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We appreciate the constructive comment by Jakobi et al. (2020), and we confirm the calculation mistakes which they discovered. In the corrected dataset, volumetric water content was more strongly correlated with sand content than with antecedent precipitation index (API) for 12 out of the 18 transect dates. For one of the dates when the API correlation was of larger magnitude than the sand content correlation, the API correlation was negative, which is physically illogical. The average correlation coefficient between volumetric water content and sand content was −0.48 across all transect dates. In contrast, the average correlation coefficient between the volumetric water content and API was only 0.25, approximately one‐half the magnitude of the sand content correlation. The spatial influence of precipitation on soil water content patterns is less stable and more complex compared to that of soil texture. We stand by the claim in our paper's title, "Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns." Key Points: We confirm the calculation mistakes discovered by Jakobi et al. (2020)Mean correlation strength between water content and antecedent precipitation index was half the magnitude of the sand content correlationInfluence of precipitation on soil water content patterns is less stable and more complex compared to that of soil texture [ABSTRACT FROM AUTHOR]

We present a data‐guided framework to explore the hydrological basis of the Budyko curve, focusing on the role of soil moisture in mediating the partitioning of precipitation into runoff and evaporation and the role of climate in this partitioning. The conceptual framework for the derivation of the Budyko curve builds on empirically based power law relationships for long‐term average scaled runoff (Q/P) and scaled evaporation (E/Ep), both as functions of long‐term average (equilibrium) soil moisture. A second component of the framework is an empirical power law relationship between equilibrium soil moisture and the aridity index, Ep/P. Combinations of these empirical power law relationships with the long‐term water balance equation give rise to a form of the Budyko curve, which is a close approximation to Budyko's original relationship and is mathematically similar to the Turc‐Pike equation, thus supporting the role of equilibrium soil moisture in giving rise to the Budyko curve. In the remainder of the paper, for special cases of the parameter values of the power law relationships, we independently derive both the soil moisture dependence on Ep/P as well as the Budyko curve itself that not only reproduces a new Budyko‐type curve, but can also explain the spread of the empirical data points away from the mean curve. The paper also raises fresh questions about how the empirical power law relationships that govern the soil‐mediated precipitation partitioning may have emerged and the role of vegetation in that mediation. Key Points: Observed long‐term average (equilibrium) soil moisture has a power law relationship with the aridity indexObserved long‐term average scaled runoff and scaled evaporation have power law relationships with equilibrium soil moistureBudyko curve manifests through the soil moisture‐mediated partitioning of precipitation into runoff and evaporation [ABSTRACT FROM AUTHOR]

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

Real‐time irrigation schedules have been shown to outperform predetermined irrigation schedules that do not consider the present state and requirements. However, implementing real‐time irrigation scheduling requires reliable present soil‐crop‐atmosphere dynamics and weather predictions; moreover, enabling farmers to adopt recommended water applications remains challenging as they rely on personal experience and knowledge. Farmers and computer‐based tools are rarely connected in a closed‐loop and farmers' feedback are usually not incorporated into a real‐time modeling procedure. To resolve these critical issues, this paper addresses the feasibility of a real‐time irrigation scheduling tool (RTIST) based on weather forecasts, field observations, and human‐machine interactions. RTIST integrates a simulation & optimization model, a data assimilation (DA) technique, and a human‐computer interaction method, and enables optimality, accuracy, and applicability of the tool. The principle of the RTIST is to engage farmers directly into computer modeling, and support irrigation scheduling decisions jointly based on model provided information and farmers' own justification. The optimization and simulation are validated by running the tool on two crop fields, showing the accuracy of present estimation and future prediction of soil moisture and leaf area index, taking advantage of field observation and DA. The applicability of RTIST is tested via virtual irrigation exercises with a group of farmers for a corn field in Eastern Nebraska. RTIST with farmers' direct engagement shows increased productivity in comparison to traditional practices. Especially, farmers' feedbacks show interest in using the tool in real‐world irrigation scheduling and providing meaningful suggestions to improve the tool for real‐world application. Key Points: Simulation‐optimization, data assimilation, and human‐computer interaction are integrated into a real‐time irrigation scheduling toolHuman‐computer interaction facilitates practical application of the tool through farmer's engagementUsing the tool leads to more accurate state estimations and higher profits in comparison to traditional techniques and practices [ABSTRACT FROM AUTHOR]

In recent years, remote sensing satellites, including Soil Moisture Active Passive (SMAP), continuously monitor SM of the earth. However, the spatial resolution of the remotely sensed SM is insufficient for some hydrological applications. In this paper, a data‐driven model is proposed based on a hybrid approach called artificial neural network kriging (ANNK) to capture the interaction between SM and ancillary data (AD), including normalized difference vegetation index (NDVI), altitude, slope, Antecedent Moisture Condition, AMC (5‐day cumulative precipitation), and daily air temperature (DAT)/land surface temperature (LST). By co‐regionalization of the estimated SM from the ANNK and SMAP 36‐km product, SM in any part of the study area is estimated more accurately and on a finer resolution (1‐km). Finally, through a linear relationship between in‐situ SM with the up‐to‐date AD including AMC (previous 5‐day cumulative precipitation), LST or DAT, slope, NDVI, and the downscaled satellite SM, the soil moisture estimates are adjusted by also adding an error term. Estimation of the final SM product in terms of the Unbiased Root Mean Square Error of less than 0.04 m3/m3 meets the accuracy requirements of the SMAP SM retrieval. Moreover, the results of SM validation show that the proposed technique has a potential for real‐time SM estimations in developing areas. Key Points: A combined data‐driven geostatistically based model is developed for uncertain downscaling Soil Moisture Active Passive (SMAP) soil moisture (SM) data utilizing ancillary data (AD)Proposing a platform for uncertain SM estimation with 5‐day antecedent moisture condition that utilizes land cover and an error termA remarkable estimation of 1 km downscaled SM data by estimating missing data in 36 km SMAP readings using cokriging and AD [ABSTRACT FROM AUTHOR]

Despite long‐standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium‐scale (200–10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre‐storm) surface soil moisture (ASM) and the storm‐scale runoff coefficient (RC; the fraction of storm‐scale precipitation accumulation converted into streamflow). In humid and semi‐humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm‐scale RC observations (available only in basins that are both lightly regulated and gauged) with high‐quality ASM values (available quasi‐globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin‐wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean‐absolute error. Plain Language Summary: Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash‐flood prediction, water‐resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi‐humid basins lacking sufficient ground‐based instrumentation for traditional streamflow calibration. Key Points: Hydrologic models still perform poorly in ungauged basinsIn relative humid areas, the correlation between pre‐storm soil moisture and the storm‐scale runoff coefficient approaches unitySuch strong correlation can be exploited to improve the precision of runoff coefficient estimates provided by models in ungaged basins [ABSTRACT FROM AUTHOR]

