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In order to explore the trends in sunshine duration in the low-latitude belt of South China, long-term sunshine duration, total clouds, low clouds, and visibility were investigated for the period 1961-2005. The results show significant declines in sunshine duration, occurring on the average of −3.2% and −2.8% per decade under all-sky and clear-sky conditions, respectively. It is noted that increased air aerosol loading due to rapid socio-economic development is the drive behind the sharp declines in sunshine duration in the study area. Though cloud is the primary regulator of sunshine duration under all-sky conditions, sunshine is strongly correlated with visibility under clear-sky conditions. Relational analysis between sunshine and cloud amount suggests 0.4% change in clouds per decade, which is well in agreement with the trend of decline in sunshine duration under all-sky conditions in the study area. Increasing low-cloud opacity could be the primary factor driving the decline in sunshine duration in the low-latitude belt of South China. [ABSTRACT FROM AUTHOR]

The seasonal patterns of total cloud amount (TCA) responsible for El Niño/La Niña-Southern Oscillation (ENSO) Sea Surface Temperature (SST) anomalies were investigated using the ISCCP-D2 cloud and NOAA OI.v2 SST datasets for the period of July 1983 to June 2008. The results show three main ENSO-sensitive regions obtained by spatial overlapping of seasonal correlations, two in the western tropical Pacific and one in the central tropical Pacific. These regions were named WTP1, WTP2, and CTP. In all three regions, except the JJA (June-August) WTP2, the TCA changes were significantly correlated with the Niño 3.4 anomalies during the four seasons (December-January-February (DJF), March-April-May, JJA, and September-October-November (SON)). Remarkable differences in the seasonal variability of TCA were observed in these regions. In the WTP1, the DJF TCA always remained the highest value among the four seasons in all years. In the WTP2, the maximum TCA occurred during JJA in most years. In the CTP, the extreme value of TCA was mainly observed in DJF or SON near the peak time of ENSO. Seasonal cross-correlation analyses also showed significant relations between TCA and Niño 3.4 SST in these regions, which may be helpful for forecasting the evolution of ENSO. [ABSTRACT FROM AUTHOR]

• Sediment connectivity is influenced by vegetation, slope and watersheds area. • Anticlockwise loop is the most frequent hysteresis type and transport the most sediment. • Indices of connectivity showed a high correlation with sediment parameters and hysteresis index. Sediment connectivity is an important property influencing landscape evolution. Understanding the link between sediment connectivity and hydrologic processes plays an important role in developing strategies for soil and water conservation measures. Based on 10 years of observations for 30 hydrologically monitored watersheds on the Loess Plateau, measurements for 926 hydrologic events were analyzed and the index of connectivity (IC) was calculated. The high-frequency runoff and sediment data were analyzed, and a normalized hysteresis index (HI) was calculated to assess the dominant hysteresis patterns for each watershed. Correlation analysis was conducted to determine the relationship between IC and HI. The results showed that the watersheds with low IC values were located in regions with dense vegetation and flat terrain and were dominated mainly by a clockwise hysteresis pattern, while the watersheds with high IC values were located in regions with sparse vegetation and steep terrain and were dominated by an anticlockwise hysteresis pattern. The anticlockwise hysteresis pattern was the most frequent and the most efficient type in terms of sediment transport among the 30 watersheds. The anticlockwise hysteresis type was more likely to occur as the IC values increased. A significant negative correlation was observed between the IC values and HI values during small and medium hydrologic events. The link is correlated with the notion that the separation into two components in connectivity indexes are similar to the two sediment sources controlling the hysteresis type. The outcomes from this study are helpful for determining the relationship between sediment connectivity and hydrologic behavior and thus for providing insight into the hydrologic processes that are essential to facilitate sustainable watershed management. [ABSTRACT FROM AUTHOR]

The fragile ecosystem in the Yellow River Basin (YRB) is sensitive to climatic changes, and previous studies have mainly focused on exploring the spatiotemporal relationship between vegetation growth and climatic change based on their spatially or temporally averaged values. However, few studies unraveled the effect of climatic changes on vegetation growth from both spatial and temporal variations separately. In the study, empirical orthogonal function (EOF), singular value decomposition (SVD), and trend analysis were used to detect the spatiotemporal variation of net primary productivity (NPP) over 2000-2019 and to analyze its response to climatic factors from spatial and temporal aspects, respectively, in the YRB. The results showed that the distribution of NPP decreased from southeast to northwest. The areas of NPP with significantly improved and degraded accounted for 80.41% and 1.3%, respectively. The NPP had greatly increased after 2012, particularly in the central region, but decreased in the eastern region. Precipitation was the dominant factor influencing NPP growth, especially in the arid and semi-arid regions. Additionally, the characterization of variability in farmland and forest land by EOF was better than SVD. This study provides insights into the relationship between vegetation growth and climatic changes at a watershed scale. [ABSTRACT FROM AUTHOR]

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50% of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66% of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In a forested country, such as Finland, over 50 % of its current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high spatial resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. Coarse spatial resolution estimates of the return levels of maximum wind speed based, e.g., on reanalysed meteorological data or climate scenarios can be downscaled to forest stand levels with the help of land cover and terrain elevation data. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data. These data were downscaled to 26 meteorological station locations to represent very diverse environments: Open Baltic Sea islands, agricultural land, forested areas, and Northern Finland treeless fells. Applying a comparison, the downscaled 10-year return levels explained 77 % of the observed variation among the stations examined. In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsP- model simulated wind. The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences. However, for areas representing a typical Finnish forested landscape with no major topographic variation, both of the methods produced very similar results. Further fine-tuning of wind multipliers could improve the downscaling for the locations with large topographic variation. However, the current results already indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]