Displays

A number of studies have shown that multivariate data assimilation into the land surface models would improve model predictive skills. Soil moisture, streamflow and Evapotranspiration are among those environmental variables that greatly affect flood forecasting, drought monitoring/prediction, and agricultural production that collectively control the land and atmospheric system. However, land surface models most often do not provide accurate and reliable estimates of fluxes and storages and are subject to large uncertainties stemming from hydrometeorological forcing, model parameters, boundary or initial condition and model structure. Here, we present the state-of-the art data assimilation methods, covering the evolution of methods, discussing their pros and cons and introduce a novel approach that couples a deterministic four‐dimensional variational (4DVAR) assimilation method with an evolutionary ensemble filtering that together  significantly improve the estimation of storages and fluxes, hence better forecasting skill. The Evolutionalry Particle Filter with MCMC (EPFM) uses the Genetic Algorithm (GA) to effectively sample the particles to better represent the posterior distribution of model prognostic variables and parameters. This is followed by coupling EPFM and 4DVAR which results in a superior DA approach, the so-called Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN). The method explicitly accounts for model structural error during the assimilation process. The application of methods is presented for both flood and drought forecasting while utilizing the remotely sensed observations.

How to cite: Moradkhani, H., Abbaszadeh, P., and Gavahi, K.: Towards an Effective and Scalable Hybrid Data Assimilation for Hydrogeophysical Applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18135, https://doi.org/10.5194/egusphere-egu2020-18135, 2020.

We present a data assimilation algorithm for the time-domain spectral-finite element code VILMA. We consider a 1D earth structure and a prescribed  glaciation history ICE5G for the external mass load forcing. We use the Parallel Data Assimilation Framework (PDAF) to assimilate sea level data into the  model in order to obtain better estimates of the viscosity structure of mantle and lithosphere. For this purpose, we apply a particle filter in which an ensemble of models is propagated in time, starting shortly before the last glacial maximum. At epochs when observations are available, each particle's performance is estimated  and they are resampled based on their performance to form a new ensemble that better resembles the true viscosity distribution. In a proof of concept we  show that with this method it is possible to reconstruct a synthetic viscosity distribution from which synthetic data were constructed. In a second step,  paleo sea level data are used to infer an optimised 1D viscosity distribution.

How to cite: Schachtschneider, R., Saynisch-Wagner, J., Bagge, M., Klemann, V., and Thomas, M.: Data assimilation for a visco-elastic Earth deformation model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3024, https://doi.org/10.5194/egusphere-egu2020-3024, 2020.

The air-sea interface is one of the most physically active interfaces of the Earth's environments and significantly impacts the dynamics in both the atmosphere and ocean. In this study, we discuss the data assimilation of surface drifters, of which the dynamic motions are highly relevant to the instant change of both surface wind field and underlying ocean flow fields. We intend to take advantage of this relationship and improve the estimation of the model initialization in both ocean and coupled atmosphere-ocean systems.

The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we first propose an augemented-state Lagrangian data assimilation (LaDA) method that is based on the Local Ensemble Transform Kalman Filter (LETKF). The algorithm is tested with “identical twin” approach of Observing System Simulation Experiments (OSSEs) using the ocean model. Examinations on both of the eddy-permitting and the eddy-resolving Modular Ocean Model of the Geophysical Fluid Dynamics Laboratory (GFDL) are tested, which is intended to update the ocean states (T/S/U/V) at both the surface and at depth by directly assimilating the drifter locations. Results show that with a proper choice of localization radius, the LaDA can outperform conventional assimilation of surface in situ temperature and salinity measurements. The improvements are seen not only in the surface state estimate, but also throughout the ocean column to deep layer. The impacts of localization radius and model error in estimating accuracy of both fluid and drifter states are further investigated. In the second section, we investigate the LaDA within a Strongly Coupled Data Assimilation (SCDA) system using the simplified Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), a three-layer truncated quasi-geostrophic model. Results show that assimilating the surface drifter locations directly is capable of improving not only the ocean states but also the atmosphere states as well. We then compare it to the conventional approach to assimilate the approximated velocities instead of the direct drifter locations and it shows that the assimilating drifter locations outperforms the other approach.