This manuscript systematically investigates the effects of gravity during soil‐limited (Stage 2) evaporation from bare soil. Analytical and numerical solutions to the one‐dimensional Richards soil moisture flow equation are developed based on simplified hydraulic functions that depend exponentially on soil water matric potential. Semi‐infinite domains with either freely draining or hydrostatic bottom boundary conditions and finite domains with no‐flow bottom boundary conditions are considered. Cases with gravity are directly compared to those without. The simplified solutions are verified by comparison to numerical solutions with fully realistic soil hydraulic functions for two soils at the extreme ends of the range of possible soil textures. Cumulative evaporation versus time and soil volumetric water content versus depth at selected times are determined for each case from the simplified solutions. These results are reported in terms of non‐dimensional variables that are universal, spanning all soil textures and other conditions. For the semi‐infinite domain cases cumulative evaporation with gravity is reduced by several orders of magnitude for very coarse soils at times of practical interest while gravity has a negligible effect for the finest textures at those times. For the finite domain cases gravity is less important because cumulative evaporation is ultimately limited by the fixed amount of water in the profile. Plain Language Summary: This manuscript examines how gravity influences evaporation of water from initially wet soils during the second stage of drying when the evaporation rate is limited by the internal resistance to water flowing up to the soil surface that exists within soil pores. The paper uses analytical and numerical solutions of the standard flow equation and produces results that should be valid for all soil textures. Key Points: Gravity has large effects on soil‐limited evaporation for coarse textured soilsGravity has negligible effect on soil‐limited evaporation for very fine textured soilsThe Richards equation with exponential hydraulic functions yields good estimates of cumulative evaporation for various boundary conditions [ABSTRACT FROM AUTHOR]

Hydraulic conductivity curves (HCCs) are important inputs in land surface modeling. The general way for predicting an HCC from a soil water retention curve (SWRC) requires an additional input of the saturated hydraulic conductivity. However, the macro effect near saturation often results in difficulty and poor performance when predicting the conductivity. In this paper, we introduce a novel method for predicting the HCC fully from the SWRC, requiring no additional parameters. This is achieved by applying an estimated conductivity (from the SWRC) in the dry range as a new matching point, in addition to modifying an existing HCC model that accounts for both capillary and adsorption forces. Testing with a total of 159 soil samples indicated that the new model substantially improves the prediction of the HCC in compared with the model with the input of the saturated hydraulic conductivity, with the R2 increased from 0.48 to 0.76 and the root‐mean‐square error value reduced from 1.60 to 0.81 cm d−1. The abrupt drop near saturation of the HCC model for soils with small n values close to 1.0, which is a parameter used in shaping the SWRC, was also overcome by forcing the water content be saturated above a fixed potential of −1 cm. Key Points: A physically based method was provided for predicting hydraulic conductivity curves (HCC) fully from soil water retention curve requiring no additional parametersThe abrupt drop near saturation of the HCC model for soils with small n values was overcome by introducing a non‐zero air‐entry valueThe new model significantly improved the predictions of conductivity compared to that requiring the input of saturated conductivity [ABSTRACT FROM AUTHOR]

Quantification of the global irrigation water use (IWU) is crucial to understanding the anthropogenic disturbance of the natural hydrological cycle and optimal agricultural water management. However, it is challenging to obtain time series data with the conventional survey‐based approach, while the current satellite‐based IWU estimations are subject to data gaps and the model structure. In this paper, we propose a comprehensive framework to couple the different processes associated with irrigation and integrate multiple satellite observations to estimate the global IWU. The ensemble IWU estimate demonstrates an improved performance when compared to the IWU obtained from individual satellite observations. The results show reasonable correlation with the survey‐based irrigation water withdrawal in states of the US (bias = −0.42 km3), provinces of China (bias = −3.10 km3), and country statistics from the Food and Agriculture Organization (bias = −10.84 km3). Large amounts of IWU are apparent in India, China, the US, Europe, and Pakistan, making up >70% of the global IWU. A general underestimation of IWU is found both in this work and previous studies, due to the coarse resolution and asynchronism of the various satellite products, the changes in irrigated areas, and the deficiency in detecting irrigation events under the case of saturated soil moisture. Nevertheless, we demonstrate advantages in integrating multiple satellite observations to reduce the uncertainty in estimating global IWU. However, additional efforts are needed to produce high‐quality and finer spatiotemporal resolution satellite‐based products, to further improve the accuracy of the global IWU estimation. Key Points: An integrated framework is proposed to estimate global irrigation water use (IWU) from multiple satellite‐based productsThe IWU estimation scheme can capture the trends of irrigation dynamics well during the study periodThe underestimation in global IWU can be mainly attributed to the coarse resolution of the satellite‐based products [ABSTRACT FROM AUTHOR]

Lateral subsurface stormflow (SSF) is the most important runoff generation mechanism for most hillslopes in temperate climates. It is influenced by pedological, biological, and topographic factors that change during landscape evolution, but so far little is known about how SSF changes over long‐time scales. Therefore, we conducted sprinkling experiments on a silicate and carbonate moraine chronosequence in the Swiss Alps. Each chronosequence consisted of four moraines ranging between a couple of decades and ∼13,500 years in age. On each moraine, we installed three plots and measured shallow SSF in a trench. We added tracers (δ2H and NaCl) to the sprinkling water to identify mixing and flow pathways in the subsurface. The coarse and drainable sediments on the young moraines provoked more frequent and larger SSF responses than for the old moraines. There was no SSF during the sprinkling experiments on the older moraines at the calcareous study area, but SSF occurred during larger natural rainfall events. The pre‐event water fractions in SSF were higher for the old moraines than the young moraines due to the increase in silt, clay, and soil organic matter content, and subsequent increase in the amount of water stored in the soil. The results of this study suggest that soil and vegetation development affect SSF characteristics and help—together with the results for overland flow (companion paper; Maier & van Meerveld, 2021, https://doi.org/10.1029/2021WR030221)—to improve hydrological models and our understanding of the changes in near‐surface runoff generation processes during the first millennia of landscape evolution in Alpine areas. Key Points: Lateral subsurface flow (SSF) occurred most frequent for the youngest moraines at the silicate study area where runoff ratios were highestSSF occurred less frequent on the moraines in the carbonate study area than in the silicate area because the soils were less developedThe pre‐event water fractions in SSF increased with moraine age because of the increase in fine‐textured material and soil water retention [ABSTRACT FROM AUTHOR]

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts. Key Points: Develop a viable approach to validate the satellite soil moisture productsTranslate model and satellite soil moisture into useful information for drought monitoringAn objective blended drought index is recommended [ABSTRACT FROM AUTHOR]

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community. [ABSTRACT FROM AUTHOR]

