River drainage networks are important landscape features that have been studied for several decades from a range of geomorphological and hydrological perspectives. However, identifying the most vital (critical) nodes on river networks and analyzing their relationships with geomorphic and climatic properties have not yet been extensively addressed in the literature. In this study, we use an algorithm that determines the set of critical nodes whose removal results in maximum network fragmentation and apply it to various topologies of simulated and natural river networks. Specifically, we consider simulated river networks obtained from optimal channel network (OCN) approach as well as extracted networks from several natural basins across the United States. Our results indicate a power-law relationship between the number of connected node pairs in the remaining network and the number of removed critical nodes. We also investigate the characteristics of sub-basins resulted from the removal of critical nodes and compare them with those of central nodes (in the context of betweenness centrality) for both natural basins and OCNs with varying energy exponent γ to understand vulnerability and resilience of river networks under potential external disruptions.

Understanding how landscapes respond to climate dynamics in terms of macroscale (average topographic features) and microscale (landform reorganization) is of interest both for deciphering past climates from today's landscapes and for predicting future landscapes in view of recent climatic trends. Although several studies have addressed macro‐scale response, only a few have focused on quantifying smaller‐scale basin reorganization. To that goal, a series of controlled laboratory experiments were conducted where a self‐organized complete drainage network emerged under constant precipitation and uplift dynamics. Once steady state was achieved, the landscape was subjected to a fivefold increase in precipitation (transient state). Throughout the evolution, high‐resolution spatiotemporal topographic data in the form of digital elevation models were collected. The steady state landscape was shown to possess three distinct geomorphic regimes (unchannelized hillslopes, debris‐dominated channels, and fluvially dominated channels). During transient state, landscape reorganization was observed to be driven by hillslopes via accelerated erosion, ridge lowering, channel widening, and reduction of basin relief as opposed to channel base‐level reduction. Quantitative metrics on which these conclusions were based included slope‐area curve, correlation analysis of spatial and temporal elevation increments, and wavelet spectral analysis of the evolving landscapes. Our results highlight that landscape reorganization in response to increased precipitation seems to follow “an arrow of scale”: major elevation change initiates at the hillslope scale driving erosional regime change at intermediate scales and further cascading to geomorphic changes at the channel scale as time evolves.

The flow duration curve (FDC) is a hydrologically meaningful representation of the statistical distribution of daily streamflows. The complexity of processes contributing to the FDC introduces challenges for the direct exploration of physical controls on FDC. In this paper, the controls of climate and catchment characteristics on FDC are explored using a stochastic framework that enables construction of the FDC from three components of streamflow: fast and slow flow (during wet days) and slow flow during dry days. The FDC during wet days (FDC_w) is computed as the statistical sum of the fast flow duration curve (FFDC) and the slow flow duration curve (SFDC_w), considering their dependency. FDC is modeled as the mixture distribution of FDC_w and the slow flow duration curve during dry days (SFDC_d), by considering the fraction of wet days (δ) for perennial streams and both δ and the fraction of days of zero streamflow for ephemeral streams. The Kappa distribution is employed to fit the FFDC, SFDC_w, and SFDC_d for 300 catchments from Model Parameter Estimation Experiment (MOPEX) across the United States. Results show that the 0–20th percentile of FDC is controlled by FFDC and SFDC_w, the 90–100th percentile of FDC is controlled by SFDC_d, and the 20–90th percentile of FDC is controlled by three components. The relationships between estimated Kappa distribution parameters and climate and catchment characteristics reveal that the aridity index, the coefficient of variation of daily precipitation, timing of precipitation, time interval between storms, snow, topographic slope, and slope of recession slope curve are dominant controlling factors.

We investigate the impact of changing nonuniform spatial and temporal precipitation patterns on the evolution of river networks. To achieve this, we develop a two‐dimensional optimal channel network (OCN) model with a controllable rainfall distribution to simulate the evolution of river networks, governed by the principle of minimum energy expenditure, inside a prescribed boundary. We show that under nonuniform precipitation conditions, river networks reorganize significantly toward new patterns with different geomorphic and hydrologic signatures. This reorganization is mainly observed in the form of migration of channels of different orders, widening or elongation of basins as well as formation and extinction of channels and basins. In particular, when the precipitation gradient is locally increased, the higher‐order channels, including the mainstream river, migrate toward regions with higher precipitation intensity. Through pertinent examples, the reorganization of the drainage network is quantified via stream parameters such as Horton‐Strahler and Tokunaga measures, order‐based channel total length and river long profiles obtained via simulation of three‐dimensional basin topography, while the hydrologic response of the evolved network is investigated using metrics such as hydrograph and power spectral density of simulated streamflows at the outlet of the network. In addition, using OCNs, we investigate the effect of orographic precipitation patterns on multicatchment landscapes composed of several interacting basins. Our results show that network‐inspired methods can be utilized as insightful and versatile models for directly exploring the effects of climate change on the evolution of river drainage systems.

The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to the movement of water. The branching angles, i.e., the angle between two adjoining channels, in drainage networks are important features related to the network topology and contain valuable information about the forming mechanisms of the landscape. Based on the channel networks extracted from 1 m Digital Elevation Models of 120 catchments with minimal human impacts across the United States, we show that the junction angles have two distinct modes with and . The observed angles are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing the slope‐area curve. Our findings suggest that the flow regimes, debris‐flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope‐area curve. These findings enable us to understand the geomorphic signature of hydrologic processes on drainage networks and develop more refined landscape evolution models.

The flow structure in the wake of a model wind turbine is explored under negligible and high turbulence in the freestream region of a wind tunnel at $Re\sim 7\times {10}^4$ . Attention is placed on the evolution of the integral scale and the contribution of the large-scale motions from the background flow. Hotwire anemometry was used to obtain the streamwise velocity at various streamwise and spanwise locations. The pre-multiplied spectral difference of the velocity fluctuations between the two cases shows a significant energy contribution from the background turbulence on scales larger than the rotor diameter. The integral scale along the rotor axis is found to grow linearly with distance, independent of the incoming turbulence levels. This scale appears to reach that of the incoming flow in the high turbulence case at $x/d\sim$ 35–40. The energy contribution from the turbine to the large-scale flow structures in the low turbulence case increases monotonically with distance. Its growth rate is reduced past $x/d\sim$ 6–7. There, motions larger than the rotor contribute ∼ $50\%$ of the total energy, suggesting that the population of large-scale motions is more intense in the intermediate field. In contrast, the wake in the high incoming turbulence is quickly populated with large-scale motions and plateau at $x/d\sim 3$ .