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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.

About 50% of U.S. water pollution problems are caused by non-point source (NPS) pollution, primarily sediment and nutrients from agricultural areas, despite the widespread implementation of agricultural Best Management Practices (BMPs). However, the effectiveness of implementation strategies and type of BMPs at watershed scale are still not well understood. In this study, the Soil and Water Assessment Tool (SWAT) ecohydrological model was used to assess the effectiveness of pollutant mitigation strategies in the Raccoon River watershed (RRW) in west-central Iowa, USA. We analyzed fourteen management scenarios based on systematic combinations of five strategies: fertilizer/manure management, changing row-crop land to perennial grass, vegetative filter strips, cover crops and shallower tile drainage systems, specifically aimed at reducing nitrate and total suspended sediment yields from hotspot areas in the RRW. Moreover, we assessed implications of climate change on management practices, and the impacts of management practices on water availability, row crop yield, and total agricultural production. Our results indicate that sufficient reduction of nitrate load may require either implementation of multiple management practices (38.5% with current setup) or conversion of extensive areas into perennial grass (up to 49.7%) to meet and maintain the drinking water standard. However, climate change may undermine the effectiveness of management practices, especially late in the 21st century, cutting the reduction by up to 65% for nitrate and more for sediment loads. Further, though our approach is targeted, it resulted in a slight decrease (~5%) in watershed average crop yield and hence an overall reduction in total crop production, mainly due to the conversion of row-crop lands to perennial grass. Such yield reductions could be quite spatially heterogeneously distributed (0 to 40%).

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

The increasingly important effect of climate change and extremes on alpine phenology highlights the need to establish accurate monitoring methods to track inter-annual variation (IAV) and long-term trends in plant phenology. We evaluated four different indices of phenological development (two for plant productivity, i.e., green biomass and leaf area index; two for plant greenness, i.e., greenness from visual inspection and from digital images) from a 5-year monitoring of ecosystem phenology, here defined as the seasonal development of the grassland canopy, in a subalpine grassland site (NW Alps). Our aim was to establish an effective observation strategy that enables the detection of shifts in grassland phenology in response to climate trends and meteorological extremes. The seasonal development of the vegetation at this site appears strongly controlled by snowmelt mostly in its first stages and to a lesser extent in the overall development trajectory. All indices were able to detect an anomalous beginning of the growing season in 2011 due to an exceptionally early snowmelt, whereas only some of them revealed a later beginning of the growing season in 2013 due to a late snowmelt. A method is developed to derive the number of samples that maximise the trade-off between sampling effort and accuracy in IAV detection in the context of long-term phenology monitoring programmes. Results show that spring phenology requires a smaller number of samples than autumn phenology to track a given target of IAV. Additionally, productivity indices (leaf area index and green biomass) have a higher sampling requirement than greenness derived from visual estimation and from the analysis of digital images. Of the latter two, the analysis of digital images stands out as the more effective, rapid and objective method to detect IAV in vegetation development.

Abstract In this paper, a multi-scale model is used to assess the multiple mineral precipitation potential in a full-scale anaerobic granular sludge system. Reactor behaviour is analysed under different operational conditions (addition/no addition of reject water from dewatering of lime-stabilized biomass) and periods of time (short/long term). Model predictions suggest that a higher contribution of reject water promotes the risk of intra-granule CaCO3 formation as a result of the increased quantity of calcium arriving with that stream combined with strong pH gradients within the biofilm. The distribution of these precipitates depends on: (i) reactor height; and (ii) granule size. The study also exposes the potential undesirable effects of the long-term addition of reject water (a decrease in energy recovery of 20% over a 100-day period), caused by loss in biomass activity (due to microbial displacement), and the reduced buffer capacity. This demonstrates how both short-term and long-term operational conditions may affect the formation of precipitates within anaerobic granules, and how it may influence methane production and consequently energy recovery.

ABSTRACTThis study investigated the interaction between microbial growth and cell elution, and their influence on resultant microbial distribution between the aqueous and solid phases during solute transport in a sandy, low‐organic‐carbon‐content porous medium. Miscible displacement experiments were conducted with salicylate as the model compound, and with different initial conditions (e.g., substrate concentrations and cell densities) to attain various degrees of microbial growth. For each experiment, salicylate and dissolved oxygen concentrations as well as cell densities were monitored in the column effluent. Cell densities were also measured in the porous medium at the beginning and end of each experiment. Total microbial growth was determined in two ways, one based on a cell mass balance for the system and the other based on total amount of salicylate degraded. For conditions yielding a considerable amount of microbial growth, the majority of the biomass was associated with the aqueous phase (68–90%). Conversely, under minimal‐growth conditions, most cells (approximately 60–70%) were attached to particle surfaces. Significant cell elution was observed for most conditions, the rate of which increased in the presence of the substrate. The results suggest that the increase in aqueous‐phase cells observed for the experiments exhibiting the greatest growth is associated with the production of new cells, and that under appropriate conditions aqueous‐phase biomass can contribute significantly to contaminant biodegradation.

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.

Plants species have been shown to improve the performance of stormwater biofiltration systems, particularly in removal of N and P. Recent research has shown that plants vary in their contribution to pollutant removal but little is known about the type of plant that is best suited to use in biofilters in terms of survival, growth rate, and performance. In this study, growth responses of 20 species to applications of semi-synthetic stormwater were measured, and the roles of key plant traits in removal of N, P, and several metals were investigated. There was no evidence of negative effects of stormwater application on plant growth, and plant traits, particularly root traits, were strongly correlated negatively with N and P concentrations of effluent stormwater. The most common and strong contributors to N and P removal appeared to be the length of the longest root, rooting depth, total root length, and root mass. The plants that made the strongest contribution to pollutant removal, e.g, Carex appressa, combined these traits with high growth rates. Investigation of other plant traits (e.g, physiology), causal mechanisms, and effects of more complex planting environments (e.g, species mixtures) should further guide the selection of plants to enhance performance of biofiltration systems.