This paper presents the assimilation of synthetic surface soil moisture retrievals and C‐band backscatter signal in a 100‐meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on footprint scale soil moisture with a time interval of three hours. The synthetic surface soil moisture map was generated by extrapolating a regression relationship between in situ measurements and open loop land surface model outputs based on the soil texture of a given pixel. The surface roughness was inverted from RADARSAT‐2 imagery using a modified Integral Equation Model (IEM) model. Three hourly synthetic backscatter maps were created from this surface roughness and the synthetic soil moisture. The Ensemble Kalman filter (EnKF) with bias correction was applied to mitigate the impact of nonlinear errors introduced by multi‐sourced perturbations. Both time series and spatial maps were examined in the evaluation of the assimilation experiments. Results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals although the later has slightly better temporal statistics. Compared to the open loop, both approaches improved the analysis of surface and root zone soil moisture with significantly lower bias. For the later, the open loop average bias was reduced from −1.25 vol/vol to 0.59 vol/vol by backscatter assimilation and to 0.41 vol/vol by soil moisture assimilation. Key Points: 3 h field scale high resolution surface soil moisture was synthesized with statistical model from land surface model and in situ observation3 h C‐band backscatter maps were generated with synthetic soil moisture and roughness information from RADARSAT‐2Ensemble Kalman filter (EnKF) assimilation of either significantly reduces the bias in modeled surface and root zone soil moisture [ABSTRACT FROM AUTHOR]

Unfrozen water content significantly affects the thermal‐hydro‐mechanical characteristics of frozen soil. Currently, theoretical explanations for the presence of unfrozen water include capillary action, surface effects, adsorption forces, and the electrical double layer. However, the relationships between unfrozen water and these actions are not well explained, except for capillary forces. In addition, although frost heave experiments indicate that the electrical double‐layer solution on a clay particle contains the main portion of unfrozen water, the electrical double‐layer theory which produces a cation solution has not previously been used to calculate unfrozen water content. In this paper, it is assumed that the residual unfrozen water at very low temperatures (below −18°C) is held within the adsorption‐layer solution and that the remaining unfrozen water within the influence of surface effects is the cationic solution in the diffuse layer. Based on these assumptions, a theoretical model of unfrozen water is established with independent variables of temperature, specific surface area, and electrical double‐layer parameters. While the input parameters of the theoretical model are numerous and difficult to obtain, the theoretical model is simplified as a parametric model. Results of the parametric model are strongly consistent with experimental data within a certain temperature range, supporting the hypothetical conditions. The similarity in the underlying mathematical structure of the derived theoretical and parametric models to current semi‐empirical models further suggests that the surface effects of clay are the main causes of unfrozen water in frozen soil. Key Points: The presence of unfrozen water is explained by surface effects, adsorption forces, and the nature of the electrical double‐layer solutionThe surface effects are defined as the combined actions of molecules and negative charges on the clay surfaceThe residual unfrozen water is a stable adsorption‐layer solution [ABSTRACT FROM AUTHOR]

Land surface heat and evaporative fluxes exchanged between the land and atmosphere play a crucial role in the terrestrial water and energy balance. Regional mapping of these fluxes is hampered by the lack of in situ measurements (with the required coverage and duration) and the high spatial heterogeneity. In this paper, we propose a Land Integrated Data Assimilation framework (LIDA) based on the variational data assimilation technique to estimate the key parameters of surface heat and evaporative fluxes by jointly assimilating Soil Moisture Active Passive (SMAP) data and Geostationary Operational Environmental Satellite (GOES) surface temperature data into a coupled parsimonious land water and energy balance model. The method is implemented over an area of 31,500 km2 in the U.S. Southern Great Plains, and its performance is evaluated through consistency tests, comparison tests, and uncertainty analyses. The maps of retrieved heat and evaporative fluxes are used to analyze a range of feedback mechanisms in land‐atmosphere interaction, such as the dependence of evapotranspiration on vegetation and water availability. Key Points: A Land Integrated Data Assimilation framework (LIDA) based on the variational data assimilation technique is proposedLIDA maps surface heat and evaporative fluxes over the U.S. Southern Great Plains by assimilating GOES LST and SMAP SMLIDA estimates the uncertainty of estimated parameters, hence fluxes, by calculating the error covariance matrix of parameters [ABSTRACT FROM AUTHOR]

This paper studies the estimation of unsaturated soil parameters based on thermal, moisture, and solute characteristics, which are important factors that affect productivity and the environment. The objective of this investigation was to determine optimal hydraulic parameters in unsaturated soil. The soil thermal, moisture, and solute transport processes are approached using one‐dimensional isothermal models characterized with nonlinear permeability effects, sorption, and reaction that generally cause considerable experimental and numerical difficulties. The spectral element method is presented for space discretization while the time scheme is constructed along the fully implicit approach with the modified Picard iterative procedure. The iterative Levenberg‐Marquardt optimization algorithm is adopted for the inverse process. The reliability and performance of the studied model is conducted using refinement indicators based on error, residual, and variance analyses, which allow drawing more representative uncertainty of model parameters estimation. Good agreement with the nonlinear optimization of soil hydraulic parameters estimated and true values is obtained. The estimated responses from respective shape curves show relatively small variability. A consideration of ionic diffusional and average temperature effects was revealed, indicating that the change of one of these factors might not necessarily improve parameter identification responses. For soil moisture content ranges between 0.04 and 0.5 cm3 cm−3, average temperature input from 20 to 40 ° C as well as when the ionic molecular diffusion coefficient is greater than 10 cm2 hr−1, a rough representation of moisture, thermal, and hydrochemical characteristics might be sufficient to estimate soil hydraulic parameters. Key Points: Intercomparisons of moisture, thermal, and hydrochemical effects are highlighted for hydraulic properties of soilSensitivity analysis reveals small variability behaviors of hydraulic parameters identification in multivariate conditionsThe change of different ionic diffusional and thermal factors might not necessarily improve parameter identification responses [ABSTRACT FROM AUTHOR]

The physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSMs accuracy over large spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff, and evapotranspiration), surface soil moisture (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM estimates from the NASA Soil Moisture Active Passive Level‐4 product (SMAP_L4) to diagnose bias in the correlation between SSM and surface runoff for multiple Noah‐Multiple Physics (Noah‐MP) LSM parameterization cases. Results demonstrate that Noah‐MP surface runoff parameterizations often underestimate the correlation between prestorm SSM and the event‐scale runoff coefficient (RC; defined as the ratio between event‐scale streamflow and precipitation volumes). This bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and applied to explain a substantial fraction of the observed basin‐to‐basin (and case‐to‐case) variability in the skill of event‐scale RC estimates from Noah‐MP. Most notably, a low bias in LSM‐predicted SSM/RC correlation squanders RC information contained in prestorm SSM and reduces LSM RC estimation skill. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poorly performing Noah‐MP parameterization cases. Plain Language Summary: Land surface models are commonly tasked with determining what fraction of incoming rainfall infiltrates into the soil versus runs off into stream channels. The key factor determining this partitioning is the amount of water in the soil column prior to a storm event (e.g., more prestorm soil moisture is generally associated with decreased amounts of infiltration and increased surface runoff). However, due to a lack of soil moisture observations available at large scales, it has generally been difficult to assess whether existing models are accurately capturing the true relationship between prestorm soil moisture and runoff. Using newly available data from the NASA Soil Moisture Active/Passive (SMAP) mission, this paper demonstrates that land surface models often misrepresent the impact of prestorm surface soil moisture on runoff generation. This misrepresentation is shown to have a strong negative impact on the ability of models to accurately estimate runoff. A new calibration technique, based on the SMAP Level 4 soil moisture product, is introduced for eliminating this bias. Overall, results demonstrate how remotely sensed soil moisture can potentially play an important role in enhancing the operational forecasting of streamflow. Key Points: Correlation between prestorm surface soil moisture and event‐scale runoff coefficient is an important diagnostic for land surface modelsImproved model representation of this correlation is associated with more skillful estimation of storm‐to‐storm variations in runoffSatellite‐based soil moisture analysis products can be applied to detect bias in model‐based correlation and correct for its impact [ABSTRACT FROM AUTHOR]

As the most recent 3‐km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel‐1 L2_SM_SP product has a unique capability to provide global‐scale 3‐km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high‐resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel‐1 radars. To address this issue, this paper presents a novel two‐layer machine learning‐based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap‐fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30‐day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two‐layer framework is validated against regional holdout SMAP/Sentinel‐1 3‐km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3‐km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33‐km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory. Key Points: A new gap‐filled soil moisture product to address the poor spatial and temporal coverage of the SMAP/Sentinel‐1 3 km product is proposedA novel two‐layer machine learning‐based framework motivated by the SMAP/Sentinel active‐passive retrieval algorithm is proposedIncorporating historical radar backscatter measurements in the proposed framework significantly improves soil moisture predictions [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture products are ideal candidates for initializing soil moisture profiles of land surface models via data assimilation. This paper investigates the possibility of using a calibrated Integral Equation Model coupled with a hyperresolution land surface model, called Soil, Vegetation, and Snow (SVS) to simulate backscatter and compares the results with C‐band RADARSAT‐2 Synthetic Aperture Radar (SAR) backscatter signals in postharvest season when the field is considered bare soil or sparsely vegetated. Modifications to SVS evaporation scheme are shown to improve the comparison against SAR measurements. An improved effective soil roughness calculation scheme was also proposed to focus on the inversion of the root mean square height (Hrms) only. Soil dielectric constant compensation was suggested to reduce the inversion error and expand the dynamic range of Integral Equation Model. The combination of SVS soil moisture and effective roughness is found superior to the absence of either of them. This method is promising considering that it does not require any in situ measurements, and yet it still outperforms the original IEM model, which uses in situ measured soil moisture and soil roughness at point scale. Key Points: C‐band backscatter is simulated on footprint scaleHyper-resolution soil moisture is obtained from land surface model with high accuracyEffective soil surface roughness is used to improve IEM model [ABSTRACT FROM AUTHOR]

The capillary length (λs) and time (ts) are dynamic scalars that emerge routinely in the infiltration problem when gravitational and pressure gradients forces are involved. During drainage, however, capillary gradients oppose gravity and retain soil moisture close to surface. In this case, the pull of capillary gradients increases with drainage time and offsets gravity resulting in a quasi‐hydrostatic pressure distribution and negligibly small drainage flux in the profile. In this paper, it is proposed to anchor the dynamic concept of field capacity—the attainment of a small negligible drainage flux—in the physics of soil moisture redistribution as influenced by gravity and capillary forces. Similar to infiltration, this dynamic approach grounds the concept of field capacity in soil hydrology and allows its estimation from readily measured intrinsic physical characteristics such as λs, ts, and Ks. Finally, we exploit an analytical solution by Broadbridge and White (1988, https://doi.org/10.1029/WR024i001p00145) to track the drainage front as soil water redistributes in an initially saturated soil profile. While initially large, the downward migrating drainage front decelerates with time reaching near steady state condition at t ≈ 1,000ts. Quasi‐hydrostatic pressure matric head and water content profiles develop above the drainage front. Key Points: Similar to infiltration, drainage is controlled by soil capillary length and time scalesA drainage front determines the attainment of field capacity in a profile [ABSTRACT FROM AUTHOR]

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network's elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring. Plain Language Summary: As climate change drives shifts in temperature and precipitation, researchers and resource managers can benefit from improved monitoring of soil moisture. Understanding the relationship between soil moisture and other system components is crucial to improving water availability projections and understanding ecosystem responses to climate change. Despite their significance, in‐ground soil‐moisture measurements are often not available across multiple elevations within a single watershed. This paper presents a network in the Southern Rocky Mountains intended to help address this data gap and compliment data from other networks. The interactive Roaring Fork Observation Network consists of nine locations across an 1,800‐m change in elevation. Each station measures soil moisture at three depths, soil temperature, air temperature, humidity, and precipitation. Some stations are equipped with cameras or snow depth gauges, and for eight sites vegetation surveys are conducted. The data are available through a simple data portal. The network was established with local resource manager support, and one of its guiding purposes is to support management and restoration planning efforts. Because of the network's ongoing monitoring across multiple elevations and habitats, interactive Roaring Fork Observation Network will provide researchers and resource managers with access to valuable information about changes in soil conditions in a changing climate. Key Points: Soil moisture is key to understanding and predicting change in hydrology and ecology amid climate variability and changeIn situ soil moisture and weather monitoring data are now available across an 1,800‐m elevation span in a mountain watershedThe network is supported and guided by resource managers and supports both research and resource management goals [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear transfer function noise model for time-series modeling of unconfined groundwater hydrographs. The motivation for its development was that existing groundwater time-series models were unable to simulate large recharge events and multiyear droughts. This was because existing methods do not partition rainfall to runoff and do not account for nonlinear soil water drainage. To account for these nonlinear processes, a vertically integrated soil moisture module was added to an existing transfer function noise model. The soil moisture module has a highly flexible structure that allowed 84 different forms to be built. Application of the time-series model requires numerical calibration of parameters for the transfer functions, noise model and, for the nonlinear models, the soil moisture module. This was undertaken using the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) global calibration scheme. However, reproducible calibration to the global optima was challenging and a number of modifications were required to the transfer function noise model. In trialing the 84 nonlinear models and 2 linear models, each was applied to eleven observation bores within a paired catchment study area in Great Western, Victoria, Australia. In comparison with existing groundwater hydrograph time-series models, the proposed nonlinear time-series model performed significantly better at all observation bores during calibration and evaluation periods. Both the linear and nonlinear models were also used to quantify the impact of revegetation within the paired catchment; however, results were inconclusive, which is likely due to time-series data for the state of the revegetation being unavailable. By analyzing the application of 84 nonlinear models to each bore, an optimal structure for the soil moisture module was identified. It is unlikely, however, that this model structure would be appropriate for all climates and geologies. To encourage further investigations, open-source code for the highly flexible groundwater time-series modeling framework is available and we invite others to develop new models. [ABSTRACT FROM AUTHOR]

When predicting flow in the unsaturated zone, any method for modeling the flow will have to define how, and to what level, the subsurface structure is resolved. In this paper, we use the Ensemble Kalman Filter to assimilate local soil water content observations from both a synthetic layered lysimeter and a real field experiment in layered soil in an unsaturated water flow model. We investigate the use of colored noise bias corrections to account for unresolved subsurface layering in a homogeneous model and compare this approach with a fully resolved model. In both models, we use a simplified model parameterization in the Ensemble Kalman Filter. The results show that the use of bias corrections can increase the predictive capability of a simplified homogeneous flow model if the bias corrections are applied to the model states. If correct knowledge of the layering structure is available, the fully resolved model performs best. However, if no, or erroneous, layering is used in the model, the use of a homogeneous model with bias corrections can be the better choice for modeling the behavior of the system. [ABSTRACT FROM AUTHOR]

Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons. [ABSTRACT FROM AUTHOR]

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex hydrologic models that simulate soil moisture flow, groundwater recharge, surface runoff, root water uptake, and river discharge at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. Gupta et al. (2008) has recently proposed steps (amongst others) toward the development of a more robust and powerful method of model evaluation. Their diagnostic approach uses signature behaviors and patterns observed in the input-output data to illuminate to what degree a representation of the real world has been adequately achieved and how the model should be improved for the purpose of learning and scientific discovery. In this paper, we introduce approximate Bayesian computation (ABC) as a vehicle for diagnostic model evaluation. This statistical methodology relaxes the need for an explicit likelihood function in favor of one or multiple different summary statistics rooted in hydrologic theory that together have a clearer and more compelling diagnostic power than some average measure of the size of the error residuals. Two illustrative case studies are used to demonstrate that ABC is relatively easy to implement, and readily employs signature based indices to analyze and pinpoint which part of the model is malfunctioning and in need of further improvement. [ABSTRACT FROM AUTHOR]

At the catchment scale, soil moisture patterns have been observed to exhibit different types of dependence on topography. Some catchments have their wettest locations in the valley bottoms, while others have their wettest locations on hillslopes that are oriented away from the sun. Additionally, some catchments have soil moisture patterns that maintain a similar organization at all times (temporal stability), while other catchments have soil moisture patterns that change through time (temporal instability). Although these tendencies are well known, the reasons for their occurrence at a particular catchment are not well understood. In this paper, we investigate conditions under which different types of topographic dependence and different degrees of temporal instability can occur through the use of a new conceptual model. The type of topographic dependence and the degree of instability are quantified by two metrics that are also introduced in the paper, and the effects of soil, vegetation, and climatic parameters on these metrics are then evaluated. The evaluations indicate that saturated horizontal hydraulic conductivity, pore disconnectedness, and a vegetation evapotranspiration efficiency parameter have strong effects on the organization and instability of the soil moisture patterns. In contrast, annual potential evapotranspiration alone has less impact on the organization or its stability. [ABSTRACT FROM AUTHOR]

Although irrigated agriculture is the primary consumer of global groundwater resources, information on recharge rates and sustainable irrigation is limited. The study objective was to fingerprint irrigation return flow to quantify percolation/recharge and to estimate sustainable irrigation levels. This paper focuses on water quantity; a companion paper addresses water quality. Soil samples from 13 boreholes drilled beneath irrigated agroecosystems in the southern High Plains were analyzed for matric potential and water-extractable Cl and NO3. Unsaturated zone pore water beneath irrigated agroecosystems can be fingerprinted by higher matric potentials (wetter soils, median mp: −40 m) and higher NO3-N (median 71 mg/L) than beneath natural ecosystems (mp −200 m; NO3-N 8.1 mg/L) and by higher Cl (720 mg/L) than beneath rain-fed agroecosystems (8.4 mg/L). The range in percolation/recharge rates beneath irrigated agroecosystems is 18-97 mm/a (median 41 mm/a; 5% of irrigation + precipitation) and occurs primarily in response to extreme precipitation events. Similarity in percolation/recharge rates beneath irrigated and rain-fed (4.8-92 mm/a) agroecosystems was unexpected and is attributed to low irrigation applications (median 300 mm/a) and increased crop yield and evapotranspiration in irrigated areas. Regional water table declines are unsustainably large (≥ 30 m over 10,000 km2) in the north and are much lower in the south. Sustainable irrigation in the south would require reduction of the irrigated area from 23% to 9%. Methods developed for quantifying recharge and sustainable irrigation application rates can be applied to groundwater-fed irrigated areas in semiarid regions globally. [ABSTRACT FROM AUTHOR]

Nonlinear storage-discharge relations may explain important hydrologic features frequently observed under a variety of environmental conditions. In this paper the catchment dynamic storage problem is addressed by incorporating nonlinear storage-discharge relations in a stochastic framework to derive the statistical distribution and the duration curve of streamflows in river basins. Such a goal is achieved by extending recent analytical solutions for the seasonal probability distribution of discharges derived from the stochastic description of soil moisture dynamics at basin scales. Long-term probability density functions (pdf's) and flow duration curves are expressed through climatic, ecohydrologic, and geomorphic parameters of the basin by relaxing the linear reservoir assumption of subsurface flow components conveniently made in previous studies. In particular, this paper focuses on three different types of nonlinear, algebraic relationships between instantaneous discharges and storage volumes (concave and convex power laws and hyperbolic). Exact expressions of the streamflow pdf are derived in all cases, which are also tested by numerical simulations. Streamflow statistics and duration curves, estimates of catchment dynamic storages, recession timescales, and sensitivity to the underlying rainfall regime are then discussed. The model shows that different shapes of the streamflow pdf (bell shaped, monotonously decreasing, and bimodal) arise depending on the degree of nonlinearity of the storage-discharge relation, providing a new approach to catchment hydrology characterization. [ABSTRACT FROM AUTHOR]

In groundwater-dependent ecosystems, interactions between rainfall, water table fluctuations, and vegetation are exerted through the soil water content. The dynamics of soil moisture, in fact, are strongly coupled to fluctuations of the water table and, together, they control the overall ecosystem dynamics. We propose here a simple process-based stochastic model for the study of soil moisture dynamics at a generic depth, to complement the stochastic model of water table depth presented in the companion paper. The model presented here is based on a local and depth-dependent water balance driven by stochastic rainfall (marked Poisson noise) and accounting for processes such as rainfall infiltration, root water uptake, and capillary rise. We obtain a semianalytical formulation of the stationary probability distribution of soil water content at different depths, which is studied for different values of soil, climate, and vegetation parameters. The probability distributions are used to investigate the ecohydrology of groundwater-dependent ecosystems, including the quantitative description of the vegetation-water table-soil moisture interplay and the probabilistic analysis of root water uptake. [ABSTRACT FROM AUTHOR]

In a companion paper, a conceptual model was presented to predict two important vegetation parameters from climatic constraints in water limited conditions, notably photosynthetic capacity and internal carbon dioxide concentration. In this study, the model is evaluated using data of four experimental forest plots in sub-Mediterranean Slovenia which were selected for their topography induced differences in climate and contrasting vegetation characteristics. Data were collected during a regular (2004) and an exceptionally dry year (2003). Measurements showed that photosynthetic capacity decreases with vapor pressure deficit, and internal carbon dioxide concentration correlates positively with available water. Variations in soil water storage at the start of the dry season and vapor pressure deficit during the dry season are responsible for a large part of these differences. Winter precipitation has a large effect on the shape of the seasonal course of transpiration during the following season. The model explained observed differences among sites and years in photosynthetic capacity and the seasonal cycle of transpiration. Although the magnitude of calculated optimum internal carbon dioxide concentrations agreed with observations, the model could not explain observed differences in internal carbon dioxide concentration or the correlation between internal carbon dioxide concentration and water availability. The optimality hypothesis, despite its limitations, can be used to predict the seasonal cycle of transpiration. [ABSTRACT FROM AUTHOR]

The present paper complements that of Isham et al. (2005), who introduced a space-time soil moisture model driven by stochastic space-time rainfall forcing with homogeneous vegetation and in the absence of topographical landscape effects. However, the spatial variability of vegetation may significantly modify the soil moisture dynamics with important implications for hydrological modeling. In the present paper, vegetation heterogeneity is incorporated through a two dimensional Poisson process representing the coexistence of two functionally different types of plants (e.g., trees and grasses). The space-time statistical structure of relative soil moisture is characterized through its covariance function which depends on soil, vegetation, and rainfall patterns. The statistical properties of the soil moisture process averaged in space and time are also investigated. These properties are especially important for any modeling that aggregates soil moisture characteristics over a range of spatial and temporal scales. It is found that particularly at small scales, vegetation heterogeneity has a significant impact on the averaged process as compared with the uniform vegetation case. Also, averaging in space considerably smoothes the soil moisture process, but in contrast, averaging in time up to 1 week leads to little change in the variance of the averaged process. [ABSTRACT FROM AUTHOR]

Coarse soil moisture fields and nonlinear relationships between fluxes and soil moisture combine to yield errors in both diagnostic and predictive estimates of large-scale mass and energy fluxes. Efforts to empirically define the dynamics of subgrid spatial variance of soil moisture have led to contradictory results. Moreover, most reports of soil moisture variability range from qualitative to descriptively quantitative, owing to the lack of a robust theoretical framework for moisture variance dynamics. In this paper we derive a conservation equation for the spatial variance of subgrid root zone soil moisture, based on first principles of statistical fluid mechanics. We arrive at a variance budget in which explicit covariances between moisture fields and land surface flux fields act to produce or destroy variance through time (according to the sign of the correlation between the flux and state fields). A series of examples are used to explore how simple forms of soil, vegetation, precipitation, topography, and initial moisture variability lead to evolving covariances between spatial fields of soil moisture and particular land surface fluxes and how these covariances relate to the temporal trajectory of the spatial variance of soil moisture. We isolate a set of processes and conditions that demonstrate variance production through time and a set that demonstrate variance destruction. Of particular interest is the tendency for transpiration and infiltration-runoff processes to either produce or destroy variance, depending on the background wetness regime. Field data are also employed and shown to demonstrate a temporal behavior of the spatial variance that is readily described by the proposed approach. Ultimately, this work should aid field data interpretation and, when supplemented with a closure model for the variance budget, lead to improved land surface flux predictability over coarse grids. [ABSTRACT FROM AUTHOR]

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models. [ABSTRACT FROM AUTHOR]

Monitoring and estimating grid-mean soil moisture is very important for assessing many hydrological, biological, and biogeochemical processes and for validating remotely sensed surface soil moisture products. Temporal stability analysis (TSA) is a valuable tool for identifying a small number of representative sampling points to estimate the grid-mean soil moisture content. This analysis was evaluated and improved using high-quality surface soil moisture data that were acquired by a wireless sensor network in a high-intensity irrigated agricultural landscape in an arid region of northwestern China. The performance of the TSA was limited in areas where the representative error was dominated by random events, such as irrigation events. This shortcoming can be effectively mitigated by using a stratified TSA (STSA) method, proposed in this paper. In addition, the following methods were proposed for rapidly and efficiently identifying representative sampling points when using TSA. (1) Instantaneous measurements can be used to identify representative sampling points to some extent; however, the error resulting from this method is significant when validating remotely sensed soil moisture products. Thus, additional representative sampling points should be considered to reduce this error. (2) The calibration period can be determined from the time span of the full range of the grid-mean soil moisture content during the monitoring period. (3) The representative error is sensitive to the number of calibration sampling points, especially when only a few representative sampling points are used. Multiple sampling points are recommended to reduce data loss and improve the likelihood of representativeness at two scales. [ABSTRACT FROM AUTHOR]

An integrated system for collecting, recording, and analyzing data describing the behavior of water flowing through a forest soil has been developed. The collecting and recording components are located on a lower terrace of the City of Seattle Cedar River watershed in western Washington. This system includes tension lysimeters installed such that the overlying soil and rooting systems are undisturbed. The lysimeters are constructed from a fused aluminum oxide disk 11 inches (28 cm) in diameter. The suction to each lysimeter is maintained through a single controlled vacuum source. Solutions collected during periods of flow are passed through flow cells measuring conductivity, acidity, and rate of water flow. The sensitivity of the soil moisture flowmeter is in excess of 0.002 cm/hr. The electrical output from the flow cells is coupled to a data-logging facility and printed as perforations in paper tape. Computer analysis converts these data to tabular and graphic form. Examples demonstrating the functioning of the system during periods of flow are presented. [ABSTRACT FROM AUTHOR]

An exact solution is derived for the second term of J. R. Philip's [1957] series expansion originally developed for his numerical method of solution of the diffusion equation with diffusivity concentration dependent. The exact solution is made possible by the adoption of simple algebraic functions to describe the water content-capillary suction and the water content-capillary conductivity relationships of the porous medium and of a solution for horizontal infiltration presented in an earlier paper [ Brutsaert, 1968]. Because of the fast convergence of the series, all terms of order higher than the second are negligible. Thus the present solution for the second term used in combination with the first term, representing the solution for horizontal infiltration, provides an adequate solution for vertical infiltration. Good agreement is obtained with published experimental data. [ABSTRACT FROM AUTHOR]

From analysis of the water profiles obtained by gamma ray absorption during infiltration into soil columns, and of the wetting branch of the soil water-suction water content relation, this paper suggests a method for checking the validity of the generalized Darcy's law and for computing the values of the hydraulic conductivity at various water contents. Tests have been made for horizontal infiltration and for capillary rise. In both tests, for a given water content, the capillary conductivity k is constant, and the generalized Darcy's law is valid and may be used to describe a transient flow in unsaturated soils. [ABSTRACT FROM AUTHOR]

Reliable estimates of regional evapotranspiration are necessary to improve water resources management and planning. However, direct measurements of evaporation are expensive and difficult to obtain. Some of the difficulties are illustrated in a comparison of several satellite-based estimates of evapotranspiration for the Upper Blue Nile (UBN) basin in Ethiopia. These estimates disagree both temporally and spatially. All the available data products underestimate evapotranspiration leading to basin-scale mass balance errors on the order of 35 percent of the mean annual rainfall. This paper presents a methodology that combines satellite observations of rainfall, terrestrial water storage as well as river-flow gauge measurements to estimate actual evapotranspiration over the UBN basin. The estimates derived from these inputs are constrained using a one-layer soil water balance and routing model. Our results describe physically consistent long-term spatial and temporal distributions of key hydrologic variables, including rainfall, evapotranspiration, and river-flow. We estimate an annual evapotranspiration over the UBN basin of about 2.55 mm per day. Spatial and temporal evapotranspiration trends are revealed by dividing the basin into smaller subbasins. The methodology described here is applicable to other basins with limited observational coverage that are facing similar future challenges of water scarcity and climate change. [ABSTRACT FROM AUTHOR]

The memory timescale that characterizes root-zone soil moisture remains the dominant measure in seasonal forecasts of land-climate interactions. This memory is a quasi-deterministic timescale associated with the losses (e.g., evapotranspiration) from the soil column and is often interpreted as persistence in soil moisture states. Persistence, however, represents a distribution of time periods where soil moisture resides above or below some prescribed threshold and is therefore inherently probabilistic. Using multiple soil moisture data sets collected at high resolution (subhourly) across different biomes and climates, this paper explores the differences, underlying dynamics, and relative importance of memory and persistence timescales in root-zone soil moisture. A first-order Markov process, commonly used to interpret soil moisture fluctuations derived from climate simulations, is also used as a reference model. Persistence durations of soil moisture below the plant water-stress level (chosen as the threshold), and the temporal spectrum of upcrossings and downcrossings of this threshold, are compared to the memory timescale and spectrum of the full time series, respectively. The results indicate that despite the differences between meteorological drivers, the spectrum of threshold-crossings is similar across sites, and follows a unique relation with that of the full soil moisture series. The distribution of persistence times exhibits an approximate stretched exponential type and reflects a likelihood of exceeding the memory at all sites. However, the rainfall counterpart of these distributions shows that persistence of dry atmospheric periods is less likely at sites with long soil moisture memory. The cluster exponent, a measure of the density of threshold-crossings in a time frame, reveals that the clustering tendency in rainfall events (on-off switches) does not translate directly to clustering in soil moisture. This is particularly the case in climates where rainfall and evapotranspiration are out of phase, resulting in less ordered (more independent) persistence in soil moisture than in rainfall. [ABSTRACT FROM AUTHOR]

Basin response and hydrologic fluxes are functions of hydrologic states, most notably of soil moisture. However, characterization of hillslope-scale soil moisture is challenging since it is both spatially heterogeneous and dynamic. This paper introduces an entropy-based and discretization-invariant dimensionless index of hydrologic complexity [ABSTRACT FROM AUTHOR]

The Noah-MP land surface model adopts a multiparameterization framework to accommodate various alternative parameterizations for more than 10 physical processes. In this paper, the parameterizations implemented in Noah-MP associated with under-canopy turbulence and root water uptake are enhanced with: (i) an under-canopy turbulence scheme currently adopted by the Community Land Model (CLM), (ii) two vertical root distribution functions, i.e., an exponential and an asymptotic formulation, and (iii) three soil water stress functions ( βt) controlling root water uptake, e.g., a soil water potential ( ψ)-based function, a nonlinear soil moisture ( θ)-based power function and an empirical threshold approach considering preferential uptake from the moist part of the soil column. A comprehensive data set of in situ micrometeorological observations and profile soil moisture/temperature measurements collected from an alpine meadow site in the northeastern Tibetan Plateau is utilized to assess the impact of the augmentations on the Noah-MP performance. The results indicate that (i) implementation of the CLM under-canopy turbulence scheme greatly resolves the overestimation of sensible heat flux and underestimation of soil temperature across the profile, (ii) both exponential and asymptotic vertical root distribution functions better represent the Tibetan conditions enabling a better representation of the measured soil moisture dynamics, and (iii) the ψ-based βt functions overestimate surface soil moisture, the default linear θ-based βt function underestimates latent heat flux during the dry-down, while both the nonlinear power function and empirical threshold approach simultaneously simulate well soil moisture, and latent and sensible heat fluxes. Additionally, the parameter uncertainty associated with soil water stress function and hydraulic parameterization is addressed. [ABSTRACT FROM AUTHOR]

Technological and methodological progress is essential to improve our understanding of fundamental processes in natural and engineering sciences. In this paper, we will address the potential of new technological and methodological advancements in soil hydrology to move forward our understanding of soil water related processes across a broad range of scales. We will focus on advancements made in quantifying root water uptake processes, subsurface lateral flow, and deep drainage at the field and catchment scale, respectively. We will elaborate on the value of establishing a science-driven network of hydrological observatories to test fundamental hypotheses, to study organizational principles of soil hydrologic processes at catchment scale, and to provide data for the development and validation of models. Finally, we discuss recent developments in data assimilation methods, which provide new opportunities to better integrate observations and models and to improve predictions of the short-term evolution of hydrological processes. [ABSTRACT FROM AUTHOR]

With the recently launched Soil Moisture Active Passive (SMAP) mission, it is very important to have a complete understanding of the radiative transfer model for better soil moisture retrievals and to direct future research and field campaigns in areas of necessity. Because natural systems show great variability and complexity with respect to soil, land cover, topography, precipitation, there exist large uncertainties and heterogeneities in model input factors. In this paper, we explore the possibility of using global sensitivity analysis (GSA) technique to study the influence of heterogeneity and uncertainties in model inputs on zero order radiative transfer (ZRT) model and to quantify interactions between parameters. GSA technique is based on decomposition of variance and can handle nonlinear and nonmonotonic functions. We direct our analyses toward growing agricultural fields of corn and soybean in two different regions, Iowa, USA (SMEX02) and Winnipeg, Canada (SMAPVEX12). We noticed that, there exists a spatio-temporal variation in parameter interactions under different soil moisture and vegetation conditions. Radiative Transfer Model (RTM) behaves more non-linearly in SMEX02 and linearly in SMAPVEX12, with average parameter interactions of 14% in SMEX02 and 5% in SMAPVEX12. Also, parameter interactions increased with vegetation water content (VWC) and roughness conditions. Interestingly, soil moisture shows an exponentially decreasing sensitivity function whereas parameters such as root mean square height (RMS height) and vegetation water content show increasing sensitivity with 0.05 v/v increase in soil moisture range. Overall, considering the SMAPVEX12 fields to be water rich environment (due to higher observed SM) and SMEX02 fields to be energy rich environment (due to lower SM and wide ranges of TSURF), our results indicate that first order as well as interactions between the parameters change with water and energy rich environments. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of hydraulic conductivity ( K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers. [ABSTRACT FROM AUTHOR]

