• Energy Research

Ejin Oasis, located in the lower reaches of the Heihe River Basin (HRB), has experienced severe ecosystem decline between the 1960s and 1990s. In response, the Chinese Government implemented the Ecological Water Diversion Project (EWDP) in 2000. To evaluate the effects of the EWDP, this study monitored changes in land use and vegetation in the Ejin Oasis since 2000 and examined driving factors behind such changes. Results demonstrated that the Ejin Oasis ecosystem generally improved between 2000 and 2011. Water body area significantly increased. Lake area of once dried-up Sogo Nuur increased to 45 km2. Accordingly, vegetation cover restoration has also significantly increased. For example, the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) has shown that 31.18% of the entire study area experienced an increase in vegetation area. On the other hand, even though the EWDP has been successful in driving vegetation recovery and lake restoration, farmland reclamation has counteracted such restoration initiatives. Farmland area almost doubled between 2000 and 2011. Thus, farmland expansion management is necessary for the full restoration of the Ejin Oasis ecosystems as well as HRB sustainable development. The results of this study can provide a reference for the management of the HRB.

The Sustainable Development Goals (SDGs) of the United Nations cover all living things on Earth. However, downscaling the SDGs to regional scales for implementation is challenging. In the paper, we convert the general SDGs into tangible and actionable goals, targets and indicators for use in integrated river basin management (IRBM). Further, we propose a decision support framework that can be used to support IRBM implementation based on the SDGs. The framework offers a context for open thinking in which IRBM decision makers envision socioeconomic and ecosystem goals and the development tracks of a river basin and explore the various paths that can be followed to reach the goals. In particular, indicators are proposed for use in IRBM, which consider five aspects of river basins, specifically water, ecosystems, socioeconomic development, ability and data. To enable decision-making that promotes progress toward the goals, five scenarios, 17 sub-scenarios and 29 key parameters are provided that form a diverse set of scenarios corresponding to specific decision schemes. Moreover, these scenarios, sub-scenarios and parameters consider future uncertainties and both engineering and non-engineering measures that can be taken to achieve the co-development of human and natural factors in a basin.

AbstractThe identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies.

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

Abstract Atmospheric methane (CH4) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007–2017 compared to 2000–2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

