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Cosmic-Ray neutron sensors are widely used to determine soil moisture on the hectare scale. Precise measurements, especially in the case of mobile application, demand for neutron detectors with high counting rates and high signal-to-noise ratios. For a long time Cosmic Ray Neutron Sensing (CRNS) instruments have relied on He-3 as an efficient neutron converter. Its ongoing scarcity demands for technological solutions using alternative converters, which are Li-6 and B-10. Recent developments lead to a modular neutron detector consisting of several B-10-lined proportional counter tubes, which feature high counting rates via its large surface area. The modularity allows for individual shieldings of different segments within the detector featuring the capability of gaining spectral information about the detected neutrons. This opens the possibility for active signal correction, especially useful when applied to mobile measurements, where the influence of constantly changing near-field to the overall signal should be corrected. Furthermore, the signal-to-noise ratio could be increased by combining pulse height and pulse length spectra to discriminate between neutrons and other environmental radiation. This novel detector therefore combines high-selective counting electronics with large-scale instrumentation technology.
The nature restoration project ‘Lenzener Elbtalaue’, realised from 2002 to 2011 at the river Elbe, included the first large scale dike relocation in Germany (420 ha). Its aim was to initiate the development of endangered natural wetland habitats and processes, accompanied by greater biodiversity in the former grassland dominated area. The monitoring of spatial and temporal variations of soil moisture in this dike relocation area is therefore particularly important for estimating the restoration success. The topsoil moisture monitoring from 1990 to 2017 is based on the Soil Moisture Index (SMI)1 derived with the triangle method2 by use of optical remotely sensed data: land surface temperature and Normalized Differnce Vegetation Index are calculated from Landsat 4/5/7/8 data and atmospheric corrected by use of MODIS data. Spatial and temporal soil moisture variations in the restored area of the dike relocation are compared to the agricultural and pasture area behind the new dike. Ground truth data in the dike relocation area was obtained from field measurements in October 2017 with a FDR device. Additionally, data from a TERENO soil moisture sensor network (SoilNet) and mobile cosmic ray neutron sensing (CRNS) rover measurements are compared to the results of the triangle method for a region in the Harz Mountains (Germany). The SMI time series illustrates, that the dike relocation area has become significantly wetter between 1990 and 2017, due to restructuring measurements. Whereas the SMI of the dike hinterland reflects constant and drier conditions. An influence of climate is unlikely. However, validation of the dimensionless index with ground truth measurements is very difficult, mostly due to large differences in scale.
Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that roads introduce a substantial bias in the CRNS estimation of field soil moisture compared to off-road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Neutron measurements on the road could overestimate the field value by up to 40 % depending on road material, width, and the surrounding field water content. The bias could be largely removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road-effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture. Plain Language Summary Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of roads. We employed physics simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that the presence of roads biased the CRNS estimation of field soil moisture compared to nonroad scenarios. Neutron measurements could overestimate the field value by up to 40 % depending on road material, width, surrounding field water content, and distance from the road. We proposed a correction function that successfully removed this bias and works even without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between corrected measurements and other field soil moisture observations. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture.
Water scarcity, adaption on climate change, and risk assessment of droughts and floods are critical topics for science and society these days. Monitoring and modeling of the hydrological cycle are a prerequisite to understand and predict the consequences for weather and agriculture. As soil water storage plays a key role for partitioning of water fluxes between the atmosphere, biosphere, and lithosphere, measurement techniques are required to estimate soil moisture states from small to large scales.
The method of cosmic-ray neutron sensing (CRNS) promises to close the gap between point-scale and remote-sensing observations, as its footprint was reported to be 30 ha. However, the methodology is rather young and requires highly interdisciplinary research to understand and interpret the response of neutrons to soil moisture. In this work, the signal of nine detectors has been systematically compared, and correction approaches have been revised to account for meteorological and geomagnetic variations. Neutron transport simulations have been consulted to precisely characterize the sensitive footprint area, which turned out to be 6--18 ha, highly local, and temporally dynamic. These results have been experimentally confirmed by the significant influence of water bodies and dry roads. Furthermore, mobile measurements on agricultural fields and across different land use types were able to accurately capture the various soil moisture states. It has been further demonstrated that the corresponding spatial and temporal neutron data can be beneficial for mesoscale hydrological modeling. Finally, first tests with a gyrocopter have proven the concept of airborne neutron sensing, where increased footprints are able to overcome local effects.
This dissertation not only bridges the gap between scales of soil moisture measurements. It also establishes a close connection between the two worlds of observers and modelers, and further aims to combine the disciplines of particle physics, geophysics, and soil hydrology to thoroughly explore the potential and limits of the CRNS method.
