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Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.

The pressure-driven flow rate through individual carbon nanotubes is precisely determined from the hydrodynamics of emerging water jets, revealing unexpectedly large and radius-dependent surface slippage. Water flow across individual nanotubes Carbon nanotubes are of interest for applications such as desalination, because of the exceptional speed with which water moves through them. But the underlying mechanism for water transport has not been well characterized, reflecting the difficulty of unambiguously measuring the permeability of single nanotubes. Lydéric Bocquet and colleagues now show that permeability can be accurately determined by observing the hydrodynamics of water jets as they emerge from single nanotubes into a surrounding fluid. The results reveal large, radius-dependent surface slippage in carbon nanotubes and no slippage in boron nitride nanotubes. This contrast is attributed to subtle, atomic-scale differences between the solid–liquid interfaces of the two systems, illustrating that nanofluidics is the frontier at which the continuum picture of fluid mechanics meets the atomic nature of matter. Measurements and simulations have found that water moves through carbon nanotubes at exceptionally high rates owing to nearly frictionless interfaces 1 , 2 , 3 , 4 . These observations have stimulated interest in nanotube-based membranes for applications including desalination, nano-filtration and energy harvesting 5 , 6 , 7 , 8 , 9 , 10 , yet the exact mechanisms of water transport inside the nanotubes and at the water–carbon interface continue to be debated 11 , 12 because existing theories do not provide a satisfactory explanation for the limited number of experimental results available so far 13 . This lack of experimental results arises because, even though controlled and systematic studies have explored transport through individual nanotubes 7 , 8 , 9 , 14 , 15 , 16 , 17 , none has met the considerable technical challenge of unambiguously measuring the permeability of a single nanotube 11 . Here we show that the pressure-driven flow rate through individual nanotubes can be determined with unprecedented sensitivity and without dyes from the hydrodynamics of water jets as they emerge from single nanotubes into a surrounding fluid. Our measurements reveal unexpectedly large and radius-dependent surface slippage in carbon nanotubes, and no slippage in boron nitride nanotubes that are crystallographically similar to carbon nanotubes, but electronically different. This pronounced contrast between the two systems must originate from subtle differences in the atomic-scale details of their solid–liquid interfaces, illustrating that nanofluidics is the frontier at which the continuum picture of fluid mechanics meets the atomic nature of matter.

Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.

The manipulation of small amounts of liquids has applications ranging from biomedical devices to liquid transfer. Direct light-driven manipulation of liquids, especially when triggered by light-induced capillary forces, is of particular interest because light can provide contactless spatial and temporal control. However, existing light-driven technologies suffer from an inherent limitation in that liquid motion is strongly resisted by the effect of contact-line pinning. Here we report a strategy to manipulate fluid slugs by photo-induced asymmetric deformation of tubular microactuators, which induces capillary forces for liquid propulsion. Microactuators with various shapes (straight, ‘Y’-shaped, serpentine and helical) are fabricated from a mechanically robust linear liquid crystal polymer. These microactuators are able to exert photocontrol of a wide diversity of liquids over a long distance with controllable velocity and direction, and hence to mix multiphase liquids, to combine liquids and even to make liquids run uphill. We anticipate that this photodeformable microactuator will find use in micro-reactors, in laboratory-on-a-chip settings and in micro-optomechanical systems. A light-actuated liquid crystal polymer material system precisely manipulates liquid drops through capillary forces, and can be formed into a variety of shapes. Light-controlled manipulation of liquid movement Liquid droplets confined within a conical capillary tube — for instance in a microfluidic device — will travel spontaneously towards the narrower end, owing to differences in curvature pressure at either end of the droplet. Now, Yanlei Yu and colleagues have designed a tubular, light-actuated, liquid crystal polymer system that can induce such asymmetric morphologies to reversibly manipulate liquid drops through capillary forces. This method of light-controlled liquid movement does not suffer from contact-line pinning, and is shown to work with a range of liquids and mixtures to achieve propulsion with controllable velocity and direction, and mixing of multiphase liquids through microactuators of various shapes (straight, 'Y'-shaped, serpentine and helical).

Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

The characteristics of the soil macropore network determine the potential for fast transport of agrochemicals and contaminants through the soil. The objective of this study was to examine the relationships between macropore network characteristics, hydraulic properties and state variables and measures of preferential transport. Experiments were carried out under near-saturated conditions on undisturbed columns sampled from four agricultural topsoils of contrasting texture and structure. Macropore network characteristics were computed from 3-D X-ray tomography images of the soil pore system. Non-reactive solute transport experiments were carried out at five steady-state water flow rates from 2 to 12 mm h−1. The degree of preferential transport was evaluated by the normalised 5% solute arrival time and the apparent dispersivity calculated from the resulting breakthrough curves. Near-saturated hydraulic conductivities were measured on the same samples using a tension disc infiltrometer placed on top of the columns. Results showed that many of the macropore network characteristics were inter-correlated. For example, large macroporosities were associated with larger specific macropore surface areas and better local connectivity of the macropore network. Generally, an increased flow rate resulted in earlier solute breakthrough and a shifting of the arrival of peak concentration towards smaller drained volumes. Columns with smaller macroporosities, poorer local connectivity of the macropore network and smaller near-saturated hydraulic conductivities exhibited a greater degree of preferential transport. This can be explained by the fact that, with only two exceptions, global (i.e. sample scale) continuity of the macropore network was still preserved at low macroporosities. Thus, for any given flow rate, pores of larger diameter were actively conducting solute in soils of smaller near-saturated hydraulic conductivity. This was associated with larger local transport velocities and, hence, less time for equilibration between the macropores and the surrounding matrix which made the transport more preferential. Conversely, the large specific macropore surface area and well-connected macropore networks associated with columns with large macroporosities limit the degree of preferential transport because they increase the diffusive flux between macropores and the soil matrix and they increase the near-saturated hydraulic conductivity. The normalised 5% arrival times were most strongly correlated with the estimated hydraulic state variables (e.g. with the degree of saturation in the macropores R2 = 0.589), since these combine into one measure the effects of irrigation rate and the near-saturated hydraulic conductivity function, which in turn implicitly depends on the volume, size distribution, global continuity, local connectivity and tortuosity of the macropore network.

An automatic workflow to measure surface flow velocities in rivers is introduced, including a Python tool. The method is based on particle-tracking velocimetry (PTV) and comprises an automatic definition of the search area for particles to track. Tracking is performed in the original images. Only the final tracks are geo-referenced, intersecting the image observations with water surface in object space. Detected particles and corresponding feature tracks are filtered considering particle and flow characteristics to mitigate the impact of sun glare and outliers. The method can be applied to different perspectives, including terrestrial and aerial (i.e. unmanned-aerial-vehicle; UAV) imagery. To account for camera movements images can be co-registered in an automatic approach. In addition to velocity estimates, discharge is calculated using the surface velocities and wetted cross section derived from surface models computed with structure-from-motion (SfM) and multi-media photogrammetry. The workflow is tested at two river reaches (paved and natural) in Germany. Reference data are provided by acoustic Doppler current profiler (ADCP) measurements. At the paved river reach, the highest deviations of flow velocity and discharge reach 4 % and 5 %, respectively. At the natural river highest deviations are larger (up to 31 %) due to the irregular cross-section shapes hindering the accurate contrasting of ADCP- and image-based results. The provided tool enables the measurement of surface flow velocities independently of the perspective from which images are acquired. With the contactless measurement, spatially distributed velocity fields can be estimated and river discharge in previously ungauged and unmeasured regions can be calculated, solely requiring some scaling information.

Since the 1950s, Europe has undergone large shifts in climate and land cover. Previous assessments of past and future changes in evapotranspiration or streamflow have either focussed on land use/cover or climate contributions or on individual catchments under specific climate conditions, but not on all aspects at larger scales. Here, we aim to understand how decadal changes in climate (e.g. precipitation, temperature) and land use (e.g. deforestation/afforestation, urbanization) have impacted the amount and distribution of water resource availability (both evapotranspiration and streamflow) across Europe since the 1950s. To this end, we simulate the distribution of average evapotranspiration and streamflow at high resolution (1 km2) by combining (a) a steady-state Budyko model for water balance partitioning constrained by long-term (lysimeter) observations across different land use types, (b) a novel decadal high-resolution historical land use reconstruction, and (c) gridded observations of key meteorological variables. The continental-scale patterns in the simulations agree well with coarser-scale observation-based estimates of evapotranspiration and also with observed changes in streamflow from small basins across Europe. We find that strong shifts in the continental-scale patterns of evapotranspiration and streamflow have occurred between the period around 1960 and 2010. In much of central-western Europe, our results show an increase in evapotranspiration of the order of 5 %–15 % between 1955–1965 and 2005–2015, whereas much of the Scandinavian peninsula shows increases exceeding 15 %. The Iberian Peninsula and other parts of the Mediterranean show a decrease of the order of 5 %–15 %. A similar north–south gradient was found for changes in streamflow, although changes in central-western Europe were generally small. Strong decreases and increases exceeding 45 % were found in parts of the Iberian and Scandinavian peninsulas, respectively. In Sweden, for example, increased precipitation is a larger driver than large-scale reforestation and afforestation, leading to increases in both streamflow and evapotranspiration. In most of the Mediterranean, decreased precipitation combines with increased forest cover and potential evapotranspiration to reduce streamflow. In spite of considerable local- and regional-scale complexity, the response of net actual evapotranspiration to changes in land use, precipitation, and potential evaporation is remarkably uniform across Europe, increasing by ∼ 35–60 km3 yr−1, equivalent to the discharge of a large river. For streamflow, effects of changes in precipitation (∼ 95 km3 yr−1) dominate land use and potential evapotranspiration contributions (∼ 45–60 km3 yr−1). Locally, increased forest cover, forest stand age, and urbanization have led to significant decreases and increases in available streamflow, even in catchments that are considered to be near-natural.

Phytoplankton blooms are an important, widespread phenomenon in open oceans, coastal waters and freshwaters, supporting food webs and essential ecosystem services. Blooms are even more important in exploited coastal waters for maintaining high resource production. However, the environmental factors driving blooms in shallow productive coastal waters are still unclear, making it difficult to assess how environmental fluctuations influence bloom phenology and productivity. To gain insights into bloom phenology, Chl a fluorescence and meteorological and hydrological parameters were monitored at high-frequency (15 min) and nutrient concentrations and phytoplankton abundance and diversity, were monitored weekly in a typical Mediterranean shallow coastal system (Thau Lagoon). This study was carried out from winter to late spring in two successive years with different climatic conditions: 2014/2015 was typical, but the winter of 2015/2016 was the warmest on record. Rising water temperature was the main driver of phytoplankton blooms. However, blooms were sometimes correlated with winds and sometimes correlated with salinity, suggesting nutrients were supplied by water transport via winds, saltier seawater intake, rain and water flow events. This finding indicates the joint role of these factors in determining the success of phytoplankton blooms. Furthermore, interannual variability showed that winter water temperature was higher in 2016 than in 2015, resulting in lower phytoplankton biomass accumulation in the following spring. Moreover, the phytoplankton abundances and diversity also changed: cyanobacteria (< 1 μm), picoeukaryotes (< 1 μm) and nanoeukaryotes (3-6 μm) increased to the detriment of larger phytoplankton such as diatoms. Water temperature is a key factor affecting phytoplankton bloom dynamics in shallow productive coastal waters and could become crucial with future global warming by modifying bloom phenology and changing phytoplankton community structure, in turn affecting the entire food web and ecosystem services.

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.