Analyzing and modelling solute and sediment transport at different spatial and temporal scales : a case study of the catchment of the Wahnbach River, Germany

Solute and sediment transport processes occur on a wide range of temporal and spatial scales. Differentiate between the observation and process scale which are defined by the extent, spacing and support of the samples and the modelling scale. Typical modelling scales are the local scale (1m), the hillslope scale (100 m), the catchment scale (10 km) and the regional scale (1000 km) and in time the event scale (1 day), the seasonal scale (1 yr) and the long-term scale (100 yrs). The modelling scale often differs from the process scale which necessitate an upscaling, downscaling or regionalization scheme. In this study process investigations at different scales are used for the temporal and spatial upscaling of a physically-based erosion model (OPUS) and its validation at different temporal and spatial scales. Numerous models for simulating erosion, transport and sedimentation are available. While the USLE model is able to simulate long-term erosion at single slopes without considering sedimentation, other approaches are calculating all processes. In this study a concept for continuously modelling of solute and sediment transport at the catchment scale for a period of 50 years is presented. The simulation is based on the application of the model system OPUS designed for long-term simulations of agricultural processes. As OPUS is a hillslope model the catchment is discretized into numerous slopes. These slopes are linked via a channel model (HEC6-model) which allows the simulation of erosion, transport and sedimentation in a catchment. After introducing some modifications and enhancements of the model it was possible to simulate interflow induced by macropores with OPUS. The simulation results are compared with measurements at different spatial and temporal scales of a meso-scale catchment used for drinking water supply. Three small sub-catchments of 22 to 29 ha are used to validate the model at the local scale. The modified and validated model is then applied to the Wahnbach catchment with an area of about 54 km². Simulations at the local and the catchment scales are presented and compared with measurements. To model the long-term behaviour of solute and sediment transport with a deterministic model a complete set of climate and landuse data is necessary. In this case study a method was developed to overcome the lack of data by using a weather generator. The long-term catchment simulations are validated by determining the mass of sediments trapped in the drinking water reservoir. In general, the obtained simulation results at different scales (from the sub-catchment scale to the catchment scale and from single events to long-term scales) are encouraging. It could be shown that process-based models can be applied at several scales in order to calculate the fluxes of matter. Furthermore, the results show that the considered flux types (water, solutes, sediment) are simulated with different success. The best results are obtained by simulating daily runoff at the sub-catchment scale and at the catchment scale. The solute concentration at the sub-catchment scale and the monthly nitrate discharge at the catchment scale is reproduced satisfactory, but the measures of model accuracy are less high. The sediment transport is simulated with the lowest accuracy

Evaluation of the ZigBee based wireless soil moisture sensor network SoilNet

A remaining challenge in hydrology is to explain the observed patterns of hydrological behaviour over multiple spacetime scales as a result of interacting environmental factors. The large spatial and temporal variability of soil water content is determined by factors like atmospheric forcing, topography, soil properties and vegetation, which interact in a complex nonlinear way (e.g. Western et al., 2004). A promising new technology for environmental monitoring is the wireless sensor network (Cardell-Oliver et al., 2005). The wireless sensor network technology allows the real-time soil water content monitoring at high spatial and temporal resolution for observing hydrological processes in small water-sheds (0.1-80 sqkm). Although wireless sensor networks can still be considered as an emerging research field, the supporting communication technology for low cost, low power wireless networks has matured greatly in the past decade (Robinson et al., 2008). Wireless environmental sensor networks will play an important role in the emerging terrestrial environmental observatories (Bogena et al., 2006), since they are able to bridge the gap between local (e.g. lysimeter) and regional scale measurements (e.g. remote sensing). This paper presents a first application of the novel wireless soil water content network SoilNet, which was developed at the Forschungszentrum Jülich using the new low-cost ZigBee radio network

Sensor-to-sensor variability of ECH2O EC-5, TE and 5TE sensors used for wireless sensor networks

Towards an improvement of measurement accuracy for the low-budget soil water content sensors ECH2O EC-5, TE and 5TE used in the wireless sensor network SoilNet, the application of a sensor-specific calibration procedure based on dielectric standard liquids reduce the RMSE of approximately 0.010 to 0.015 cm^3 cm^-3 in high soil water content range

An empirical vegetation correction for soil water content quantification using cosmic ray probes

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity

Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.</p

Fuentes de variabilidad que intervienen en la medicion de contenido de agua en el suelo en parcelas de manzano regadas por goteo

Comunicación presentada al XXXVII Congreso Nacional de Riegos, celebrado en Don Benito del 4 al 6 de Junio de 2019 y organizada por la Asociación Española de Riegos y Drenajes y la Universidad de ExtremaduraEl riego por goteo aplica el agua en el suelo de manera eficiente, reduciendo la superficie mojada y aportando el agua cerca de las raíces. Pero ello determina una distribución de la humedad del suelo heterogénea, que complica la monitorización con sensores. La finalidad de este trabajo es caracterizar la incertidumbre que aportan distintas fuentes de variabilidad implicadas en la medición con sensores de tipo capacitivo en un cultivo de manzanos regado por goteo. Para ello se ha analizado (1) cómo varía en condiciones reales de campo la extensión superficial de los bulbos húmedos y su centrado respecto a los goteros, (2) el ajuste en laboratorio, para el suelo objeto de estudio, entre la permitividad dieléctrica y el contenido relativo de agua, que es el principio en que se basan los sensores de tipo capacitivo, y (3) la respuesta individual de los sensores Decagon 10HS a medios de permitividad dieléctrica conocida. Los resultados muestran que, aun fijando la posición de los sensores en relación a los goteros, la mayor fuente de desviación entre mediciones del contenido relativo de agua en el suelo corresponde a la propia variabilidad de los bulbos húmedos (RMSE = 0.088 m3 m-3), mientras que la respuesta de distintos sensores individuales a la permitividad es muy repetitiva (RMSE = 0.003 m3 m-3). Un uso adecuado de sensores en condiciones de riego localizado deberá contemplar la variabilidad entre bulbos húmedos, tanto en la instalación de los sensores como en su interpretación.Drip irrigation applies water to the soil in an efficient way, reducing the wet surface and supplying water close to the root zone. However, it determines a heterogeneous distribution of water in the soil that complicates its monitoring with sensors. The purpose of this work was to characterize the main sources of uncertainty involved in the measurement with capacitive-type sensors in a drip-irrigated apple orchard. To that end, we analyzed (1) the variability in real field conditions of the extension and centering of wet bulbs, (2) the relationship in laboratory conditions, for the soil of interest, between dielectric permittivity and water content, which is the principle on which capacitive-type sensors are based; and (3) the response of individual sensors Decagon 10HS to different media of known electrical permittivity. The results show that, even at fixed positions relative to drippers, the largest uncertainties in the assessment of relative soil water content comes from the variability of the wet bulbs (RMSE = 0.088 m3 m-3), while the response of individual sensors to permittivity was very repetitive (RMSE = 0.003 m3 m-3) An adequate use of sensors under localized irrigation should consider the variability between wet bulbs, both in the installation of the sensors and in their interpretation.Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) (Proyecto RTA2013- 00045-C04-01 y FPI INIA), por el Fondo Social Europeo y por el Fondo Europeo de Desarrollo Regional (FEDER

Evaluating the value of a network of cosmic-ray probes for improving land surface modelling

Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased