[1] Ecohydrological systems may be characterized as nonlinear, complex, open dissipative systems. Such systems consist of many coupled processes, and the couplings change depending on the system state or scale in space and time at which the system is analyzed. The arrangement of couplings in a complex system may be represented as a network of information flow and feedback between variables that measure system processes. The occurrence of feedback on such a network provides sufficient conditions for self-organized and nonlinear behaviors to emerge. We adapt an information-theoretic statistical method called transfer entropy for the purposes of robustly measuring the directionality, relative strength, statistical significance, and time scale of information flow between pairs of ecohydrological variables using time series data. A process network may be delineated where variables are cast as nodes and information flows as weighted directional links between them. The process network captures key couplings and time scales and represents the state of the complex system as a whole, including functional groups of variables (subsystems) and synchronization resulting from feedbacks. It is therefore able to identify interactions which are not detectable using methods which examine the system using one relationship at a time. We assemble an information flow process network using July 2003 FLUXNET data for a Midwestern corn-soybean ecohydrological system in a healthy, peak growing season state and compare the results with those using July 2005 data for the same site during a severe drought. We find that the process network during drought is substantially decoupled, and regional-scale information feedback is reduced during the drought. We conclude that the proposed process network methodology is able to identify the differences between two states of an ecohydrological system on the basis of variations in the pattern of feedback coupling on the network.

Methane (CH4) exchange in wetlands is complex, involving nonlinear asynchronous processes across diverse time scales. These processes and time scales are poorly characterized at the whole‐ecosystem level, yet are crucial for accurate representation of CH4 exchange in process models. We used a combination of wavelet analysis and information theory to analyze interactions between whole‐ecosystem CH4 flux and biophysical drivers in two restored wetlands of Northern California from hourly to seasonal time scales, explicitly questioning assumptions of linear, synchronous, single‐scale analysis. Although seasonal variability in CH4 exchange was dominantly and synchronously controlled by soil temperature, water table fluctuations, and plant activity were important synchronous and asynchronous controls at shorter time scales that propagated to the seasonal scale. Intermittent, subsurface water table decline promoted short‐term pulses of methane emission but ultimately decreased seasonal CH4 emission through subsequent inhibition after rewetting. Methane efflux also shared information with evapotranspiration from hourly to multiday scales and the strength and timing of hourly and diel interactions suggested the strong importance of internal gas transport in regulating short‐term emission. Traditional linear correlation analysis was generally capable of capturing the major diel and seasonal relationships, but mesoscale, asynchronous interactions and nonlinear, cross‐scale effects were unresolved yet important for a deeper understanding of methane flux dynamics. We encourage wider use of these methods to aid interpretation and modeling of long‐term continuous measurements of trace gas and energy exchange.

Traditional infrastructure adaptation to extreme weather events (and now climate change) has typically been techno‐centric and heavily grounded in robustness—the capacity to prevent or minimize disruptions via a risk‐based approach that emphasizes control, armoring, and strengthening (e.g., raising the height of levees). However, climate and nonclimate challenges facing infrastructure are not purely technological. Ecological and social systems also warrant consideration to manage issues of overconfidence, inflexibility, interdependence, and resource utilization—among others. As a result, techno‐centric adaptation strategies can result in unwanted tradeoffs, unintended consequences, and underaddressed vulnerabilities. Techno‐centric strategies that lock‐in today's infrastructure systems to vulnerable future design, management, and regulatory practices may be particularly problematic by exacerbating these ecological and social issues rather than ameliorating them. Given these challenges, we develop a conceptual model and infrastructure adaptation case studies to argue the following: (1) infrastructure systems are not simply technological and should be understood as complex and interconnected social, ecological, and technological systems (SETSs); (2) infrastructure challenges, like lock‐in, stem from SETS interactions that are often overlooked and underappreciated; (3) framing infrastructure with a SETS lens can help identify and prevent maladaptive issues like lock‐in; and (4) a SETS lens can also highlight effective infrastructure adaptation strategies that may not traditionally be considered. Ultimately, we find that treating infrastructure as SETS shows promise for increasing the adaptive capacity of infrastructure systems by highlighting how lock‐in and vulnerabilities evolve and how multidisciplinary strategies can be deployed to address these challenges by broadening the options for adaptation.

Emerging interdisciplinary science efforts are providing new understanding of the interdependence of food, energy, and water (FEW) systems. These science advances, in turn, provide critical information for coordinated management to improve the affordability, reliability, and environmental sustainability of FEW systems. Here we describe the current state of the FEW nexus and approaches to managing resource conflicts through reducing demand and increasing supplies, storage, and transport. Despite significant advances within the past decade, there are still many challenges for the scientific community. Key challenges are the need for interdisciplinary science related to the FEW nexus; ground‐based monitoring and modeling at local‐to‐regional scales; incorporating human and institutional behavior in models; partnerships among universities, industry, and government to develop policy relevant data; and systems modeling to evaluate trade‐offs associated with FEW decisions.

