Land surface processes and their coupling to the atmosphere over the Tibetan Plateau (TP) play an important role in modulating the regional and global climate. Therefore, identifying and quantifying uncertainty in these land surface model (LSM) processes are essential for improving climate models. The specifications of land cover and soil texture types, intertwined with the uncertainties in associated vegetation and soil parameters in LSMs, are significant sources of uncertainty due to the lack of detailed land survey in the TP. To differentiate the effects of land cover or soil texture specifications in the Noah with Multiple Parameterizations (Noah-MP) LSM from the effects of uncertainties in the model parameters, this study first identified the most sensitive vegetation and soil parameters through global sensitivity analysis and then conducted parametric ensemble simulations using two land cover data sets and two soil texture data sets over the central TP to estimate their corresponding impacts on the overall model responses. The distinction level and the Kolmogorov-Smirnov test were then applied to assess the differences between the results from parametric ensemble simulations using different land cover or soil texture data sets. The results show that the simulated energy and water fluxes over the central TP are dominated by soil parameters. The canopy height is the most sensitive vegetation parameter, and the Clapp-Hornberger b parameter (the exponent in the function that relates soil water potential and water content) is the most sensitive soil parameter. Relative to the background parametric uncertainties, the Noah-MP LSM could not sufficiently distinguish the effects of changes between forested types or soil texture types, which highlight the need for further quantifying and reducing the parametric uncertainties in LSMs. Further analysis shows significant sensitivities of the distinction level and changes in model response to annual precipitation and vegetation fraction. This work provides a scientific reference for assessing the impacts of land cover or soil texture changes on Noah-MP simulations under future climate change conditions.
Structural and parametric problems associated with physical parameterizations are often tied together in weather and climate models. This study examines the sensitivities of turbine‐height wind speeds to structural and parametric uncertainties associated with the planetary boundary layer (PBL) parameterizations in the Weather Research and Forecasting model over an area of complex terrain. The sensitivity analysis is based on experiments from two perturbed parameter ensembles using the Mellor‐Yamada‐Nakanishi‐Niino (MYNN) and Yonsei University (YSU) PBL schemes, respectively. In each scheme, most of the intermember variances can be explained by a few parameters. Compared to the YSU parameters, the MYNN parameters induce relatively weaker (stronger) impacts on wind speeds during daytime (nighttime). The two schemes can overall reproduce the observed diurnal features of turbine‐height wind speeds. Differences in the daytime wind speeds are evident between the two ensembles. The daytime biases exist even with well‐tuned parameter values in MYNN, indicating the structural error. The YSU scheme better matches monthly mean daytime observations, partly due to the compensation among the biases in different wind strengths. Compared to YSU, MYNN generally better agrees with observations in both weak and strong wind conditions. However, the improvements accomplished for one condition by parameter tuning may degrade model performances for others, suggesting the relationships that link different conditions are not accurately represented in the parameterizations. Simulated biases due to structural errors are further identified by evaluating them for different time of day and locations. Ultimately, this study improves understanding of structural limitations in the PBL schemes and provides insights on further parameterization development.
Selecting suitable species for vegetation restoration presents a notable challengefor land managers and scientists. Recently developed trait-based approaches may be an effective means of overcoming this challenge. However, we lack a traitbased species screening model that can be used to select potential species for restoration of degraded ecosystems. 2.Here, we developed a species screening model based on quantitative trait-based theory and a maximum entropy algorithm. The objective was to select more species that have comparable restoration abilities to the target species that have high survival rates for vegetation restoration based on species' functional traits. Thus, species diversity will be improved to facilitate restoration. We also developed a software platform that can be used to implement the model. We then applied our model and software platform to select species for restoration efforts in a tropical coral island which is part of Hainan Island, China.3. As a prerequisite, we started with three target species which have high potential for restoring the island. Likewise, 66 non-native species were selected as the potential species pool. For each species, we identified and measured 28 traits that are strongly associated with harsh environments. Harsh environments are those with drought stress, high temperatures, intensive UV radiation, lack of real soil and nutrients, and high salinity and alkalinity. Then, our software platform was used to run the species selection model. Finally, 12 out of 66 species being identified as suitable species for restoration. 4. We transplanted seedlings of all 66 species to the island to monitor seedlings survival. We found that the 12 species identified from our model had high survival rates, which ranged from 86% to 91%. In contrast, the mean survival rate for species not identified from our model was less than 40%. These results suggest that our species screening procedure was appropriate for selecting candidate species for use in vegetation restoration.5. We show that by using species natural history information, as well as functional trait data, candidate species for restoration efforts can be successfully identified in a timely manner. Importantly, our proposed method is faster and less costly than more commonly used 'trial-and-error' method. The most time-consuming aspect of our approach is the need to measure the functional traits of target and potential S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section.
1. Traditionally, restoration ecologists and land managers have used the trial-anderror method to select candidate restoration species. This method, however, is time consuming (usually more than 3 years) and has a relatively low success rate.Recently, Wang et al. ( 2020) developed a trait-based species selection framework which can quickly (within 1 month) and successfully select many appropriate species for ecological restoration. They used 28 traits that are associated with tolerance to harsh environmental conditions to select candidate restoration species for a tropical coral island in Hainan Province, China. However, it is likely that some of the 28 traits used in this study may not be very important for species selection, providing the potential use of fewer overall traits. This is important since in many situations land managers will have limited data and resources on species traits.2. In this study, we used Wang et al. ( 2020)'s trait data to test which traits are necessary to achieve a similar success rate when screening species for restoration applications. We performed principal component analysis (PCA) to compute each trait's relative contribution. Then, we used the backward stepwise approach where the trait that had the least contribution among all remaining traits was removed one at a time, and the screening model was then run again using the smaller set of traits.Species which are proven very appropriate for ecological restoration in Wang et al.(2020) were the standard to quantify how many and which traits should be used to acquire similar screening results. We also classified all 28 traits into four types of functional traits to test if a small set of traits can mimic Wang et al. ( 2020)'s selection results. 3.Our results indicate that it is hard to simultaneously reduce trait numbers and maintain the right screening results; especially for tree species. Likewise, vine species and herbaceous species still required most of the original traits used by Wang et al. (2020). Our results also indicate that multiple trait types are required, rather than one single group of functional traits.
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