Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought.

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.

Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales.

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

From hillslope to small catchment scales (< 50 km2), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m2) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a - 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.

Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape.

Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO2) 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 (r2 = 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.