidad del recurso hídrico es de vital importancia para la planificación hidrológica, este depende de múltiples causas como la variabilidad climática y la cobertura vegetal. La precipitación, temperatura y evapotranspiración han sufrido cambios en diferentes partes de España. Para la cuenca del río Júcar además de otras cuencas en España se ha producido un significativo cambio en el valor promedio de las aportaciones hidrológicas desde 1980. El presente estudio se centra en analizar cómo han afectado los cambios en las variables climáticas a las aportaciones hidrológicas y en qué grado explican las reducciones registradas en las mismas. Adicionalmente se considera la posibilidad de que otros factores como cambios en la cobertura vegetal también puedan haber influido en la reducción de escorrentía. Para modelar la hidrología de la cuenca del Júcar se utilizó el modelo lluviaescorrentía HBV. El modelo fue calibrado para periodo anterior a la fecha de cambio considerada (1950-1979) y se simuló y adaptó para los años posteriores a 1980 (1980-2007) ajustando parámetros que puedan ser explicados por cambios en las condiciones de la cuenca. La zona más susceptible es la cabecera del Júcar donde se genera la mayor cantidad del recurso hídrico y se presenta la mayor disminución en las aportaciones. En la cuenca media del Júcar se presenta una menor disminución en las aportaciones, sin embargo en la cuenca baja del Júcar se registra un incremento en las aportaciones. Los cambios en las variables hidrológicas (precipitación y temperatura) explican una parte de los cambios en las aportaciones. Otras causas posibles como los cambios en la cobertura vegetal pueden completar la explicación del cambio en las aportaciones. Para probarlo se ha recalibrado el modelo hidrológico alterando solo uno o dos parámetros relacionados con esta condición.</p>

Spatial data on Low-and-Middle-Income-Country (LMIC) cities, and deprived areas within cities, are often not readily available in support of local and global information needs. To address this information gap, we propose the systematic semi-automated SLUMAP framework that provides policy-relevant information on deprived urban areas in Sub-Saharan Africa (SSA), based on free open-source software (FOSS). First, we assess user needs for spatial information on deprivation (ranging from local communities to global research and policy support). Second, we show how free or low-cost image datasets can be used for mapping the location of deprived areas at the city scale and providing an overall assessment of their spatial patterns. This is implemented as a grid-based approach using machine learning and assessing the contribution of a large number of spectral and spatial features derived from open or low-cost imagery. Third, we show how higher (spatial and spectral) resolution data can provide a detailed characterization of such areas, with a GEOBIA/machine-learning workflow and deep learning techniques. We illustrate the experiments and results on the city of Nairobi (Kenya)and discuss transferability to SSA cities.

Regulatory inspection and violation reports provide insight into the impact of natural gas extraction on the surrounding environment, human health, and public safety. Inspection reports for natural gas wells in Pennsylvania were collected from the Pennsylvania DEP Compliance Report from 2000 to 2014. Analysis of 215,444 inspection records for 70,043 conventional and unconventional wells was conducted in order to compare the odds of violations occurring under different circumstances. Logistic regression models were used to estimate the probability of violations occurring for both conventional and unconventional wells. When inspected, conventional wells had 40% higher odds of having a violation. However, unconventional wells had higher odds for environmental violations related to waste discharge as well as cementing and casing failures. Large operators had 40% lower odds of having any violation than smaller operators. While larger operators had fewer violations, a few of the largest companies had rates of violation much higher than the average for all operators, with some reaching violation rates as high as 1 in 4 active wells. A well also has a higher chance of being in violation if it is in the first year (85%) or second year (109%) since its spud date.

The StormTac Web model, representing a low-complexity conceptual model (LCCM), was applied to two urban catchments featuring stormwater quality controls, a stormwater pond or a biofilter. The model calculates annual average runoff from annual precipitation and land-use specific volumetric runoff coefficients and baseflows (in storm sewers), which are multiplied by the corresponding mean stormwater quality constituent concentrations obtained from the recently upgraded StormTac Database, to yield constituent loads. The resulting runoff loads pass through the stormwater quality control facilities (a stormwater pond or a biofilter) where treatment takes place and its efficacy is described by “reduction efficiencies”. For the four selected stormwater quality constituents (TP, Cu, Zn, TSS) and two study catchments, a 201-ha residential Ladbrodammen and an 8.2-ha Sundsvall traffic corridor, the compositions of stormwater entering and leaving the control facilities were calculated by StormTac Web and compared against the measured data. In general, the calculated concentrations were smaller than the measured ones, and these differences were reduced, but not eliminated in all cases, by considering uncertainties in both calculated and measured data. Uncertainties in calculated values consisted of two components, a flow component (assumed as 20%) and a concentration component, which was assumed equal to the relative standard error (RSE) of the data in the StormTac Database. Explanations of differences in calculated and measured stormwater data were discussed with respect to temporal changes and trends in environmental practices and stormwater quality monitoring and enhancement by treatment.

Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.