While large‐scale terrestrial evapotranspiration (ET) information is essential for our understanding of the Earth's water and energy cycles, substantial differences exist in current global ET ...products due partly to uncertainties in soil‐ and vegetation‐related parameters and/or precipitation forcing. Here a calibration‐free complementary relationship (CR) model, driven purely by routine meteorological forcing (air and dew‐point temperature, wind speed, and net radiation), mainly from ERA5, was employed to estimate global ET rates during 1982–2016. Modeled ET agrees favorably with (a) monthly eddy‐covariance measurements of 129 global FLUXNET sites, and; (b) water‐balance‐derived ET of 52 basins at the multiyear mean and annual scales. Additional evaluations demonstrate that the CR is very competitive, in comparison with other 12 widely used global ET products. The 35‐years mean global land ET rate from the CR is 500 ± 6 mm yr−1 (72.3 ± 0.9 × 103 km3 yr−1) with more than 70% of the land area exhibiting increasing annual ET rates over the study period. Globally, CR ET significantly increased at a rate of 0.31 mm yr−1 during 1982–2016, suggesting a 2.2% increase in global land ET over last 35 years. Model inter‐comparisons indicate that global annual CR ET values and their trend are close to the median of not only the 12 ET products chosen but also that of 20 CMIP6 models. Since this calibration‐free CR model requires no precipitation (except in sea‐shore deserts for a subsequent ET correction), vegetation or soil data, it could be incorporated into complex hydrological and/or climate models, thereby facilitating large‐scale hydrological and climate simulations.
Key Points
A global, 30‐years‐plus complementary relationship evapotranspiration (ET) product is developed and validated at the plot and basin scales
This new ET product, derived from a minimal number of inputs without vegetation or soil data, may improve upon previous global ET estimates
Global terrestrial ET rates increased significantly during 1982–2016, particularly in the Northern Hemisphere
•Long-term mean TP-averaged annual ET is 353±24 mm, with 64% from soil evaporation.•Annual ET from TP increased significantly during 1982–2016.•Precipitation enhancement is the dominant driver of ...increase in TP-averaged ET.•The dominant drivers of ET trend are spatially heterogeneous over TP.•Vegetation change plays a less important role than climatic factors in ET trend.
While terrestrial evapotranspiration (ET) from the Tibetan Plateau (TP) plays a key role in modulating water storage change in the Asian Water Tower, the magnitude, trend, and drivers of ET remain poorly understood in this region due partially to sparse ground measurements. This study used a water-carbon coupled biophysical model, Penman-Monteith-Leuning Version 2 (PML_V2), to characterize the variations in ET across TP during 1982–2016 and its drivers. Model parameters of PML_V2 were calibrated against ground-observed data from 14 eddy-covariance flux towers. Plot- and basin-scale validations demonstrate that the PML_V2 is robust enough in simulating both magnitude and trend in ET. The 35-year mean annual ET rates decrease from the southeastern to the northwestern TP, leading to a TP-averaged value of 353 ± 24 mm yr−1. Soil evaporation is the main component (64%) of ET, followed by plant transpiration (31%) and canopy evaporation (5%). From 1982 to 2016, TP-averaged ET increased significantly with a rate of 1.87 ± 0.25 mm yr−2 (p < 0.001) due primarily to precipitation enhancement. Spatially, precipitation is the dominant driver that controls ET trend over most parts of TP except certain regions in the southeastern and eastern TP, where net radiation and temperature do so instead, respectively. This is because 68% of the TP area is dryland with the aridity index < 0.65. While LAI appears less important than climate factors over much of TP, its relative contribution to ET trend exceeds 20% in many parts of eastern TP, indicating that vegetation change played a nonnegligible role in regulating annual ET variations over certain regions where LAI varied substantially. Our results are of vital importance for facilitating the understanding of hydrological processes over the Asian Water Tower.
We have carried out modeling and computation of mass transfer in a membrane contactor for removal of organic compounds from aqueous solutions. Both computational fluid dynamics (CFD) and Artificial ...Intelligence (AI) methods were utilized for modeling separation process. For the AI, we explored the application of three distinct regression models, namely Kernel Ridge Regression, Gaussian Process Regression, and Poisson Regression to predict the concentration of a component, C, based on r and z. To enhance the performance of these models, the hyper-parameter tuning process employs Glowworm Swarm Optimization (GSO). The findings illustrate the effectiveness of the utilized models. Gaussian Process Regression achieves a noteworthy R2 score of 0.99791, with a RMSE of 3.9666×101(mol/m3) and an AARD% of 4.52000×10-1. Kernel Ridge Regression, while slightly less accurate, achieves a commendable R2 value of 0.97865, with an RMSE of 1.2446×102(mol/m3) and an AARD% of 2.63808. Poisson Regression offers a respectable performance, yielding an R2 score of 0.95509, along with an RMSE of 1.8011×102(mol/m3) and an AARD% of 4.28969. Moreover, the separation efficiency was estimated to be greater than 70 % using the membrane process.
