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As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments., Key Points Simulations reproduce observed drought duration and severity in terms of the Standardized Precipitation Index (SPI) but simulated low precipitation is considerably biasedVariance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zonesGood model performance in terms of SPI does not imply that low precipitation values are well simulated by the climate models

This paper presents the results of the Ensemble Riemannian Data Assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in Lorenz-96 (QG) model.

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Non-point source pollution of phosphorus (P) is a primary cause of eutrophication of aquatic ecosystems, and poses a persistent management challenge due to the dynamic and poorly understood processes controlling the transport and transformation of P at the watershed scale. We examined phosphorus inputs, retention, and riverine losses in 62 diverse watersheds that included a wide range of land cover and use (minimally disturbed to human dominated) and human P inputs in Minnesota, USA. Fertilizer inputs from row crop cultivation were the dominant source of P to agricultural watersheds. A large majority of P inputs was retained in watershed soils or removed in agricultural products. However, fertilizer input was the most important factor associated with average annual river transport of total, dissolved, and particulate phosphorus (PP). Mean annual runoff increased total and dissolved P yields and decreased P retention. Dissolved P made up a significant portion of annual river loads at sites with high rates of P inputs and river TP export, with the ratio of dissolved to PP export increasing with crop cover and fertilizer inputs. PP export rose with larger extents of eroding bluffs near channels. Bluffs constitute only a small proportion of watershed area, but make a disproportionately high contribution to sediment loads due to their close proximity to river channels that are increasingly affected by human and climate-driven changes to river hydrology. Together, our results suggest that rising discharge and flow variability due to climate change and agricultural intensification coupled with high rates of P inputs will maintain elevated fluvial P export into the future. Without reductions in P inputs and reductions in both soluble P losses and stream bank erosion, reversal of water quality degradation will be difficult to achieve.

Author(s): Mamalakis, Antonios; Yu, Jin-Yi; Randerson, James T; AghaKouchak, Amir; Foufoula-Georgiou, Efi

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

The quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100 kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5 mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal climate variability, having profound impacts on a wide 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. By construction, wsPCA has the 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 intraseasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the 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 also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent., Key Points A wavelet‐based spectral principal component analysis is used to examine CMIP6 models in reproducing the Madden‐Julian Oscillation (MJO)CMIP6 models capture the average MJO propagation speed but significantly underestimate the MJO contribution to the total intraseasonal climate variabilityPrecipitation variability related to MJO over the Amazonia, Southwest Africa, and Maritime Continent is underestimated in CMIP6 models

Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4±5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

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...

Bedload particle hops are defined as successive motions of a particle from start to stop, characterizing one of the most fundamental processes describing bedload sediment transport in rivers. Although two transport regimes have been recently identified for short- and long-hops, respectively (Wu et al., Water Resour Res, 2020), there still lacks a theory explaining how the mean hop distance-travel time scaling may extend to cover the phenomenology of bedload particle motions. Here we propose a velocity-variation based formulation, and for the first time, we obtain analytical solution for the mean hop distance-travel time relation valid for the entire range of travel times, which agrees well with the measured data (Wu et al., J Fluid Mech, 2021). Regarding travel times, we identify three distinct regimes in terms of different scaling exponents: respectively as ~1.5 for an initial regime and ~5/3 for a transition regime, which define the short-hops; and 1 for the so-called Taylor dispersion regime defining long-hops. The corresponding probability density function of the hop distance is also analytically obtained and experimentally verified.

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space–time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction.

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system., Key Points We developed a global fire emissions forecasting system with lead times of 1 to 6 months, building on a statistical ARIMAX modeling frameworkThe system draws upon different data sources to optimize fire prediction in over 1,300 individual fire cohesive regionsOur results suggested that the joint use of past fire observations within the fire season and climate data can improve S2S fire prediction

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...

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 addres...

The channelization cascade observed in terrestrial landscapes describes the progressive formation of large channels from smaller ones starting from diffusion-dominated hillslopes. This behavior is reminiscent of other non-equilibrium complex systems, particularly fluids turbulence, where larger vortices break down into smaller ones until viscous dissipation dominates. Based on this analogy, we show that topographic surfaces emerging between parallel zero-elevation boundaries present a logarithmic scaling in the mean-elevation profile, which resembles the well-known logarithmic velocity profile in wall-bounded turbulence. Within this region of elevation fluctuation, the power spectrum exhibits a power-law decay resembling the Kolmogorov -5/3 scaling of turbulence. We also demonstrate that similar scaling behaviors emerge in surfaces from a laboratory experiment, natural basins, and constructed following optimality principles. In general, we show that the steady-state solutions of the governing equations of landscape evolution are the stationary surfaces of a functional defined as the average domain elevation. Depending on the exponent of the specific drainage area in the erosion term (m), the steady-state surfaces are local minimum (m1) of the average domain elevation.

The abundant lakes dotting arctic deltas are hotspots of methane emissions and biogeochemical activity, but seasonal variability in lake extents introduces uncertainty in estimates of lacustrine carbon emissions, typically performed at annual or longer time scales. To characterize variability in lake extents, we analyzed summertime lake area loss (i.e., shrinkage) on two deltas over the past 20 years, using Landsat‐derived water masks. We find that monthly shrinkage rates have a pronounced structured variability around the channel network with the shrinkage rate systematically decreasing farther away from the channels. This pattern of shrinkage is predominantly attributed to a deeper active layer enhancing near‐surface connectivity and storage and greater vegetation density closer to the channels leading to increased evapotranspiration rates. This shrinkage signal, easily extracted from remote sensing observations, may offer the means to constrain estimates of lacustrine methane emissions and to develop process‐based estimates of depth to permafrost on arctic deltas., Key Points Summertime lake shrinkage in arctic deltas is highly structured around the channel networkHigher shrinkage rates closer to channels are attributed to enhanced near‐surface flow and storage as well as greater vegetation densityLake methane emission estimates on arctic deltas should account for spatially and seasonally variable lake extents to reduce uncertainty

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 ...

Satellite precipitation products are essential for global analysis of water cycle dynamics as well as for regional analyses in regions where no ground observations are available. For any climatic or hydrologic application, it is important to know down to which scale a gridded satellite precipitation product can accurately resolve the spatial patterns of precipitation. This scale, which we call “effective resolution”, is a complex combination of the instrument resolution (especially so for multisensor products such as IMERG), the multi-sensor retrieval or merging algorithm, and the type of the precipitating system, and it can differ substantially from the grid size of the satellite product. Here, we use a wavelet-based framework to quantitatively define the effective resolution of the IMERG multi-satellite product by comparison with the MRMS ground radar product at the hourly time scale over the continental United States. Our findings show that the effective resolution varies across geographical areas, seasons and types of precipitation and provide insight for the use of those products in hydrologic applications and for algorithmic improvements.

Tidal asymmetry in estuaries and lagoons (tidal basins) controls residual sediment transport, and quantifying tidal asymmetry is important for understanding the factors contributing to long-term morphological changes. Asymmetry in peak flood and ebb currents (Peak Current Asymmetry – PCA) controls residual transport of coarse sediment, and asymmetry in slack water duration preceding flood and ebb currents (Slack Water Asymmetry – SWA) controls residual transport of fine sediment. PCA and SWA are analyzed herein for Newport Bay, a tidal embayment in southern California, based on the skewness of tidal currents predicted for several stations by a hydrodynamic model. Use of skewness for tidal asymmetry is relatively new and offers several advantages over a traditional harmonic method including the ability to resolve variability over a wide range of time scales. Newport Bay is externally forced by mixed oceanic tides that are shown to be ebb dominant because of shorter falling tide than rising tide. Both PCA and SWA indicate ebb dominance that favors export of coarse and fine sediments, respectively, to the coastal ocean. However, we show that the ebb dominance of SWA is derived from the basin geometry and not the external forcing, while ebb dominance of PCA is linked to the external forcing and the basin geometry. We also show that tidal flats in Newport Bay play an important role in maintaining ebb dominated transport of both coarse and fine sediments. Loss of tidal flats could weaken PCA and reverse SWA to become flood dominant. Specifically, we show that sea level rise > 0.8 m that inundates tidal flats will begin to weaken ebb dominant PCA and SWA and that sea level rise > 1.0 m will reverse SWA to become flood dominant. This feedback mechanism is likely to be important for predicting long-term evolution of tidal basins under accelerating sea level rise.

Expansion and intensification of managed landscapes for agriculture have resulted in severe unintended global impacts, including degradation of arable land and eutrophication of receiving water bodies. Modern agricultural practices rely on significant direct and indirect human energy inputs through farm machinery and chemical use, respectively, which have created imbalances between increased rates of biogeochemical processes related to production and background rates of natural processes. We articulate how these imbalances have cascaded through the deep inter-dependencies between carbon, soil, water, nutrient and ecological processes, resulting in a critical transition of the critical zone and creating emergent inter-dependencies and co-evolutionary trajectories. Understanding of these novel organizations and function of the critical zone is vital for developing sustainable agricultural practices and environmental stewardship.

This research was funded by NSF grant EAR-1209402 under the Water Sustainability and Climate Program (WSC): REACH (REsilience under Accelerated CHange)

Intensively managed row crop agriculture has fundamentally changed Earth surface processes within the Mississippi River basin through large-scale alterations of land cover, hydrology and reactive nitrogen availability. These changes have created leaky landscapes where excess agriculturally derived nitrate degrades riverine water quality at local, regional and continental scales. Individually, wetlands are known to remove nitrate but the conditions under which multiple wetlands meaningfully reduce riverine nitrate concentration have not been established. Only one region of the Mississippi River basin—the 44,000 km2 Minnesota River basin—still contains enough wetland cover within its intensively agriculturally managed watersheds to empirically address this question. Here we combine high-resolution land cover data for the Minnesota River basin with spatially extensive repeat water sampling data. By clearly isolating the effect of wetlands from crop cover, we show that, under moderate–high streamflow, wetlands are five times more efficient per unit area at reducing riverine nitrate concentration than the most effective land-based nitrogen mitigation strategies, which include cover crops and land retirement. Our results suggest that wetland restorations that account for the effects of spatial position in stream networks could provide a much greater benefit to water quality then previously assumed.

The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)—the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the “effective resolution” of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed.

Climatic or geologic controls, such as tectonics or glacial drainage, might impose constraints on landscape self-organization resulting in spatial patterns of rivers and valleys which do not obey the typical self-similar relationships found in most landscapes. The goal of this study is to quantify how such geologic constraints express themselves on channel network topology, spatial heterogeneity of drainage patterns, and emergence of preferred scales of landscape dissection. We use as an example a basin located in the Upper Midwestern United States where successive glaciations over the past thousand years have led to a pronounced spatially anisotropic channel network structure which defeats most scaling laws of fluvial landscapes. This is contrasted with another river basin in the North-Central U.S. which has been organized under the absence of major geologic influences and follows a typical self-similar channel network organization. We show how the geologic constraints have imposed a competition for space which is captured in the slope–local drainage density probabilistic structure, in the failure of self-similarity in basin-wide river network topology, and in the length-area scaling relationship being not typical of fluvial landscapes. Via a two-dimensional wavelet analysis and synthesis, we demonstrate the occurrence of a gap in the power spectrum which corresponds to the presence of preferred scales of organization, and characterize them through multi-scale detrending. The developed methodologies can be useful in advancing our geomorphologic understanding of how external controls might manifest themselves in creating a landscape dissection that is outside the norm and how this dissection can be studied objectively for understanding cause and effect.

Meandering river planform evolution is driven by the interaction of local nonlinear processes and cutoff dynamics. Despite the known nonlinear dynamics governing the evolution of meandering rivers, previous attempts have found at most a weak signature of these process nonlinearities on the static meander planform morphologies (form nonlinearities). In this work, we present a framework to measure form nonlinearity from centerline curvature signals and unambiguously quantify its presence in both a numerically simulated meandering river and three natural rivers. The degree of nonlinearity (DNL) metric is introduced to measure the strength of form nonlinearities embedded in the centerlines. The DNL's evolution through time is computed for annual observations over 30 years of an active, tropical meandering river and for the simulated centerline to understand how cutoffs and bend growths affect form nonlinearity. We find that although cutoffs reduce the overall form nonlinearity, they also act as a source of nonlinearity themselves by creating scales that contribute disproportionately to DNL.

Understanding how sediment moves along source to sink pathways through watersheds—from hillslopes to channels and in and out of floodplains—is a fundamental problem in geomorphology. We contribute to advancing this understanding by modeling the transport and in-channel storage dynamics of bed material sediment on a river network over a 600 year time period. Specifically, we present spatiotemporal changes in bed sediment thickness along an entire river network to elucidate how river networks organize and process sediment supply. We apply our model to sand transport in the agricultural Greater Blue Earth River Basin in Minnesota. By casting the arrival of sediment to links of the network as a Poisson process, we derive analytically (under supply-limited conditions) the time-averaged probability distribution function of bed sediment thickness for each link of the river network for any spatial distribution of inputs. Under transport-limited conditions, the analytical assumptions of the Poisson arrival process are violated (due to in-channel storage dynamics) where we find large fluctuations and periodicity in the time series of bed sediment thickness. The time series of bed sediment thickness is the result of dynamics on a network in propagating, altering, and amalgamating sediment inputs in sometimes unexpected ways. One key insight gleaned from the model is that there can be a small fraction of reaches with relatively low-transport capacity within a nonequilibrium river network acting as “bottlenecks” that control sediment to downstream reaches, whereby fluctuations in bed elevation can dissociate from signals in sediment supply.

