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Summary Modeling soil water flow requires the knowledge of numerous parameters associated to the water content and the soil hydraulic properties. Direct estimations of those parameters in laboratory require expensive equipment and the obtained parameters are generally not representative at the field scale because of the limitation of core sample size. Indirect methods such as inverse modeling are known to get efficient estimations and are easier to set up and process for large-scale studies. In this study, we investigated the capacity of an inverse modeling procedure to estimate the soil and the bedrock hydrodynamic properties only from in situ soil water content measurements at multiple depths under natural conditions. Multi-objective parameter optimization was performed using the HYDRUS-1D software and an external optimization procedure based on the NSGA-II algorithm. In a midslope shallow soil, water content was monitored at 3 depths, 20, 40, and 60 cm during 12 intense rainfall events, whose amounts ranged between 50 and 250 mm and duration between 1 and 5 days. The vertical profile was considered as 2 layers of soils above a third layer representing the weathered schist rock. This deep layer acted as a deep boundary condition, which features the bedrock permeability and water storage. Each layer was described trough the 6 parameters of the Mualem–van Genuchten formulation. The calibrated parameters appeared to have very low uncertainty while allowing a good modelisation of the observed water content variations. The calibrated saturated water content was close to the laboratory porosity measurements while the saturated hydraulic conductivity showed that the soil was highly permeable, as measured in the field. The inverse modeling approach allowed an estimation of the hydraulic properties of the bedrock layer where no measurement was available. The bedrock layer was found to have a low saturated hydraulic conductivity (<5 mm h −1 ), which means that the schist bedrock is poorly weathered and that saturated area can be generated above this depth, as it was observed. The simulated water contents were generally close to the measured water contents, but the model failed sometimes to reproduce the saturation of the soil in the deeper layers, probably because of sub-surface flux at the soil/bedrock interface. In these cases, further investigation will have to be made by using a 2D-model. [ABSTRACT FROM AUTHOR]

A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model developed by Météo-France was used. This study was done in the Gardon catchment (545 km²) in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R² values increasing from 0.61 to 0.72. [ABSTRACT FROM AUTHOR]

A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a fundamental role in irrigation and drainage, soil conservation, as well as water and contaminant transport in the vadose zone. This study evaluates the accuracy, performance, and prediction capabilities of 15 SWR models. A total of 140 soil samples were collected from different sites, covering all textural classes. Standard suction tests, using both hanging column and ceramic pressure plate extractors, were conducted to compile the SWR databank. 15 SWR models were selected and fitted to the SWR data points. Soil texture, bulk density, and organic matter were used to determine their effect on the performance of the SWR models. The results indicate that the Tani and Russo models exhibit the lowest levels of accuracy and performance among the selected models. Based on the Akaike and Bayesian information criteria analysis, the van Genuchten model exhibits the lowest values among the selected models, with poor prediction capabilities in estimating the SWR curve. The significance test at the 0.05 level (95% confidence interval) shows that according to the calculated p-values for the Pearson correlation coefficient between RMSE and texture, the Brooks-Corey and van Genuchten models are poorly influenced by soil properties. The performance of the models is not significantly affected by the soil organic matter. Similarly, bulk density does not significantly affect model performance except for the Brooks–Corey, van Genuchten, Tani, and Russo models. Among the SWR models considered, the double exponential, Groenevelt and Grant, and Khlosi et al. models demonstrate superior accuracy and performance in predicting the SWR curve. This is supported by lower values for RMSE, Akaike, and Bayesian information criteria. [ABSTRACT FROM AUTHOR]

Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement: The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations. [ABSTRACT FROM AUTHOR]

The electrical resistivity of the vadose zone is highly dependent on soil moisture content (VWC), making ERT a potential tool for non-intrusive infiltration monitoring. There's a long history of using Archie's law to convert resistivity values to VWC, often under the assumption that the pore-fluid conductivity is constant. Analysis of two years of soil conductivity and VWC data from sensors buried in a bioswale in Philadelphia, Pennsylvania showed that the characteristic curve of VWC versus resistivity exhibited hysteresis. The same value of resistivity was associated with different VWC during imbibition and drying. Accurate VWC could be estimated using Archie's law only after incorporating changes in pore-fluid conductivity. Additionally, pore-fluid conductivity varied seasonally, with the highest values recorded during winter from road salt runoff. Archie exponents differed for sensors within 25 cm of each other despite both the uniform nature of the bioswale soil and fits to Archie's Law for individual sensors, indicating considerable heterogeneity. This pattern was confirmed at two additional sites. Assuming the conductivity of the pore-fluid remains constant during infiltration not only produces erroneous soil moisture estimates, it can even reverse the apparent soil moisture trend. We conclude that to use ERT to monitor infiltration it is essential to independently record changes in pore-fluid conductivity, and that multiple sensors are required because changes in pore-fluid conductivity and Archie parameters can vary on a sub-meter scale for even apparently homogeneous sites. Additionally, long-term changes in Archie parameters, notably m, are possible, and may indicate long-term changes in soil fabric. [ABSTRACT FROM AUTHOR]

Adopting new technologies to calibrate hydrological models to produce good simulations for water resources or flood risk management is one of the important research topics in hydrology field. Incorporating hydrological signatures and concepts in hydrological model calibration becomes prevalent in recent years. A good realistic simulation of hydrological processes within the catchment is more significant than only achieving good performance at watershed outlet with single-statistical-objective function. This research adopts the concept of flood scaling property, which describes the statistical relationship between flood peak, its contributing areas and additional catchment attributes, to behave as one of the constraints under multi-objective model calibration framework. Several designed calibration scenarios are tested by employing the soil and water assessment tool hydrological model. In comparison with single-objective calibration scenarios, multi-objective calibration method is recommended to obtain both good simulation at catchment outlet and sub-basins reaches. The multi-objective calibration method assists in improving the model performance in terms of flow duration curve and also in reducing the bias of long-term runoff ratio. The proposed model calibration approach reflects the hydrological interaction of flood peaks across sub-basins and takes into account the catchment antecedent wetness and climatic condition for each flood event. [ABSTRACT FROM AUTHOR]

