Influence of processing parameters and composition on the effective compatibilization of polypropylene–poly(ethylene terephthalate) blends

The effects of the addition of different functionalized compatibilizers on toughness, morphology and rheological properties of a polypropylene (PP) - poly(ethylene terephthalate) (PET) (85-15 wt%) blend were studied. The three compatibilizers compared were: (Styrene Ethylene Butylene Styrene)- grafted(glycidyl methacrylate); (Styrene Ethylene Butylene Styrene) - grafted - (maleic anhydryde); (polyolefin) - grafted - (glycidyl methacrylate), abbreviated to: SEBS-g-GMA, SEBS-g-MA and POE-g-GMA respectively. The effective grafting content was the same for all three compatibilizers. Before the comparison of the different compatibilizers was done, first the effects of three different processing temperatures and three different compatibilizer contents were investigated, based on the addition of SEBS-g-GMA. The compatibilization effect was significantly improved with an increase in processing temperature from 250 to 300 degrees C. The toughness was increased with almost a factor two and a decrease in the average domain size of the dispersed phase was observed. An increase in compatibilizer content from 0.25 to 2.5 wt% resulted in a finer dispersity as well as in a steep increase in toughness, which was noted to approach the brittle-to-ductile transition. The comparison of the three compatibilizers was subsequently done at the most promising processing temperature and content: 300 degrees C and 2.5 wt%. The results showed that the addition of SEBS-g-MA and POE-g-GMA had a less significant positive effect on the compatibilization compared to SEBS-g-GMA. The difference is attributed to a higher reactivity for GMA compared to MA and a higher possibility for migration towards the PP-PET interface for the SEBS chain compared to the POE chain

Assessment of MERRA-2 Land Surface Energy Flux Estimates

In MERRA-2, observed precipitation is inserted in place of model-generated precipitation at the land surface. The use of observed precipitation was originally developed for MERRA-Land(a land-only replay of MERRA with model-generated precipitation replaced with observations).Previously shown that the land hydrology in MERRA-2 and MERRA-Land is better than MERRA. We test whether the improved land surface hydrology in MERRA-2 leads to the expected improvements in the land surface energy fluxes and 2 m air temperatures (T2m)

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill

Assessment and enhancement of MERRA land surface hydrology estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979 present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies

Prediction of Hydrological Drought: What Can We Learn From Continental-Scale Offline Simulations?

Land surface model experiments are used to quantify, across the coterminous United States, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Forecasted streamflows are compared to naturalized streamflow observations where available and to synthetic (model-generated) streamflow data elsewhere. We find that snow initialization has a major impact on skill in the mountainous western U.S. and in a portion of the northern Great Plains; a mid-winter (January 1) initialization of snow in these areas leads to significant skill in the spring melting season. Soil moisture initialization also contributes to skill, and although the maximum contributions are not as large as those seen for snow initialization, the soil moisture contributions extend across a much broader geographical area. Soil moisture initialization can contribute to skill at long leads (up to 5 or 6 months), particularly for forecasts issued during winter

Land-Focused Changes in the Updated GEOS FP System (Version 5.25)

Many of the changes imposed in the January 2020 upgrade from Version 5.22 to 5.25 of the Goddard Earth Observing System (GEOS) Forward Processing (FP) analysis system were designed to increase the realism of simulated land variables. The changes, which consist of both land model parameter updates and improvements to the physical treatments employed for various land processes, have generally positive or neutral impacts on the character of the FP product, as documented here

Assessment of MERRA-2 Land Surface Energy Flux Estimates

In the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here we assess aspects of the MERRA-2 land surface energy budget and 2 m air temperatures (T(sup 2m)). For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W/sq m), the sensible heating (by 6 W/sq m), and the downwelling shortwave radiation (by 14 W/sq m), while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10-15 W/sq m each). These results differ only slightly from those for NASA's previous reanalysis, MERRA. Comparison to various gridded reference data sets over Boreal summer (June-July-August) suggests that MERRA-2 has particularly large positive biases (>20 W/sq m) where LH is energy-limited, and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during Boreal summer, the globally averaged anomaly correlations (R(sub anom)) with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs. GLEAM data) and the daily maximum T(sup 2m) (from 0.69 to 0.75 vs. CRU data). In regions where T(sup 2m) is particularly sensitive to the precipitation corrections (including the central US, the Sahel, and parts of south Asia), the changes in the T(sup 2m) R(sub anom) are relatively large, suggesting that the observed precipitation influenced the T(sup 2m) performance

Soil temperature simulation results in Alaska (1980 - 2014) – Data archive for “Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model”

The datasets archived here include simulation results discussed in the paper, “Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model”, to be published in Journal of Advances in Modeling Earth Systems. Specifically, subsurface soil temperatures for 1980-2014 across Alaska were produced by a baseline simulation with the NASA Catchment Land Surface Model (CLSM). Five sets of point simulations were also conducted at permafrost sites in Alaska, including 1) T1BC - the top layer temperature is prescribed to observations, 2) T1BC_OrgC – repeat of the T1BC simulation but using the updated model version that incorporates soil thermal impacts of organic carbon content, 3) T2BC - the temperatures of both the 1st and 2nd layer are prescribed to observations, 4) T2BC_OrgC – repeat of the T2BC simulation but using the updated model version, and 5) M2_OrgC – simulations with the updated model version driven by MERRA-2 forcing. Details about the model configuration and the changes defining the updated model version can be found in the paper. The major findings in this paper include: a) profile-average RMSE of simulated soil temperature versus in situ observations is reduced by using corrected local forcing and land cover; b) subsurface heat transport is mostly realistic, and when not, it is improved via treatment of soil organic carbon-related thermal properties; and c) mean bias and RMSE of climatological ALT between simulations and observations are significantly reduced with updated model version.NASA Interdisciplinary Science progra

A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

NASAs Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2-3 days and a latency of 24 hours. Here, to enhance the utility of the SMAP data, we present an approach for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States (CONUS) is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence