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Summary: Inverse stochastic modelling and direct moment propagation is applied for deriving effective land surface parameters of a Soil–Vegetation–Atmosphere-Transfer (SVAT) model. Special attention is given to the proper representation of latent heat fluxes at grid scale when an underlying subgrid-scale heterogeneity is assumed. Derived effective land surface and vegetation parameters yield the same total latent heat fluxes on the grid scale as the averaged heat fluxes on the subgrid-scale. Upscaling relations are derived that relate mean and variance (first and second moment) of subgrid-scale heterogeneity to a corresponding effective parameter at grid-scale. Explicit upscaling relations are exemplary derived for (a) land surface property parameters roughness length, albedo, emissivity and (b) vegetation parameters wilting point soil moisture, field capacity, leaf area index, vegetation fraction, and minimal and maximal stomata resistance. It is shown that the value of effective parameters depends on the sign of the first and second derivatives of aggregated fluxes with respect to the parameter. It is demonstrated that the Second-Order-First-Moment (SOFM) Method yields results that are comparable to those obtained with inverse stochastic Monte Carlo simulations. Effective parameters were found to be independent of meteorological forcing conditions and initial conditions for a 7-day time period. [Copyright &y& Elsevier]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [ABSTRACT FROM AUTHOR]

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm). [ABSTRACT FROM AUTHOR]

Coupled numerical models, which simulate water and energy fluxes in the subsurface–land-surface–atmosphere system in a physically consistent way, are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a simulated reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the meteorological Consortium for Small-scale Modeling (COSMO) model, the land-surface Community Land Model (CLM), and the subsurface ParFlow model, to generate a simulated catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany, the virtual Neckar catchment. Simulations for this catchment are made for the period 2007–2015 and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modeling challenges, the model performance is evaluated based on observations covering several variables of the water cycle. We find that the simulated catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g., concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement, such as overland flow, and in the realism of some observation operators like the satellite-based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): 10.26050/WDCC/Neckar_VCS_v1. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km 2 : the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: 10.23728/b2share.282675586fb94f44ab2fd09da0856883 and 10.23728/b2share.bd89f066c26a4507ad654e994153358b. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while well established sensors typically suffer from a small spatial support. With a sensor footprint up to several hectares, Cosmic-Ray Neutron Sensing (CRNS) is an emerging technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km²: the pre-alpine Rott headwater catchment in Southern Germany which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, that network was designed to study root zone soil moisture dynamics at the catchment-scale. The observations of the dense CRNS network were complemented by extensive measurements that allow to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from Unmanned Areal Systems (UAS), permanent and temporary wireless sensor networks, profile probes as well as comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive dataset to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatio-temporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land - atmosphere exchange processes. The data set is available through EUDAT, splitted into the two subsets https://doi.org/10.23728/b2share.85fe0f9dac0f48df9215c17e65d1f1e1 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.93ed99e486904d48a8a6a68083066198 (Fersch et al., 2020b). [ABSTRACT FROM AUTHOR]

Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007-2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar%5FVCS%5Fv1. [ABSTRACT FROM AUTHOR]

• Remarkable differences of soil texture in southern Africa are found in global soil data. • Land-atmosphere interactions are investigated by using coupled WRF-Hydro with four global soil data. • Soil texture changes lead to differences in temperature up to 1.7°C and sensible heat flux up to 25 W/m2 during austral summer. • Soil data discrepancies demonstrate physical feedback on atmospheric processes. • Consistent consideration of soil data uncertainties is needed in modeling applications. Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications. [ABSTRACT FROM AUTHOR]

Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April-October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%-20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%-0.7%. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science. [ABSTRACT FROM AUTHOR]

The analysis of land-atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff-infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff-infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff-infiltration partitioning and resolved overland flow on land-atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff-infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff-infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash-Sutcliffe model efficiency coefficient of 0.43. [ABSTRACT FROM AUTHOR]

With the aim of developing a fully coupled atmosphere-hydrology model system, the Weather Research and Forecasting (WRF) model was enhanced by integrating a new set of hydrologic physics parameterizations accounting for lateral water flow occurring at the land surface. The WRF-Hydro modeling system was applied for a 3 year long simulation in the Crati River Basin (Southern Italy), where output from the fully coupled WRF/WRF-Hydro was compared to that provided by original WRF model. Prior to performing coupled land-atmosphere simulations, the stand-alone hydrological model ('uncoupled' WRF-Hydro) was calibrated through an automated procedure and validated using observed meteorological forcing and streamflow data, achieving a Nash-Sutcliffe Efficiency value of 0.80 for 1 year of simulation. Precipitation, runoff, soil moisture, deep drainage, and land surface heat fluxes were compared between WRF-only and WRF/WRF-Hydro simulations and validated additionally with ground-based observations, a FLUXNET site, and MODIS-derived LST. Since the main rain events in the study area are mostly dependent on the interactions between the atmosphere and the surrounding Mediterranean Sea, changes in precipitation between modeling experiments were modest. However, redistribution and reinfiltration of local infiltration excess produced higher soil moisture content, lower overall surface runoff, and higher drainage in the fully coupled model. Higher soil moisture values in WRF/WRF-Hydro slightly influenced precipitation and also increased latent heat fluxes. Overall, the fully coupled model tended to show better performance with respect to observed precipitation while allowing more water to circulate in the modeled regional water cycle thus, ultimately, modifying long-term hydrological processes at the land surface. [ABSTRACT FROM AUTHOR]

