ALBERT

Description: Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution will develop an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach was evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60%, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30% for the first four principal components, respectively. Besides, both the mean absolute errors and mean root mean squared errors of the reconstructed time series by ISSA are also much smaller than those by SSAM. The respective improvements are 34.45 and 33.91% when the missing data accounts for 60%. The results from real incomplete time series also show that the SD derived by ISSA is 12.27 mg L−1, smaller than 13.48 mg L−1 derived by SSAM.

Description: Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution develops an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach is evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60 %, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30 % for the first four principal components, respectively. Both the mean absolute error and mean root mean squared error of the reconstructed time series by ISSA are also smaller than those by SSAM. The respective improvements are 34.45 and 33.91 % when the missing data accounts for 60 %. The results from real incomplete time series also show that the standard deviation (SD) derived by ISSA is 12.27 mg L−1, smaller than the 13.48 mg L−1 derived by SSAM.

Description: To assess global water resources from the perspective of subannual variation in water availability and water use, an integrated water resources model was developed. In a companion report, we presented the global meteorological forcing input used to drive the model and six modules, namely, the land surface hydrology module, the river routing module, the crop growth module, the reservoir operation module, the environmental flow requirement module, and the anthropogenic withdrawal module. Here, we present the results of the model application and global water resources assessments. First, the timing and volume of simulated agriculture water use were examined because agricultural use composes approximately 85% of total consumptive water withdrawal in the world. The estimated crop calendar showed good agreement with earlier reports for wheat, maize, and rice in major countries of production. In major countries, the error in the planting date was ±1 mo, but there were some exceptional cases. The estimated irrigation water withdrawal also showed fair agreement with country statistics, but tended to be underestimated in countries in the Asian monsoon region. The results indicate the validity of the model and the input meteorological forcing because site-specific parameter tuning was not used in the series of simulations. Finally, global water resources were assessed on a subannual basis using a newly devised index. This index located water-stressed regions that were undetected in earlier studies. These regions, which are indicated by a gap in the subannual distribution of water availability and water use, include the Sahel, the Asian monsoon region, and southern Africa. The simulation results show that the reservoir operations of major reservoirs (〉1 km3) and the allocation of environmental flow requirements can alter the population under high water stress by approximately −11% to +5% globally. The integrated model is applicable to assessments of various global environmental projections such as climate change.

Description: Terrestrial vegetation dynamics are closely influenced by both climate and by both climate and by land use and/or land cover change (LULCC) caused by human activities. Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute trends in the fractional green vegetation cover to climate variability and to human activity in Ejina Region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16-day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low but that the mean growing season vegetation cover has increased from 3.4% in 2000 to 4.5% in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We further report that the area of land irrigated each year can be predicted using the runoff gauged 1 year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52% of the total variance in regional vegetation cover from 2000 to 2010. The results demonstrate that it is possible to separate the satellite-observed changes in green vegetation cover into components due to climate and human modifications. Such results inform management on the implications for water allocation between oases in the middle and lower reaches and for water management in the Ejina oasis.

Description: To assess global water availability and use at a subannual timescale, an integrated global water resources model was developed consisting of six modules: land surface hydrology, river routing, crop growth, reservoir operation, environmental flow requirement estimation, and anthropogenic water withdrawal. The model simulates both natural and anthropogenic water flow globally (excluding Antarctica) on a daily basis at a spatial resolution of 1°×1° (longitude and latitude). This first part of the two-feature report describes the six modules and the input meteorological forcing. The input meteorological forcing was provided by the second Global Soil Wetness Project (GSWP2), an international land surface modeling project. Several reported shortcomings of the forcing component were improved. The land surface hydrology module was developed based on a bucket type model that simulates energy and water balance on land surfaces. The crop growth module is a relatively simple model based on concepts of heat unit theory, potential biomass, and a harvest index. In the reservoir operation module, 452 major reservoirs with 〉1 km3 each of storage capacity store and release water according to their own rules of operation. Operating rules were determined for each reservoir by an algorithm that used currently available global data such as reservoir storage capacity, intended purposes, simulated inflow, and water demand in the lower reaches. The environmental flow requirement module was newly developed based on case studies from around the world. Simulated runoff was compared and validated with observation-based global runoff data sets and observed streamflow records at 32 major river gauging stations around the world. Mean annual runoff agreed well with earlier studies at global and continental scales, and in individual basins, the mean bias was less than ±20% in 14 of the 32 river basins and less than ±50% in 24 basins. The error in the peak was less than ±1 mo in 19 of the 27 basins and less than ±2 mo in 25 basins. The performance was similar to the best available precedent studies with closure of energy and water. The input meteorological forcing component and the integrated model provide a framework with which to assess global water resources, with the potential application to investigate the subannual variability in water resources.