Changes in climate and agricultural practices are putting pressure on agro-environmental systems all over the world. Predicting the effects of future management or conservation actions has proven exceptionally challenging in these complex landscapes. We present a perspective, gained from a decade of research and stakeholder involvement in the Minnesota River Basin, where research findings have influenced solutions and policy in directions not obvious at the outset. Our approach has focused on identifying places, times, and processes of significant change and developing reduced complexity predictive frameworks that can inform mitigation actions.

Quantifying planform changes of large and actively migrating rivers such as those in the tropical Amazon at multidecadal time scales, over large spatial domains, and with high spatiotemporal frequency is essential for advancing river morphodynamic theory, identifying controls on migration, and understanding the roles of climate and human influences on planform adjustments. This paper addresses the challenges of quantifying river planform changes from annual channel masks derived from Landsat imagery and introduces a set of efficient methods to map and measure changes in channel widths, the locations and rates of migration, accretion and erosion, and the space-time characteristics of cutoff dynamics. The techniques are assembled in a comprehensive MATLAB toolbox called RivMAP (River Morphodynamics from Analysis of Planforms), which is applied to over 1500 km of the actively migrating and predominately meandering Ucayali River in Peru from 1985 to 2015. We find multiscale spatial and temporal variability around multidecadal trends in migration rates, erosion and accretion, and channel widths revealing a river dynamically adjusting to sediment and water fluxes. Confounding factors controlling planform morphodynamics including local inputs of sediment, cutoffs, and climate are parsed through the high temporal analysis.

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

Bed load transport is a highly stochastic, multiscale process, where particle advection and diffusion regimes are governed by the dynamics of each sediment grain during its motion and resting states. Having a quantitative understanding of the macroscale behavior emerging from the microscale interactions is important for proper model selection in the absence of individual grain-scale observations. Here we develop a semimechanistic sediment transport model based on individual particle dynamics, which incorporates the episodic movement (steps separated by rests) of sediment particles and study their macroscale behavior. By incorporating different types of probability distribution functions (PDFs) of particle resting times Tr, under the assumption of thin-tailed PDF of particle velocities, we study the emergent behavior of particle advection and diffusion regimes across a wide range of spatial and temporal scales. For exponential PDFs of resting times Tr, we observe normal advection and diffusion at long time scales. For a power-law PDF of resting times (i.e., f(Tr)∼Tr−ν), the tail thickness parameter ν is observed to affect the advection regimes (both sub and normal advective), and the diffusion regimes (both subdiffusive and superdiffusive). By comparing our semimechanistic model with two random walk models in the literature, we further suggest that in order to reproduce accurately the emerging diffusive regimes, the resting time model has to be coupled with a particle motion model able to produce finite particle velocities during steps, as the episodic model discussed here.

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.

Freshwater faunal diversity and abundance have declined dramatically worldwide, concurrent with changes in streamflow and sediment loads in rivers. Cumulative effects and interdependencies of chronic covarying environmental stressors can obscure causal linkages that may be controlling the population dynamics of longer-lived freshwater fauna, such as mussels. To understand changes in long-term mussel population density, we developed a dynamic, process-based interaction model that couples streamflow, suspended sediment, phytoplankton, and mussel abundance under the hypothesis that chronic exposure to increased suspended sediment and food limitation are the primary factors controlling native mussel population density in a midwestern USA agricultural river basin. We calibrated and validated the model with extensive survey data from multiple time periods and used it to evaluate changes in mussel abundance at a subbasin scale over decades. We evaluated sensitivity of simulated mussel densities across a ...

Energy, water, and food resources are highly interdependent. Agricultural irrigation accounts for 84% of global consumptive freshwater use, the food supply chain demands up to 30% of global primary energy use, and roughly 80% of global electricity generation depends on water for cooling (an average of nearly 100 L of water withdrawn per kWh). Improving understanding of the complex interactions of this resource nexus is, therefore, a top priority for human well-being, sustainable development, and policymaking. Here, we present an interactive analysis toolbox, Nexus of Food, Energy, and Water (NeFEW), that synthesizes available global data to enable modeling and analysis of these resources and their interdependencies at the country-level and for user-specified categories and quantities. Sample analyses also presented here include country-specific estimates of water resources required to produce different types of food and energy, energy required per quantity of water or agricultural product supplied, and C O 2 -equivalent emissions associated with water and energy provision.

Complete transformations of land cover from prairie, wetlands, and hardwood forests to homogenous row crop agriculture scattered with urban centers are thought to have caused profound changes in hydrology in the Upper Midwestern US since the 1800s. Continued intensification of land use and drainage practices combined with increased precipitation have caused many Midwest watersheds to exhibit higher streamflows today than in the historical past. While changes in crop type and farming practices have been well documented over the past few decades, changes in artificial surface (ditch) and subsurface (tile) drainage systems have not. This makes it difficult to quantitatively disentangle the effects of climate change and artificial drainage intensification on the observed hydrologic change, often spurring controversial interpretations with significant implications for management actions. In this study, we investigate four large (23,000–69,000 km2) Midwest river basins that span climate and land use gradients to understand how climate and agricultural drainage have influenced basin hydrology over the last 79 years. We use daily, monthly, and annual flow metrics to document streamflow changes and discuss those changes in the context of climate and land use change. While we detect similar timing of precipitation and streamflow changes in each basin, overall the magnitude and significance of precipitation changes are much less than we detect for streamflows. Of the basins containing greater than 20 % area drained by tile and ditches, we observe 2 to 4 fold increases in low flows and 1.5 to 3 fold increases in high and extreme flows. Monthly precipitation has increased slightly for some months in each basin, mostly in fall and winter months (August – March), but total monthly streamflow has increased in all months for the Minnesota River Basin (MRB), every month but April for the Red River Basin (RRB), September-December and March in the Illinois River Basin (IRB), and no months in the Chippewa River basin (CRB). Using a water budget, we determined that the soil moisture/groundwater storage term for the intensively drained and cultivated MRB, IRB, and RRB, has decreased by about 200 %, 100 %, and 30 %, respectively while increased by roughly 30 % in the largely forested CRB since 1975. We argue that agricultural land use change, through wetland removal and artificial drainage installation, has decreased watershed storage and amplified the streamflow response to precipitation increases in the Midwest. Highly managed basins with large reservoirs and urban centers, such as the Illinois River basin (IRB), may be able to buffer some of these impacts better than largely unregulated systems such as the Minnesota River (MRB) and Red River of the North (RRB) basins. The reported streamflow increases in the MRB, IRB, and RRB are large (18 %–318 %), and should have important implications for channel adjustment and sediment and nutrient transport. Acknowledging both economic benefits and apparent detrimental impacts of artificial drainage on river flows, sediments, and nutrients, we question whether any other human activity has comparably altered critical zone activities, while remaining largely unregulated and undocumented. We argue that better documentation of existing and future drain tile and ditch installation is greatly needed.

Author(s): Tejedor, A; Longjas, A; Foufoula-Georgiou, E; Georgiou, TT; Moreno, Y | Abstract: Multiplex networks have been intensively studied during the last few years as they offer a more realistic representation of many interdependent and multilevel complex networked systems. However, even if most real networks have some degree of directionality, the vast majority of the existent literature deals with multiplex networks where all layers are undirected. Here, we study the dynamics of diffusion processes acting on coupled multilayer networks where at least one layer consists of a directed graph; we call these directed multiplex networks. We reveal a new and unexpected signature of diffusion dynamics on directed multiplex networks, namely, that different from their undirected counterparts, they can exhibit a nonmonotonic rate of convergence to steady state as a function of the degree of coupling, resulting in a faster diffusion at an intermediate degree of coupling than when the two layers are fully coupled. We use synthetic multiplex examples and real-world topologies to illustrate the characteristics of the underlying dynamics that give rise to a regime in which an optimal coupling exists. We further provide analytical and numerical evidence that this new phenomenon is solely a property of directed multiplex, where at least one of the layers exhibits sufficient directionality quantified by a normalized metric of asymmetry in directional path lengths. Given the ubiquity of both directed and multilayer networks in nature, our results have important implications for studying the dynamics of multilevel complex systems.