Search

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Sorptivity is one of the most important parameters for the quantification of water infiltration into soils. proposed a specific formulation to derive sorptivity as a function of the soil water retention and hydraulic conductivity functions, as well as initial and final soil water contents. However, this formulation requires the integration of a function involving hydraulic diffusivity, which may be undefined or present numerical difficulties that cause numerical misestimations. In this study, we propose a mixed formulation that scales sorptivity and splits the integrals into two parts: the first term involves the scaled degree of saturation, while the second involves the scaled water pressure head. The new mixed formulation is shown to be robust and well-suited to any type of hydraulic function – even with infinite hydraulic diffusivity or positive air-entry water pressure heads – and any boundary condition, including infinite initial water pressure head, h→-∞. Lastly, we show the benefits of using the proposed formulation for modeling water into soil with analytical models that use sorptivity. [ABSTRACT FROM AUTHOR]

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, {t∼i,I∼i}i=1n$\{ {\tilde t_i},{\tilde I_i}\} _{i = 1}^n$, of cumulative infiltration measurements using curve fitting techniques. The two‐term infiltration equation, I(t)=St+cKst$I(t) = S\sqrt t + c{K_{\rm{s}}}t$, 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, I∼(t∼)$\tilde I(\tilde t)$, beyond t=tvalid$t = {t_{{\rm{valid}}}}$ 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 c(β)$c({{\beta}})$ relationship for 0≤t≤tvalid$0 \le t \le {t_{{\rm{valid}}}}$, 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. Core Ideas: We describe a method that can determine the time validity of infiltration equations.This method infers as well the hydraulic properties of unsaturated soils.We present a robust and efficient numerical solution of Parlange's three‐parameter infiltration equation.Posterior distribution determines how well parameters are defined by calibration to data.Two and a half centimeters of cumulative infiltration is a good proxy for time validity Philip's two‐term equation. [ABSTRACT FROM AUTHOR]

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures. Plain Language Summary: Hydrological models need information about the soil physical characteristics (soil hydraulic parameters), which are in general not available if the models are applied at larger scales (region to global scale). Therefore, pedotransfer functions (PTFs) are classically used, which relate easily available soil properties such as sand‐, silt‐, clay‐content, soil organic carbon content, and soil bulk density, which are available from soil maps, to the soil hydraulic parameters. Unfortunately, there are many different PTFs available in literature. In the study presented, we analyzed the impact of different PTFs on the simulation results of water fluxes and found, that the choice of PTF impacts the simulation results. Further, some PTFs were identified as being less robust compared to others. In general, the study shows that harmonizing PTFs in model‐inter‐comparisons is needed to avoid artifacts originating from the choice of PTF rather from different model structures. Key Points: Using different PTFs in hydrological models causes substantial variability in predicted fluxesWe strongly recommend to harmonize the PTFs used in model inter‐comparison studies [ABSTRACT FROM AUTHOR]

Future crop production will be affected by climatic changes. In several regions, the projected changes in total rainfall and seasonal rainfall patterns will lead to lower soil water storage (SWS), which in turn affects crop water uptake, crop yield, water use efficiency (WUE), grain quality and groundwater recharge. Effects of climate change on those variables depend on the soil properties and were often estimated based on model simulations. The objective of this study was to investigate the response of key variables in four different soils and for two different climates in Germany with a different aridity index (AI): 1.09 for the wetter (range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done by using high-precision weighable lysimeters. According to a "space-for-time" (SFT) concept, intact soil monoliths that were moved to sites with contrasting climatic conditions have been monitored from April 2011 until December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier climate, whereas crop yield was significantly higher, without affecting grain quality. Especially "non-productive" water losses (evapotranspiration out of the main growing period) were lower, which led to a more efficient crop water use in the drier climate. A characteristic decrease of the SWS for soils with a finer texture was observed after a longer drought period under a drier climate. The reduced SWS after the drought remained until the end of the observation period which demonstrates carry-over of drought from one growing season to another and the overall long-term effects of single drought events. In the relatively drier climate, water flow at the soil profile bottom showed a small net upward flux over the entire monitoring period as compared to downward fluxes (groundwater recharge) or drainage in the relatively wetter climate and larger recharge rates in the coarser- as compared to finer-textured soils. The large variability of recharge from year to year and the long-lasting effects of drought periods on the SWS imply that long-term monitoring of soil water balance components is necessary to obtain representative estimates. Results confirmed a more efficient crop water use under less-plant-available soil moisture conditions. Long-term effects of changing climatic conditions on the SWS and ecosystem productivity should be considered when trying to develop adaptation strategies in the agricultural sector. [ABSTRACT FROM AUTHOR]

Weighable lysimeters were used to study the relation between soil water content (SWC) and the actual evapotranspiration (ETa) of grassland under two different climate regimes of Rollesbroich and Selhausen but for an identical soil from Rollesbroich. All components of the water balance were determined from 2012 until 2018. Budyko analysis was used to characterize the hydrological status of the studied sites. Wavelet analysis was also applied to study the power spectrum of ETa, vegetationheight- adjusted reference evapotranspiration (ETcrop), and water stress index (WSI) defined as ETa/ETcrop, as well as SWC at three different depths and the coherence between SWC and ETa and WSI. The Budyko analysis showed that 2018 resulted in a shift of both locations towards more water-limited conditions, although Rollesbroich remained an energy-limited system. Based on the power spectrum analysis, the annual timescale is the dominant scale for the temporal variability of ETa, ETcrop, and SWC. The results also showed that increasing dryness at the energy-limited site led to more temporal variability of SWC at all depths at the annual timescale. Wavelet coherence analysis showed a reduction of the phase shift between SWC and ETa at an annual scale caused by the increase in dryness during the measurement period. We found that phase shifts between SWC and ETa and SWC and WSI were stronger at the water-limited site than at the energy-limited site. The wavelet coherence analysis also showed that from 2014 to 2018, the control of ETa and WSI on SWC increased due to higher dryness of soil. [ABSTRACT FROM AUTHOR]

A close correlation between water conductivity (K(θ)) and air permeability (Ka), measured at various water contents, is expected due to tight dependence of water filled porosity to air filled porosity of soils. Finding such a relation will greatly facilitate the prediction of unsaturated water conductivity (K(θ)). So, the purpose of the current investigation was to find out if a reliable relation or function between the two permeabilities can be established. In this regard, K(θ) and Ka were measured by pressure plate outflow and variable head methods, respectively, at the range of 0 to -100 kPa matric potential (ψm). A linear regression function between relative water conductivity (Kr(θ)) and Ka(LogK, (θ)=a+bLogKa) with the correlation coefficient (R) from 0.884 to 0.999 were established for the 22 examined soils. The overall R for 128 data pairs (Kr(θ) and Ka) became 0.821 (being significant at P<0.01) with the slope (b) of -2.54 and intercept (a) of -10.93. For the comparison propose Kr(θ) were also predicted from RETC using experimental SMC data and van Genuchten and Brooks-Corey models. The reliability of the Kr(θ) prediction from Ka based on root mean square error (RMSE), geometric mean error ratio (GMER), and geometric standard deviation of error ratio (GSDER) criteria became considerable greater than those predicted from the two mentioned models. [ABSTRACT FROM AUTHOR]