European forests are suffering considerably from the consequences of the droughts of recent years, and the exact reasons and influencing factors for this are still not fully understood. This study was conducted to characterize the changes and dynamics of soil moisture in a mixed forest in northern Bavaria within 1 year. Since electrical resistivity correlates well with soil water content, we used two‐dimensional electrical resistivity tomography (ERT) monitoring and time‐lapse analyses to supplement punctual measurements by sensors and soil analyses to show soil moisture changes throughout a whole year (2020–2021). While the topsoil dries out significantly from summer to autumn down to a depth of about 3 m, a clear increase in soil water content and a decrease in resistivity below 3 m can be observed during winter period. Anomalies in the topsoil (0–1 m) showing lower resistivities than the surrounding substrate could be related to tree positions by additional terrestrial laser scans. A significant relationship could be found between tree crown projection area and resistivity in 1–2 m depth. We found a trend that mean resistivity below pine is lower as below beech. ERT data were also used to estimate the soil water content via Archie's law and the results correlate strongly with the measured values, but the degree of correlation varies depending on the depth level. ERT as a noninvasive method, in combination with additional data, for example, on the vitality status of individual trees, could help to better understand root water uptake and water supply to trees, especially during periods of drought. Core Ideas: In situ soil moisture monitoring for realistic assessment of tree water supply.ERT monitoring can help understand root water uptake in a mixed forest.Resistivity of subsurface anomalies correlates with the crown projection area of trees.Resistivity anomalies show descriptive differences between tree species.Volumetric water content can be estimated via Archie's law, calibrated with in situ data. [ABSTRACT FROM AUTHOR]

The data assimilation of remotely sensed soil moisture observations provides a feasible path of improving river flow simulation. In this work, we studied the performance of the error subspace transform Kalman filter (ESTKF) assimilation algorithm on the assimilation of remotely sensed soil moisture from SMAP, including the improvement of soil moisture and river flow in the hydrological model. Additionally, we discussed the advantages and added value of ESTKF compared to the ensemble Kalman filter (EnKF) in a hydrological model. To achieve this objective, we solved the spatial resolution gap between the remotely sensed soil moisture and the simulated soil moisture of the hydrological model. The remotely sensed soil moisture from SMAP was assimilated into the first layer soil moisture in the distributed hydrological model. The spatial resolution of the hydrological model was 600 m, while the spatial resolution of the SMAP remotely sensed soil moisture was 9 km. There is a considerable gap between the two spatial resolutions. By employing observation operators and observation localization based on geolocation, the distributed hydrological model assimilated multiple remotely sensed soil moisture values for each grid, thereby ensuring the consistent updates of soil moisture in the model. The results show the following: (1) In terms of improving soil moisture, we found that both ESTKF and EnKF were effective, and the ubRMSE of ESTKF was lower than that of EnKF. (2) ESTKF improved most cases where open-loop high river flow simulations were too low, but EnKF did not improve this situation. (3) In ESTKF, the relative error of flood volume was reduced on average to 2.52%, but the relative error of flood peak did not improve. The results provide evidence of the value of ESTKF in the hydrological model. [ABSTRACT FROM AUTHOR]

In arid and semi-arid areas, water vapor transport is an important form of soil water movement and plays a crucial role in the overall water and energy balance. For better prediction of soil water and heat fluxes and understanding of root zone soil water dynamics for effective crop management, soil moisture, temperature, soil texture and micrometeorological data have been collected from field trials. Based on the data collected, a Hydrus 1D model was established to simulate the coupled transport of liquid water, water vapor and heat under summer maize (summer maize treatment; SMT) and bare soil (bare soil treatment; BT) for a 100 cm soil profile. Calibration and validation data for the model revealed a good level of agreement between simulated and measured data. Results indicated that the isothermal vapor flux was close to zero throughout the profile, while the isothermal water flux dominated the soil water movement for both SMT and BT. The vapor flux was mainly contributed by thermal vapor flux and increased with soil desiccation. Evaporation and transpiration showed two distinct phases, increasing immediately after irrigation and decreasing gradually as soil water content decreased. SMT had lower evaporation rates due to the protection provided by crop canopy. Irrigation significantly altered the dynamic characteristics of thermal liquid water and thermal vapor fluxes in the vadose, emphasizing the importance of considering the coupled transport of liquid water, vapor, and heat transport at interfaces in the soil–plant–atmosphere continuum for accurate estimates of water flux, especially under prolonged drought conditions. [ABSTRACT FROM AUTHOR]

North Africa is characterized by several ungauged basins, especially in Morrocco, where satellite products could be an alternative of the lack ground-based measurements. In this study, the Land Parameter Retrieval Model (LPRM) soil moisture and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) were used in flood modeling in Moroccan ungauged basins (Bourrous, Al Wiza, El Hallouf and Jamala). We started with comparing GPM-IMERG Early with ground measurements from five rain gauges. Next, the Soil Conservation Service – Curve Number (SCS-CN) was applied with ground precipitation measurements, LPRM and GPM-IMERG Early datasets to simulated flood events in a gauged basin, the Ghdat. Finally, this SCS-CN model was transposed with these satellite data sets validated to those ungauged basins in order to reproduce flood events. The results show that the GPM-IMERG Early is best with in situ measurements (correlation coefficient = 0.50; relative bias = 27.51%; probability of detection = 0.77; false alarm rate = 0.23), on a daily scale. The observed precipitation, LPRM and GPM-IMERG Early were performed well in validation to simulate floods in the Ghdat, where Nash–Sutcliffe criterion range from 0.43 and 0.98 using the SCS-CN model. For Bourrous, Al Wiza, El Hallouf and Jamala all flood events and hydrographs were reproduced by GPM-IMERG Early and LPRM products. Furthermore, LPRM products were validated against soil moisture measurements with a coefficient of determine R2 between 0.72 and 0.84. The results of this work provided interesting insights for flood modeling using GPM-IMERG and LPRM satellite products in ungauged basins. [ABSTRACT FROM AUTHOR]