Complex relationship between process parameters and critical quality attributes in nanonization of drugs based on supercritical processing demands for developing advanced hybrid computational models. ...In this research, the complicated relationships between temperature, pressure, solvent density, and solubility of Nystatin in supercritical carbon dioxide for green processing of nanoparticles was explored via advanced hybrid models. For the first time, three base regression models: K-Nearest Neighbors (KNN), Bayesian Ridge Regression (BRR), and Polynomial Regression (PR) in combination with Political Optimizer (PO) for correlation of solubility of organic compound as well as solvent density was developed. Additionally, an ensemble approach, Bagging, is employed to enhance predictive performance of the models. Bagging ensemble technique combined with BRR predicts Nystatin solubility with an R2 of 0.89459. Bagging combined with KNN produces a score of 0.9162, showcasing its strong predictive capabilities. Moreover, bagging coupled with PR exhibits exceptional predictive performance, attaining a score of 0.99819. When predicting solvent density, bagging in conjunction with BRR yields an R2 value of 0.81069, while bagging paired with KNN achieves a higher score of 0.90542, demonstrating its proficiency in modeling solvent density. Bagging with PR surpasses the other models, attaining a significant R2 of 0.99035, highlighting its superior predictive accuracy.
Vegetation plays an important role in regulating terrestrial water and energy fluxes. Recent research reported that China is the largest contributor to the global net increase in green foliage. Such ...large‐scale vegetation greenness change would inevitably affect the water budget across China. In this study, we evaluated the effects of vegetation greenness change on evapotranspiration (ET) and water yield in China from 1982 to 2014 with a process‐based conceptual hydrologic model and a detrending experiment of vegetation index. On the national scale, the change in vegetation greenness was dominated by an increasing trend (greening), which has led to an increase in ET and a decline in water yield from 1982 to 2014. The contribution of vegetation greenness change to the trend of annual ET and water yield is 0.25 mm/yr and −0.16 mm/yr, respectively. Spatially, the relative change in ET and runoff caused by vegetation greenness change is significantly greater in water‐limited areas than in energy‐limited areas. Vegetation greening exacerbated water scarcity in water‐limited areas. Therefore, the ecological restoration policy should fully consider the negative effect of vegetation greening on water yield in water‐limited regions and balance the conflicting water demands between the ecosystem and human beings.
Key Points
We evaluated the effects of vegetation greenness change on annual ET and water yield across China from 1982 to 2014
On the national scale, the change in vegetation greenness has led to an increase in ET and a decline in water yield
The water‐reducing effect caused by vegetation greening is more pronounced in water‐limited areas than in energy‐limited areas
Mapping floods is important for policy makers to make timely decisions in regards to emergency responses and future planning. It is therefore crucial to develop a rapid inundation modelling framework ...to map flood inundation. This study develops an airborne scanning laser altimetry (LiDAR) digital elevation model (DEM) based Rapid flood Inundation Modelling framework (LiDAR-RIM) for assessment of inundation extent, depth, volume and duration for flood events. The modelling framework has been applied to the mid-Murrumbidgee region in the southeast Murray-Darling Basin, Australia for two flood events occurred in December 2010 and March 2012. The inundation extents estimated using this methodology compared well to those obtained from two Landsat ETM+ images, demonstrating suitability and applicability of this method. For testing possibility of larger area application, the framework also uses 30-m resolution shuttle radar topography mission (SRTM)-DEM to replace LiDAR-DEM for the same modelling. The inundation extents obtained by using the SRTM-DEM are smaller than those obtained using the LiDAR-DEM, especially for large flood events. A possible reason is that the river cross sections obtained from the SRTM-DEM are not accurate enough for inundation modelling. The LiDAR-RIM has an advantage for process modelling and scenario modelling under future climatic conditions.
•The cluster ensemble technique improved the final cluster structures.•The spatiotemporal ensemble clustering revealed four different patterns of GWL.•LSTM was not appropriate for monthly GWL ...forecasting compared to ensemble methods.•The neural ensemble has the lowest disorder and a large reduction in uncertainty.•The simultaneous clustering-forecasting ensemble techniques enhanced the outcomes.
In this study, first to identify the patterns of groundwater level (GWL) over the Ghorveh-Dehgolan plain (GDP) located in western Iran, as a data pre-processing scheme, three different types of clustering algorithms were applied to monthly GWL data sets of the piezometers. Then, the best structures of all clustering methods were integrated by Combining Multiple Clusterings via Similarity Graph (COMUSA) method to obtain the most homogenous patterns of GWL. The final results of the clustering step indicated that applying COMUSA could enhance the homogeneity of the clusters up to 25%. After dividing the GWL of GDP into four patterns, three single artificial intelligence (AI)-based models were applied to forecast multi-step-ahead GWL of centroid piezometer of each cluster. To benefit from the advantages of the single models, the outcomes were then combined with a neural averaging ensemble (NAE) technique as a post-processing step. Additionally, the assessment of the deep learning (DL) -based long-short-term memory (LSTM) application in multi-step ahead forecasting of GWL showed this method is not an appropriate choice compared to ensemble techniques for modeling the process with limited observed data. The comparison of the proposed GWL forecasting models of this study revealed the superiority of the NAE technique that enhanced the accuracy of the single models up to 23% in the testing phase. It could be concluded that the combination of cluster ensemble and model ensemble techniques could improve the performance of the individual method in reliable forecasting of the future GWL condition and the methodology of this study can be applied to the GWL of other plains.
