Explore the vast range of titles available.

For estimating the annual mean of daily solar irradiation in plateau mountainous regions, observed data from 15 radiation stations were used to validate different empirical estimation methods over the Tibetan Plateau. Calibration indicates that sunshine-based site-dependent models perform better than temperature-based ones. Then, the highly rated sunshine-based Ångström model and temperature-based Bristow model were selected for regional application. The geographical models perform much better than the average models, but still not ideally. To achieve better performance, the Ångström-type model was improved using altitude and water vapor pressure as the leading factors. The improved model can accurately predict the coefficients at all the stations, and performs the best among all models with an average Nash-Sutcliffe Efficiency value of 0.856. Spatial distribution of the annual mean of daily solar irradiation was then estimated with the improved model. It is indicated that there is an increasing trend of radiation from east to west, with a great center of the annual mean of daily solar irradiation on southwest Tibetan Plateau ranging from 20 to 24 MJ·m−2. The improved model should be further validated against observations before its applications in other plateau mountainous regions.

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.

Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO 2 ) transfer between land surfaces and the atmosphere. Improved estimates of NEE can serve to better constrain spatiotemporal characteristics of terrestrial carbon fluxes, improve verification of land models, and advance monitoring of Earth’s terrestrial ecosystems. Spatiotemporal NEE information developed by combining ground-based flux tower observations and spatiotemporal remote sensing datasets are of potential value in benchmarking land models. We apply a machine learning approach (Random Forest (RF)) to develop spatiotemporally varying NEE estimates using observations from a flux tower and several variables that can potentially be retrieved from satellite data and are related to ecosystem dynamics. Specific variables in model development include a mixture of remotely sensed (fraction of photosynthetically active radiation (fPAR), Leaf Area Index (LAI)) and ground-based data (soil moisture, downward solar radiation, precipitation and mean air temperature) in a complex landscape of the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. Predicted results show good agreement with the observed data for the NEE (r 2  = 0.87). We then validate the temporal pattern of the NEE generated by the RF model for two independent years at the two sites not used in the development of the model. The model development process revealed that the most important predictors include LAI, downward solar radiation, and soil moisture. This work provides a demonstration of the potential power of machine learning methods for combining a variety of observational datasets to create spatiotemporally extensive datasets for land model verification and benchmarking.

Differences in water and carbon fluxes along a climate/elevation gradient within a sagebrush ecosystem are quantified, and inferences are made about climate warming using a network of eddy covariance systems. Sites are located within the Reynolds Creek Critical Zone Observatory in southwestern Idaho, USA, with elevations ranging from 1425 to 2111 m, annual precipitation ranging from 290 to 795 mm and annual temperature ranging from 9.1 to 5.4 °C. Annual gross ecosystem production (GEP) for the sites averaged (± uncertainty) 385 ± 6, 549 ± 19, 684 ± 25, and 818 ± 26 gC m⁻² from lowest to highest elevation. Annual net ecosystem production indicated that the sites are carbon sinks with annual uptake typically ranging from 100 ± 10 to 200 ± 30 gC m⁻². Exceptions to this are: the lowest elevation site which was carbon neutral (1 ± 16 gC m⁻²) during a year with a summer rainfall respiration pulse, and the highest elevation site, where carbon uptake dropped to 42 ± 20 gC m⁻² during a heavy snow year. Carbon flux and evapotranspiration (ET) peaked about a month earlier at the lower elevation sites, but with limited precipitation, these sites encountered water stress for much of the growing season. Model simulations suggest that climate warming will likely have a negligible impact on annual ET and GEP at lower elevations, but rather shift ET and GEP earlier in the season and prolong the period of water stress. ET and GEP may increase with climate warming at higher elevations where precipitation is above a threshold of about 450 mm.

The impact of land management actions such as prescribed fire remains a key uncertainty in understanding the spatiotemporal patterns of carbon cycling in the Western USA. We therefore quantified carbon exchange and aboveground carbon stocks following a prescribed fire in a mountain big sagebrush ecosystem located in the northern Great Basin, USA. Specifically, we examined the changes in plant functional type, leaf area index, standing aboveground carbon stocks, net ecosystem production (NEP), gross ecosystem production (GEP), and ecosystem-level respiration (Reco) for 2 years before and 7 of 9 years after a prescribed fire. Post-burn GEP and Reco exceeded pre-burn GEP and Reco within 2 years and remained elevated. The variation in GEP and Reco provided no evidence of a large and prolonged net efflux of carbon in the 9 years after the fire. Rather, NEP indicated the site was a sink before and after the fire, with little change in sink strength associated with the burn. Re-sprouting and recruitment of grasses and forbs drove the post-burn increase in GEP. Woody shrub growth was the dominant control on aboveground biomass accumulation after fire, with shrub aboveground biomass reaching 11% of pre-burn biomass after 5 years. The rapid recovery of GEP and the growth of mid-successional shrubs suggest ecosystem-level carbon fluxes and stocks can recover rapidly after fire in mesic mountain big sagebrush ecosystems.

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model.

Correct simulation of overwinter condition is important for the growth of winter crops and for initial growth of spring crops. The objective of this study was to investigate overwinter soil water and temperature dynamics with the simultaneous heat and water (SHAW) model and with its linkage to the root zone water quality model (RZWQM), a hybrid model of RZWQM and SHAW (RZ-SHAW) in a Siberian wildrye grassland under two irrigation treatments (non-irrigation and pre-winter irrigation) in two seasons (2005–2006 and 2006–2007). Experimental results showed that pre-winter irrigation considerably increased soil water content for the top 60-cm soil profile in the following spring, but had little effect on soil temperature. Both SHAW and RZ-SHAW simulated these irrigation effects equally well, which demonstrated a correct linkage between RZWQM and SHAW. Across the treatments and years, the average root mean square deviation (RMSD) for simulated total soil water content (liquid plus frozen) was 0.031 m3 m−3 for both RZ-SHAW and SHAW models, and that for liquid water content alone was 0.028 m3 m−3 for both models. Both models provided better simulation of total and liquid soil water contents under non-irrigation condition than under pre-winter irrigation conditions. On average, RZ-SHAW simulated soil temperature slightly better with an average RMSD of 1.4°C compared to that of 1.8°C by SHAW. Both RZ-SHAW and SHAW simulated the soil freezing process well, but were less accurate in simulating the soil thawing processes, where further improvements are desirable. These simulation results show that the SHAW model is correctly implemented in RZWQM (RZ-SHAW), which adds the capability of RZWQM in simulating overwinter soil conditions that are critical for winter crops.

The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two- source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snowcover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.

Understanding and simulating plant canopy conditions can assist in better acknowledgment of plant microclimate characteristics, its effect on plant processes, and the influence of management and climate scenarios. The ability of the Simultaneous Heat and Water (SHAW) model to simulate the surface energy balance and profiles of leaf temperature and micrometeorological variables within a maize canopy and the underlying soil temperatures was tested using data collected during 1999 and 2003 at Yucheng, in the North China Plain. The SHAW model simulates the near-surface heat and water movement driven by input meteorological variables and observed plant characteristic (leaf area index [LAI], height, and rooting depth). For 1999, the model accurately simulated air temperature and relative humidity in the upper one-third of the canopy, but overpredicted midday temperature in the lower canopy. For 2003, although the surface energy balance was simulated quite well, radiometric canopy surface temperature and midday leaf temperature in the upper portion of the canopy were overpredicted, by approximately 5°C. Model efficiency (the fraction of variation in observed values explained by the model) for leaf temperature in the lower two-thirds of the canopy ranged from 0.82 to 0.90, but fell to 0.38 for the uppermost canopy layer. Weaknesses in the model were identified and potentially include: the use of K-theory to simulate turbulent transfer within the canopy; and simplifying assumptions with regard to long-wave radiation transfer within the canopy. Model modifications are planned to address these weaknesses.

In the Pacific Northwest (PNW), concern about the impacts of climate and land cover change on water resources and flood-generating processes emphasizes the need for a mechanistic understanding of the interactions between forest canopies and hydrologic processes. Detailed measurements during the 1999 and 2000 hydrologic years were used to modify the Simultaneous Heat and Water (SHAW) model for application in forested systems. Major changes to the model include improved representation of rainfall interception and stomatal conductance dynamics. The model was developed for the 1999 hydrologic year and tested for the 2000 hydrologic year without modification of the site parameters. The model effectively simulated throughfall, soil water content profiles, and shallow soil temperatures for both years. The largest discrepancies between soil moisture and temperature were observed during periods of discontinuous snow cover due to spatial variability that was not explicitly simulated by the model. Soil warming at bare locations was delayed until most of the snow cover ablated because of the large heat sink associated with the residual snow patches. During the summer, simulated transpiration decreased from a maximum monthly mean of 2.2 mm day−1in July to 1.3 mm day−1in September as a result of decreasing soil moisture and declining net radiation. The results indicate that a relatively simple representation of the vegetation canopy can accurately simulate seasonal hydrologic fluxes in this environment, except during periods of discontinuous snow cover.