How to cite: Sun, L.: Lagrangian Data Assimilation of Surface Drifters to Support Ocean and Coupled Model Initialization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10809, https://doi.org/10.5194/egusphere-egu2020-10809, 2020.

In this study we compare the results of strongly coupled data assimilation (SCDA) and weakly coupled data assimilation (WCDA), and among the different WCDAs by analyzing the assimilation effect on the prediction of the ocean as well as the atmosphere variables. We have implemented the parallel data assimilation framework (PDAF, http://pdaf.awi.de) with the AWI climate model (AWI-CM), which couples the ocean model FESOM and the atmospheric model ECHAM. In the WCDA, the assimilation acts separately on each component in the coupled model and observations of one component only directly influence its own component. The other components can benefit from the DA through the model dynamics. The alternative to WCDA is SCDA, in which the atmosphere as well as the ocean variables are updated jointly using cross-covariances between the two components. Our current system allows both the SCDA and the WCDA. For the SCDA configuration, either the ocean observations (e.g., satellite sea surface temperature, profiles of temperature and salinity) or the atmosphere observations (e.g., air temperature, surface pressure) or both of them can be assimilated to update the ocean as well as the atmosphere variables. For the WCDA, it allows 1) assimilating only the ocean observations into the ocean state; 2) assimilating only the atmosphere observations into the atmosphere state; 3) assimilating both types of observations into the corresponding component models. The results are evaluated by comparing the estimated ocean and atmosphere variables with the observational data.

How to cite: Tang, Q., Mu, L., Sidorenko, D., and Nerger, L.: Weakly and strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5023, https://doi.org/10.5194/egusphere-egu2020-5023, 2020.

The temporal and spatial development of the atmospheric boundary layer is coupled to soil conditions via latent and sensible heat flux. Information about soil conditions is following encoded in atmospheric screen-level observations. To infer the soil moisture, these observations are usually assimilated with a Simplified Extended Kalman Filter (SEKF). This data assimilation technique is simplified in comparison to Ensemble Kalman Filters (EnKF), which are often used for data assimilation in the atmosphere. To make full use of the interface between atmosphere and land, we want to use strongly-coupled data assimilation with a unified system. We will present which problems have to be solved within an EnKF framework to use it as unified data assimilation system. We initialized an observing system simulation experiment with the TerrSysMP system, where a limited area model for the atmosphere is coupled with the Community Land Model. Here, we assimilate the two-metre temperature with an EnKF to update the soil moisture for a dry time period. We use initial soil moisture and soil temperature perturbations as only method to create an ensemble.

We show a positive observation impact during daytime. The analysis and forecast are further improved compared to assimilation with a SEKF. During daytime, the atmosphere and soil are strongly coupled, while they are almost uncoupled during night-time. Following, we have a slightly negative observation impact during night-time. This negative impact is induced by sampling errors of the ensemble. The negative impact is further amplified in the transition time between night and day. We can attribute this amplification to horizontal heterogeneities and multiplicative ensemble inflation in soil. We can therefore say that the inflation is wrongly tuned for the soil during night-time, while it works for the atmosphere and during daytime. We hypothesize that these problems during night-time can be avoided by using additional models, like a time-dependent localization radius and inflation factor.

How to cite: Finn, T. S., Geppert, G., and Ament, F.: Towards an ensemble-based assimilation of boundary-layer observations for soil moisture, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15057, https://doi.org/10.5194/egusphere-egu2020-15057, 2020.

The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).

The sensitivity of 7 land surface parameters is tested using the Latin-Hypercube One-factor-At-a-Time (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST).

The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.

How to cite: Fersch, B., Senatore, A., Adler, B., Arnault, J., Mauder, M., Schneider, K., Völksch, I., and Kunstmann, H.: High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17855, https://doi.org/10.5194/egusphere-egu2020-17855, 2020.

Land-atmosphere feedback processes are key components of the Earth climate system. In general, it is questionable to which extend the state of the land surface feeds back to the state of the atmosphere. This question can be addressed with a coupled land surface – atmospheric model, and the realism of the simulated feedbacks can be evaluated with a model-to-observation comparison. This study investigates the particular case of the process chain linking lateral terrestrial water flow, soil moisture, surface evaporation and precipitation. The focus is on summer precipitation in the European region. The study period is set to four months in June-September 2008. The tool to conduct this study is the coupled atmospheric – hydrological model WRF-Hydro, which allows surface and subsurface water routing. For the setup of the atmospheric part, a horizontal grid of 700x500 grid points with a grid spacing of 5 km, that is covering an area of 3500 km x 2500 km, and 50 vertical levels up to 10 hPa is chosen. For the setup of the land water routing, a horizontal grid of 14000x10000 grid points with a grid spacing of 250 m and 4 soil layers down to 2 m depth is chosen. The employed model version includes a surface evaporation tagging procedure in order to quantify the fraction of European precipitation originating from evaporation from all over the European continent. The method consists of generating a set of WRF-Hydro simulations with and without land water routing by using random realizations of the stochastic kinetic energy backscatter scheme, and assess the impact of lateral terrestrial water flow on precipitation with the daily gridded observational dataset for precipitation in Europe (E-OBS). An ensemble size of twenty members is used to disentangle the contribution of two processes responsible for precipitation differences between WRF-Hydro simulations with and without land water routing, namely the changes in surface evaporation and the atmosphere chaotic behavior. It is found that the consideration of lateral terrestrial water flow increases the amount of summer precipitation through enhanced surface evaporation up to 10%, which reduces the bias to E-OBS.

How to cite: Arnault, J., Fersch, B., Rummler, T., Zhang, Z., Wei, J., Quenum, M., Graf, M., Laux, P., and Kunstmann, H.: Contribution of lateral terrestrial water flow to precipitation – A WRF-Hydro ensemble analysis and continental evaporation tagging for Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10223, https://doi.org/10.5194/egusphere-egu2020-10223, 2020.

Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration.  

How to cite: Zhao, H., Baatz, R., Montzka, C., Vereecken, H., and Hendricks Franssen, H.-J.: Multi-scale assimilation of SMAP data: comparison between land surface and land surface-subsurface model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-465, https://doi.org/10.5194/egusphere-egu2020-465, 2020.

It is recognized that groundwater (GW) may play an important role in the subsurface–land-surface–atmosphere system and that pumping of GW may affect soil moisture which in turn influences local weather and climate through land-atmosphere interactions. In this study effects of GW pumping on ground surface temperature (GST) in the North China Plain (NCP) were investigated with a coupled ParFlow.CLM model of subsurface and land-surface processes and their interactions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the impacts of GW pumping and irrigation on GST. Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges between 1–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to raise GST for about 20 years from the pre-pumping WTD in the NCP. The increase of GST is expected to be faster initially and gradually slow down. The findings from this study may implicate similar GST increases may occur in other regions with GW depletion.

How to cite: Zhang, Y.-K., Yang, C., and Yang, X.: Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3198, https://doi.org/10.5194/egusphere-egu2020-3198, 2020.

The Yellow River Basin (795,000 km²) in Northern China has been greatly affected by intensive human activity and climate change over the past decades. In this study, a coupled atmospheric and hydrological modelling system is applied to investigating the long-term hydrological cycle and short-term forecasting of hydrological events in the Yellow River Basin. This modelling system (AHMS) combines a hydrological model (HMS) with the Weather Research and Forecast model (WRF) and the Noah land surface scheme (NoahMP-LSM), which has been recently improved to account for topographic influences in the infiltration scheme and to allow for interactions between the unsaturated and saturated zones by applying the Darcy-flux boundary condition. Here, simulations are performed using the offline AHMS mode over the Yellow River Basin by considering a time span of 25 years (1979-2003) and a spatial resolution of 20 km. The NCEP reanalysis dataset and observed precipitation data for the referred period are used as meteorological forcing data. The most important parameters affecting the hydrological process are identified by means of a parametric sensitivity analysis. Specifically, these main parameters are the Manning's roughness coefficient of channel, the soil infiltration capacity and the hydraulic conductivity of riverbed. To calibrate the values of these parameters for the Yellow River Basin, model predictions for daily streamflow are compared with the corresponding observational data at four hydrological gauging stations including Tangnaihe (TNH), Lanzhou (LZ), Toudaoguai (TDG) and Huanyuankou (HYK) on the mainstream of the Yellow River. Quantitative agreement is found between these observations and the simulation results for all stations. The progress achieved in the present work paves the way for a sediment flux model over the Yellow River Basin and demonstrates the good performance of AHMS for long-term hydrological simulations. 

How to cite: Jiang, C., J. R. Parteli, E., and Shao, Y.: Application of a Coupled Atmospheric and Hydrological Modelling System (AHMS) to the Yellow River Basin, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5197, https://doi.org/10.5194/egusphere-egu2020-5197, 2020.

Integrated terrestrial systems modeling is important for the comprehensive investigation of the coupled terrestrial water, energy and biogeochemical cycles. In this work, we applied the Terrestrial Systems Modeling Platform (TSMP) to the two meso-scale catchments in Germany (Rur and Bode) to conduct a long time hydrologic simulation with a focus on variables such as soil moisture, evapotranspiration (ET) and groundwater recharge. Simulations for the Rur and Bode catchments were performed at three different spatial horizontal model resolutions (1000, 500, and 200m) with CLM and CLM-PF in TSMP. Each of the three resolution models was run for 24 years (1995-2018) with transient atmospheric forcings derived from COSMO-REA6 data. The long term simulation results show that the summer of 2018 resulted in the lowest soil moisture content over the time series that is around 0.20, lower than the dry summers of 1995 and 2003. ET was more reduced in July-August 2018 due to the decrease of soil moisture content during this period. Nevertheless, actual evapotranspiration was even in the summer of 2018 often not limited by soil moisture content. For these catchments ET is most of the time energy limited. In addition, the vegetation evaporation (resulting from interception) accounts for the smallest percentage of the ET (ca. 20%), whereas the vegetation transpiration and soil evaporation account for almost the same percentage of the total ET (each 40% approximately). Both the CLM and CLM-PF simulation results indicate that grid coarsening (lower model resolution) leads to larger ET and soil moisture content, which is related to the decreasing slope gradient with grid coarsening. The analysis of groundwater recharge is underway.

How to cite: Yang, Z., Kurtz, W., Gebler, S., Schüler, L., Kollet, S., Vereecken, H., and Hendricks-Franssen, H.-J.: Long term hydrologic simulations for the meso-scale catchments Rur and Bode in Germany by TSMP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5696, https://doi.org/10.5194/egusphere-egu2020-5696, 2020.

The terrestrial carbon cycle is an important part of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in estimating carbon fluxes, which impacts global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. In this context, accurate soil moisture data will improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36 years (1980-2015) of surface soil moisture data as provided by the ESA CCI in combination with atmospheric CO₂ concentration observations at global scale. We will present the methods used for assimilating long-term remotely sensed soil moisture into the terrestrial biosphere model, and demonstrate the importance of soil moisture in modeling ecosystem carbon cycle processes. We will also investigate the impacts of soil moisture on the terrestrial carbon cycle during climate extremes at various scales.

How to cite: Wu, M., Scholze, M., Jiang, F., Wang, H., Zhang, W., Lu, Z., He, W., Wang, S., Kaminski, T., Vossbeck, M., Wang, J., and Ju, W.: Impacts of soil hydrologial modeling on long-term terrestrial carbon cycle inferred from CCDAS (Carbon Cycle Data Assimilation System), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6374, https://doi.org/10.5194/egusphere-egu2020-6374, 2020.