Both hydrological and geophysical data can be used to calibrate hillslope hydrologic models. However, these data often reflect hydrological dynamics occurring at disparate spatial scales. Their use as sole objectives in model calibrations may thus result in different optimum hydraulic parameters and hydrologic model behavior. This is especially true for mountain hillslopes where the subsurface is often heterogeneous and the representative elementary volume can be on the scale of several m3. This study explores differences in hydraulic parameters and hillslope‐scale storage and flux dynamics of models calibrated with different hydrological and geophysical data. Soil water content, groundwater level, and two time‐lapse electrical resistivity tomography (ERT) data sets (transfer resistance and inverted resistivity) from two mountain hillslopes in Wyoming, USA, are used to calibrate physics‐based surface–subsurface hydrologic models of the hillslopes. Calibrations are performed using each data set independently and all data together resulting in five calibrated parameter sets at each site. Model predicted hillslope runoff and internal hydrological dynamics vary significantly depending on the calibration data set. Results indicate that water content calibration data yield models that overestimate near‐surface water storage in mountain hillslopes. Groundwater level calibration data yield models that more reasonably represent hillslope‐scale storage and flux dynamics. Additionally, ERT calibration data yield models with reasonable hillslope runoff predictions but relatively poor predictions of internal hillslope dynamics. These observations highlight the importance of carefully selecting data for hydrologic model calibration in mountain environments. Poor selection of calibration data may yield models with limited predictive capability depending on modeling goals and model complexity. Key Points: Water content calibration data yield models that overestimate near‐surface water storage in mountain hillslopesGroundwater level calibration data yield models that reasonably represent hillslope‐scale storage and flux dynamicsResistivity calibration data yield models with reasonable hillslope runoff predictions but poor predictions of internal hillslope dynamics [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures. [ABSTRACT FROM AUTHOR]

Resistivity is used to evaluate soil water content (SWC), which has the advantages of not causing soil disturbance and in low price. It is an effective way to assess the SWC variability. This paper aims to evaluate the variability of loess slope SWC through the change of resistivity. It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors. In this context, geoelectric and environmental factors such as soil temperature and SWC were monitored for three years. The prediction model of apparent resistivity and SWC was calibrated. The post processing of geoelectric data was introduced. In addition, the SWC collected by Time-Domain Reflectometry (TDR) was used to verify the feasibility of electrical resistivity tomography (ERT) data. The SWC variability in the process of rainfall, the evolution of four seasons, and the alternation of drying and wetting were evaluated. The research results show that: i) the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5–3.0 m depth. ii) The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring. iii) From 2017 to 2020, the SWC increased in August, and the soil became dry in January. iv) Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity. The outcome can provide the change information of SWC to a great extent without excavating boreholes. [ABSTRACT FROM AUTHOR]

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future. [ABSTRACT FROM AUTHOR]

River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa. [ABSTRACT FROM AUTHOR]

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation. [ABSTRACT FROM AUTHOR]

Field-scale estimation of soil hydraulic parameters is important for simulating water movement, particularly in the vadose zone. The accuracy of soil hydraulic parameters significantly affects the output of models using these parameters as input variables. A new approach for an inverse method, often referred to as inverse modelling, was presented to calibrate soil hydraulic parameters. The new method is based on the use of uniform design theory to generate uncertainty estimates of the soil hydraulic parameters. The uncalibrated and calibrated soil hydraulic parameters were evaluated by comparing the predicted and measured soil water contents using the HYDRUS-1D model. The results show that the calibrated soil hydraulic parameters that were inversely estimated by uniform design theory were accurate and reasonable. In addition, the results provided recommendations for improving the estimation of soil hydraulic parameters when there are no available observation data for calibrating and optimizing soil hydraulic parameters. [ABSTRACT FROM AUTHOR]

Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements. [ABSTRACT FROM AUTHOR]

Soil wetness is an important property in determining the variable disposition of hillslopes to shallow landslides. Recent studies have demonstrated the potential of in situ soil wetness information for landslide early warning. However, the spatial representativeness of in situ sensors may be affected by local heterogeneities of soil properties and hydrological processes, and their installation may be destructive. Electrical resistivity tomography (ERT) has been used in the past to estimate plot‐scale soil moisture variation and may overcome these limitations. In this study, we installed and operated an automated ERT monitoring system on a landslide‐prone hillslope in the Napf region (Switzerland). The system was operational during a period of 9 mo, and measurements were conducted at high temporal resolution and under different soil hydrological conditions. Electrical resistivity was measured along two perpendicular profile lines in Wenner–Schlumberger configuration at 0.25‐m electrode spacing. Soil saturation was calculated by the Archie's law and the parameters were fitted with colocated soil moisture sensors. Comparison of ERT‐derived soil moisture with soil wetness from in situ sensors showed a good correlation, and infiltration properties critical for landslide early warning could be reliably reproduced. Further, analysis of spatial saturation variation revealed that ERT was capable to detect heterogeneities of soil hydrological process. Under highly saturated conditions, the reliability of the saturation estimation was affected by an increased number of faulty measurements and the spatial heterogeneity of the infiltration process. Core Ideas: In situ soil wetness monitoring is used for landslide early warning.Electrical resistivity tomography (ERT) is a noninvasive alternative to sensors for soil moisture estimation.Spatial variation of soil hydrological processes can be assessed using ERT.Temporal soil moisture variation can be well reproduced at high temporal resolution.Data quality depends on the water saturation and the petrophysical function parametrization. [ABSTRACT FROM AUTHOR]

The sustainable development of agriculture in Xinjiang Province, China has been threatened by soil salinization. Biochar can be an effective amendment to improve salt‐affected soils. An appropriate amount of biochar application and incorporation depth are key factors for amending performance. However, few studies have investigated the effects of differing biochar application amounts on saline soil properties, including soil water infiltration, using a combination of experiments and simulations. In this study, acidulated biochar was applied at rates of 0, 10, 25, 50 and 100 t ha−1 to a farmland topsoil to investigate the impacts of biochar on Xinjiang saline soil's physical and chemical properties and infiltration characteristics. The soil's physical and chemical properties that were investigated included soil pH, soil organic carbon content, soil salt content, saturated hydraulic conductivity (Ks), soil water retention curves, and infiltration characteristics including cumulative infiltration (CI) and wetting front (Zf). HYDRUS‐1D was applied to predict soil water movement under different biochar incorporation depths. Results showed that the rate of change of soil pH, soil organic carbon content, soil salt content and Ks were −0.009, 0.102 g kg−1, 0.045 g kg−1 and 0.035 cm day−1, respectively, per ton of biochar applied. Soil water retention curves showed that biochar enhanced soil water retention capacity and available soil water content (AWC) in the silt clay loam soil. The Philip model was a good (R2 > 0.80) fit to soil water infiltration and indicated that biochar amendment promoted infiltration rates. The van Genuchten model was good for describing soil hydraulic parameters (R2 > 0.99) and could be used for HYDRUS‐1D simulations (R2 > 0.99, RRMSE <6.8% and NSE >0.98). The optimum biochar application amount for the light salinised soil in Xinjiang was recommended as 25 t ha−1 incorporated to 30 cm depth based on the AWC, soil salt content and incorporation depths. The study provides a reference for future field experiment design. Highlights: Effects of biochar on salt‐affected soil studied using a combination of experiments and simulations.Acidulated biochar amendment of saline soils can reduce pH and increase SOC.Biochar incorporation depth was considered to affect soil water infiltration.The biochar application amount at 25 t ha−1 incorporated to 30 cm depth was recommended [ABSTRACT FROM AUTHOR]

The high sensitivity of loess slopes to water has been emphasized in many studies. However, it is still limited in terms of the understanding of slope morphological differentiation on the overall and local failure patterns in slopes, as well as on the acquisition method of hydrological dynamics. In this study, rainfall characteristics and slope surface morphological differences were introduced. Geoelectric and environmental factors were monitored. On this basis, apparent resistivity corrected by seasonal temperature and its relationship with soil water content was calibrated. The water migration characteristics and potential failure patterns of three slope morphologies were evaluated. The results are: (i) the improved resistivity method can better reflect the water flow movement within the slope, and it performs well after being corrected by temperature; (ii) the characteristics of surface runoff and water infiltration are directly affected by the cumulative rainfall value, and especially when the cumulative rainfall is >70 mm threshold, the surface runoff quickly infiltrates into the deep of the slope along the preferential paths; (iii) the interception ability of loess slope morphology to the surface runoff is concave slope > convex slope > linear slope; (iv) with the continuous rainfall, the convex surface of a slope is prone to be damaged by saturated mud flow. When the cumulative rainfall threshold is 70 mm, the preferential flow is easily excited on the concave surface of the slope, resulting in local collapse at the slope toe and mid-deep landslides. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable for drought monitoring, but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative for simulating soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land-surface model. To estimate the soil water holding capacity, the sole parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions or an indirect estimation by a machine learning approach, random forests, using as predictors altitude, temperature, precipitation, potential evapotranspiration and land use. Results showed that the random forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases. [ABSTRACT FROM AUTHOR]

On 12 and 13 September 2019, a long-lasting heavy precipitation episode (HPE) affected the València, Murcia and Almería regions in eastern Spain. Observed rainfall amounts were close to 500 mm in 48 h, being the highest cumulative precipitation registered in some rain gauges for the last century. Subsequent widespread flash flooding caused seven fatalities and estimated economical losses above EUR 425 million. High-resolution precipitation estimates from weather radar observations and flood response from stream gauges are used in combination with a fully distributed hydrological model to examine the main hydrometeorological processes within the HyMeX programme. This HPE was characterized by successive, well-organized convective structures that impacted a spatial extent of 7500 km 2 , with rainfall amounts equal to or larger than 200 mm. The main factors driving the flood response were quasi-stationarity of heavy precipitation, very dry initial soil moisture conditions and large storage capacities. Most of the examined catchments exhibited a dampened and delayed hydrological response to cumulative precipitation: until runoff thresholds were exceeded, infiltration-excess runoff generation did not start. This threshold-based hydrological behaviour may impact the shape of flood peak distributions, hindering strict flood frequency statistical analysis due to the generally limited lengths of data records in arid and semi-arid catchments. As an alternative, a power-law relationship is analytically derived to specifically link flood magnitude to rainfall amount, with the parameters depending on basin and precipitation features. This power law could aid in developing empirical envelope curves so as to assess hydrologic risk in arid and semi-arid catchments with a threshold-based hydrological behaviour. [ABSTRACT FROM AUTHOR]

Although shallow (<1.5 m) soil water storage has been extensively studied, the significance of deeper unsaturated zone water storage to flow generation is poorly documented. However, a limited but growing body of empirical work shows that the weathered bedrock vadose zone, not soil, stores the majority of plant available water in many seasonally dry and semi‐arid landscapes. Moreover, this storage dynamic mediates recharge to hillslope groundwater systems that generate stream discharge and support ecologically significant baseflows. Explicit representations of bedrock vadose zone processes are rarely incorporated into runoff models, due in part to a paucity of observations that can constrain simulations. Here, we develop a simple representation of the weathered bedrock vadose zone that is guided by in situ field observations. We incorporate this representation into a rainfall‐runoff model, and calibrate it on streamflow alone, on rock moisture (i.e., weathered bedrock vadose zone moisture) alone, and on both using the concept of Pareto optimality. We find that the model is capable of accurately simultaneously simulating dynamics in rock moisture and streamflow, in terms of Kling‐Gupta Efficiency, when using Pareto optimal parameter sets. Calibration on streamflow alone, however, is insufficient to accurately simulate rock moisture dynamics. We further find that the posterior distributions of some model parameters are sensitive to choice of calibration scenario. The posterior distribution of high‐performing model parameters resulting from the streamflow only calibration scenario include physically unrealistic values that are not yielded by the rock moisture only or Pareto calibration strategies. These results suggest that the accuracy of some model results can be increased and parameter uncertainty decreased via incorporation of rock moisture data in calibration, without sacrificing streamflow simulation quality. Emerging recognition of the global significance of weathered bedrock water storage in seasonally dry and semi‐arid regions motivates more observations of weathered bedrock moisture and integration of this variable into earth system models. [ABSTRACT FROM AUTHOR]

