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We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.

The basins of the Indus and Ganges rivers cover 2.20 million km2 and are inhabited by more than a billion people. The region is under extreme pressures of population and poverty, unregulated utilization of the resources and low levels of productivity. The needs are: (1) development policies that are regionally differentiated to ensure resource sustainability and high productivity; (2) immediate development and implementation of policies for sound groundwater management and energy use; (3) improvement of the fragile food security and to broaden its base; and (4) policy changes to address land fragmentation and improved infrastructure. Meeting these needs will help to improve productivity, reduce rural poverty and improve overall human development.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Freshwater scarcity is a significant concern due to climate change in some regions of Brazil; likewise, evaporation rates have increased over the years. Floating photovoltaic systems can reduce water evaporation from reservoirs by suppressing the evaporating area on the water surface. This work evaluated the effects of floating photovoltaic systems on water evaporation rates in the Passaúna Reservoir, southeastern Brazil. Meteorological data such as temperature, humidity, wind speed, and solar radiation were used to estimate the rate of water evaporation using FAO Penman–Monteith, Linacre, Hargreaves–Samani, Rohwer, and Valiantzas methods. The methods were tested with the Kruskal–Wallis test, including measured evaporation from the nearest meteorological station to determine whether there were significant differences between the medians of the methods considering a 95% confidence level for hypothesis testing. All methods differed from the standard method recommended by the FAO Penman–Monteith. Simulations with more extensive coverage areas of the floating photovoltaic system were carried out to verify the relationship between the surface water coverage area and the evaporation reduction efficiency provided by the system and to obtain the avoided water evaporation volume. For the floating photovoltaic system with a coverage area of 1265.14 m2, an efficiency of 60.20% was obtained in reducing water evaporation; future expansions of the FPS were simulated with coverage areas corresponding to energy production capacities of 1 MWp, 2.5 MWp, and 5 MWp. The results indicated that for a floating photovoltaic system coverage area corresponding to 5 MWp of energy production capacity, the saved water volume would be enough to supply over 196 people for a year. More significant areas, such as covering up the entire available surface area of the Passaúna reservoir with a floating photovoltaic system, could save up to 2.69 hm3 of water volume annually, representing a more significant value for the public management of water resources.

To support their Integrated Water Supply Grid, Wessex Water recognized the need for a sophisticated control system. The Servelec Technologies pump optimization system OptiMISER was identified as the best tool for this function and has been in use in the Control Room since September 2014. Since then it has been controlling pumps and valves across the Warminster area, ensuring pro-active optimal management of that part of the network, as a precursor to full implementation over the Grid.

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.

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