Summary: A number of future water availability scenarios in the Crati River Basin (Southern Italy) are made by applying the outputs of three Regional Climate Models (RCMs) RegCM, HIRHAM and COSMO-CLM to the newly developed Intermediate Space Time Resolution Hydrological Model (In-STRHyM). In-STRHyM is a fully distributed hydrological model detailed enough to describe the hydrological processes of several small-medium sized Mediterranean basins. It has a relatively simple structure and is suitable for long period simulations to be undertaken within acceptable time frames. The analysis was performed using two time slices (1961–1990 and 2070–2099) with the SRES A2 (HAD3AM) and A1B (ECHAM5/MPI-OM) scenarios. Observed biases in simulated precipitation and temperature fields during the control period (1961–1990) were corrected before using meteorological outputs from each RCM as input for In-STRHyM. A sensitivity analysis is used to help verify this procedure. While hydrological model simulations are based on different greenhouse gas emission scenarios, GCMs, RCMs and bias correction parameters and hence show noticeable differences, they all agree on a general reduction in future water resource availability. An increase in the average annual temperature between +3.5°C and +3.9°C and a decrease between −9% and −21% in the cumulative annual precipitation are projected, leading to a drastic reduction in snow accumulation of between −82% and −92%. Evapotranspiration is expected to increase in the wintertime and decrease in summertime, with the water stress period increasing on average by 15days. Mean annual reductions are predicted for root zone soil moisture (between −12.8±1.9% and −20.7±1.9%, with reductions reaching −37.7±2.4% during summer), groundwater storage (−6.5±1.4% and −11.6±1.6%), surface runoff (−25.4±6.0% and −41.2±5.0%) and a significant increase in runoff variability. [Copyright &y& Elsevier]

Abstract: Savannahs are highly dynamic ecosystems but many of their properties and the related balances of energy, carbon, nitrogen, and water are still poorly understood. A particular scientific issue is the quantification of trace gases emitted from the soil of savannah ecosystems and their interaction with regional and global climate and air chemistry. Therefore it is important to develop and evaluate land-surface models that on the one hand represent vegetation and soil dynamics and on the other hand provide energy and water fluxes in a temporal resolution suitable for the application in climate/air chemistry models. In this paper, we present a consistent coupling between a common land-surface model (OSU) and a widely used biogeochemical model (DNDC) that is a first step for a full coupling of climate/air chemistry and biogeochemical processes. For consistency reasons, both models are linked to a general physiologically based plant model to provide the physical boundary conditions as well as the carbon and nitrogen in- and output variables. Evaluation is carried out with measurements of soil temperature, latent heat flux, soil water content, and soil emission data from two vegetation periods collected at a natural grassland site in Bontioli Nature Reserve, Burkina Faso (Africa). The results demonstrate that simulations of biogeochemical processes based on soil environmental conditions, calculated either with the land-surface model or with the unchanged biogeochemical model, do not differ significantly from each other. The OSU model simulates more realistic day-to-day variation of soil temperature as DNDC but the sensitivity of the biogeochemical simulation to this variation is small. In contrast, the sensitivity to differences in soil water content is high, but simulation results of both models are very similar on the daily scale and hardly depend on spatial soil resolution. [Copyright &y& Elsevier]

Water resources management requires an accurate knowledge of the behavior of the regional hydrological cycle components, including precipitation, evapotranspiration, river discharge and soil water storage. Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate these components. Recent model developments have focused on coupled atmospheric-hydrological modeling systems, such as WRF-Hydro, in order to account for subsurface, overland and river flow and potentially improve the representation of land-atmosphere interactions. The aim of this study is to investigate the contribution of lateral terrestrial water flow to the regional hydrological cycle, with the help of a joint soil-atmospheric moisture tagging procedure newly developed in the so-called WRF-tag and WRF-Hydro-tag models. An application of both models for the high precipitation event on 15 August 2008 in the German and Austrian parts of the Danube river basin (94,100 km2) is presented. The precipitation having fallen in the basin during this event is considered as a moisture source, is tagged and tracked for a 40 month-period until December 2011. At the end of the study period, in both simulations, approximately 57% of the tagged moisture has run off, while 41% has evaporated back to the atmosphere, including 2% that has recycled in the Danube river basin as precipitation. In WRF-Hydro-tag, the surface evaporation of tagged moisture is slightly enhanced by surface flow infiltration, and slightly reduced by subsurface lateral water flow in areas with low topography gradients. This affects the source precipitation recycling by +/- 0.02%. [ABSTRACT FROM AUTHOR]

