• Energy Research

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.

The comprehensive assessment of climatic and hydrological droughts in terms of their temporal and spatial evolutions is very important for water resources management and social development in the basin scale. To study the spatial and temporal changes of climatic and hydrological droughts and the relationships between them, the SPEI and SDI are adopted to assess the changes and the correlations of climatic and hydrological droughts by selecting the Jialing River basin, China as the research area. The SPEI and SDI at different time scales are assessed both at the entire Jialing River basin and at the regional levels of the three sub basins. The results show that the SPEI and SDI are very suitable for assessing the changes and relationships of climatic and hydrological droughts in large basins. Based on the assessment, for the Jialing River basin, climatic and hydrological droughts have the increasing tendency during recent several decades, and the increasing trend of climatic droughts is significant or extremely significant in the western and northern basin, while hydrological drought has a less significant increasing trend. Additionally, climatic and hydrological droughts tend to increase in the next few years. The results also show that on short time scales, climatic droughts have one or two months lag impact on hydrological droughts in the north-west area of the basin, and have one month lag impact in south-east area of the basin. The assessment of climatic and hydrological droughts based on the SPEI and SDI could be very useful for water resources management and climate change adaptation at large basin scale.

Sustainable agriculture in China is threatened by rapid socioeconomic development, urbanization, and climate change. In addition, the distribution of freshwater resources between regions is highly unequal, and water shortages are common in arid regions. The virtual water trade can help to ease water shortages in arid areas by utilizing the comparative advantage of water resources in other areas. However, sometimes the patterns of the virtual water trade do not fit the distribution of water resources and, in these instances, inter-regional coordination would help to improve the level of equality in the virtual water trade. We combined the concept of the Gini coefficient with a multi-objective optimization model to investigate the inter-regional coordination of the virtual water trade in an arid region of China. Agricultural data from different regions of Gansu Province in 2014 were used to explore methods of improving the equality of virtual water flow patterns in the agricultural sector. Three constraints (a crop supply constraint, an irrigation water constraint, and an economic benefit constraint) were set up to investigate the relationship between different challenges and the effects of inter-regional coordination. Our results validated the use of the proposed method in Gansu Province and indicated that it could be applied to other arid regions. Variations in crop supply, irrigation water, and economic benefits were found among the different constraint scenarios, illustrating the trade-offs between water-saving and agricultural objectives. Our results also showed the balance between various factors, including the equality of the virtual water patterns, water-saving measures, and economic benefits. These results support the effectiveness of inter-regional coordination and indicate that the improvement in equality and the adjustment cost should be balanced. Our findings will help to guide the planning of local crop acreages to achieve the best virtual water balance model between regions.

The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) parameters. In order to illustrate the novelty and usefulness of the MGARCH model in energy applications, the conditional covariances and correlation coefficients between the global solar radiation and the meteorological variables among stations were obtained by dynamic conditional correlation (DCC) models. The resulting conditional covariances and correlation coefficients were found to be large. It was also observed that the conditional correlation coefficients between global solar radiation and sunshine duration were higher than that between global solar radiation and temperature difference. The results of this study will provide a better understanding of the associations between global solar radiation and meteorological variables and will provide a basis for the investigation of this relationship in models.