This cumulative dissertation explored the use of the detection of natural background of fast neutrons, the so-called cosmic-ray neutron sensing (CRS) approach to measure field-scale soil moisture in cropped fields. Primary cosmic rays penetrate the top atmosphere and interact with atmospheric particles. Such interaction results on a cascade of high-energy neutrons, which continue traveling through the atmospheric column. Finally, neutrons penetrate the soil surface and a second cascade is produced with the so-called secondary cosmic-ray neutrons (fast neutrons). Partly, fast neutrons are absorbed by hydrogen (soil moisture). Remaining neutrons scatter back to the atmosphere, where its flux is inversely correlated to the soil moisture content, therefore allowing a non-invasive indirect measurement of soil moisture. The CRS methodology is mainly evaluated based on a field study carried out on a farmland in Potsdam (Brandenburg, Germany) along three crop seasons with corn, sunflower and winter rye; a bare soil period; and two winter periods. Also, field monitoring was carried out in the Schaefertal catchment (Harz, Germany) for long-term testing of CRS against ancillary data. In the first experimental site, the CRS method was calibrated and validated using different approaches of soil moisture measurements. In a period with corn, soil moisture measurement at the local scale was performed at near-surface only, and in subsequent periods (sunflower and winter rye) sensors were placed in three depths (5 cm, 20 cm and 40 cm). The direct transfer of CRS calibration parameters between two vegetation periods led to a large overestimation of soil moisture by the CRS. Part of this soil moisture overestimation was attributed to an underestimation of the CRS observation depth during the corn period ( 5-10 cm), which was later recalculated to values between 20-40 cm in other crop periods (sunflower and winter rye). According to results from these monitoring periods with different crops, vegetation played an important role on the CRS measurements. Water contained also in crop biomass, above and below ground, produces important neutron moderation. This effect was accounted for by a simple model for neutron corrections due to vegetation. It followed crop development and reduced overall CRS soil moisture error for periods of sunflower and winter rye. In Potsdam farmland also inversely-estimated soil hydraulic parameters were determined at the field scale, using CRS soil moisture from the sunflower period. A modelling framework coupling HYDRUS-1D and PEST was applied. Subsequently, field-scale soil hydraulic properties were compared against local scale soil properties (modelling and measurements). Successful results were obtained here, despite large difference in support volume. Simple modelling framework emphasizes future research directions with CRS soil moisture to parameterize field scale models. In Schaefertal catchment, CRS measurements were verified using precipitation and evapotranspiration data. At the monthly resolution, CRS soil water storage was well correlated to these two weather variables. Also clearly, water balance could not be closed due to missing information from other compartments such as groundwater, catchment discharge, etc. In the catchment, the snow influence to natural neutrons was also evaluated. As also observed in Potsdam farmland, CRS signal was strongly influenced by snow fall and snow accumulation. A simple strategy to measure snow was presented for Schaefertal case. Concluding remarks of this dissertation showed that (a) the cosmic-ray neutron sensing (CRS) has a strong potential to provide feasible measurement of mean soil moisture at the field scale in cropped fields; (b) CRS soil moisture is strongly influenced by other environmental water pools such as vegetation and snow, therefore these should be considered in analysis; (c) CRS water storage can be used for soil hydrology modelling for determination of soil hydraulic parameters; and (d) CRS approach has strong potential for long term monitoring of soil moisture and for addressing studies of water balance.
A comprehensive hydrometeorological dataset is presented spanning the period 1 January 201131 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km(2)) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high-gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The number of observed variables, the various sensors involved (both in situ and remote) and the space-time resolution (similar to km(2), similar to min) of this comprehensive dataset make it a unique contribution to research communities focused on hydrometeorology, surface hydrology and erosion. Given that rainfall is highly variable in space and time in this region, the observation system enables assessment of the hydrological response to rainfall fields. Indeed, (i) rainfall data are provided by rain gauges (both a research network of 21 rain gauges with a 5 min time step and an operational network of 10 rain gauges with a 5 min or 1 h time step), S-band Doppler dual-polarization radars (1 km(2), 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100m height resolution). Additionally, during the special observation period (SOP-1) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). (ii) Other meteorological data are taken from the operational surface weather observation stations of Meteo-France (including 2m air temperature, atmospheric pressure, 2 m relative humidity, 10m wind speed and direction, global radiation) at the hourly time resolution (six stations in the region of interest). (iii) The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations estimate water discharge at a 2-10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water samples are collected automatically during floods, allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 to 116 km(2)) at a time resolution of 2-5 min. A network of soil moisture sensors enables the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, concomitant observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. Finally, this dataset is considered appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling.
