Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

Acquisition of Rainfall in Ungauged Basins: a Study of Rainfall Distribution Heterogeneity Based on a New Method

High-density precipitation data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involves high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the Proper Orthogonal Decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall data in ungauged basins. (2) Compared with the commonly used Thiessen Polygon method, the Inverse Distance Weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

Sea-ice retreat suggests re-organization of water mass transformation in the Nordic and Barents Seas

Water mass transformation in the Nordic and Barents Seas, triggered by air-sea heat fluxes, is an integral component of the Atlantic Meridional Overturning Circulation (AMOC). These regions are undergoing rapid warming, associated with a retreat in ice cover. Here we present an analysis covering 1950−2020 of the spatiotemporal variability of the air-sea heat fluxes along the region’s boundary currents, where water mass transformation impacts are large. We find there is an increase in the air-sea heat fluxes along these currents that is a function of the currents’ orientation relative to the axis of sea-ice change suggesting enhanced water mass transformation is occurring. Previous work has shown a reduction in heat fluxes in the interior of the Nordic Seas. As a result, a reorganization seems to be underway in where water mass transformation occurs, that needs to be considered when ascertaining how the AMOC will respond to a warming climate.

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

Analysis and Sources Identification of Atmospheric PM10 and Its Cation and Anion Contents in Makkah, Saudi Arabia

In this paper, atmospheric water-soluble cation and anion contents of PM10 are analysed in Makkah, Saudi Arabia. PM10 samples were collected at five sites for a whole year. PM10 concentrations (µg/m3) ranged from 82.11 to 739.61 at Aziziyah, 65.37 to 421.71 at Sanaiyah, 25.20 to 466.60 at Misfalah, 52.56 to 507.23 at Abdeyah, and 40.91 to 471.99 at Askan. Both daily and annual averaged PM10 concentrations exceeded WHO and Saudi Arabia national air quality limits. Daily averaged PM10 concentration exceeded the national air quality limits of 340 µg/m3, 32% of the time at Aziziyah, 8% of the time at Sanaiyah, and 6% of the time at the other three sites. On average, the cations and anions made a 37.81% contribution to the PM10 concentrations. SO42−, NO3−, Ca2+, Na+, and Cl− contributed 50.25%, 16.43%, 12.11%, 11.12%, and 8.70% to the total ion concentrations, respectively. The minor ions (F−, Br−, Mg2+, NO2−, and PO43−) contributed just over 1% to the ion mass. Four principal components explained 89% variations in PM10 concentrations. Four major emission sources were identified: (a) Road traffic, including emission from the exhaust, wear-and-tear, and the resuspension of dust particles (F−, SO42−, NO3−, Ca2+, Na+, Mg+, Br−, Cl−, NO2−, PO43−); (b) Mineral dust (Cl−, F−, Na+, Ca2+, Mg2+, PO43−); (c) Industries and construction–demolition work (F−, SO42−, Ca2+, Mg2+); and (d) Seaspray and marine aerosols (Cl−, Br−, Mg2+, Na+). Future work would include an analysis of the metal contents of PM10 and their spatiotemporal variability in Makkah.

Extreme Weather Events as Risk Factors for Oil and Gas Facilities

The article discusses issues related to the influence of changes in the frequency, intensity and duration of extreme meteorological events on the objects of the oil and gas industry. Climate indices recommended by World Meteorological Organization (WMO) are used as characteristics of the extreme events. The possibilities of using climatic indices for identifying physical risks applicable to the objects of the oil and gas industry are demonstrated. The features of the spatiotemporal variability of climatic indices are considered and the geographical regions of the Commonwealth of Independent States (CIS) are identified as the most and least vulnerable to changes in the extreme characteristics of the climate. The results obtained can be used both at the level of individual enterprises and facilities of the oil and gas industry, and in the development of regulations common for the industry.

Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa

Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa.