Estimation of the Surface Net Radiation Under Clear-Sky Conditions in Areas With Complex Terrain: A Case Study in Haihe River Basin

The surface net radiation as an important component of the surface radiation budget has attracted wide attention; however, it is still an enormous challenge to carry out an accurate estimation of the surface net radiation in areas with complex terrain due to the scarcity of radiation observation sites and high-spatial heterogeneity of the influencing factors of the surface net radiation. Taking the Haihe River Basin as the study area, this study estimated the surface net radiation under clear-sky conditions from 2001∼2019 based on an improved algorithm of the net long-wave radiation, and the solar short-wave radiation in terms of direct radiation, diffuse sky radiation, and reflected radiation from the surrounding terrain. In this study, the regional meteorological factors were inverted based on remote sensing data to make up for the deficiency of meteorological factor interpolation. The solar short-wave radiation was corrected by considering the comprehensive influence of the atmosphere, underlying surface, and terrain, and the net long-wave radiation was optimized by localizing the algorithm coefficients. The results showed the correlation coefficient between the estimated and observed surface net radiation reached approximately 0.9, indicating the accuracy of this improved method is acceptable. Besides, the results suggested the surface net radiation was significantly influenced by the terrain, the highest value of which occurred on the south slope, followed by that on the southwest slope, west or southeast slopes, and the lowest value occurred on the north slope. In addition, there was the highest surface net radiation in summer, and there was the lowest and most frequently negative surface net radiation in winter. This study makes up for the shortcomings of the traditional spatial interpolation of meteorological factors and previous empirical formulas, and can therefore provide an important methodological foundation for the research on the surface radiation, climate, and hydrology in the areas with complex terrain.

Merging High-Resolution Satellite Surface Radiation Data with Meteorological Sunshine Duration Observations over China from 1983 to 2017

Surface solar radiation (Rs) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR), the recent release of International Satellite Cloud Climatology Project (ISCCP) HXG cloud products provide a promising opportunity for building long-term Rs data with high resolutions (3 h and 10 km). In this study, we compare three satellite Rs products based on AVHRR cloud products over China from 1983 to 2017 with direct observations of Rs and sunshine duration (SunDu)-derived Rs. The results show that SunDu-derived Rs have higher accuracy than the direct observed Rs at time scales of a month or longer by comparing with the satellite Rs products. SunDu-derived Rs is available from the 1960s at more than 2000 stations over China, which provides reliable decadal estimations of Rs. However, the three AVHRR-based satellite Rs products have significant biases in quantifying the trend of Rs from 1983 to 2016 (−4.28 W/m2/decade to 2.56 W/m2/decade) due to inhomogeneity in satellite cloud products and the lack of information on atmospheric aerosol optical depth. To adjust the inhomogeneity of the satellite Rs products, we propose a geographically weighted regression fusion method (HGWR) to merge ISCCP-HXG Rs with SunDu-derived Rs. The merged Rs product over China from 1983 to 2017 with a spatial resolution of 10 km produces nearly the same trend as that of the SunDu-derived Rs. This study makes a first attempt to adjust the inhomogeneity of satellite Rs products and provides the merged high-resolution Rs product from 1983 to 2017 over China, which can be downloaded freely.

The accuracy improvement of clear-sky surface shortwave radiation derived from CERES SSF dataset with a simulation analysis

Towards the Xiaotang region along the northern margin of the China's largest desert, a quantitative assessment of the precision of clear-sky satellite observations (the Single Scanner Footprint TOA/Surface Fluxes and Clouds downward surface shortwave radiation product of Clouds and the Earth's Radiant Energy System (CERES), DSSR_CER) is conducted, the localized inversion mode of "absolutely clear-sky" downward surface shortwave radiation (DSSR) is established, and the "absolutely clear-sky" DSSR in Xiaotang during 2005-2018 is simulated by the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. In general, under the "absolutely clear-sky" condition of Xiaotang region, there is a significant error in DSSR_CER, and the simulated results of SBDART (DSSR_SBD) with same input parameters as DSSR_CER is better and more comparable. Single scattering albedo (SSA), asymmetry parameter (ASY) and aerosol optical depth (AOD) play crucial roles in deciding the accuracy of DSSR, and after parameter adjustment, the DSSR_SBD is better than the initial, which is improved remarkably with all indexes of the fitting results greatly improved. The temporal variation of the DSSR during 2005-2018 indicates that the highest annual average value is found in 2008 (770.00 W·m^-2), while the lowest appears in 2010 (600.97 W·m^-2). Besides, the highest seasonal mean DSSR appears in summer, which between 860.6 and 935.07 W·m^-2, while reaches the lowest in winter (403.79-587.53 W·m^-2). Moreover, the monthly average DSSR changes as a curve with a single peak and is close to normal distribution, the highest appears in June (934.61 W·m^-2), while the minimum with the value of 390.34 W·m^-2 is found in December. All of the solar elevation angle, the characteristics of climate and aerosol particles in different seasons may contribute to the temporal variation.

Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method

Incident surface shortwave radiation (ISR) is a key parameter in Earth’s surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization; (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values; and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m2 and 66.80 W/m2, respectively. The corresponding daily RMSE values were 27.78 W/m2 and 23.51 W/m2. The RMSE at BSRN sites was 105.87 W/m2 for instantaneous ISR and 32.76 W/m2 for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.

Intercomparison of Satellite-Derived Solar Irradiance from the GEO-KOMSAT-2A and HIMAWARI-8/9 Satellites by the Evaluation with Ground Observations

Solar irradiance derived from satellite imagery is useful for solar resource assessment, as well as climate change research without spatial limitation. The University of Arizona Solar Irradiance Based on Satellite–Korea Institute of Energy Research (UASIBS-KIER) model has been updated to version 2.0 in order to employ the satellite imagery produced by the new satellite platform, GK-2A, launched on 5 December 2018. The satellite-derived solar irradiance from UASIBS-KIER model version 2.0 is evaluated against the two ground observations in Korea at instantaneous, hourly, and daily time scales in comparison with the previous version of UASIBS-KIER model that was optimized for the COMS satellite. The root mean square error of the UASIBS-KIER model version 2.0, normalized for clear-sky solar irradiance, ranges from 4.8% to 5.3% at the instantaneous timescale when the sky is clear. For cloudy skies, the relative root mean square error values are 14.5% and 15.9% at the stations located in Korea and Japan, respectively. The model performance was improved when the UASIBS-KIER model version 2.0 was used for the derivation of solar irradiance due to the finer spatial resolution. The daily aggregates from the proposed model are proven to be the most reliable estimates, with 0.5 km resolution, compared with the solar irradiance derived by the other models. Therefore, the solar resource map built by major outputs from the UASIBS-KIER model is appropriate for solar resource assessment.

Evaluation of Reanalysis Surface Incident Solar Radiation Data in China

Surface incident solar radiation (R_s) of reanalysis products is widely used in ecological conservation, agricultural production, civil engineering and various solar energy applications. It is of great importance to have a good knowledge of the uncertainty of reanalysis R_s products. In this study, we evaluated the R_s estimates from two representative global reanalysis (ERA-Interim and MERRA-2) using quality- controlled surface measurements from China Meteorological Administration (CMA) and Multi-layer Simulation and Data Assimilation Center of the Tibetan Plateau (DAM) from 2000 to 2009. Error causes are further analyzed in combination radiation products from the Earth's Radiant Energy System (CERES) EBAF through time series estimation, hotspot selection and Geodetector methods. Both the ERA-Interim and MERRA-2 products overestimate the R_s in China, and the MERRA-2 overestimation is more pronounced. The errors of the ERA-Interim are greater in spring and winter, while that of the MERRA-2 are almost the same in all seasons. As more quality-controlled measurements were used for validation, the conclusions seem more reliable, thereby providing scientific reference for rational use of these datasets. It was also found that the main causes of errors are the cloud coverage in the southeast coastal area, aerosol optical depth (AOD) and water vapor content in the Sichuan Basin, and cloud coverage and AOD in the northeast and middle east of China.

Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study.

A New Method to Estimate Reference Crop Evapotranspiration from Geostationary Satellite Imagery: Practical Considerations

Reference crop evapotranspiration (ETo) plays a role in irrigation advisory being of crucial importance for water managers dealing with scarce water resources. Following the ETo definition, it can be shown that total solar radiation is the main driver, allowing ETo estimates from satellite observations. As such, the EUMETSAT LSA-SAF operationally provides ETo primarily derived from the European geostationary satellite MSG. ETo estimations following the original FAO report require several meteorological observations gathered over actual well-watered grass. Here we will consider the impact of two effects on ETo using the LSA-SAF and FAO methodologies: (i) local advection, related to the impact of advection of surrounding warm dry air onto the reference non-water stressed surface; and (ii) the so-called surface aridity error, which occurs when calculating ETo according to FAO, but with input data not collected over well-watered grass. The LSA-SAF ETo is not sensitive to any of these effects. However, it is shown that local advection may increase evapotranspiration over a limited field by up to 30%, while ignoring aridity effects leads to a great overestimation. The practical application of satellite estimates of ETo provided by the LSA-SAF are discussed here, and, furthermore, water managers are encouraged to consider its advantages and ways for improvement.

Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products

Assessment of Land Surface Models (LSMs) at heterogeneous terrain and climate regimes is essential for understanding complex hydrological and biophysical parameterization. This study utilized the two LSMs, Community Land Model (CLM 4.0) and three layer Variable Infiltration Capacity (VIC-3L), to estimate the interaction between land surface and atmosphere by means of energy fluxes including net radiation (R_N), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G). The modeled energy fluxes were analyzed at two sites: Freeman Ranch-2 (FR2) located in the lowland region of Texas (272m), and Providence 301 (P301) located on the mountains of Sierra Nevada in California (2015m) from 2003 to 2013. R_N was underestimated by CLM with bias -25.06Wm^-2 due to its snow hydrology scheme at P301. LE was overestimated by the VIC during summer precipitation and had a positive bias of 5.51Wm^-2, whereas CLM showed a negative bias of -6.58Wm^-2 at the FR2 site. G was considered as a residual term in CLM, which caused weak performance at P301, while VIC calculated G as a function of soil temperature, depth, and hydraulic conductivity. In addition, The MOD16 showed similar results with models at FR2; however, at P301, they yielded a correlation value of 0.85 and 0.21 for LSMs and MOD16, respectively. The later has lower correlation with in situ specifically in summer season caused by erroneous biophysical or meteorological inputs to the algorithms. The sensitivity analysis between soil moisture and turbulent fluxes, exhibited negative trend (especially for LE at P301) due to topography and snow cover. The results from this study are conducive to improvements in models and satellite based characterization of water and energy fluxes, especially at rugged terrain with high elevation, where observational experiments are difficult to conduct.

A water balance based, spatiotemporal evaluation of terrestrial evapotranspiration products across the contiguous United States

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy-balance based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long term climatology and inter-annual variability in ET derived from a terrestrial water budget (ETWB) for 671 gaged basins across the CONUS. All three ETEB products have low spatial bias and accurately capture inter-annual variability of ETWB in the central US, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on inter-annual timescales, ΔTWS and error in precipitation estimates are non-negligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.

Effects of Shade Intensity and Duration on Asian Soybean Rust Caused by Phakopsora pachyrhizi

Field studies to quantify the effects of shade intensity and duration on soybean rust caused by Phakopsora pachyrhizi were carried out in Florida in 2006 and 2007. Soybean plants at the V4 stage were inoculated with urediniospores at 2100, 0000, and 0200 h. Inoculated plants were either placed in cages that were covered with shade cloths of different mesh sizes allowing 70, 50, or 20% transmission of sunlight or were not covered so that the plants received 100% of sunlight. Plants kept under 20 and 100% sunlight were sampled 12, 18, and 36 h after inoculation to determine the in vivo germination percentage of urediniospores and the percentage of germ tubes that formed appressoria. In separate experiments, inoculated plants were placed under the shade (20% sunlight) and moved to unshaded conditions after 1, 2, and 7 days. For all experiments, soybean rust incidence and severity were rated 12 days after inoculation. Higher levels of disease incidence and severity were detected in plants under shade compared with those under full sunlight. Shade duration greater than 2 days favored disease development. Within 36 h, in vivo germination of urediniospores and formation of appressoria were not significantly affected by the treatments. These results may explain why soybean rust is more severe in the lower canopy and shaded areas in the field.

Variation in the sensitivity of organismal body temperature to climate change over local and geographic scales

Global climate change is expected to have broad ecological consequences for species and communities. Attempts to forecast these consequences usually assume that changes in air or water temperature will translate into equivalent changes in a species' organismal body temperature. This simple change is unlikely because an organism's body temperature is determined by a complex series of interactions between the organism and its environment. Using a biophysical model, validated with 5 years of field observations, we examined the relationship between environmental temperature change and body temperature of the intertidal mussel Mytilus californianus over 1,600 km of its geographic distribution. We found that at all locations examined simulated changes in air or water temperature always produced less than equivalent changes in the daily maximum mussel body temperature. Moreover, the magnitude of body temperature change was highly variable, both within and among locations. A simulated 1 degrees C increase in air or water temperature raised the maximum monthly average of daily body temperature maxima by 0.07-0.92 degrees C, depending on the geographic location, vertical position, and temperature variable. We combined these sensitivities with predicted climate change for 2100 and calculated increases in monthly average maximum body temperature of 0.97-4.12 degrees C, depending on location and climate change scenario. Thus geographic variation in body temperature sensitivity can modulate species' experiences of climate change and must be considered when predicting the biological consequences of climate change.

Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area

The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NOx and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different LULC category definitions and uncertainties in the vegetation distributions.