Le réchauffement rapide récent a eu des impacts inégaux sur la composition, la structure et le fonctionnement des écosystèmes nordiques. On ne sait toujours pas comment les facteurs climatiques contrôlent les tendances linéaires et non linéaires de la productivité des écosystèmes. Sur la base d'un produit d'indice de phénologie végétale (IPP) à une résolution spatiale de 0,05° sur 2000-2018, nous avons utilisé un schéma d'ajustement polynomial automatisé pour détecter et caractériser les types de tendances (c.-à-d. tendances polynomiales et non tendances) dans l'IPP annuel intégré (PPIINT) pour les écosystèmes nordiques (> 30°N) et leur dépendance aux facteurs climatiques et aux types d'écosystèmes. La pente moyenne pour les tendances linéaires (p < 0,05) de PPIINT était positive dans tous les écosystèmes, parmi lesquels les forêts de feuillus à feuilles larges et les forêts de feuillus à aiguilles (ENF) ont montré les pentes moyennes les plus élevées et les plus basses, respectivement. Plus de 50% des pixels dans les ENF, les arbustes arctiques et boréaux et les zones humides permanentes (PW) avaient des tendances linéaires. Une grande fraction de PW a également montré des tendances quadratiques et cubiques. Ces tendances concordent bien avec les estimations de la productivité globale de la végétation basées sur la fluorescence de la chlorophylle induite par le soleil. Dans tous les biomes, PPIINT dans les pixels avec des tendances linéaires a montré des valeurs moyennes plus faibles et des coefficients de corrélation partielle plus élevés avec la température ou les précipitations que dans les pixels sans tendances linéaires. Dans l'ensemble, notre étude a révélé l'émergence d'une convergence latitudinale et d'une divergence dans les contrôles climatiques sur les tendances linéaires et non linéaires de PPIINT, ce qui implique que les déplacements nordiques de la végétation et le changement climatique peuvent potentiellement augmenter la nature non linéaire des contrôles climatiques sur la productivité des écosystèmes. Ces résultats peuvent améliorer notre compréhension et notre prévision des changements induits par le climat dans la phénologie et la productivité des plantes et faciliter la gestion durable des écosystèmes en tenant compte de leur résilience et de leur vulnérabilité au changement climatique futur. El rápido calentamiento reciente ha causado impactos desiguales en la composición, estructura y funcionamiento de los ecosistemas del norte. Se desconoce cómo los impulsores climáticos controlan las tendencias lineales y no lineales en la productividad de los ecosistemas. Con base en un producto de índice de fenología vegetal (PPI) a una resolución espacial de 0.05° durante 2000-2018, utilizamos un esquema de ajuste polinómico automatizado para detectar y caracterizar los tipos de tendencias (es decir, tendencias polinómicas y no tendencias) en el PPI integrado anual (PPIINT) para los ecosistemas del norte (> 30°N) y su dependencia de los impulsores climáticos y los tipos de ecosistemas. La pendiente promedio para las tendencias lineales (p < 0.05) de PPIINT fue positiva en todos los ecosistemas, entre los cuales los bosques caducifolios de hoja ancha y los bosques perennifolios de hoja de aguja (ENF) mostraron las pendientes medias más altas y más bajas, respectivamente. Más del 50% de los píxeles en ENF, matorrales árticos y boreales y humedales permanentes (PW) tuvieron tendencias lineales. Una gran fracción de PW también mostró tendencias cuadráticas y cúbicas. Estos patrones de tendencia coincidieron bien con las estimaciones de la productividad global de la vegetación basadas en la fluorescencia de la clorofila inducida por el sol. En todos los biomas, PPIINT en píxeles con tendencias lineales mostró valores medios más bajos y coeficientes de correlación parcial más altos con la temperatura o la precipitación que en píxeles sin tendencias lineales. En general, nuestro estudio reveló la aparición de convergencia latitudinal y divergencia en los controles climáticos sobre las tendencias lineales y no lineales de PPIINT, lo que implica que los cambios septentrionales de la vegetación y el cambio climático pueden aumentar potencialmente la naturaleza no lineal de los controles climáticos sobre la productividad de los ecosistemas. Estos resultados pueden mejorar nuestra comprensión y predicción de los cambios inducidos por el clima en la fenología y la productividad de las plantas y facilitar la gestión sostenible de los ecosistemas al tener en cuenta su resiliencia y vulnerabilidad al cambio climático futuro. Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000-2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change. تسبب الاحترار السريع الأخير في تأثيرات غير متكافئة على تكوين وبنية وأداء النظم الإيكولوجية الشمالية. لا يزال من غير المعروف كيف تتحكم المحركات المناخية في الاتجاهات الخطية وغير الخطية في إنتاجية النظام البيئي. استنادًا إلى منتج مؤشر الفينولوجيا النباتية (PPI) بدقة مكانية تبلغ 0.05درجة خلال الفترة 2000-2018، استخدمنا مخططًا آليًا للتركيب متعدد الحدود لاكتشاف وتمييز أنواع الاتجاهات (أي الاتجاهات متعددة الحدود وعدم وجود اتجاهات) في مؤشر أسعار المنتجين السنوي المتكامل (PPIINT) للنظم الإيكولوجية الشمالية (> 30درجةشمالًا) واعتمادها على الدوافع المناخية وأنواع النظم الإيكولوجية. كان المنحدر المتوسط للاتجاهات الخطية (p < 0.05) لـ PPIINT إيجابيًا في جميع النظم الإيكولوجية، من بينها الغابات المتساقطة ذات الأوراق العريضة والغابات دائمة الخضرة ذات الأوراق الإبرية (ENF) التي أظهرت أعلى وأدنى المنحدرات المتوسطة، على التوالي. كان لأكثر من 50 ٪ من وحدات البكسل في ENF، والشجيرات القطبية والشمالية، والأراضي الرطبة الدائمة (PW) اتجاهات خطية. أظهر جزء كبير من المياه الصالحة للشرب أيضًا اتجاهات تربيعية ومكعبة. اتفقت أنماط الاتجاه هذه بشكل جيد مع تقديرات إنتاجية الغطاء النباتي العالمي بناءً على فلورة الكلوروفيل المستحثة بالطاقة الشمسية. في جميع المناطق الحيوية، أظهر PPIINT بالبكسل مع الاتجاهات الخطية قيمًا متوسطة أقل ومعاملات ارتباط جزئية أعلى مع درجة الحرارة أو هطول الأمطار مقارنة بالبكسل بدون اتجاهات خطية. بشكل عام، كشفت دراستنا عن ظهور تقارب وتباعد خطوط العرض في الضوابط المناخية على الاتجاهات الخطية وغير الخطية لـ PPIINT، مما يعني أن التحولات الشمالية للغطاء النباتي وتغير المناخ قد تزيد من الطبيعة غير الخطية للضوابط المناخية على إنتاجية النظام الإيكولوجي. يمكن أن تحسن هذه النتائج فهمنا وتنبؤنا بالتغيرات الناجمة عن المناخ في علم الظواهر النباتية والإنتاجية وتسهيل الإدارة المستدامة للنظم الإيكولوجية من خلال مراعاة مرونتها وقابليتها للتأثر بتغير المناخ في المستقبل.

Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series.

AbstractThis study focuses on the trends and the causes of variation in actual evapotranspiration (AET) around the warming hiatus over China by a comprehensive analysis applying various temporal–spatial methods. It is observed that the annual AET showed a different trend around 2000 for China as a whole. By employing segmented regression analysis for detecting warming hiatus points, high temporal inconsistency can be found in eight climatic regions of China. The impacts of meteorological variables on AET were further identified by affecting the intensity and relative change of meteorological factors. AET was highly correlated (p < .01) with solar radiation in the southeast (R = 0.80) and air specific humidity in the northwest areas (R = 0.83). AET changes presented the highest sensitivity to specific humidity in Northwest before 2006 and in north central China after 2003, with sensitivity coefficients of 1.48 and 1.74, respectively. Three variables, including air specific humidity (with an average contribution rate of ~17% in the northwest), short‐wave radiation and air temperature, can be the main factors that lead to the changes in AET. The specific meteorological factors varied from region to region: the changes in AET can be ascribed to the increased wind and short‐wave radiation in north central China and east China, the decreased air temperature in Tibetan Plateau, the increased specific humidity in southeast China during warming hiatus, and so on. After the warming hiatus occurred, the dominant factor of AET trends changed from air specific humidity to short‐wave radiation and other factors. Generally, air specific humidity and air temperature have played leading roles in AET trends during the past 30 years.

Simulations of solar radiation have become increasingly common in recent years because of the rapid global development and deployment of solar energy technologies. The effect of air pollution on solar radiation is well known. However, few studies have attempting to evaluate the potential of the air pollution index in estimating solar radiation. In this study, meteorological data, solar radiation, and air pollution index data from three sites having different air pollution index conditions are used to develop random forest models. We propose different random forest models with and without considering air pollution index data, and then compare their respective performance with that of empirical methodologies. In addition, a variable importance approach based on random forest is applied in order to assess input variables. The results show that the performance of random forest models with air pollution index data is better than that of the empirical methodologies, generating 9.1–17.0% lower values of root-mean-square error in a fitted period and 2.0–17.4% lower values of root-mean-square error in a predicted period. Both the comparative results of different random forest models and variance importance indicate that applying air pollution index data is improves estimation of solar radiation. Also, although the air pollution index values varied largely from season to season, the random forest models appear more robust performances in different seasons than different models. The findings can act as a guide in selecting used variables to estimate daily solar radiation and improve the accuracy of solar radiation estimation.