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The two-term infiltration equation (Formula presented.) is commonly used to determine the sorptivity, (Formula presented.) (Formula presented.), and product, (Formula presented.) (Formula presented.), of the dimensionless multiple (Formula presented.) and saturated soil hydraulic conductivity (Formula presented.) (Formula presented.) from cumulative vertical infiltration measurements (Formula presented.) (L) at times (Formula presented.) (T). This reduced form of the quasi-analytical power series solution of Richardson's equation of Philip enjoys a solid physical underpinning but at the expense of a limited time validity. Using simulated infiltration data, Jaiswal et al. have shown this time validity to equal about 2.5 cm of cumulative infiltration. The goals of this work are twofold. First, we investigate the extent to which cumulative infiltration measurements larger than 2.5 cm bias the estimates of (Formula presented.) and (Formula presented.). Second, we investigate the impact of epistemic errors on the inferred time validities and parameters. Partial infiltration curves up to 2.5 cm of cumulative vertical infiltration improve substantially the agreement between actual and least squares estimates of (Formula presented.) and (Formula presented.). But this only holds if the data generating infiltration process follows Richardson's equation and experimental conditions satisfy assumptions of soil homogeneity and a uniform initial water content. Otherwise, autocorrelated cumulative infiltration residuals will bias the least squares estimates of (Formula presented.) and (Formula presented.). Our findings reiterate and reinvigorate earlier conclusions of Haverkamp et al. and show that epistemic errors deteriorate the physical significance of the coefficients of infiltration functions. As a result, the parameters of infiltration functions cannot simply be used in storm water and vadose zone flow models to forecast runoff and recharge at field and landscape scales unless these predictions are accompanied by realistic uncertainty bounds. We conclude that the time validity of Philip's two-term equation is an elusive theoretical quantity with arbitrary physical meaning.

Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world.

Many different equations have been proposed to describe quantitatively one-dimensional soil water infiltration. The unknown coefficients of these equations characterize soil hydraulic properties and may be estimated from a n record, (Formula presented.), of cumulative infiltration measurements using curve fitting techniques. The two-term infiltration equation, (Formula presented.), of Philip has been widely used to describe measured infiltration data. This function enjoys a solid mathematical–physical underpinning and admits a closed-form solution for the soil sorptivity, S [L T−1/2], and multiple, c [−], of the saturated hydraulic conductivity, Ks [L T−1]. However, Philip's two-term equation has a limited time validity, tvalid [T], and thus cumulative infiltration data, (Formula presented.), beyond (Formula presented.) will corrupt the estimates of S and Ks. This paper introduces a novel method for estimating S, c, Ks, and tvalid of Philip's two-term infiltration equation. This method, coined parasite inversion, use as vehicle Parlange's three-parameter infiltration equation. As prerequisite to our method, we present as secondary contribution an exact, robust and efficient numerical solution of Parlange's infiltration equation. This solution admits Bayesian parameter estimation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and yields as byproduct the marginal distribution of Parlange's β parameter. We evaluate our method for 12 USDA soil types using synthetic infiltration data simulated with HYDRUS-1D. An excellent match is observed between the inferred values of S and Ks and their “true” values known beforehand. Furthermore, our estimates of c and tvalid correlate well with soil texture, corroborate linearity of the (Formula presented.) relationship for (Formula presented.), and fall within reported ranges. A cumulative vertical infiltration of about 2.5 cm may serve as guideline for the time-validity of Philip's two-term infiltration equation.

Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.

Many different equations ranging from simple empirical to semi-analytical solutions of the Richards equation have been proposed for quantitative description of water infiltration into variably saturated soils. The sorptivity, S, and the saturated hydraulic conductivity, Ks, in these equations are typically unknown and have to be estimated from measured data. In this paper, we use so-called characteristic time (tchar) to design a new method, referred to as the characteristic time method (CTM) that estimates S, and Ks, from one-dimensional (1D) cumulative infiltration data. We demonstrate the usefulness and power of the CTM by comparing it with a suite of existing methods using synthetic cumulative infiltration data simulated by HYDRUS-1D for 12 synthetic soils reflecting different USDA textural classes, as well as experimental data selected from the Soil Water Infiltration Global (SWIG) database. Results demonstrate that the inferred values of S and Ks are in excellent agreement with their theoretical values used in the synthetically simulated infiltration experiments with Nash–Sutcliffe criterion close to unity and RMSE values of 0.04 cm h−1/2 and 0.05 cm h−1, respectively. The CTM also showed very high accuracy when applied on synthetic data with added measurement noise, as well as robustness when applied to experimental data. Unlike previously published methods, the CTM does not require knowledge of the time validity of the applied semi-analytical solution for infiltration and, therefore, is applicable to infiltrations with durations from 5 min to several days. A script written in Python of the CTM method is provided in the supplemental material.

Multi-walled carbon nanotubes (MWCNTs) are increasing used in commercial applications and may be released into the environment with anionic surfactants, such as sodium dodecylbenzenesulfonate (SDBS), in sewer discharge. Little research has examined the transport, retention, and remobilization of MWCNTs in the presence or absence of SDBS in porous media with controlled chemical heterogeneity, and batch and column scale studies were therefore undertaken to address this gap in knowledge. The adsorption isotherms of SDBS on quartz sand (QS), goethite coated quartz sand (GQS), and MWCNTs were determined. Adsorption of SDBS (MWCNTs » GQS > QS) decreased zeta potentials for these materials, and produced a charge reversal for goethite. Transport of MWCNTs (5 mg L-1) dramatically decreased with an increase in the fraction of GQS from 0 to 0.1 in the absence of SDBS. Conversely, co-injection of SDBS (10 and 50 mg L-1) and MWCNTs radically increased the transport of MWCNTs when the GQS fraction was 0, 0.1, and 0.3, especially at a higher SDBS concentration, and altered the shape of retention profile. Mathematical modeling revealed that competitive blocking was not the dominant mechanism for the SDBS enhancement of MWCNT transport. Rather, SDBS sorption increased MWCNT transport by increasing electrostatic and/or steric interactions, or creating reversible interactions on rough surfaces. Sequential injection of pulses of MWCNTs and SDBS in sand (0.1 GQS fraction) indicated that SDBS could mobilize some of retained MWCNTs from the top to deeper sand layers, but only a small amount of released MWCNTs were recovered in the effluent. SDBS therefore had a much smaller influence on MWCNT transport in sequential injection than in co-injection, presumably because of a greater energy barrier to MWCNT release than retention. This research sheds novel insight on the roles of competitive blocking, chemical heterogeneity and nanoscale roughness, and injection sequence on MWCNT retention and release.

Infiltration in soils is a key process that partitions precipitation at the land surface into surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in land surface models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hillslope, and grid cell scales of LSMs. Despite the progress that has been made, upscaling of local-scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows that there is a large variety of approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few LSMs consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. To tackle these challenges and integrate current knowledge on soil processes affecting infiltration processes into LSMs, we advocate a stronger exchange and scientific interaction between the soil and the land surface modeling communities.

Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic-ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area-average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast-neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS-1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model-predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters a and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near-surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs.

Reliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity. Plain Language Summary: Onshore wind energy is expected to play a major role in the global energy transition. However, reliable and highly resolved information on the onshore wind energy potential (WEP) crucial for expansion planning is missing over southern Africa. This study evaluated high resolution 3.3 km ICON‐LAM atmospheric simulations and 31 km ERA5 reanalysis against 60 m wind speed (ws60m) observations and compared the corresponding derived WEPs. The results show that ERA5 underestimates ws60m by 27%, resulting in a 48% lower WEP assessment than ICON‐LAM, whose ws60m simulation results show a very small bias. Underestimation of wind energy yields may hinder further expansion of wind energy, as less economic performance is expected, which underlines the importance of highly resolved weather data. Key Points: Simulated ERA5 and km‐scale ICON‐LAM wind speeds are evaluated and corresponding southern Africa wind energy potentials are calculatedERA5 underestimates 60 m wind speed, whereas ICON‐LAM produces lower biases in the wind speed simulationsHigher wind energy potentials are revealed from wind speeds simulated by ICON‐LAM compared to ERA5, which is often used for such assessments [ABSTRACT FROM AUTHOR]

This comment challenges the perspective of Gao et al. (2023) that is rejecting the role of soil processes in hydrology. We argue that the authors present a false dichotomy between soil-centric and ecosystem-centric views. These two views of hydrology are complementary and reflect on the inherent multiscale complexity of hydrology where soil processes dominate at certain scales but other processes may become important at the catchment scale. We recognize the need for a new scale-aware framework that reconciles the interplay between soil processes at small scales with emergent behaviors driven by vegetation, topography, and climate at large scales. [ABSTRACT FROM AUTHOR]