To expand the knowledge base on natural infrastructure for erosion mitigation in the Andes, it is necessary to move beyond case by case empirical studies to comprehensive assessments. This study reviews the state of evidence on the effectiveness of interventions to mitigate soil erosion by water and is based on Andean case studies published in gray and peer-reviewed literature. Based on a systematic review of 118 case studies from the Andes, this study addressed the following research questions. (1) Which erosion indicators allow us to assess the effectiveness of natural infrastructure? (2) What is the overall impact of working with natural infrastructure on on-site and off-site erosion mitigation? (3) Which locations and types of studies are needed to fill critical gaps in knowledge and research? Three major categories of natural infrastructure were considered: restoration and protection of natural vegetation, such as forest or native grasslands, forestation with native or exotic species and implementation of soil and water conservation measures for erosion mitigation. From the suite of physical, chemical and biological indicators commonly used in soil erosion research, two indicators were particularly relevant: soil organic carbon of topsoil and soil loss rates at plot scale. The protection and conservation of natural vegetation has the strongest effect on soil quality, with 3.01±0.893 times higher soil organic carbon content in the topsoil compared to control sites. Soil quality improvements are significant but lower for forestation and soil and water conservation measures. Soil and water conservation measures reduce soil erosion to 62.1 % ± 9.2 %, even though erosion mitigation is highest when natural vegetation is maintained. Further research is needed to evaluate whether the reported effectiveness holds during extreme events related to, for example, El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (at 10 locations with three different depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. The soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, soil moisture difference index, and standard deviation of the peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classifications in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided more insights into the relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events could be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns attributable to combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas. [ABSTRACT FROM AUTHOR]

The saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climate models. Ksat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of Ksat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 Ksat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of Ksat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat , other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived Ksat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these Ksat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between Ksat data collected in temperate and tropical regions and Ksat measured in the laboratory or field. The SoilKsatDB dataset is available at 10.5281/zenodo.3752721 and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license. [ABSTRACT FROM AUTHOR]

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyzes the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS, the Advanced SCATterometer on MetOp, the Global Land Data Assimilation System, and the WaterGAP Global Hydrology Model. In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission, and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems. [ABSTRACT FROM AUTHOR]

Time‐lapse electrical resistivity tomography (ERT) can be used to image a wide variety of subsurface processes. It is often necessary to remove the effects of seasonal near‐surface temperature variability in order to interpret the signal of interest. Here, we use a synthetic modeling approach to explore the challenges related to removing temperature effects from ERT data in seasonally frozen ground. Electrical resistivity tomography data collection and processing methods that are often used to improve resolution in frozen ground do not appreciably improve the accuracy of temperature corrected data. A sensitivity analysis showed that errors in the temperature corrected data are primarily caused by error in the inverted resistivity models, and that this sensitivity to model error is much higher in partially frozen ground than in unfrozen ground. Core Ideas: The temperature signal in time‐lapse data can interfere with the signal of interest.When the ground surface freezes in winter, the resistivity perturbation is large.Standard data processing methods cannot remove effects of a frozen surface layer.Temperature correction is ultimately limited by accuracy of inverted models. [ABSTRACT FROM AUTHOR]

Soil water and nutrient status are two of the most important factors for plant development and crop yield. Vermicompost and biochar are supposed to amend soil attributes and increase the productivity. However, little is known about their mixture application on soil quality and nutrient uptake under natural conditions. The aim of this investigation was to understand the impact of soil amendments (control, vermicompost, biochar, and vermicompost + biochar) on yield, soil quality, physiological and biochemical attributes, as well as nutrient uptake of wheat plants grown at different irrigation water treatments (50%, 75%, and 100% of field capacity [FC]) in saline sodic soil. Vermicompost improved wheat growth and yield. Biochar-treated plants had higher growth performance and yield than control plants in all traits and than vermicompost in some cases, thus confirming its potential for enhancing soil quality and increasing nutrient uptake, which stimulates soil chemical properties. When vermicompost was added in combination with biochar, further enhancement in the growth and yield was recorded, highlighting the beneficial effect of vermicompost on plant yield. Vermicompost-biochar mixture application followed by biochar as a singular application caused significant improvements in relative water content, chlorophyll content, stomatal conductance, cytotoxicity, leaf K+ content with respect to nutrient uptake (N, P, and K), while reducing oxidative stress (i.e., activities of catalase [CAT] and ascorbate peroxidase [APX], and expression levels of CAT, APX, and Mn- SOD genes), leaf Na+ content, and proline content. This resulted in increases in yieldrelated traits and productivity owing to the enhancement in soil chemical characteristics and soil moisture content. Grain yield and nutrient uptake attained the highest values at 75% FC in wheat plants treated by the combination of vermicompost and biochar. In summary, this investigation revealed that the synergistic effect of vermicompost and biochar can not only enhance crop production but also eliminates the detrimental effects of soil salinity and water stress. [ABSTRACT FROM AUTHOR]

Africa is severely affected by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. Here, a database of 399 African stream gauges is used to analyze the seasonality of observed annual maximum flood, precipitation and soil moisture between 1981 and 2018. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river flood with annual maximum rainfall and soil moisture. The results show that the annual maximum flood in most areas is more strongly linked to the annual peak of soil moisture than of annual maximum precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture than by the variability in the annual maximum precipitation. These results have important implications for flood forecasting and the analysis of the long‐term evolution of these hydrological hazards in relation with their drivers. Key Points: Seasonality of annual maximum flood, precipitation and soil moisture is evaluated in AfricaStronger association of the flood timing with soil moisture, rather than with 5‐day or 1‐day annual maximum precipitationInterannual variability in flood magnitude is mostly related to variability in annual maximum soil moisture [ABSTRACT FROM AUTHOR]

In this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small‐ and medium‐scale (150–10,000 km2) European catchments to assess the impact of satellite soil moisture (SM) DA on streamflow simulations for different climatic and hydrologic conditions. In the experiment, Climate Change Initiative SM active, passive and combined products are assimilated over a time period 2003–2016 via an Ensemble Kalman Filter (EnKF). The results show that, on average, the assimilation of the three products provides relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median Kling‐Gupta Efficiency equal to 0.0048, 0.0033, and 0.0022 [−] for the active, the passive, and the combined products, respectively). OL performance itself is found to be a significant driver of the assimilation results: greater improvements are observed in catchments with poor OL streamflow predictions and inaccurate precipitation estimates. The remotely sensed product accuracy also emerges as relevant for assimilation efficiency, while factors affecting SM retrievals such as vegetation density, topographic complexity and basin area are found to have only a limited impact on the spatial pattern of performance. Small and detrimental effects of SM assimilation are observed over fully humid catchments and at high latitudes where pre‐storm soil moisture has reduced control on runoff generation as well as in basins where the hydrological model contains structural limitations. Key Points: The added value of satellite soil moisture assimilation on runoff simulations is assessed within 700 European catchmentsThe assimilation of the European Space Agency Climate Change Initiative, satellite soil moisture products show limited improvements, with the active product providing the best resultsThe impact of open loop model performance, remotely sensed data accuracy, climate conditions and catchment characteristics are evaluated [ABSTRACT FROM AUTHOR]

The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events. A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved. It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary. [ABSTRACT FROM AUTHOR]

The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements. [ABSTRACT FROM AUTHOR]

Determining and predicting both the spatial and temporal variability of soil moisture content is a critical factor in crop production in semi-arid agricultural systems. The objective of this research was to predict the spatial distribution of soil moisture content using ordinary kriging (OK), regression kriging (RK) and cokriging (CK) models. For that purpose, the soil moisture was monitored in the wet period (March, April and May) and dry period (August, September and October) in Kadınhanı district of Konya Province, Turkey. In each monthly sampling period of the study, a total of 278 samples were collected from locations within an area of 29,608.6 ha. The coefficients of variation (CV) of the soil moisture in dry and wet soil conditions were 0.39 and 0.20, respectively. Soil texture, bulk density (BD) and land use were the most important factors influencing soil moisture. In addition, dry soil generally had a smaller nugget effect than wet soil. Overall, CK was able to better estimate the soil moisture content than OK and RK, as evidenced by the smaller mean absolute error (MAE) and mean square error (MSE). RK estimation is more vulnerable to error than CK and OK estimations because the low coefficients of determination in the regression equation lead to increased error and lower confidence in the estimates. The insights generated by the modeling utilized in this study can assist land use planners, land managers and farmers to more efficiently predict, manage and utilize soil moisture content and hence produce larger crops more sustainably. [ABSTRACT FROM AUTHOR]

Future climate scenarios for the Mediterranean region indicate a possible decrease in annual precipitation associated with an intensification of extreme rainfall events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the impact of climate change on soil moisture since it is a proxy for agricultural droughts, and the antecedent soil moisture condition plays a key role on runoff generation. This study focuses on 10 sites, located in southern France, with available soil moisture, temperature, and precipitation observations for a 10-year time period. Soil moisture is simulated at each site at the hourly time step using a model of soil water content. The sensitivity of the simulated soil moisture to different changes in precipitation and temperature is evaluated by simulating the soil moisture response to temperature and precipitation scenarios generated using a delta change method for temperature and a stochastic model (the Neyman–Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Overall, increased temperature and precipitation intensity associated with more intermittent precipitation leads to decreased soil moisture and an increase in the annual number of days with dry soil moisture conditions. In particular, a temperature increase of +4 ∘ C combined with a decrease of annual rainfall between 10 % and 20 %, corresponding to the current available climate scenarios for the Mediterranean, lead to a lengthening of the drought period from June to October with an average of +28 d of soil moisture drought per year. [ABSTRACT FROM AUTHOR]

Soil erosion and suspended sediment transport understanding is an important issue in terms of soil and water resources management in the critical zone. In mesoscale watersheds (>10 km2) the spatial distribution of potential sediment sources within the catchment associated with rainfall dynamics is considered to be the main factor in the observed suspended sediment flux variability within and between runoff events. Given the high spatial heterogeneity that can exist for such scales of interest, distributed physically based models of soil erosion and sediment transport are powerful tools to distinguish the specific effect of structural and functional connectivity on suspended sediment flux dynamics. As the spatial discretization of a model and its parameterization can crucially influence how the structural connectivity of the catchment is represented in the model, this study analyzed the impact of modeling choices in terms of the contributing drainage area (CDA) threshold to define the river network and of Manning's roughness parameter (n) on the sediment flux variability at the outlet of two geomorphologically distinct watersheds. While the modeled liquid and solid discharges were found to be sensitive to these choices, the patterns of the modeled source contributions remained relatively similar when the CDA threshold was restricted to the range of 15 to 50 ha , with n restricted to the range 0.4–0.8 on the hillslopes and to 0.025–0.075 in the river. The comparison of the two catchments showed that the actual location of sediment sources was more important than the choices made during discretization and parameterization of the model. Among the various structural connectivity indicators used to describe the geological sources, the mean distance to the stream was the most relevant proxy for the temporal characteristics of the modeled sedigraphs. [ABSTRACT FROM AUTHOR]

In many orchards, irrigation scheduling is designed based on data from meteorological networks and considering homogeneous soil properties. Such assumptions may result in inefficient irrigation, which is difficult to constrain without expensive or invasive techniques. Here we have evaluated the ability of the electrical resistivity tomography (ERT) for detecting meter-scale irrigation uniformity and deep percolation during irrigation. The spatiotemporal variability of soil volumetric water content (VWC) in a vineyard located near Santiago (Chile) was inferred using ERT monitoring of two irrigation cycles. The electrical resistivity structure up to 4 m depth was estimated using two-dimensional inversion of ERT data. ERT results were verified by comparing resistivity models with VWC measured with soil moisture sensors, soil properties mapped in a 2 m-depth soil pit, and the spatiotemporal evolution of VWC obtained by solving numerically Richards equation. Largest temporal variations of resistivity were observed within the root depth (1 m) and are consistent with expected relative changes in VWC during irrigation. ERT images exhibit lateral changes in resistivity at these depths, likely indicating non-uniform infiltration of water controlled by observed soil texture variations. Resistivity changes were also observed below the root zone, suggesting that a fraction of the irrigation water percolates downward. These findings can be explained by an excess of irrigation water applied during the monitoring, which was planned considering regional evapotranspiration (ET) data that overestimated the actual ET measured at the vineyard. Altogether, our results suggest that ERT monitoring during irrigation is a cost-effective tool to constrain the performance of irrigation systems. [ABSTRACT FROM AUTHOR]

Drought-induced evaporation can reduce soil water content and significantly alter soil hydro-mechanical behavior. Understanding the temporal and spatial distribution characteristics of soil water content during evaporation is of great significance for evaluating the encountered geotechnical and geo-environmental problems in arid or semi-arid regions. In this study, an electrical resistivity/resistance method (ERM) with a high spatial resolution of centimeter-level was developed for a small-scale laboratory test and applied to quantitatively characterize the evaporation-induced water content variations along a depth gradient. A total of 8 groups of initially saturated sandy soil columns (84 mm in diameter and 290 mm in height) were prepared, and eight pairs of mini electrodes (3.5 mm in diameter) were installed in each soil sample with a vertical distance of 30 mm. The soil columns were subjected to continuous drying. The changes in soil electrical resistance at different depths were monitored by the electrode couples. The gravimetric water contents at different depths were also measured at the end of drying. It is found that soil water content decreases exponentially with increasing electrical resistance. Based on the obtained data, a calibration relationship between soil gravimetric water content and corrected electrical resistance was well established with consideration of temperature effect. This relationship was validated successfully by the experimental results, indicating the feasibility of the developed ERM to characterize the soil water content dynamics during the drying process. Besides, the drying process with the movement of the evaporation front was discussed. The results of this study demonstrate the good performance of ERM in the estimation of temporal and spatial variations of soil water content and its potential application in arid or semi-arid regions with frequent droughts. [ABSTRACT FROM AUTHOR]

In this study, scale models of typical urban surfaces and two green infrastructures (concave grassland and porous pavement) were constructed, and two simulated rainfall intensities (low intensity was 0.3 mm/min with 25.4 mm depth, and high intensity was 0.6 mm/min with 42.0 mm) were utilized to investigate their runoff responses and the impacts of pervious surface positions and initial soil moisture on the runoff processes. Results indicated that impervious concrete surface exhibited a faster generation of runoff and with a runoff coefficient of 89%. Grassland surface represented that time to runoff was about 25 times than that of the impervious surface and recorded the smallest runoff coefficient of 34 and 53%. Compared with the impervious area, concave grassland was able to effectively delay time to runoff, while the porous pavement was able to significantly reduce runoff discharge and peak flow rate. A high rainfall intensity led to a reduction in time to runoff and an acceleration of runoff discharge and peak flow rate. Pervious surface under the lower side generated runoff at a slower rate, and registered a smaller runoff coefficient compared with the pervious surface under the upper side. The initial soil moisture and time to runoff had a significant negative correlation, and a positive correlation was found between the initial soil moisture and runoff coefficient. These findings facilitate a better understanding of runoff processes of urban surfaces and green infrastructures that may be able to help in better hydrology system design for mitigating urban flooding. [ABSTRACT FROM AUTHOR]

The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking. [ABSTRACT FROM AUTHOR]

This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC‐I‐AMC‐III) by coupling the improved initial abstraction (Ia‐λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness‐of‐fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE‐observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process. [ABSTRACT FROM AUTHOR]

In January 2011, the Mountainous Region of Rio de Janeiro state underwent one of the largest natural hazards in Brazil, affecting more than 300,000 people, causing more than 900 dead and irreparable human losses and enormous economic losses. From a hydrometeorological point of view, for the prevention of this type of disaster, alert systems are used worldwide, seeking to anticipate these phenomena by issuing warnings to the population, using tools such as hydrometeorological monitoring and modeling. This study evaluated the applicability of the hydrological soil moisture accounting procedure (SMAP) model with daily discretization as a flood prevention tool in Brazil. It was done by adjusting sub-watershed parameters in the semiautomatic calibration process, inserting observed data of precipitation and stream flow, as well as evapotranspiration in the calibration of the model, between 2008 and 2010. The model was validated twice, between the years of 2011 and 2013, and during the disaster period. The results show that the efficiency coefficient reached in the calibration stage was 1.713, with a relative error of 12% between the calculated and the observed stream flow, and in the validation, the results were 1.401 and 28%, respectively. In the validation of the disaster, the calculated stream flow was overestimated, with an average relative error of 53%. Flash flood prevention through the use of the daily SMAP model has shown promise for use in operational centers. [ABSTRACT FROM AUTHOR]

Soil physical properties play an important role in estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at different scales using the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) LSM, forced by several complex or globally uniform soil texture maps. At the point scale, the model shows a realistic sensitivity of runoff processes and soil moisture to soil texture and reveals that loamy textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets, although important differences can be found at the regional scale, particularly in areas where the different maps disagree on the prevalence of clay soils. The three tested soil texture maps are also found to be similar by construction, with a shared prevalence of loamy textures, and have a spatial overlap over 40 % between each pair of maps, which explains the overall weak impact of soil texture map change. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling, but the added value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies. [ABSTRACT FROM AUTHOR]

The current study has been derived to understand morphometric parameters to pledge the reduction in the proclaimed drought influence for climate change. Watershed prioritization has been studied using the geographical information system and remote sensing techniques for soil erosion and water preservation measure at Plio–Pleistocene elevated tract in Bangladesh. Secondary data, i.e., SRTM DEM and the topographical sheets, have been used for the drainage network identification. Seventeen watersheds of fifth order and three watersheds of sixth order are selected for the whole study. Sixteen basic morphometric parameters that are categorized as areal, linear and relief aspects have been used for the study area. The study area comprises sixth stream order that has been obtained using a stream threshold value of 100-m length. Morphometric analysis is suitable for water and soil conservation practice where groundwater and relevant data sets are not available. For the categorization and correlation of the morphometric parameters, principal component analysis (PCA) has been used in the present study. PCA analysis is more suitable, well-known and widely used method for its simplicity to choose more influencing parameters (correlated parameters) that are responsible for watershed prioritization. Strongly correlated components (Dd, Rc, Re and Rb) are used for the compound parameter (Cp) and final priority (Rp) calculation. Low Cp value is suitable for water and soil conservation measure. Therefore, the corresponding low Cp has been taken as one for final priority ranking (Rp) purpose, then second lowest value as two, and so on. [ABSTRACT FROM AUTHOR]

On 18 May 2015, a severe rainfall event triggered a flash flood in the municipality of Salgar, located in the northwestern Colombian Andes. This work aims to reconstruct the main hydrological features of the flash flood to better understand the processes modulating the occurrence of the event. Radar quantitative precipitation estimates (QPEs), satellite information, and post-event field visits are used to reconstruct the Salgar flash flood, in an ungauged basin, addressing the relationship among rainfall spatiotemporal structure, soil moisture, and runoff generation during successive rainfall events by using a conceptual modeling framework including landslide and hydraulic submodels. The hydrological model includes virtual tracers to explore the role of runoff and subsurface flow and the relative importance of convective and stratiform precipitation in flash flood generation. Despite potential shortcomings due to the lack of data, the modeling results allow an assessment of the impact of the interactions between runoff, subsurface flow, and convective–stratiform rainfall on the short-term hydrological mechanisms leading to the flash flood event. The overall methodology reproduces the magnitude and timing of the La Liboriana flash flood peak discharge considerably well, as well as the areas of landslide occurrence and flood spots, with limitations due to the spatial resolution of the available digital elevation model. Simulation results indicate that the flash flood and regional landslide features were strongly influenced by the antecedent rainfall, which was associated with a northeasterly stratiform event. The latter recharged the gravitational and capillary storages within the model, moistening the entire basin before the occurrence of the flash flood event and impacting the subsurface–runoff partitioning during the flash flood event. Evidence suggests that the spatial structure of the rainfall is at least as important as the geomorphological features of the basin in regulating the occurrence of flash flood events. [ABSTRACT FROM AUTHOR]

Electromagnetic induction (EMI) data are often used to investigate spatial and temporal patterns of soil texture, soil water content and soil salinity. We hypothesized that the EMI methodology might thus also offer potential to detect agricultural legacy effects originating from fertilizer application and irrigation of different fields. Therefore, we performed EMI measurements on two long‐term field experiments (LTFE) at Thyrow near Berlin (Germany) that differed in agricultural management with regard to long‐term irrigation in combination with mineral (NPK and lime) and organic amendments (straw and farmyard manure). Two different rigid‐boom multi‐coil EMI instruments were used to measure simultaneously the apparent electrical conductivity (ECa) over nine different depth ranges to study the entire soil profile from topsoil to deep subsoil. Additionally, soil samples were taken from the different treatments to ground‐truth the measurements and disentangle the nutrient application or irrigation effects from natural soil heterogeneity. The soil samples indicated a rather homogenous soil and the correlation between soil parameters or states were not significant. However, the treatments showed significant differences in measured ECa values. In general, ECa values were largest on regularly irrigated as well as on mineral and organic fertilized plots, with regular irrigation exhibiting the largest impact on EMI records even though the last application was months before the EMI measurement. Overall, this study reveals that EMI data can support the classical in situ assessment of agricultural management effects within LTFE, while offering new potentials in detecting and understanding legacy effects of agricultural management on spatial soil properties at farm level. [ABSTRACT FROM AUTHOR]

Floods have strong impacts in the Mediterranean region and there are concerns about a possible increase in their intensity due to climate change. In this study, a large database of 171 basins located in southern France with daily discharge data with a median record length of 45 years is considered to analyze flood trends and their drivers. In addition to discharge data, outputs of precipitation, temperature, evapotranspiration from the SAFRAN reanalysis and soil moisture computed with the ISBA land surface model are also analyzed. The evolution of land cover in these basins is analyzed using the CORINE database. The trends in floods above the 95th and 99th percentiles are detected by the Mann–Kendall test and quantile regression techniques. The results show that despite the increase in extreme precipitation reported by previous studies, there is no general tendency towards more severe floods. Only for a few basins is the intensity of the most extreme floods showing significant upward trends. On the contrary, most trends are towards fewer annual flood occurrences above both the 95th and 99th percentiles for the majority of basins. The decrease in soil moisture seems to be an important driver for these trends, since in most basins increased temperature and evapotranspiration associated with a precipitation decrease are leading to a reduction in soil moisture. These results imply that the observed increase in the vulnerability to these flood events in recent decades is mostly caused by human factors such as increased urbanization and population growth rather than climatic factors. [ABSTRACT FROM AUTHOR]

The present work focuses on an assessment of the applicability of groundwater table (GWT) measures in the modelling of soil water retention characteristics (SWRC) using artificial neural network (ANN) methods. Model development, testing, validation and verification were performed using data collected across two decades from soil profiles at full‐scale research objects located in Southwest Poland. A positive effect was observed between the initial GWT position data and the accuracy of soil water reserve estimation. On the other hand, no significant effects were observed following the implementation of GWT fluctuation data over the entire growing season. The ANN tests that used data of either soil water content or GWT position gave analogous results. This revealed that the easily obtained data (temperature, precipitation and GWT position) are the most accurate modelling parameters. These outcomes can be used to simplify modelling input data/parameters/variables in the practical implementation of the proposed SWRC modelling variants. [ABSTRACT FROM AUTHOR]