Description: An integrated global water resources model was developed consisting of six modules: land surface hydrology, river routing, crop growth, reservoir operation, environmental flow requirement estimation, and anthropogenic water withdrawal. It simulates both natural and anthropogenic water flow globally (excluding Antarctica) on a daily basis at a spatial resolution of 1°×1° (longitude and latitude). The simulation period is 10 years, from 1986 to 1995. This first part of the two-feature report describes the input meteorological forcing and natural hydrological cycle modules of the integrated model, namely the land surface hydrology module and the river routing module. The input meteorological forcing was provided by the second Global Soil Wetness Project (GSWP2), an international land surface modeling project. Several reported shortcomings of the forcing component were improved. The land surface hydrology module was developed based on a bucket type model that simulates energy and water balance on land surfaces. Simulated runoff was compared and validated with observation-based global runoff data sets and observed streamflow records at 32 major river gauging stations around the world. Mean annual runoff agreed well with earlier studies at global, continental, and continental zonal mean scales, indicating the validity of the input meteorological data and land surface hydrology module. In individual basins, the mean bias was less than ±20% in 14 of the 32 river basins and less than ±50% in 24 of the basins. The performance was similar to the best available precedent studies with closure of energy and water. The timing of the peak in streamflow and the shape of monthly hydrographs were well simulated in most of the river basins when large lakes or reservoirs did not affect them. The results indicate that the input meteorological forcing component and the land surface hydrology module provide a framework with which to assess global water resources, with the potential application to investigate the subannual variability in water resources. GSWP2 participants are encouraged to re-run their model using this newly developed meteorological forcing input, which is in identical format to the original GSWP2 forcing input.

Description: Terrestrial vegetation dynamics are closely influenced by both climate change and by direct human activities that modify land use and/or land cover (LULCC). Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute the trend of fractional green vegetation cover to climate change and to human activity in Ejina region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16 day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low, but that the mean growing season vegetation cover has increased from 3.4% in 2000 to 4.5% in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We found that the area of land irrigated each year was mostly dependent on the runoff gauged one year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52% of the total variance in regional vegetation cover from 2000 to 2010.

Description: The North China Plain is one of the most important crop production regions in China. However, water resources in the area are limited. Accurate modeling of water consumption and crop production in response to the changing environment is important. To better describe the two-way interactions among climate, irrigation, and crop growth, the crop phenology and physiology scheme of the SiBcrop model was coupled with the Simple Biosphere model version 2 (SiB2) for simulating crop phenology, as well as the crop production and evapotranspiration of winter wheat and summer maize, two of the main crops in the region. In the coupled model, the Leaf Area Index (LAI) produced by the crop phenology and physiology scheme was used in estimating the sub-hourly energy and carbon fluxes. Observations obtained from two typical eddy covariance sites located in this region were used to validate the model. The coupled model was able to simulate carbon and energy fluxes, soil water content, biomass carbon, and crop yield with high accuracy, especially for the latent heat flux and carbon flux. The LAI was also well-simulated by the model. Therefore, the coupled model is capable of assessing the responses of water resources and crop production to the changes of future climate and irrigation schedules.

Description: Dissolved organic carbon (DOC) and total dissolved amino acids (TDAA) were measured in high (Chukchi Sea) and low (Beaufort Sea) productivity regions of the Western Arctic Ocean to investigate the composition and bioavailability of dissolved organic matter (DOM). Concentrations and DOC-normalized yields of TDAA in Chukchi surface waters were relatively high, indicating an accumulation of bioavailable DOM. High yields of TDAA were also observed in the upper halocline of slope and basin waters, indicating off-shelf transport of bioavailable DOM from the Chukchi Sea. In contrast, concentrations and yields of TDAA in Beaufort surface waters were relatively low, indicting DOM was of limited bioavailability. Yields of TDAA in the upper halocline of slope and basin waters were also low, suggesting the Beaufort is not a major source of bioavailable DOM to slope and basin waters. In shelf waters of both systems, elevated concentrations and yields of TDAA were often observed in waters with higher chlorophyll concentrations and productivity. Surface concentrations of DOC were similar (p 〉 0.05) in the two systems despite the contrasting productivity, but concentrations and yields of TDAA were significantly higher (p 〈 0.0001) in the Chukchi than in the Beaufort. Unlike bulk DOC, TDAA concentrations and yields reflect ecosystem productivity in the Western Arctic. The occurrence of elevated bioavailable DOM concentrations in the Chukchi implies an uncoupling between the biological production and utilization of DOM and has important implications for sustaining heterotrophic microbial growth and diversity in oligotrophic waters of the Central Arctic basins.

Description: Aerosol optical properties were measured with a seven-wavelength aethalometer and a three-wavelength nephelometer at the suburban site SORPES in Nanjing, China, in September 2013–January 2015. The aethalometer compensation parameter k, calculated with the Virkkula et al. (2007) method depended on the backscatter fraction, measured with an independent method, the integrating nephelometer. At λ = 660 nm the daily averaged compensation parameter k ≈ 0.0017 ± 0.0002 and 0.0042 ± 0.0013 when backscatter fraction at λ = 635 nm was in the ranges of 0.100 ± 0.005 and 0.160 ± 0.005, respectively. Also, the wavelength dependency of the compensation parameter depended on the backscatter fraction: when b(λ = 525 nm) was less than approximately 0.13 the compensation parameter decreased with wavelength and at larger b it increased with wavelength. This dependency has not been considered in any of the algorithms that are currently used for processing aethalometer data. The compensation parameter also depended on the single-scattering albedo ω0 so that k decreased with increasing ω0. For the green light (λ = 520 nm) in the ω0 range 0.870 ± 0.005, the average (± standard deviation) k ≈ 0.0047 ± 0.006 and in the ω0 range 0.960 ± 0.005, k ≈ 0.0028 ± 0.0007. This difference was larger for the near-infrared light (λ = 880 nm): in the ω0 range 0.860 ± 0.005, k ≈ 0.0055 ± 0.0023 and in the ω0 range 0.960 ± 0.005, k ≈ 0.0019 ± 0.0011. The negative dependence of k on ω0 was also shown with a simple theoretical analysis.