Establishment of a network of soil moisture and cosmic ray neutron sensors for data assimilation and optimization of high-resolution, real-time predictions

2021 ◽  
Author(s):  
Patrizia Ney ◽  
Alexandre Belleflamme ◽  
Maksim Iakunin ◽  
Niklas Wagner ◽  
Sebastian Bathiany ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
High Resolution ◽  
Real Time ◽  
Cosmic Ray ◽  
Hydrologic Model ◽  
Direct Access ◽  
Extreme Weather Events ◽  
Observation Data ◽  
Community Land Model

<p>Climate change and its impacts at local scales, such as the more frequent occurrence of extreme weather events like droughts or floods, pose an increasing problem for agriculture. Our aim is to support farmers with soil condition and weather forecasting products that provide the basis for optimal adaptation to short-term weather variability and extremes as well as to long-term, regional climate change.</p><p>For this purpose, a prototypical monitoring and real-time forecasting system was established. The monitoring networks consist of a novel cosmic ray neutron sensor (Styx Neutronica), soil moisture and temperature sensors in four depths between 5 and 60 cm (SoilNet) and an all-in-one weather station (ATMOS-41, METER Environment) to measure the atmospheric conditions including air temperature, humidity, pressure, solar irradiance, wind speed and precipitation at 2 meter height above ground. The observation data are transmitted in real time to a cloud server via the cellular solution NBIoT (Narrow Band Internet of Things). After data post-processing the meteorological and hydrological parameters measured on site are directly assimilated into the fully coupled multi-physical numerical model system TSMP (Terrestrial Systems Modeling Platform, www.terrsysmp.org) at Forschungszentrum Jülich. ParFlow hydrologic model (www.parflow.org) is used in combination with the Community Land Model (CLM) to predict hourly, high-resolution (near plot level) information on soil moisture or other soil and meteorological parameters for the next 10 days. A special feature here is the prediction on the temporal development of plant-available water between 0-60cm depth for the sites of our monitoring network partners.</p><p>Observation data as well as the forecasting products are published in near real time on the digital product platform www.adapter-projekt.de. Users thus have direct access to relevant information that support them in planning agricultural management, e.g. irrigation and fertilization requirements, trafficability or workability.</p>

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

scholarly journals Evaluation of daily forecasts by the coupled Terrestrial Systems Modelling Platform (TSMP) over a small convection-permitting model domain in Central Europe

2021 ◽  
Author(s):  
Maksim Iakunin ◽  
Niklas Wagner ◽  
Alexander Graf ◽  
Klaus Görgen ◽  
Stefan Kollet
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Simulated Data ◽  
Cosmic Ray ◽  
Hydrologic Model ◽  
Small Scale ◽  
System Modelling ◽  
Community Land Model ◽  
Model Domain ◽  
Wide Range

<p>In many of today’s resource management and climate change adaptation challenges, versatile and  reliable numerical model simulations are the basis for informed decision making. The integration of multiple compartmental  models into simulation platforms allows us to reproduce interacting geosystem processes and thereby solve a wide range of problems in a variety of applications. The Terrestrial System Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from the groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the COSMO (Consortium for Small-scale Modeling) atmospheric model, the CLM (Community Land Model), and the hydrologic model ParFlo, coupled through OASIS3-MCT. TSMP is used in various studies from climate change simulations to near-real time forecasting and monitoring. Here we present the results of the evaluation of the TSMP in a monitoring setup, providing daily forecasts with a lead time of 10days of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in Western Germany. The model domain covers an area of 150km x 150km at 1km (atmosphere) and 0.5km (land surface and subsurface) resolution. The simulated data is compared with observations from the TERENO (Terrestrial Environmental Observatories, https://www.tereno.net) Eifel/Lower Rhine Valley network. This TERENO observatory comprises a total area of 2354 km² and provides data from a very dense measurement network of 12 climate stations, 6 eddy covariance stations, 6 lysimeter stations, and 13 cosmic-ray neutron stations. To assess the quality and suitability of the TSMP as a monitoring system of the geosystem’s state and evolution with agricultural applications in mind,  forecasts from July 2019 to October 2020 are analyzed with reference to the observations. Results show that the TSMP can well represent the main subsurface hydrological and relevant meteorological features.</p>

scholarly journals Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

2015 ◽  
Vol 19 (1) ◽  
pp. 615-629 ◽  
Author(s):  
X. Han ◽  
H.-J. H. Franssen ◽  
R. Rosolem ◽  
R. Jin ◽  
X. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Cosmic Ray ◽  
Systematic Bias ◽  
Area Index ◽  
Community Land Model ◽  
Independent Field

Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research – HiWATER, 2012) where no irrigation data were available as model input although for the area a significant amount of water was irrigated. In the study, the measured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the local ensemble transform Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

scholarly journals Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

2019 ◽  
Vol 23 (1) ◽  
pp. 277-301 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Land Surface ◽  
European Space Agency ◽  
Loop Model ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

2012 ◽  
Vol 13 (1) ◽  
pp. 338-350 ◽  
Author(s):  
Menberu M. Bitew ◽  
Mekonnen Gebremichael ◽  
Lula T. Ghebremichael ◽  
Yared A. Bayissa
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Mountainous Watershed ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

scholarly journals A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US

2014 ◽  
Vol 18 (1) ◽  
pp. 67-84 ◽  
Author(s):  
A. A. Oubeidillah ◽  
S.-C. Kao ◽  
M. Ashfaq ◽  
B. S. Naz ◽  
G. Tootle
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Assessment ◽  
Spatial Resolution ◽  
Climate Impact ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Data Set ◽  
Model Parameter ◽  
Climate Impact Assessment

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation – including meteorologic forcings, soil, land class, vegetation, and elevation – were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.

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

2016 ◽  
Author(s):  
R. Baatz ◽  
Harrie-Jan Hendricks Franssen ◽  
Xujun Han ◽  
Tim Hoar ◽  
Heye R. Bogena ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cosmic Ray ◽  
Land Surface Models ◽  
Lower Boundary Condition ◽  
Soil Map ◽  
Community Land Model ◽  
Verification Period ◽  
Surface Models ◽  
Soil Hydraulic

Abstract. 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.

scholarly journals High-resolution drought simulations and comparison to soil moisture observations in Germany

2021 ◽  
Author(s):  
Friedrich Boeing ◽  
Oldrich Rakovech ◽  
Rohini Kumar ◽  
Luis Samaniego ◽  
Martin Schrön ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Information Systems ◽  
High Resolution ◽  
Water Cycle ◽  
Cosmic Ray ◽  
Vegetation Period ◽  
Moisture Index ◽  
Distributed Sensor ◽  
Local Risk ◽  
The One

Abstract. The 2018–2020 consecutive drought events in Germany resulted in impacts related with several sectors such as agriculture, forestry, water management, industry, energy production and transport. A major national operational drought information system is the German Drought Monitor (GDM), launched in 2014. It provides daily soil moisture (SM) simulated with the mesoscale hydrological model (mHM) and its related soil moisture index at a spatial resolution of 4 × 4 km2. Key to preparedness for extreme drought events are high-resolution information systems. The release of the new soil map BUEK200 allowed to increase the model resolution to ~1.2 × 1.2 km2, which is used in the second version of the GDM. In this paper, we explore the ability to provide drought information on the one-kilometer scale in Germany. Therefore, we compare simulated SM dynamics using homogenized and deseasonalized SM observations to evaluate the high-resolution drought simulations of the GDM. These SM observations are obtained from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations and lysimeters at 40 sites in Germany. The results show that the agreement of simulated and observed SM dynamics is especially high in the vegetation period (0.84 median correlation R) and lower in winter (0.59 median R). Lower agreement in winter results from methodological uncertainties in simulations as well as in observations. Moderate but significant improvements between the first and second GDM version to observed SM were found in correlations for autumn (+0.07 median R) and winter (+0.12 median R). The annual drought intensity ranking and the spatial structure of drought events over the past 69 years is comparable for the two GDM versions. However, the higher resolution of the second GDM version allows a much more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality observational soil moisture database.

scholarly journals High-resolution simulation and validation of soil moisture in the arid region of Northwest China

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xianyong Meng ◽  
Hao Wang ◽  
Ji Chen ◽  
Mingxiang Yang ◽  
Zhihua Pan
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Arid Region ◽  
Arid Regions ◽  
Northwest China ◽  
Agricultural Drought ◽  
Data Set ◽  
Study Region ◽  
Community Land Model

AbstractSoil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0–10, 0–20, and 0–50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.

scholarly journals High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

2013 ◽  
Vol 14 (4) ◽  
pp. 1175-1193 ◽  
Author(s):  
Irena Ott ◽  
Doris Duethmann ◽  
Joachim Liebert ◽  
Peter Berg ◽  
Hendrik Feldmann ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Modelling ◽  
Community Land Model ◽  
The Impact ◽  
River Catchments

Abstract The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

Sign in / Sign up

Export Citation Format

Share Document