Dynamics of Terrestrial Ecosystems, Water, and Climate in The United States

Water, climate, and vegetation all play major roles in keeping ecosystems alive. By looking at satellite gathered data through NCL, (a unix based coding language) patterns, trends, and change over time, or a lack thereof, were observed. A warming trend in the United States was noticed. The Leaf Area Index suffered an important drop in the American South, Eastern Texas, and the Bay Area, California. Precipitation, overall, stayed about the same, seeing drops in some areas, like the American South West, or increases,in the American Midwest as well as the North East. Oddly, Texas and the American South had significant rainfall over time. Also, there was an increase in vegetation in Arizona, Nevada, and Eastern California,despite the drop in precipitation in these areas

Examination on the Efficacy of Noah-Multiparameterization (Noah-MP) as a New Land Model in Earth Systems Modeling

Along with measurements and observations, models (both physical and mathematical) are among the most important tools used by Earth scientists to make hypotheses, test hypotheses, and form predictions. These models are based upon information and theories already believed to be true. As newer and better information is discovered, the models can become more accurate and precise. Mathematical models, sometimes called numerical models, also rely on technology; as the technology used to make models becomes more advanced, more complex models can be made and used to test more variables at once. This report examines a new mathematical model called Noah-Multiparameterization (Noah-MP) as a possible new Land System Model (LSM). To assess this model, atmospheric and hydrological data collected at multiple sites were obtained. The data were then run through the Noah-MP model to generate simulated data. The simulated data were then compared to observed data at the same site during the same time period to determine if the model produced reasonable figures. Initial results indicate the model’s simulated data are relatively close to observed data

Using Satellite Remotely Sensed Data to Improve Understanding of the Coupled Dynamics between Terrestrial Water and Ecosystems under the Recent California Drought

Over the past decade, California has experienced a serious drought. Using the National Center for Atmospheric Research Command Language (NCL), we first analyzed the changes in temperature and the water budgets including precipitation, river runoff, evapotranspiration, and terrestrial water storage (TWS), and snow cover over California. Results show that there were decreasing trends in precipitation and runoff, more apparently in snow cover and TWS, and an increasing trend in temperature over California from 2002-2015, indicating a “hot” drought. In addition, we analyzed the satellite data of leaf area index (LAI) and normalized difference vegetation index (NDVI) to investigate how ecosystems respond to the hot drought. Either LAI or NDVI does not show as much decreasing trends as TWSA and snow cover. There was even an increasing trend in both LAI and NDVI in the forest ecosystem over the mountainous region with very little decreasing trend or no change in LAI and NDVI in many desert areas of California. This may be caused by the elevated CO2 level, which facilitates plant photosynthesis, and adaption of vegetation to the drought, for instance, through deeper roots, or increased groundwater pumping. There are also some patches of forest ecosystems showing a significant trend, possibly related to the tree “die-off” occurring worldwide caused by the hot drought

Quantifying the Impact of Climate Change and Human Activities on Streamflow in a Semi-Arid Watershed with the Budyko Equation Incorporating Dynamic Vegetation Information

Understanding hydrological responses to climate change and land use and land cover change (LULCC) is important for water resource planning and management, especially for water-limited areas. The annual streamflow of the Wuding River Watershed (WRW), the largest sediment source of the Yellow River in China, has decreased significantly over the past 50 years at a rate of 5.2 mm/decade. Using the Budyko equation, this study investigated this decrease with the contributions from climate change and LULCC caused by human activities, which have intensified since 1999 due to China’s Grain for Green Project (GFGP). The Budyko parameter that represents watershed characteristics was more reasonably configured and derived to improve the performance of the Budyko equation. Vegetation changes were included in the Budyko equation to further improve its simulations, and these changes showed a significant upward trend due to the GFGP based on satellite data. An improved decomposition method based on the Budyko equation was used to quantitatively separate the impact of climate change from that of LULCC on the streamflow in the WRW. Our results show that climate change generated a dominant effect on the streamflow and decreased it by 72.4% in the WRW. This climatic effect can be further explained with the drying trend of the Palmer Severity Drought Index, which was calculated based only on climate change information for the WRW. In the meantime, although human activities in this watershed have been very intense, especially since 1999, vegetation cover increase contributed a 27.6% decline to the streamflow, which played a secondary role in affecting hydrological processes in the WRW

Assessment of the Effects of Climate Change on Evapotranspiration with an Improved Elasticity Method in a Nonhumid Area

Climatic elasticity is a crucial metric to assess the hydrological influence of climate change. Based on the Budyko equation, this study performed an analytical derivation of the climatic elasticity of evapotranspiration (ET). With this derived elasticity, it is possible to quantitatively separate the impacts of precipitation, air temperature, net radiation, relative humidity, and wind speed on ET in a watershed. This method was applied in the Wuding River Watershed (WRW), located in the center of the Yellow River Watershed of China. The estimated rate of change in ET caused by climatic variables is −10.69 mm/decade, which is close to the rate of change inET (−8.06 mm/decade) derived from observable data. The accurate estimation with the elasticity method demonstrates its reliability. Our analysis shows that ET in the WRW had a significant downward trend, but the ET ratio in the WRW has increased continually over the past 52 years. Decreasing precipitation is the first-order cause for the reduction of ET, and decreasing net radiation is the secondary cause. Weakening wind speed also contributed to this reduction. In contrast, regional warming led to an increase in ET that partly offset the negative contributions from other climatic variables. Moreover, reforestation can affect the energy budget of a watershed by decreasing albedo, compensating for the negative influence of global dimming. The integrated effect from precipitation and temperature can affect the energy budget of a watershed by causing a large fluctuation in winter albedo

Assessment of the Effects of Climate Change on Evapotranspiration with an Improved Elasticity Method in a Nonhumid Area

Climatic elasticity is a crucial metric to assess the hydrological influence of climate change. Based on the Budyko equation, this study performed an analytical derivation of the climatic elasticity of evapotranspiration (ET). With this derived elasticity, it is possible to quantitatively separate the impacts of precipitation, air temperature, net radiation, relative humidity, and wind speed on ET in a watershed. This method was applied in the Wuding River Watershed (WRW), located in the center of the Yellow River Watershed of China. The estimated rate of change in ET caused by climatic variables is −10.69 mm/decade, which is close to the rate of change inET (−8.06 mm/decade) derived from observable data. The accurate estimation with the elasticity method demonstrates its reliability. Our analysis shows that ET in the WRW had a significant downward trend, but the ET ratio in the WRW has increased continually over the past 52 years. Decreasing precipitation is the first-order cause for the reduction of ET, and decreasing net radiation is the secondary cause. Weakening wind speed also contributed to this reduction. In contrast, regional warming led to an increase in ET that partly offset the negative contributions from other climatic variables. Moreover, reforestation can affect the energy budget of a watershed by decreasing albedo, compensating for the negative influence of global dimming. The integrated effect from precipitation and temperature can affect the energy budget of a watershed by causing a large fluctuation in winter albedo

LIVE, ATTENUATED VACCINES AND METHODS OF MAKING AND USING

A live, attenuated HIV vaccine is provided, and methods of making a atenuated HIV vaccine are provided

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.U.S. Department of Energy [DE-SC0019438, DE-SC0008272]; U.S. National Science Foundation [EAR-1552329, OIA-1557349]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Audio-Visual Instance Segmentation

In this paper, we propose a new multi-modal task, namely audio-visual instance segmentation (AVIS), in which the goal is to identify, segment, and track individual sounding object instances in audible videos, simultaneously. To our knowledge, it is the first time that instance segmentation has been extended into the audio-visual domain. To better facilitate this research, we construct the first audio-visual instance segmentation benchmark (AVISeg). Specifically, AVISeg consists of 1,258 videos with an average duration of 62.6 seconds from YouTube and public audio-visual datasets, where 117 videos have been annotated by using an interactive semi-automatic labeling tool based on the Segment Anything Model (SAM). In addition, we present a simple baseline model for the AVIS task. Our new model introduces an audio branch and a cross-modal fusion module to Mask2Former to locate all sounding objects. Finally, we evaluate the proposed method using two backbones on AVISeg. We believe that AVIS will inspire the community towards a more comprehensive multi-modal understanding

River network routing on the NHDPlus dataset

International audienceThe mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into net- works. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides stream- flow observations at about 20 thousand gauges located on theNHDPlus river network.Ariver networkmodel called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff com- putations provided by land surfacemodels.RAPIDis applied to theGuadalupe and SanAntonioRiver basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditionalMuskingummethod when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin