•A total of 39 models to predict frozen soil thermal conductivity were reviewed.•A large dataset was compiled to evaluate performance of the models.•BB1992, TZ2016, ZM2018 and WL2017 are the best ...performing models, but overall performance of the models is not satisfactory.•A call for new model conceptualization, development and evaluation.
Frozen soil thermal conductivity (λeff) is a critical thermo-physical property that is required for environmental, earth science, geotechnical and geo-environmental applications and associated numerical modeling. Measurement of λeff in frozen soils is difficult and prone to errors, especially near the freezing/thawing point of soil water (e.g., −4 to 0 °C). Available steady-state or transient methods to measure λeff based on the soil temperature response to applied heat often result in melting of soil ice, violating the conduction-only assumptions of these methods and result in biased λeff measurements. Therefore, the choice of λeff models is often influence by their ease of implementation. A great number of such models have been developed during the last few decades since the latest comprehensive review of frozen soil thermal conductivity models in the early 1980s. There is a need to revisit this topic by comparing the models and evaluating their performance to provide information to the novice and expert alike, in order to guide them on their advantages, limitations and applications. A total of 39 models were categorized as: 1) linear and non-linear regression models (8 models); 2) physical models (6 models); 3) mixing models (6 models); 4) normalized models (17 models); and 5) models of other types based on their characteristics (2 models). These models were assessed with a large compiled dataset consisting of 331 λeff measurements taken at temperatures <−4 °C on 27 soils from seven studies, using steady-state or transient methods. Three performance indices including root mean square error (RMSE), average deviations (AD) and Nash-Sutcliff Efficiency (NSE) were used to evaluate performance of these models. The results showed that the models of Becker et al. (1992) (BB1992), Tian et al. (2016) (TZ2016), Zhang et al. (2018) (ZM2018) and Wang et al. (2017) (WL2017) were the best performing models in their affiliated category. However, none of the models performed satisfactorily, with NSE = 0.51, RMSE = 0.46 W m−1 °C−1 and AD = −0.04 W m−1 °C−1 for the best performing model among all the models investigated. Future studies that focus on conceptualization and development of new frozen soil thermal conductivity models for accurate and wide application is recommended.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Limited precipitation restricts crop yield in the North China Plain, where high level of production depends largely on irrigation. Establishing the optimal irrigation scheduling according to the crop ...water requirement (CWR) and precipitation is the key factor to achieve rational water use. Precipitation data collected for about 40 years were employed to analyze the long-term trend, and weather data from 1984 to 2005 were used to estimate the CWR and irrigation water requirements (IWR). Field experiments were performed at the Luancheng Station from 1997 to 2005 to calculate the soil water consumption and water use efficiency (WUE). The results showed the CWR for winter wheat and summer maize were similar and about 430
mm, while the IWR ranged from 247 to 370
mm and 0 to 336
mm at the 25% and 75% precipitation exceedance probabilities for winter wheat and summer maize, respectively. The irrigation applied varied in the different rainfall years and the optimal irrigation amount was about 186, 161 and 99
mm for winter wheat and 134, 88 and 0
mm for summer maize in the dry, normal and wet seasons, respectively. However, as precipitation reduces over time especially during the maize growing periods, development of water-saving management practices for sustainable agriculture into the future is imperative.
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Detailed assessment of small‐scale heterogeneity in local surface water balance is essential to accurate estimation of evapotranspiration in semiarid climates. However, meteorological approaches are ...often impractical to implement in sites with sparse and diverse vegetation composition, especially with seasonally variable leaf canopy features. Ground‐based infrared thermometry (TIR) provides spatially and temporally continuous resolution of surface skin temperature that can be directly related to the land surface energy balance. We made repeated measurements with a portable TIR camera to capture seasonal replicates for patch scale heat images for four sagebrush communities. The heat images near peak foliage and near the end of the growing season were compared by computation of surface energy fluxes from TIR sensing to surface energy balance (SEB) and Bowen ratio (BR). Estimates of sensible (H) and latent heat flux (LE) were evaluated with eddy covariance measurements to disaggregate the expression of seasonal phenology of sagebrush species across wetness and elevation. Estimations showed reasonable agreement with ground‐based LE observations for most cases (r2 = 0.59–0.76 for SEB and 0.22–0.72 for BR; root mean squared error = 73.4–106.4 W m−2 for SEB and 109.9–204.0 W m−2 for BR). Predictability declined as the fraction of senescent foliage increased in dry conditions. The field trials suggest the methods have the potential for monitoring land surface energy fluxes and plant health at a very fine spatial scale. The ability to partition heat fluxes from various plant communities over a range of moisture availability will provide valuable information associated with the consumptive water use and phenological processes in the semiarid West.
Ground‐based thermal infrared (TIR) temperature can predict spatio‐temporally continuous land surface energy balance. Proposed TIR methods showed good agreement with ground‐based eddy covariance observations in turbulent heat fluxes for most cases. Predictability declined substantially as the fraction of senescent foliage increased in dry conditions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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.
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This work advances a unified approach to process‐based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in ...the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within‐canopy and below‐canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.