L'indice de végétation à différence normalisée (NDVI) est l'indice le plus couramment utilisé pour évaluer la végétation. Cependant, des différences significatives entre divers ensembles de données satellitaires, un terrain complexe et l'impact des nuages sur les capteurs optiques limitent l'évaluation du changement de végétation basée sur NDVI. Pour résoudre ces problèmes, cette étude utilise des données satellitaires multi-sources (GIMMS3g NDVI, CdR AVHRR NDVI, SPOT NDVI et MODIS NDVI) pour surveiller la dynamique de la végétation à différentes échelles de temps de 1990 à 2020 dans la province du Sichuan, en Chine. Les résultats indiquent qu'au fil du temps, les valeurs de l'IVDN des quatre produits de l'IVDN dans la province du Sichuan ont montré une tendance à la hausse. Il existe certaines différences dans la distribution spatiale et l'hétérogénéité spatiale du taux de variation des valeurs NDVI entre les quatre produits NDVI à différentes échelles de temps, et les différences sont principalement concentrées dans le bassin du Sichuan (SB) et la région du plateau alpin occidental du Sichuan (WS). Par rapport aux trois autres produits NDVI, GIMMS NDVI a la valeur la plus élevée mais la plus faible augmentation au cours de la période d'étude. La valeur SPOT NDVI est la plus petite, mais l'augmentation est relativement importante. Cependant, au cours de la période de chevauchement des quatre ensembles de données NDVI, seule la moyenne annuelle de CdR AVHRR NDVI a montré une tendance à la baisse (pente2000-2013 = −0,0001·a−1). La fluctuation annuelle de CdR AVHRR NDVI est la plus faible, et par rapport à d'autres ensembles de données NDVI, sa corrélation avec les facteurs climatiques montre une variabilité spatiale significativement plus faible. De plus, la capacité du CdR AVHRR NDVI à distinguer différents types de couverture végétale est significativement faible (MST = 0,045). Les résultats de cette étude serviront de référence pour d'autres recherches sur les changements de la végétation dans la province du Sichuan et la reconstruction de l'IVDN dans les zones nuageuses. El Índice de Diferencia Normalizada de Vegetación (NDVI) es el más utilizado para evaluar la vegetación. Sin embargo, las diferencias significativas entre varios conjuntos de datos satelitales, el terreno complejo y el impacto de las nubes en los sensores ópticos limitan la evaluación del cambio de vegetación basada en NDVI. Para abordar estos problemas, este estudio utiliza datos satelitales de múltiples fuentes (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI y MODIS NDVI) para monitorear la dinámica de la vegetación en diferentes escalas de tiempo desde 1990 hasta 2020 en la provincia de Sichuan, China. Los resultados indican que a lo largo del tiempo, los valores de NDVI de los cuatro productos de NDVI en la provincia de Sichuan han mostrado una tendencia al alza. Existen ciertas diferencias en la distribución espacial y la heterogeneidad espacial de la tasa de cambio de los valores de NDVI entre los cuatro productos de NDVI en diferentes escalas de tiempo, y las diferencias se concentran principalmente en la cuenca de Sichuan (SB) y la región de la meseta alpina de Sichuan occidental (WS). En comparación con los otros tres productos de NDVI, GIMMS NDVI tiene el valor más alto, pero el aumento más pequeño durante el período de estudio. El valor de NDVI al CONTADO es el más pequeño, pero el aumento es relativamente grande. Sin embargo, dentro del período de superposición de los cuatro conjuntos de datos de NDVI, solo el promedio anual de CDR AVHRR NDVI mostró una tendencia a la baja (pendiente 2000-2013 = −0,0001· a −1). La fluctuación anual de CDR AVHRR NDVI es la más pequeña y, en comparación con otros conjuntos de datos de NDVI, su correlación con los factores climáticos muestra una variabilidad espacial significativamente más débil. Además, la capacidad de CDR AVHRR NDVI para distinguir diferentes tipos de cobertura vegetal es significativamente pobre (Std = 0.045). Los hallazgos de este estudio proporcionarán una referencia para futuras investigaciones sobre los cambios en la vegetación en la provincia de Sichuan y la reconstrucción del NDVI en áreas nubladas. The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi-source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope2000–2013 = −0.0001·a−1). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas. مؤشر الاختلاف الطبيعي للغطاء النباتي (NDVI) هو المؤشر الأكثر استخدامًا لتقييم الغطاء النباتي. ومع ذلك، فإن الاختلافات الكبيرة بين مجموعات البيانات الساتلية المختلفة، والتضاريس المعقدة، وتأثير السحب على أجهزة الاستشعار البصرية تحد من تقييم تغير الغطاء النباتي بناءً على NDVI. لمعالجة هذه القضايا، تستخدم هذه الدراسة بيانات الأقمار الصناعية متعددة المصادر (GIMMS3g NDVI و CDR AVHRR NDVI و SPOT NDVI و MODIS NDVI) لمراقبة ديناميكيات الغطاء النباتي على نطاقات زمنية مختلفة من 1990 إلى 2020 في مقاطعة سيتشوان، الصين. تشير النتائج إلى أنه بمرور الوقت، أظهرت قيم NDVI من منتجات NDVI الأربعة في مقاطعة سيتشوان اتجاهًا تصاعديًا. هناك اختلافات معينة في التوزيع المكاني وعدم التجانس المكاني لمعدل التغير في قيم NDVI بين منتجات NDVI الأربعة على نطاقات زمنية مختلفة، وتتركز الاختلافات بشكل أساسي في حوض سيتشوان (SB) ومنطقة هضبة جبال سيتشوان الغربية (WS). مقارنة بالمنتجات الثلاثة الأخرى لمؤشر NDVI، فإن مؤشر GIMMS لمؤشر NDVI له أعلى قيمة ولكنه أقل زيادة خلال فترة الدراسة. قيمة NDVI الفورية هي الأصغر، لكن الزيادة كبيرة نسبيًا. ومع ذلك، خلال الفترة المتداخلة لمجموعات البيانات الأربعة لمؤشر بيانات التنمية الوطنية، أظهر المتوسط السنوي فقط لمؤشر بيانات التنمية المجتمعية AVHRR NDVI اتجاهًا هبوطيًا (المنحدر 2000-2013 = -0.0001·a-1). يعد التذبذب السنوي لمؤشر التباين المكاني لمؤشر التباين المكاني لمؤشر التباين المكاني لمؤشر التباين المكاني لمؤشر التباين المكاني لمؤشر التباين المكاني هو الأصغر، وبالمقارنة مع مجموعات بيانات مؤشر التباين المكاني الأخرى، فإن ارتباطه بالعوامل المناخية يظهر تقلبًا مكانيًا أضعف بكثير. علاوة على ذلك، فإن قدرة المؤشر الوطني للغطاء النباتي AVHRR على التمييز بين أنواع الغطاء النباتي المختلفة ضعيفة للغاية (STD = 0.045). ستوفر نتائج هذه الدراسة مرجعًا لمزيد من البحث حول تغيرات الغطاء النباتي في مقاطعة سيتشوان وإعادة إعمار NDVI في المناطق الملبدة بالغيوم.

AbstractVegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l'Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.