We implement multivariate data assimilation in a seamless sea ice prediction system based on the fully-coupled AWI Climate Model (AWI-CM, v1.1). AWI-CM has an ocean/ice component with unstructured-mesh discretization and smoothly varying spatial resolution, which aims for seamless sea ice prediction across a wide range of space and time scales. The assimilation uses a Local Error Subspace Transform Kalman Filter coded in the Parallel Data Assimilation Framework. To test the robustness of the assimilation system, a perfect-model experiment is configured to assimilate synthetic observations. Real observations from sea ice concentration, thickness, drift, and sea surface temperature are further assimilated in the system. The analysis results are evaluated against independent in-situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the analysis step. Particularly we find that assimilating sea ice drift improves the sea ice thickness estimate in the Antarctic, and assimilating sea surface temperature is able to avert a circulation bias of the free-running model in the Arctic Ocean at mid-depth. We also test the performance of an extended experiment where the atmosphere is constrained by nudging toward reanalysis data. The second version of the system assimilating more observations also with a new atmospheric model is currently under development.

How to cite: Mu, L., Nerger, L., Tang, Q., Losa, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., and Goessling, H. F.: Multivariate data assimilation in a seamless sea ice prediction system based on AWI-CM, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9214, https://doi.org/10.5194/egusphere-egu2020-9214, 2020.

Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.

How to cite: Patil, A., Fersch, B., Hendricks-Franssen, H.-J., and Kunstmann, H.: Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10419, https://doi.org/10.5194/egusphere-egu2020-10419, 2020.

Given the expected increase in the frequency and intensity of severe weather events due to global warming, improving weather forecasting capability in terms of both spatial resolution and lead times is a key factor for reducing extreme events impact. The climate of the Calabrian peninsula (southern Italy) is dominated by the interactions of the air masses with the surrounding Mediterranean Sea and strongly influenced by its complex steep orography, which often amplifies precipitation amounts worsening ground effects.

With the aim of investigating the capability of a state-of-the-art modelling chain to deliver accurate forecasts for civil protection purposes in the Calabria Region, an experimental high-resolution hydrometeorological modelling system has been developed recently at the Department of Environmental Engineering of the University of Calabria, providing forecasts up to the hydrological impact. The system is based on the Advanced Research WRF (ARW) mesoscale model in its version 3.9.1, with two one-way nested domains, the innermost having 2-km resolution. The boundary and initial conditions are provided operationally by the Global Forecasting System (GFS) in its high-resolution version and, for back-analysis purposes, by the European Centre for Medium-range Weather Forecasts’ Integrated Forecasting System (IFS). Finally, to simulate the hydrological impact of the atmospheric forcing, the WRF-Hydro 5.0 modelling system in a one-way mode with a horizontal resolution of 200 m is linked to the system and applied on all the main river networks of the region.

The accuracy and efficiency of the system have been tested with two events occurred in Autumn 2019. Though the synoptic conditions showed some significant differences, both the events affected mainly the central part of the region, causing about 230 mm and 200 mm of rainfall in 72 hours, on the 11-13 November 2019 and on the 24-26 November 2019, respectively. The analysis focused particularly on the predictability of the events, evaluating the forecast accuracy by considering lead times from one week early.

Preliminary results highlight the ability to forecasts the events well in advance, proved by the comparison of the simulated rainfall with the ground-based observations and the reproduction of the main hydrological signals in the basins affected by the events.

How to cite: Furnari, L., Senatore, A., Magnusson, L., and Mendicino, G.: Developing an operational high-resolution hydrometeorological system in a Mediterranean region: predictability analysis of two case studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14140, https://doi.org/10.5194/egusphere-egu2020-14140, 2020.