The spatial and temporal distribution of soil water is a key constituent for the coupled moisture processes between the land surface and the atmospheric boundary layer. The scales of soil moisture observations and simulations with land-surface models are typically mismatching with respect to spatial support. Cosmic-ray neutron sensing (CRNS) offers a competitive method to observe soil moisture at the spatial scales of modern LSMs as typically used in local area climate and atmospheric models. In our study, we investigate how the assimilation of CRNS observations helps to improve the spatial structures of soil moisture in these models and the local soil moisture – atmospheric boundary layer interaction. The modeling chain consists of Noah-MP LSM, the WRF-Hydro model in both, observation driven and fully WRF coupled configuration, and the Data Assimilation Research Testbed (DART) coupled to WRF-Hydro (Hydro-DART). The data assimilation is based on Ensemble-Kalman Fitler (EnKF) with the fully coupled WRF-Hydro-Dart model system.Extensive observations from the Terrestrial Environmental Observatories (TERENO, www.tereno.net), will be used along with data from MOSES (Helmholtz Modular Observation Solutions for Earth Science https://moses.eskp.de/), and with data from the DFG research unit Cosmic Sense. We present the structure and a first evaluation of the model system for the Ammer river catchment in southern Germany based on observations from the TERENO preAlpine network. [ABSTRACT FROM AUTHOR]

The exchange of water between atmosphere, biosphere, and hydrosphere can be viewed as a result of complex interactions of dynamic feedback mechanisms, where soil moisture content acts as the key state variable. Approaches are required to handle land surface complexity and scale dependency of water fluxes. State-of-the-art observations of soil moisture content are ranging from continuous point-scale measurements via field-scale snapshots to remote sensing products on the basin scale and beyond. They have to deal with a space-time trade-off since the measurement frequency typically decreases with the covered spatial scale. Cosmic-ray neutron sensing is one of the growing techniques to move soil hydrological observations forward. We have established a research unit (DFG FOR 2694) named "Cosmic Sense" to jointly improve such measurements with or in combination with cosmic-ray neutron sensing, especially in annual joint field campaigns at small catchment scale. Key aims are to- bridge existing gaps between scales through additional techniques and sources of information about the soil water storage. Cosmic-ray neutron sensing measures the presence of water by sensing changes in neutron density above the ground. We want to develop a quantitative, adaptable, and transferable approach for observing representative soil moisture content values on the field scale while accounting for other dynamic water pools on the land surface, such as biomass, interception, and snow. Furthermore, by sensor clusters and mobile neutron detectors the mapping of soil moisture can be transferred to larger scales. - bridge gaps between disciplines. Cosmic-ray neutron sensing shall function as a unique combination of invasive and non-invasive observations in joint field campaigns. The measurement and interpretation of soil moisture by Cosmic-ray neutron sensing requires advanced knowledge of soil hydrology as well as particle physics including neutron transport modeling. Efforts will be made for detector development that aims to substantially improve the temporal resolution and spatial coverage. - foster an approach that is especially tailored to resolve the discrepancy in support volume and timing between hydrological models and field observations. Comprehensive observations by cosmic-ray neutron sensing, remote sensing and complementary methods will be used concertedly with hydrological and land-surface models at the regional scale to infer also groundwater recharge and atmospheric fluxes. Thereby, models and observations together will allow for identifying patterns and processes at the scale of small catchments with improved spatio-temporal resolution. [ABSTRACT FROM AUTHOR]

Lateral terrestrial water flow contributes to the Earth system dynamics by redistributing the moisture fluxes at the land surface. Fully coupled atmospheric-hydrological modeling, which considers the lateral hydrological processes, is increasingly used to investigate the land-atmosphere interactions. Planetary boundary layer (PBL) parameterizations which depict vertical energy and mass exchanges from the surface to lower atmosphere are pivotal to atmosphere modeling applications. This study aims at quantitatively evaluating to what extend the PBL scheme influences land-atmosphere interactions and how it impacts the regional climate modeling by coupled atmospheric-hydrological modeling approach. In this case study, the hydrologically enhanced version of the Weather Research and Forecasting modeling WRF-Hydro is used as fully coupled atmospheric-hydrological modeling experiment, and the standard WRF modeling is used as the control experiment. The regional simulation is applied to the Heihe river basin, an endorheic basin located in northwest China. The atmospheric modeling is configured at convection permitting scale 3 km, and the lateral terrestrial water processes are resolved at 300 m fine hydrological sub-grid. An ensemble of WRF/WRF-Hydro simulations is produced by varying the PBL scheme (YSU, MYJ, ACM2) for three continuous years from 2008-2010. The sensitivity of land-atmosphere interactions is evaluated with a regional precipitation recycling analysis and a three-dimensional atmospheric moisture tracing method (E-tagging). The interannual variability of the regional water budgets and land-atmosphere interactions are quantified with the model ensemble. We find a low precipitation recycling and a clearly positive soil moisture-precipitation feedback triggered by lateral terrestrial water flow in the study area. Lateral terrestrial water flow increases the local precipitation recycling irrespective of the modeling variability obtained with the different PBL parameterizations. The robust impact of lateral terrestrial water flow on local precipitation recycling indicates that the fully coupled modeling should be applied for climate projections. [ABSTRACT FROM AUTHOR]

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture. [ABSTRACT FROM AUTHOR]