Floods and debris flows in small Alpine torrent catchments (<10km(2)) arise from a combination of critical antecedent system state conditions and mostly convective precipitation events with high precipitation intensities. Thus, climate change may influence the magnitude-frequency relationship of extreme events twofold: by a modification of the occurrence probabilities of critical hydrological system conditions and by a change of event precipitation characteristics. Three small Alpine catchments in different altitudes in Western Austria (Ruggbach, Brixenbach and Langentalbach catchment) were investigated by both field experiments and process-based simulation. Rainfall-runoff model (HQsim) runs driven by localized climate scenarios (CNRM-RM4.5/ARPEGE, MPI-REMO/ECHAM5 and ICTP-RegCM3/ECHAM5) were used in order to estimate future frequencies of stormflow triggering system state conditions. According to the differing altitudes of the study catchments, two effects of climate change on the hydrological systems can be observed. On one hand, the seasonal system state conditions of medium altitude catchments are most strongly affected by air temperature-controlled processes such as the development of the winter snow cover as well as evapotranspiration. On the other hand, the unglaciated high-altitude catchment is less sensitive to climate change-induced shifts regarding days with critical antecedent soil moisture and desiccated litter layer due to its elevation-related small proportion of sensitive areas. For the period 2071-2100, the number of days with critical antecedent soil moisture content will be significantly reduced to about 60% or even less in summer in all catchments. In contrast, the number of days with dried-out litter layers causing hydrophobic effects will increase by up to 8%-11% of the days in the two lower altitude catchments. The intensity analyses of heavy precipitation events indicate a clear increase in rain intensities of up to 10%.
1. The complex, nonlinear response of dryland systems to grazing and climatic variations is a challenge to management of these lands. Predicted climatic changes will impact the desertification of drylands under domestic livestock production. Consequently, there is an urgent need to understand the response of drylands to grazing under climate change. 2. We enhanced and parameterized an ecohydrological savanna model to assess the impacts of a range of climate change scenarios on the response of a semi-arid African savanna to grazing. We focused on the effects of temperature and CO2 level increase in combination with changes in inter- and intra-annual precipitation patterns on the long-term dynamics of three major plant functional types. 3. We found that the capacity of the savanna to sustain livestock grazing was strongly influenced by climate change. Increased mean annual precipitation and changes in intra-annual precipitation pattern have the potential to slightly increase carrying capacities of the system. In contrast, decreased precipitation, higher interannual variation and temperature increase are leading to a severe decline of carrying capacities owing to losses of the perennial grass biomass. 4. Semi-arid rangelands will be at lower risk of shrub encroachment and encroachment will be less intense under future climatic conditions. This finding holds in spite of elevated levels of atmospheric CO2 and irrespective of changes in precipitation pattern, because of the drought sensitivity of germination and establishment of encroaching species. 5. Synthesis and applications. Changes in livestock carrying capacities, both positive and negative, mainly depend on the highly uncertain future rainfall conditions. However, independent of the specific changes, shrub encroachment becomes less likely and in many cases less severe. Thus, managers of semi-arid rangelands should shift their focus from woody vegetation towards perennial grass species as indicators for rangeland degradation. Furthermore, the resulting reduced competition from woody vegetation has the potential to facilitate ecosystem restoration measures such as re-introduction of desirable plant species that are only little promising or infeasible under current climatic conditions. On a global scale, the reductions in standing biomass resulting from altered degradation dynamics of semi-arid rangelands can have negative impacts on carbon sequestration.
Site-specific soil moisture and groundwater levels are key input parameters for ecological modeling. Obtaining such information in a comprehensive manner is difficult for large regions. We studied a floodplain region in the Federal State of Brandenburg, Germany, to examine the degree to which the average depth of groundwater tables can be derived from surface temperatures obtained by the ASTER radiospectrometer (spatial resolution of 90 m per pixel). A floristic ecological indicator representing the site-specific moisture level was applied to develop a proxy between the thermal satellite data and groundwater table depth. The use of spring scenes (late April to early May) from 2 years proved to be well suited for minimizing the effects of weather and land use. Vegetation surveys along transects that were 2 m wide across the pixel diagonals allowed for the calculation of average ecological moisture values of pixel-sites by applying Ellenberg-numbers. These values were used to calibrate the satellite data locally. There was a close relationship between surface temperature and the average ecological moisture value (R2 = 0.73). Average ecological moisture values were highly indicative of the average groundwater levels during a 7-year measurement series (R2 = 0.93). Satellite-supported thermal data from spring were suitable for estimating the average groundwater levels of low-lying grasslands on a larger scale. Ecological moisture values from the transect surveys effectively allowed the incorporation of relief heterogeneity within the thermal grid and the establishment of the correlation between thermal data and average groundwater table depth. Regression functions were used to produce a map of groundwater levels at the study site.
Soil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition
(2013)
The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multiangular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6-8 vol.% including all test fields with a variety of crop types.