The regional terrestrial-atmospheric water cycle is strongly altered by human activities. Among them, reservoir regulation is a way to spatially and temporally allocate the water resources in a basin for the purpose of, for example, flood control, agriculture development, ecosystem maintenance. However, it is still not well understood how the reservoir regulation modifies the regional terrestrial-atmospheric water cycle. To address this question, this study employs a fully-coupled regional Earth system modelling system WRF-HMS, which has a closed description of the water cycle in a ground-soil-vegetation-atmosphere continuum. A process-based reservoir regulation module is for the first time now implemented into WRF-HMS, which allows to represent reservoir regulation in one seamless atmosphere-hydrology modeling system. In addition, an online budget analysis of atmospheric moisture is implemented into WRF-HMS, so that the impact of reservoir regulation on the atmospheric branch of the water cycle is quantitatively analyzed. Our study focuses on the basin of the largest fresh water lake in China, the Poyang Lake basin. Four simulations with a horizontal resolution of 10 km are conducted for the investigation period of 1979 to 1986: the standalone HMS with/without the reservoir regulation module and the fully-coupled WRF-HMS with/without the reservoir regulation module. For the standalone simulations, the basin-averaged, multi-year mean results show that incorporating reservoir regulation leads to an increased evapotranspiration, a wetter soil, and a higher groundwater level. In addition, the interactions among river water, unsaturated zone, and groundwater are enhanced as well. Overall, the reservoir-enabled HMS model improves the streamflow simulation over the Poyang Lake basin on daily and monthly scales than the reservoir-disabled HMS model. For the fully coupled simulations, our preliminary results show that incorporating reservoir regulation also modifies the regional atmospheric branch of the water cycle, for example, moistening planetary boundary layer due to the enhanced evapotranspiration. Details about the results of the fully-coupled simulations will be presented in the conference.

How to cite: Wei, J., Dong, N., Arnault, J., Fersch, B., Wagner, S., Zhang, Z., Laux, P., Yang, C., Yang, Q., Yu, Z., and Kunstmann, H.: How reservoir regulation modifies the regional terrestrial-atmospheric water cycle: Incorporation of a reservoir network module into a fully-coupled hydrological-atmospheric model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16219, https://doi.org/10.5194/egusphere-egu2020-16219, 2020.

Land degradation, as a major issue in South Africa, undermines water resources and land potential productivity, and threatens the ecosystem biodiversity and human activities. In the scope of accurately assessing the land degradation processes in multi-use landscapes, the atmosphere-land surface relations and the dynamics of land surface state variabilities need to be addressed in a detail. This requires Earth System modeling approaches jointly considering high-resolution atmospheric modeling, land surface and hydrological modeling frameworks. This study investigates the atmosphere-land interactions and land surface water-energy budget for South Africa using the Earth System Model WRF-Hydro. WRF-Hydro is the fully coupled atmosphere-land surface-hydrology modeling system, which enhances the Weather Research and Forecasting model with the overland and subsurface water routing processes. In the WRF-Hydro modeling setup, the atmospheric part is configured in a convection-permitting spatial resolution at 4 km, with horizontal grids of 650 × 500 points, covering area of Southern Africa. In the land surface, the gridded hydrological processes are routed on a 400 m fine hydrological subgrid, within a soil depth of 2 m. In this study, we perform the coupled simulation for the year of 2010 and show the validation of modeling results with multiple reference datasets. The water-energy budget in the land surface from coupled WRF-Hydro simulation is assessed on 22 primary hydrological drainage regions. Model results show that coupled atmospheric-hydrological modeling is able to represent the regional water and energy budget, and to resolve atmosphere-land surface interactions. This allows the further usage of the coupled atmospheric-hydrological modeling in the context of land degradation studies, e.g. under different land-use scenarios.

How to cite: Zhang, Z., Arnault, J., Laux, P., Baade, J., and Kunstmann, H.: Assessing the interactions of atmosphere and land surface over South Africa with convective-permitting coupled atmospheric-hydrological modeling , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17772, https://doi.org/10.5194/egusphere-egu2020-17772, 2020.

Accurate basic soil properties information is fundamental for obtaining reliable soil moisture using land surface models. In view of the passive microwave remote sensing, basic soil properties have an impact on soil dielectric constant, together with soil moisture and temperature. The common link enables to use coupled land surface model with microwave emission model for retrieving basic soil properties in space, especially in remote areas such as the third pole region. The Maqu site in the eastern Tibetan Plateau, including ELBARA-III radiometry observations, was taken as the case. This paper employed an improved observation operator— a discrete scattering-emission model of L-band radiometry with an air-to-soil transition model embedded in, which considers both geometric and dielectric roughness impacts from heterogeneous topsoil structure on surface emission. Community Land Model 4.5 together with Local Ensemble Transform Kalman Filter algorithm were used by mean of the Open Source Multivariate Land Data Assimilation Framework. The retrieved basic soil properties were compared to in situ measurements, as well as the update soil moisture and temperature and energy fluxes. The impacts from surface roughness consideration and polarization configuration on parameter retrieval were also evaluated. To gain an insight on the impact from time interval of observations on parameter retrieval, results using observations at SMAP descending and ascending time were discussed.

How to cite: Zhao, H., Zeng, Y., Su, B., and Han, X.: Retrieval of basic soil physical properties by assimilating radiometry observations in the community land surface model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19116, https://doi.org/10.5194/egusphere-egu2020-19116, 2020.

This research aims to observe the behaviour between heat flow at the limit of the unsaturated area and the earth's surface (evaporation) through different methods based on the surface energy balance. This behavior has been determined by the DRUtES. DRUtES is a free software able to determine the evaporation in the surface using climate and hydraulic parameters determined by the Richard equation. Richards’ equation describes the flow of water in an unsaturated porous medium due to the actions of gravity and capillarity neglecting the flow of the non-wetting phase, usually air (Farthing & Ogden, 2017). 

The results obtained have been compared with the Penman-Monteith potential evapotranspiration model, this one as a referenced value. The results obtained help to understand the loss of water in the unsaturated area. This first approach using DRUtES and evaporation methods will allow a deeper investigation in the future regarding the impact of climate change on climate variables and their effects on soil moisture (unsaturated area) and natural aquifer recharge.

Key words: Evaporation, surface energy balance, Richard's Equation, zone unsaturated, Penman-Monteith.

How to cite: Cárdenas-Castillero, G. and Arbelaez, J.: Application of the Surface Energy Balance in Richard's equation-based model using climatic data to calculate soil evaporation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20055, https://doi.org/10.5194/egusphere-egu2020-20055, 2020.

The flood events in the mountainous area of northern China has the characteristics of high intensity and strong sudden occurrence, and atmospheric-hydrological coupling system can improve the forecast accuracy and prolong the lead time. This paper discusses the simulations of the enhanced WRF-Hydro model on a historical flood that occurrs in a mesoscale catchment of Taihang mountain on July 21, 2012. Firstly, the precipitation accuracy of WRF, WRF data assimilation, co-kriging merging method of radar QPE data are as three different input sources for WRF-Hydro. The results show that the rainfall of merging QPE can achieve better simulations in time and space. In addition, the rainfall of WRF assimilation data is obviously better than that of WRF, but still underestimates the rainfall values. The extreme event rainstorm mainly proceeds in 5 hours, and for the assimilation data, the spatio-temporal simulations of the rainfall data in the first 2 hours are slightly poor. Hence we compare the combination of the first few hours to use the merging QPE and following by assimilation precipitation as the model input. In addition, according to the parameters of the WRF-Hydro model, a gridding parameter calibration method based on topographic index is constructed.

How to cite: Wang, W., Liu, J., Li, C., Qiu, Q., and Liu, Y.: Forecasting of an extreme flood in mountainous area of North China based on WRF-Hydro with distributing parameters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21037, https://doi.org/10.5194/egusphere-egu2020-21037, 2020.