The freezing temperature of water in soil is not constant but varies over a range determined by soil texture. Consequently, the amounts of unfrozen water and ice change with temperature in frozen soil, which in turn affects hydraulic, thermal, and mechanical properties of frozen soil. In this paper, an Am-241 gamma ray source and time-domain reflectometry (TDR) were combined to measure unfrozen water content and ice content in frozen soil simultaneously. The gamma ray attenuation was used to determine total water content. The TDR was used to determine the dielectric constant of the frozen soil. Based on a four-phase mixing model, the amount of unfrozen water content in the frozen soil could be determined. The ice content was inferred by the difference between total water content and unfrozen water content. The gamma ray attenuation and the TDR were both calibrated by a gravimetric method. Water contents measured by gamma ray attenuation and TDR in an unfrozen silt column under infiltration were compared and showed that the two methods have the same accuracy and response to changes of water content. Unidirectional column freezing experiments were performed to apply the combined method of gamma ray attenuation and TDR for measuring unfrozen water content and ice content. The measurement error of the gamma ray attenuation and TDR was around 0.02 and 0.01 m3/m3, respectively. The overestimation of unfrozen water in frozen soil by TDR alone was quantified and found to depend on the amount of ice content. The higher the ice content, the larger the overestimation. The study confirmed that the combined method could accurately determine unfrozen water content and ice content in frozen soil. The results of soil column freezing experiments indicate that total water content distribution is affected by available pore space and the freezing front advance rate. It was found that there is similarity between the soil water characteristic and the soil freezing characteristic of variably saturated soil. Unfrozen water content is independent of total water content and affected only by temperature when the freezing point is reached. [ABSTRACT FROM AUTHOR]

This paper characterizes the long-term, spatiotemporal variation of drought propensity through a newly proposed, namely Drought Management Index (DMI), and explores its predictability in order to assess the future drought propensity and adapt drought management policies for a location. The DMI was developed using the reliability-resilience-vulnerability (RRV) rationale commonly used in water resources systems analysis, under the assumption that depletion of soil moisture across a vertical soil column is equivalent to the operation of a water supply reservoir, and that drought should be managed not simply using a measure of system reliability, but should also take into account the readiness of the system to bounce back from drought to a normal state. Considering India as a test bed, 5 year long monthly gridded (0.5° Lat × 0.5° Lon) soil moisture data are used to compute the RRV at each grid location falling within the study domain. The Permanent Wilting Point (PWP) is used as the threshold, indicative of transition into water stress. The association between resilience and vulnerability is then characterized through their joint probability distribution ascertained using Plackett copula models for four broad soil types across India. The joint cumulative distribution functions (CDF) of resilience and vulnerability form the basis for estimating the DMI as a five-yearly time series at each grid location assessed. The status of DMI over the past 50 years indicate that drought propensity is consistently low toward northern and north eastern parts of India but higher in the western part of peninsular India. Based on the observed past behavior of DMI series on a climatological time scale, a DMI prediction model comprising deterministic and stochastic components is developed. The predictability of DMI for a lead time of 5 years is found to vary across India, with a Pearson correlation coefficient between observed and predicted DMI above 0.6 over most of the study area, indicating a reasonably good potential for drought management in the medium term water resources planning horizon. [ABSTRACT FROM AUTHOR]

Field monitoring of isotopes and isotope analyses have been used to approximate water travel time at hillslope scales. This paper introduces an alternate method for estimating a point-scaled transit time in a soil layer, namely hydrometric transit time (HTT). HTT uses hydrometric measurements to address both matrix flow and bypass flow. Soil matrix flux is approximated through integration of continuous soil moisture profiles and soil water computations. Free surface film modeling of unsaturated flow is used to estimate bypass flow. This flux and storage estimation scheme is applied to a steep hillslope using hydrometric measurements that were estimated over an 8 month period using a continuous daily approximation of soil moisture profiles. Transit times at several designated points are also evaluated using isotope analyses. Results show that rainfall strongly controls temporal fluctuations. This work highlights the potential role of HTT in revealing the spatial and seasonal variations in the transit time probability density function (PDF) along transects. Mean transit time does not sufficiently characterize transit time variations on a hillslope scale. Accumulated flux distributions identify distinct hydrologic contributions and efficient redistribution of soil water in the downslope area of the hillside. [ABSTRACT FROM AUTHOR]

The pressure on water resources, deteriorating water quality, and uncertainties associated with the climate change create an environment of conflict in large and complex river system. The Black Sea Basin (BSB), in particular, suffers from ecological unsustainability and inadequate resource management leading to severe environmental, social, and economical problems. To better tackle the future challenges, we used the Soil and Water Assessment Tool (SWAT) to model the hydrology of the BSB coupling water quantity, water quality, and crop yield components. The hydrological model of the BSB was calibrated and validated considering sensitivity and uncertainty analysis. River discharges, nitrate loads, and crop yields were used to calibrate the model. Employing grid technology improved calibration computation time by more than an order of magnitude. We calculated components of water resources such as river discharge, infiltration, aquifer recharge, soil moisture, and actual and potential evapotranspiration. Furthermore, available water resources were calculated at subbasin spatial and monthly temporal levels. Within this framework, a comprehensive database of the BSB was created to fill the existing gaps in water resources data in the region. In this paper, we discuss the challenges of building a large-scale model in fine spatial and temporal detail. This study provides the basis for further research on the impacts of climate and land use change on water resources in the BSB. [ABSTRACT FROM AUTHOR]

The Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux. [ABSTRACT FROM AUTHOR]

The cosmic-ray probe (CRP) provides continuous estimates of soil moisture over an area of ∼30 ha by counting fast neutrons produced from cosmic rays which are predominantly moderated by water molecules in the soil. This paper describes the setup, measurement correction procedures, and field calibration of CRPs at nine locations across Australia with contrasting soil type, climate, and land cover. These probes form the inaugural Australian CRP network, which is known as CosmOz. CRP measurements require neutron count rates to be corrected for effects of atmospheric pressure, water vapor pressure changes, and variations in incoming neutron intensity. We assess the magnitude and importance of these corrections and present standardized approaches for network-wide analysis. In particular, we present a new approach to correct for incoming neutron intensity variations and test its performance against existing procedures used in other studies. Our field calibration results indicate that a generalized calibration function for relating neutron counts to soil moisture is suitable for all soil types, with the possible exception of very sandy soils with low water content. Using multiple calibration data sets, we demonstrate that the generalized calibration function only applies after accounting for persistent sources of hydrogen in the soil profile. Finally, we demonstrate that by following standardized correction procedures and scaling neutron counting rates of all CRPs to a single reference location, differences in calibrations between sites are related to site biomass. This observation provides a means for estimating biomass at a given location or for deriving coefficients for the calibration function in the absence of field calibration data. [ABSTRACT FROM AUTHOR]