[1] There is no consensus on how changes in both temperature and precipitation will affect regional vegetation. We investigated controls on hydrologic partitioning at the catchment scale across many different ecoregions, and compared the resulting estimates of catchment wetting and vaporization (evapotranspiration) to remotely sensed indices of vegetation greenness. The fraction of catchment wetting vaporized by plants, known as the Horton index, is strongly related to the ratio of available energy to available water at the Earth's surface, the aridity index. Here we show that the Horton index is also a function of catchment mean slope and elevation, and is thus related to landscape characteristics that control how much and how long water is retained in a catchment. We compared the power of the components of the water and energy balance, as well as landscape characteristics, to predict Normalized Difference Vegetation Index (NDVI), a surrogate for vegetation productivity, at 312 Model Parameter Estimation Experiment (MOPEX) catchments across the United States. Statistical analysis revealed that the Horton index provides more precision in predicting maximum annual NDVI for all catchments than mean annual precipitation, potential evapotranspiration, or their ratio, the aridity index. Models of vegetation productivity should emphasize plant-available water, rather than just precipitation, by incorporating the interaction of climate and landscape. Major findings related to the Horton index are: (1) it is a catchment signature that is relatively constant from year-to-year; (2) it is related to specific landscape characteristics ; (3) it can be used to create catchment typologies; and (4) it is related to overall catchment greenness.

Context With rapidly expanding urban regions, the effects of land cover changes on urban surface temperatures and the consequences of these changes for human health are becoming progressively larger problems. Objectives We investigated residential parcel and neighborhood scale variations in urban land surface temperature, land cover, and residents' perceptions of landscapes and heat illnesses in the subtropical desert city of Phoenix, AZ USA. MethodsWe conducted an airborne imaging campaign that acquired high resolution urban land surface temperature data (7 m/pixel) during the day and night. We performed a geographic overlay of these data with high resolution land cover maps, parcel boundaries, neighborhood boundaries, and a household survey. Results Land cover composition, including percentages of vegetated, building, and road areas, and values for NDVI, and albedo, was correlated with residential parcel surface temperatures and the effects differed between day and night. Vegetation was more effective at cooling hotter neighborhoods. We found -015-0284-3 consistencies between heat risk factors in neighborhood environments and residents' perceptions of these factors. Symptoms of heat-related illness were correlated with parcel scale surface temperature patterns during the daytime but no corresponding relationship was observed with nighttime surface temperatures. Conclusions Residents' experiences of heat vulnerability were related to the daytime land surface thermal environment, which is influenced by micro-scale variation in land cover composition. These results provide a first look at parcel-scale causes and consequences of urban surface temperature variation and provide a critically needed perspective on heat vulnerability assessment studies conducted at much coarser scales.Landscape Ecol (2016) 31:745-760 DOI 10.1007/s10980

[1] Ecohydrological systems are complex, open dissipative systems characterized by couplings and feedback between subsystems at many scales of space and time. The information flow process network approach is developed to analyze such systems, using time series data to delineate the feedback, time scales, and subsystems that define the complex system's organization. Network statistics are used to measure the statistical feedback, entropy, and net and gross information production of subsystems on the network to study monthly process networks for a Midwestern corn-soybean ecosystem for the years 1998-2006. Several distinct system states are identified and characterized. Particularly interesting is the midsummer state that is dominated by regional-scale information feedback and by information flow originating from the ecosystem's photosynthetic activity. In this state, information flows both ''top-down'' from synoptic weather systems and ''bottom-up'' from the plant photosynthetic activity. A threshold in air temperature separates this summer state where increased organization appears from other system states. The relationship between Shannon entropy and information flow is investigated. It is found that information generally flows from high-entropy variables to low-entropy variables, and moderate-entropy variables participate in feedback.

ABSTRACT:Observations of local-scale urban surface energy balance (SEB), which include fluxes of net all-wave radiation (Q*), and eddy covariance measurements of sensible (Q H ) and latent heat (Q E ) were collected in an arid Phoenix, AZ suburb from January to December 2012. We studied diurnal variations in SEB partitioning over four distinct seasons: winter, equinoxes, and summer; the latter period is further subdivided into (1) months prior to and (2) months occurring during the North American Monsoon. Largest flux densities were observed in summer, with most available energy partitioned into Q H . Much less energy is partitioned into Q E , but this term is strongly affected by monsoonal precipitation, where greater-than-average Q E can be discerned for several days after storm events. The presence of a positive daily flux residual (RES) [i.e. Q* − (Q H + Q E )] for most of the summer indicates that anthropogenic heat (Q F ) from residential cooling is likely a significant factor influencing SEB. Analysis of hourly ensemble SEB fluxes during all seasons also indicates that RES is largest in the morning, but Q H dominates in the afternoon. Results of SEB trends and magnitudes from Phoenix were also compared with other urban sites, especially in (sub)tropical cities. When normalized with net radiation terms, a consistent diurnal hysteresis between ensemble Q H and RES occurs, suggesting a robust parameterization of this relationship for model development during clear-sky conditions. SEB dynamics also appear to be affected by local surface characteristics, with regular nocturnal negative Q H associated with a high urban sky-view factor. Measured Q E fluxes during dry seasons were larger than expected based on the small proportion of irrigated plan area vegetated surfaces. A probable explanation could be an enhanced micro-scale advective forcing of evapotranspiration arising from leading-edge effects over patchy residential lawns, which has possible implications for modelling evapotranspiration in hot arid cities.