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On behalf of the journal, AGU, and the scientific community, the Editors would like to sincerely thank those who reviewed manuscripts for Geophysical Research Letters in 2018. The hours reading and commenting on manuscripts not only improves the manuscripts but also increases the scientific rigor of future research in the field. We particularly appreciate the timely reviews, in light of the demands imposed by the rapid review process at Geophysical Research Letters. With the revival of the “major revisions” decisions, we appreciate the reviewers' efforts on multiple versions of some manuscripts. Many of those listed below went beyond and reviewed three or more manuscripts for our journal, and those are indicated in italics. In total, 4,484 referees contributed to 7,557 individual reviews in journal. Thank you again. We look forward to the coming year of exciting advances in the field and communicating those advances to our community and to the broader public.

Advances in computing technology, new and ongoing restoration initiatives, concerns about climate change's effects, and the increasing interdisciplinarity of research have encouraged the development of landscape-scale mechanistic models of coupled ecological-geophysical systems. However, communication barriers and uneven infiltration of new strategies for data-driven induction persist in the context of simulation model development across disciplines. One challenge is that ecology and the geosciences have embraced different modeling epistemologies, with ecologists historically favoring inductive inference from generalized, phenomenological models and geoscientists favoring deductive inference from detailed first-principles models. Today, many models used for environmental management, particularly for aquatic ecosystems, tend to be highly detailed, with ecological and geophysical components represented in different modules that are linked but often not closely integrated. These observations highlight a need for cross-disciplinary dialogue about landscape-scale modeling objectives and approaches. The philosophies of pattern-oriented modeling in ecology and exploratory modeling in geophysics have yielded advances in theoretical and applied knowledge in both of those disciplines, but they are not comprehensive across all aspects of landscape-scale modeling. Here we define and synthesize the “Appropriate-Complexity Method” (ACME), which builds upon these two philosophies to guide the development of process-oriented models across a spectrum of scientific and management objectives. ACME helps modelers efficiently converge upon an optimal modeling structure through: i) systematic evaluation of the attributes that comprise computational and representational detail, for which we have developed an operational decision tree; ii) iterative adjustment of models based on pattern-oriented model evaluation strategies; and iii) the use of appropriate datasets (where applicable) to build conceptual models and formulate predictions. Decisions about aspects of computational and representational detail are based on the landscape's emergent properties. They are also based on a hierarchy of classes of questions governing model objectives that represent a multi-attribute tradeoff among validation potential, interpretability, tractability, and generality as functions of computational and representational detail. Tradeoff curves, together with model objectives, provide further guidance for determining the “appropriate” level of complexity for representation of processes in models. Once deemed adequate for addressing the original research question of interest, models may be used for projection and scenario testing. They may next undergo expansion that moves them down the hierarchy, where they can then be used to address research questions of higher specificity, detail, and validation potential, though at a cost of lower tractability and interpretability on the tradeoff curves. This practical, systematic procedure provides clear guidance for the design and improvement of landscape models that may be used to address a wide variety of questions relevant to restoration, over a spectrum of scales.

As humans continue to inhabit and modify river deltas, the natural processes governing material transport through these landscapes are altered. Two common engineering projects undertaken on deltas are the dredging of channels to enable shipping and the construction of embankments to reduce flooding. While the impact of these topographic modifications has been studied at a local level for specific sites, there is a gap in our generalized understanding of how these landscape modifications impact material transport. To narrow this gap, we conduct exploratory numerical modeling to develop deltaic landscapes with different input sediment compositions, modify their topography to mimic dredging and embankments, simulate different flow conditions, and then model the transport of passive particles. We find that human modification of topography lowers hydrological connectivity by reducing the area visited by fluvial inputs. The amount of time particles spend within the delta is reduced by the construction of polders and is lengthened by dredging. Material buoyancy has a greater impact on nourishment areas and exposure times than flow regime or topographic modification, with positively buoyant particles spending longer and visiting a greater area of the delta than neutral and negatively buoyant material. The results of this study can help guide the design of future engineering projects by providing estimates of their likely impact on transport processes. [ABSTRACT FROM AUTHOR]