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Soil moisture at the plot or hill-slope scale is an important link between local vadose zone hydrology and catchment hydrology. However, so far only a few methods are on the way to close this gap between point measurements and remote sensing. One new measurement methodology that could determine integral soil moisture at this scale is the aboveground sensing of cosmic-ray neutrons, more precisely of ground albedo neutrons. The present study performed ground albedo neutron sensing (GANS) at an agricultural field in northern Germany. To test the method it was accompanied by other soil moisture measurements for a summer period with corn crops growing on the field and a later autumn-winter period without crops and a longer period of snow cover. Additionally, meteorological data and aboveground crop biomass were included in the evaluation. Hourly values of ground albedo neutron sensing showed a high statistical variability. Six-hourly values corresponded well with classical soil moisture measurements, after calibration based on one reference dry period and three wet periods of a few days each. Crop biomass seemed to influence the measurements only to minor degree, opposed to snow cover which has a more substantial impact on the measurements. The latter could be quantitatively related to a newly introduced field neutron ratio estimated from neutron counting rates of two energy ranges. Overall, our study outlines a procedure to apply the ground albedo neutron sensing method based on devices now commercially available, without the need for accompanying numerical simulations and suited for longer monitoring periods after initial calibration.
Soil moisture dynamics are affected by complex interactions among several factors. Understanding the relative importance of these factors is still an important challenge in the study of water fluxes and solute transport in unsaturated media. In this study, the spatio-temporal variability of surface soil moisture was investigated in a 10 ha flat cropped field located in northern Italy. Soil moisture was measured on a regular 50 x 50 m grid on seven dates during the growing season. For each measurement campaign, the spatial variability of the soil moisture was compared with the spatial variability of the soil texture and crop properties. In particular, to better understand the role of the vegetation, the spatio-temporal variability of two different parameters - leaf area index and crop height - was monitored on eight dates at different crop development stages. Statistical and geostatistical analysis was then applied to explore the interactions between these variables. In agreement with other studies, the results show that the soil moisture variability changes according to the average value within the field, with the standard deviation reaching a maximum value under intermediate mean soil moisture conditions and the coefficient of variation decreasing exponentially with increasing mean soil moisture. The controls of soil moisture variability change according to the average soil moisture within the field. Under wet conditions, the spatial distribution of the soil moisture reflects the variability of the soil texture. Under dry conditions, the spatial distribution of the soil moisture is affected mostly by the spatial variability of the vegetation. The interaction between these two factors is more important under intermediate soil moisture conditions. These results confirm the importance of considering the average soil moisture conditions within a field when investigating the controls affecting the spatial variability of soil moisture. This study highlights the importance of considering the spatio-temporal variability of the vegetation in investigating soil moisture dynamics, especially under intermediate and dry soil moisture conditions. The results of this study have important implications in different hydrological applications, such as for sampling design, ranking stability application, indirect measurements of soil properties and model parameterisation.
We used inverse modelling techniques and soil moisture measured by the cosmic-ray neutron sensing (CRS) to estimate root-zone soil hydraulic properties at the field scale. A HYDRUS-1D model was developed for inverse modelling and calibrated with parameter estimation software (PEST) using a global optimizer. Integral CRS measurements recorded from a sunflower farm in Germany comprised the model input. Data were transformed to soil water storage to enable direct model calibration with a HYDRUS soil-water balance. Effective properties at the CRS scale were compared against local measurements and other inversely estimated soil properties from independent soil moisture profiles. Moreover, CRS-scale soil properties were tested on the basis of how field soil moisture (vertical distribution) and soil water storage were reproduced. This framework provided good estimates of effective soil properties at the CRS scale. Simulated soil moisture at different depths at the CRS scale agreed with field observations. Moreover, simulated soil water storage at the CRS scale compared well with calculations from point-scale profiles, despite their different support volumes. The CRS-scale soil properties estimated with the inverse model were within the range of variation of properties identified from all inverse simulations at the local scale. This study demonstrates the potential of CRS for inverse estimation of soil hydraulic properties.
The present study proposes a General Probabilistic Framework (GPF) for uncertainty and global sensitivity analysis of deterministic models in which, in addition to scalar inputs, non-scalar and correlated inputs can be considered as well. The analysis is conducted with the variance-based approach of Sobol/Saltelli where first and total sensitivity indices are estimated. The results of the framework can be used in a loop for model improvement, parameter estimation or model simplification. The framework is applied to SWAP, a 113 hydrological model for the transport of water, solutes and heat in unsaturated and saturated soils. The sources of uncertainty are grouped in five main classes: model structure (soil discretization), input (weather data), time-varying (crop) parameters, scalar parameters (soil properties) and observations (measured soil moisture). For each source of uncertainty, different realizations are created based on direct monitoring activities. Uncertainty of evapotranspiration, soil moisture in the root zone and bottom fluxes below the root zone are considered in the analysis. The results show that the sources of uncertainty are different for each output considered and it is necessary to consider multiple output variables for a proper assessment of the model. Improvements on the performance of the model can be achieved reducing the uncertainty in the observations, in the soil parameters and in the weather data. Overall, the study shows the capability of the GPF to quantify the relative contribution of the different sources of uncertainty and to identify the priorities required to improve the performance of the model. The proposed framework can be extended to a wide variety of modelling applications, also when direct measurements of model output are not available.
Cosmic-Ray neutron sensing (CRS) is a unique approach to measure soil moisture at field scale filling the gap of current methodologies. However, CRS signal is affected by all the hydrogen pools on the land surface and understanding their relative importance plays an important role for the application of the method e.g., validation of remote sensing products and data assimilation. In this study, a soil moisture scaling approach is proposed to estimate directly the correct CRS soil moisture based on the soil moisture profile measured at least in one position within the field. The approach has the advantage to avoid the need to introduce one correction for each hydrogen contribution and to estimate indirectly all the related time-varying hydrogen pools. Based on the data collected in three crop seasons, the scaling approach shows its ability to identify and to quantify the seasonal biomass water equivalent. Additionally, the analysis conducted at sub-daily time resolution is able to quantify the daily vertical redistribution of the water biomass and the rainfall interception, showing promising applications of the CRS method also for these types of measurements. Overall, the study underlines how not only soil moisture but all the specific hydrological processes in the soil-plant-atmosphere continuum should be considered for a proper evaluation of the CRS signal. For this scope, the scaling approach reveals to be a simple and pragmatic analysis that can be easily extended to other experimental sites. (C) 2015 Elsevier B.V. All rights reserved.
Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
The characteristics of an aboveground cosmic-ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point-scale snow depth (SD) and snow-water-equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230-270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m(-3) and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point-scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.
Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.
Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.