As climate changes, many regions of the world are projected to experience more intense droughts, which can drive changes in plant community composition through a variety of mechanisms. During drought, community composition can respond directly to resource limitation, but biotic interactions modify the availability of these resources. Here, we develop the Community Response to Extreme Drought framework (CRED), which organizes the temporal progression of mechanisms and plantplant interactions that may lead to community changes during and after a drought. The CRED framework applies some principles of the stress gradient hypothesis (SGH), which proposes that the balance between competition and facilitation changes with increasing stress. The CRED framework suggests that net biotic interactions (NBI), the relative frequency and intensity of facilitative (+) and competitive (À) interactions between plants, will change temporally, becoming more positive under increasing drought stress and more negative as drought stress decreases. Furthermore, we suggest that rewetting rates affect the rate of resource amelioration, specifically water and nitrogen, altering productivity responses and the intensity and importance of NBI, all of which will influence droughtinduced compositional changes. System-specific variables and the intensity of drought influence the strength of these interactions, and ultimately the system's resistance and resilience to drought.

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change.Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two Accepted ArticleThis article is protected by copyright. All rights reserved remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change and broadening the impact of ecological research.

A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.

Soil water storage dynamics link landscape and climate processes with consequent impacts on hydrologic and biogeochemical cycles, and on ecosystem functions. We explore regional-scale, spatiotemporal dynamics of root-zone soil water storage using spatially explicit, long-term simulations from a land data assimilation system over an area of ∼2 million km 2 in the U.S. Midwest. We synthesize the dataset to examine spatial patterns of hydroclimatic forcing and soil and vegetation properties to identify the key drivers of soil water storage across the region. To identify differences in temporal hydrologic variability between sites with different soil and vegetation characteristics, we focus on daily data from 10 locations, representative of three major land cover types in the region. We use the concepts of (a) soil water memory to evaluate differences in landscape buffering of rainfall and (b) persistence to evaluate the threshold-crossing properties of statistically defined "wet" and "dry" soil water conditions. Power spectral analyses of soil water storage reveal that regionally consistent patterns emerge in memory at multiple temporal scales. Threshold-crossing analyses reveal corresponding similarity in persistence between sites. The analyses show that stochastic rainfall is the key driver of landscape hydrologic dynamics, with rainfall frequency as the primary determinant of persistence across the region, whereas differences in land cover and soil properties across the region have second-order influence. The synthesis approach we present here is useful for ecological and agricultural risk assessments of climate change impacts, including increasing frequency of extreme events such as droughts and floods, but primarily points to the need for better understanding of future precipitation changes.

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