Delta shoreline structure has long been hypothesized to encode information on the relative influence of fluvial, wave, and tidal processes on delta formation and evolution. We introduce here a novel multiscale characterization of shorelines by defining three process-informed morphological metrics. We show that this characterization yields self-emerging classes of morphologically similar deltas, i.e., delta morphotypes, and also predicts the dominant forcing of each morphotype. Then we show that the dominant forcings inferred from shoreline structure generally align with those estimated via relative sediment fluxes, while positing that misalignments arise from spatiotemporal heterogeneity in deltaic sediment fluxes not captured in their estimates. The proposed framework for shoreline characterization advances our quantitative understanding of how shoreline features reflect delta forcings, and may aid in deciphering paleoclimate from images of ancient deposits and projecting delta morphologic response to changes in sediment fluxes.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of coupled cloud physics and land-surface models. Here, we use functional decomposition of satellite-derived SM (SMAP/L4) and cloud vertical profiles (CVP: GPM/DPR/L2A) in the central US to quantify the relationship between SM and the vertical distribution of cloud water. High-dimensional model representation disentangles the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show the sign and strength of this feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in the antecedent 7-hour SM and land-surface temperature can increase reflectivity up to 4 dBZ in the lower atmosphere (1-3 km above the surface). The presented approach brings new insights into observational understanding of SM-precipitation feedback and possesses the potential for diagnosing cloud models regarding land-atmosphere coupling representation.

Wave-influenced deltas are the most abundant delta type and are also potentially the most at-risk to human-caused changes, owing to the effects of wave-driven sediment transport processes and the short timescales on which they operate. Despite this, the processes controlling wave-influenced growth are poorly understood, and the role of fine-grained cohesive sediment (mud) is typically neglected. Here we simulate idealized river deltas in Delft3D across a range of conditions to interrogate how relative wave-influence and fluvial sediment composition impact delta evolution on decadal-millennial timescales. Our simulations capture the barrier-spit formation and accretion process characteristic of prograding wave-influenced deltas, such as those of the Red (Vietnam), Sinu (Colombia), and Coco (Nicaragua) rivers. Barrier-spit accretion exhibits multi-decadal cyclicity driven by subaqueous accumulation of fluvial sediment near river mouths. Using a range of metrics, we quantify how waves and mud influence delta morphology and dynamics. Results show that waves stabilize and simplify channel networks, smooth shorelines, increase shoreline reworking rates, reduce mud retention in the delta plain, and rework mouth bar sediments to form barrier-spits. Higher fluvial mud concentrations produce simpler and more stable distributary networks, rougher shorelines, and limit back-barrier lagoon preservation without altering shoreline reworking rates. Our findings reveal distinct controls on shoreline change between river-dominated and wave-influenced deltas and demonstrate that mud plays a critical role in delta evolution, even under strong wave influence. These insights could enhance paleoenvironmental reconstructions and inform predictions of delta responses to climate and land-use changes.

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. The advantages of wsPCA are its ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intra-seasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the complex wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent.

We present a new metric for braiding intensity to characterize multi-thread systems (e.g., braided and anastomosing rivers) called the Entropic Braiding Index, eBI. This metric is a generalization of the widely used Braiding Index (BI) which is simply the average count of intercepted channels per cross-section. The co-existence of diverse channels (widely different widths and discharges) within river cross-sections distorts the information conveyed by BI, since its value does not reflect the diversity and natural variability of the system. Moreover, the fact that BI is extremely sensitive to resolution (BI increases at higher resolution as smaller scale channels can be resolved) challenges its applicability. eBI, addresses these main drawbacks of BI. eBI is rooted in the concept of Shannon Entropy, and its value can be intuitively interpreted as the equivalent number of equally important (in terms of discharge) channels per cross-section. Thus, if the channels observed in a multi-thread system are all carrying the same amount of discharge, eBI has the same value of BI. On the other hand, if a very dominant channel in terms of discharge co-exists with much smaller channels, eBI would take a value slightly larger than 1 (note that the actual value would depend on the number of small channels and their relative size with respect to the dominant channel). We present a comparative study of BI and eBI for different multi-thread rivers obtained from numerical simulations and remote-sensing data and for different discharge stages. We also provide evidence of the robustness of eBI in contrast to BI when a given river system is studied under different resolutions. Finally, we explore the potential of eBI as a metric to characterize different types of multi-thread systems and their stability.

A long-standing question in geomorphology concerns the applicability of statistical models for elevation data based on fractal or multifractal representations of terrain. One difficulty with addressing this question has been the challenge of ascribing statistical significance to metrics adopted to measure landscape properties. In this paper, we use a recently developed surrogate data algorithm to generate synthetic surfaces with identical elevation values as the source dataset, while also preserving the value of the Hölder exponent at any point (the underpinning characteristic of a multifractal surface). Our primary data are from an experimental study of landscape evolution. This allows us to examine how the statistical properties of the surfaces evolve through time and the extent to which they depart from the simple (multi)fractal formalisms. We also study elevation data from Florida and Washington State. We are able to show that the properties of the experimental and actual terrains depart from the simple statistical models. Of particular note is that the number of sub-basins of a given channel order (for orders sufficiently small relative to the basin order) exhibit a clear increase in complexity after a flux steady-state is established in the experimental study. The actual number of basins is much lower than occur in the surrogates. The imprint of diffusive processes on elevation statistics means that, at the very least, a stochastic model for terrain based on a local formalism needs to consider the joint behavior of the elevations and their scaling (as measured by the pointwise Hölder exponents).

Understanding how thermokarst lakes on arctic river deltas will respond to rapid warming is critical for projecting how carbon storage and fluxes will change in those vulnerable environments. Yet, this understanding is currently limited partly due to the complexity of disentangling significant interannual variability from the longer-term surface water signatures on the landscape, using the summertime window of optical spaceborne observations. Here, we rigorously separate perennial lakes from ephemeral wetlands on 12 arctic deltas and report distinct size distributions and climate trends for the two waterbodies. Namely, we find a lognormal distribution for lakes and a power-law distribution for wetlands, consistent with a simple proportionate growth model and inundated fractal topography, respectively. Furthermore, while no trend with temperature is found for wetlands, a statistically significant decreasing trend of mean lake size with warmer temperatures is found, attributed to colder deltas having deeper and thicker permafrost preserving larger lakes.

Understanding the complex interplay between erosional and depositional processes, and their relative roles in shaping landscape morphology is a question at the heart of geomorphology. A unified framework for examining this question can be developed by simultaneously considering terrain elevation statistics over multiple scales. We show how a long-standing tool for landscape analysis, the elevation-area or hypsometry, can be complemented by an analysis of the elevation scalings to produce a more sensitive tool for studying the interplay between processes, and their impact on morphology. We then use this method, as well as well-known geomorphic techniques (slope-area scaling relations, the number of basins and basin size as a function of channel order) to demonstrate how the complexity of an experimental landscape evolves through time. Our primary result is that the complexity increases once a flux equilibrium is established as a consequence of the role of diffusive processes acting at intermediate elevations. We gauge landscape complexity by comparing results between the experimental landscape surfaces and those produced from a new algorithm that fixes in place the elevation scaling statistics, but randomizes the elevations with respect to these scalings. We constrain the degree of randomization systematically and use the amount of constraint as a measure of complexity. The starting point for the method is illustrated in the figure, which shows the original landscape (top-left) and three synthetic variants generated with no constraints to the randomization. The value quoted in these panels is the root-mean-squared difference in the elevation values for the synthetic cases relative to the original terrain. This value is greatest where the original ridge becomes a valley. All these landscapes contain the same elevation values (i.e. the same probability distribution functions), and the same elevation scalings at a point. The differences emerge because the elevations themselves are distributed randomly across the surface.

This paper presents the results of an ensemble data assimilation methodology over the Wasserstein space for high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic model of atmospheric circulation. Unlike Euclidean data assimilation, this approach is equipped with a Riemannian geometry and formulates data assimilation as a Wasserstein barycenter between the forecast probability distribution and the normalized likelihood function. The methodology does not rely on any Gaussian assumptions and can intrinsically treat systematic model and observation errors. To cope with the computational cost of the Wasserstein distance, the paper examines the efficiency of the entropic regularization. Comparisons with the standard particle and stochastic ensemble Kalman filters demonstrate that under systematic errors the presented methodology could extend the forecast skills of nonlinear dynamical systems.

Wildfire indices are used globally to quantify and communicate a wide range of fire characteristics, including fire danger and fire behaviour. Wildfire terminologies, definitions and variables used to compute fire indices vary broadly. This makes it difficult to compare them under a common framework for regional assessment and for future improvements under changing climate and land-use/land-cover conditions. This paper reviews 24 fire indices used worldwide and proposes a simple framework within which they can be classified based on constitutive inputs used for their computation. We differentiate between constitutive inputs that are raw or directly measurable variables such as fuel, weather and topography (referred to as Level 1 inputs) and calculated constitutive inputs such as fuel moisture (as a function of ecology and hydrometeorology); fire behaviour (as a function of spread, energy, and ignition); and dynamic meteorology. These six calculated constitutive inputs are referred to as Level 2 inputs. Based on this classification, our findings indicate that the Burning Index from the United States National Fire Danger Rating System (NFDRS) and the Fire Weather Index from the Canadian Forest Fire Danger Rating System (CFFDRS), used by many countries worldwide, utilize the most comprehensive set of Level 2 inputs. In addition, the Level 2 input that is most frequently used by all fire indices is fuel moisture as a function of hydrometeorology and the least integrated input is that of fire ignition. We further group the fire indices in three types: fire weather, fire behaviour, and fire danger indices, according to the open literature definition of their predictant outputs and examine the specific constitutive inputs used in their computation. Most fire indices are based on Level 2 inputs (which use Level 1 inputs) and are predominantly fire danger and fire behaviour indices. This is followed by fire indices that use a combination of both Level 1 and Level 2 inputs, separately and are mostly fire danger indices. Only a few fire indices are computed solely with raw Level 1 inputs and are mainly fire behaviour indices. Providing a comprehensive view of the existing wildfire indices’ utilization and computational structure is expected to be a helpful resource for wildfire researchers and operational experts worldwide. 2

The Braiding Index (BI), defined as the average count of intercepted channels per cross-section, is a widely used metric for characterizing multi-thread river systems. However, it does not account for the diversity of channels (e.g., in terms of discharge) within different cross-sections, omitting important information related to system complexity. Here we present a modification of BI (the Entropic Braiding Index, eBI) which augments the information content in BI by using Shannon Entropy to encode the diversity of channels in each cross section. eBI is interpreted as the number of “effective channels” per cross-section, allowing a direct comparison with the traditional BI. We demonstrate the superior capabilities of eBI via analysis of synthetic, numerical and field examples. In addition, we show that interrogating cross-sections via the ratio BI/eBI has the potential to quantify channel disparity, differentiate types of multi-thread systems, and inform about cross-section stability to forcing variability (e.g., seasonal flooding).