•Estimating recession constant is key to the one-parameter digital filtering.•The Eckhardt method is sensitive to the maximum baseflow index.•Difference among the improved digital filtering methods ...is small.•The improved Lyne–Hollick method with two passes performs overall best.
Baseflow is the portion of streamflow that comes from shallow and deep subsurface flow, and is the key for catchment ecology and water resource management. This paper comprehensively evaluates four widely used non-tracer baseflow separation methods against tracer-based hydrograph separation for five Eastern Australian catchments. The four methods include United Kingdom Institute of Hydrology (UKIH) method and three digital filtering methods: Lyne–Hollick method, Chapman–Maxwell method and Eckhardt method. The first two filtering methods include a parameter of recession constant, and the last one has two parameters: the recession constant and the maximum baseflow index. We used an Automatic Baseflow Identification Technique (ABIT) to estimate the recession constant, which varies from 0.943 to 0.987 for the five catchments that is evidently higher than the default value of 0.925, and used the default Eckhardt and UKIH methods to estimate the maximum baseflow index, respectively. All modelling results are evaluated against the tracer-based hydrograph separation. Using the recession constant estimated from the ABIT method performs noticeably better than using the default parameter, indicated by the absolute bias reduced about 20% in average. For the two-parameter Eckhardt method, estimating the maximum baseflow index has larger effect on baseflow separation than estimating the recession constant. Compared to the different parameterisation schemes, the difference among the improved non-tracer methods is small. Using multiple passes into the Lyne–Hollick method can only slightly improve or deteriorate baseflow index estimates. Our results suggest that it is critical to get appropriate parameter(s) before applying the digital filtering methods.
•ETwb of 56 large river basins (1983–2016) are derived from multi-source datasets.•Gauge undercatch in precipitation is corrected to mitigate underestimation of ETwb.•The 12 ETwb time-series are ...merged using Bayesian-based three-cornered hat method.•The uncertainty in ETwb is due primarily to the uncertainty in precipitation.
A thorough validation of global terrestrial evapotranspiration (ET) models requires reliable ground-observed ET data. While the open-access eddy-covariance flux measurements are widely used for such a purpose, they have certain drawbacks, and thus, other alternative publicly available reference datasets are urgently needed. Using remote sensing and ground-based observational data, this study provides water-balance-based evapotranspiration (ETwb) estimates for 56 large (>105 km2) river basins of the world over the 1983–2016 period. For each basin, the observed runoff and four different precipitation (Prec) data sources were combined with three types of terrestrial water storage change (dS) estimates, yielding altogether 12 annual ETwb time-series. An optimally merged ETwb time-series was eventually produced using the Bayesian-based three-cornered hat method. The relative uncertainty in the ETwb estimates is less than 10 % in most basins and it stems primarily from the uncertainty in Prec. In summary, this new ETwb dataset has the following advantages: i) The gauge undercatch in Prec was corrected, thus mitigating the well-known general underestimation of ETwb in mid- and high-latitudes; ii) Multiple Prec and dS datasets were combined to account for the uncertainty in the water balance approach, thereby enabling the quantification of the uncertainty in ETwb and its sources, and; iii) The ETwb dataset stretches more than three decades, making it appropriate for evaluating long-term trends in global ET models. This ETwb dataset is publicly available (https://data.tpdc.ac.cn/en/data/e010cd0d-0881-4e7e-9d63-d36992750b04) and may serve as a benchmarking tool to calibrate/validate large-scale ET models for an improved understanding of regional- and/or global-scale ET processes.
Improving the traffic capacity of multilayer networks has been the subject of extensive interest and research. This paper proposes an effective strategy to improve traffic capacity of three‐layer ...networks via the process of removing links linking to nodes with large degrees. In order to improve the traffic capacity, two different link deletion rules are proposed to remove a portion of logical layer links without the change of physical layer topology structure. It is found that the proposed strategy can improve traffic capacity; furthermore, the link deletion rule that two logical layers delete the same number of links at the same time is better than the link deletion rule that only the links of one logical layer are deleted. This work contributes to the design and optimization of realistic multilayer networks. This paper proposes an effective strategy to improve traffic capacity of three‐layer networks via the process of removing links linking to nodes with large degrees. Under the premise that the structure of physical layer in three‐layer networks does not change, some logical layer links between the nodes with large degrees are removed to improve the traffic capacity, and two different link deletion rules are proposed.
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