Key Points:
Flexible model implementation enables evaluation of key modeling decisions
Case studies illustrate capabilities to identify preferable modeling approaches
Accelerates improvements in model fidelity & uncertainty characterization
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Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO
) 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
= 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.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Soil moisture and evapotranspiration (ET) are important components of boreal forest hydrology that affect ecological processes and land‐atmosphere feedbacks. Future trends in soil moisture in ...particular are uncertain. Therefore, accurate modeling of these dynamics and understanding of concomitant sources of uncertainty are critical. Here, we conduct a global sensitivity analysis, Monte Carlo parameterization, and analysis of parameter uncertainty and its contribution to future soil moisture and ET uncertainty using a physically based ecohydrologic model in multiple boreal forest types. Soil and plant hydraulic parameters and LAI have the largest effects on simulated summer soil moisture at two contrasting sites. In future scenario simulations, the selection of parameters and global climate model (GCM) choice between two GCMs influence projected changes in soil moisture and ET about as much as the projected effects of climate change in the less sensitive GCM with a late‐century, high‐emissions scenario, though the relative effects of parameters, GCM, and climate vary among hydrologic variables and study sites. Saturated volumetric water content and sensitivity of stomatal conductance to vapor pressure deficit have the most statistically significant effects on change in ET and soil moisture, though there is considerable variability between sites and GCMs. The results of this study provide estimates of: (a) parameter importance and statistical significance for soil moisture modeling, (b) parameter values for physically based soil‐vegetation‐atmosphere transfer models in multiple boreal forest types, and (c) the contributions of uncertainty in these parameters to soil moisture and ET uncertainty in future climates.
Plain Language Summary
Future changes in soil water content and evapotranspiration (ET) in boreal regions will influence forest dynamics, permafrost thaw rates and have the potential to alter feedbacks between the land surface and the atmosphere, potentially influencing regional climate dynamics. These changes are uncertain and difficult to model because of the complexity of the boreal ecological and hydrological systems. In this study, we conduct experiments with a hydrological model to better understand which parameters most influence soil water content and ET, how much uncertainty these parameters contribute, and how specific parameter values affect whether soils become wetter or drier and by how much. We find that the parameters that control how tightly soils retain water and how much plants close their stomata and hence reduce transpiration in response to dry conditions, have the largest influences. Interactions between parameters are also important. Even when these parameter values are constrained by model performance in comparison with observations, parameter choice can determine whether soils become wetter or drier in future conditions. These findings suggest that modelers should carefully consider how parameter selection influences simulated future hydrologic conditions and highlight specific parameters that are worthy of further attention.
Key Points
Modeled summer soil moisture at rooting depth is most sensitive to soil and plant hydraulic parameters and parameter interactions.
Parameter uncertainty affects projected change in evapotranspiration and θVWC about as much as climate change or choice between two global climate models.
We present parameter values useful for hydrologic modeling in multiple boreal forest types while quantifying the impacts of equifinality.
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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.
Abstract The Long‐Term Agroecosystem Research Network (LTAR), through its Common Experiment (CE) framework, contrasts prevailing and alternative agricultural practices for efficacy and sustainability ...within the indicator domains of environment, productivity, economics, and society. Invasive species, wildfire, and climate change are principal threats to Great Basin agroecosystems. Prescribed grazing may be an effective tool for restoring lands degraded by these disturbances. At the Great Basin (GB) LTAR site headquartered in Boise, ID, our contribution to the CE contrasts a prevailing (PRV), cattle grazing practice of fixed moderate stocking and duration with an alternative (ALT), prescribed grazing practice called high‐intensity low‐frequency (HILF) grazing where stocking and duration are tailored to suppress invasive annual grass competition with native or desirable plant species and thus promote recovery of rangelands degraded by annual grass invasion and recurrent wildfire. Preliminary results indicate cheatgrass density and fuel height have been reduced in ALT‐treated paddocks compared to PRV paddocks. Since its inception in 2014, our GB CE has been a research co‐production effort among ranchers, public land managers, and researchers. Future directions for this research will center on expanding the experiment to multiple study areas to better address the scope of the annual grass/wildfire problem. We expect this research will lead to effective and sustainable grazing practices for restoring >41 million hectares of degraded rangelands in the Great Basin and other areas of the western United States.
Core Ideas Extensive areas of the Great Basin have been degraded by wildfire and cheatgrass invasion. Prescribed grazing may be a viable tool for promoting restoration of these degraded lands. The Great Basin Long‐Term Agroecosystem Research common experiment (CE) contrasts fixed moderate beef cattle stocking and duration, the prevailing practice on United States Department of the Interior Bureau of Land Management (BLM) public lands, with an alternative, prescribed grazing practice called high‐intensity low‐frequency cattle grazing. The ranchers, BLM, and the USDA‐ARS have co‐produced this research from its onset. Future directions include expanding the CE to additional study areas to address the broad scope of this problem.
Plain Language Summary Invasive species, wildfire, and climate change are principal disturbance threats to Great Basin agroecosystems. Prescribed grazing may be a tool for restoring lands degraded by these disturbances. Our CE contrasts a prevailing, cattle grazing practice of fixed moderate stocking and duration with an alternative practice of high‐intensity grazing of flexible duration tailored to suppress invasive annual grass competition in native or desirable species restorations. Since its inception, this has been a research co‐production effort among ranchers, public land managers, and researchers. Future directions will center on expanding the scope of the experiment to multiple study areas.
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Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant ...declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long-term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere-ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze-thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow-covered days. This shortening of the frost-covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil. Published in 2010 by John Wiley & Sons, Ltd.
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