<p>This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at &#8776;200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset [4]. The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of &#8776;10&#176; x 10&#176; blocks. Model performance is finally evaluated against a range of observed and reference data since 1985.&#160;</p><p>The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES [6], which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.</p><ul><li>[1] https://www.ufz.de/mhm</li> <li>[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327</li> <li>[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195</li> <li>[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803</li> <li>[5] https://www.ufz.de/ulysses</li> </ul>
High-resolution geophysical techniques are now capable of routine assessment of sea-floor geology relevant to oil and gas exploration and production in the Gulf of Mexico to water depths of 7700 ft. Survey methods pioneered in deepwater areas off the Atlantic east coast have been significantly improved. Present practice uses satellite navigation for ship positioning and bottom-mounted telemetering transponder arrays for accurate positioning of deeply towed sensors. Deeply towed subbottom profiler and side-scan sonar systems provide very high resolution data for near-surface sediments and seafloor morphology. Digital recording provides capability for real-time image processing and enhancement.Bathymetric mapping uses surface-towed narrow-beam fathometers calibrated for water column velocity and bottom slope.Medium-penetration seismic data are displayed through a control module to reduce vertical exaggeration and improve resolution. The new techniques allow comprehensive engineering geologic evaluations of deepwater prospects in a cost-and time-effective manner.
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Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modeling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) have been recognized as a promising tool for soil moisture monitoring due to their hectare-scale footprint and decimeter-scale measurement depth. Different approaches exists that could be the basis for incorporating CRNS data into distributed hydrologic models, but largely still need to be implemented, thoroughly compared, and tested across different soil and vegetation types. This study establishes a framework to accommodate neutron count measurements and assess the accuracy of soil water content simulated by the mesoscale Hydrological Model (mHM) for the first time. It covers CRNS observations across different vegetation types in Germany ranging from agricultural areas to forest. We include two different approaches to estimate CRNS neutron counts in mHM based on the simulated soil moisture: a method based on the Desilets equation and another one based on the Cosmic-ray Soil Moisture Interaction Code (COSMIC). Within the Desilets approach, we further test two different averaging methods for the vertically layered soil moisture, namely uniform vs. non-uniform weighting scheme depending on the CRNS penetrating depth. A Monte Carlos simulation with Latin hypercube sampling approach (with N = 100,000) is employed to explore and constrain the (behavioral) mHM parameterizations against observed CRNS neutron counts. Overall, the three methods perform well with Kling-Gupta efficiency > 0.8 and percent bias < 1 % across the majority of investigated sites. We find that the non-uniform weighting scheme in the Desilets method provide the most reliable performance, whereas the more commonly used approach with uniformly weighted average soil moisture overestimates the observed CRNS neutron counts. We then also demonstrate the usefulness of incorporating CRNS measurements into mHM for the simulations of both soil moisture and evapotranspiration and add a broader discussion on the potential and guidelines of incorporating CRNS measurements in large-scale hydrological and land surface models.
Evaluation of geohazards on the Louisiana continental slope using a combination of high-resolution acoustic data (standard geohazards survey data), 3D-seismic amplitude maps of the sea floor, and direct observation/sampling by a manned submersible reinforces the value of 3D-seismic amplitude data for feature identification. Amplitude extraction data from surface and near-surface horizons are valuable for establishing the links between high-resolution seismic signature and actual sea floor response, particularly in settings characterized by various types and rates of hydrocarbon venting/seepage.It was found that amplitude extraction data could accurately define the areas, configurations, and relative rates of hydrocarbon seepage (from anomaly strength and target size). In areas evaluated with 3D-seismic amplitude extraction data, this procedure provided a rapid method of identifying sites of hydrocarbon venting/seepage, their relative activities, and the likelihood of encountering sensitive chemosynthetic communities and other features such as mud vents, gas hydrate mounds, hardgrounds, and sizable buildups of authigenic carbonates. Results of this study support the value of using 3D-seismic amplitude extraction data for improving our understanding and predictability of the slope's surface geology and seep-related benthic habitats.
<p>Land surface and hydrologic models (LSM/HMs) have been typically calibrated with streamflow for selected river basins. This procedure, although it is the current standard, it is highly disadvantageous because the resulting model 1) is not transferable to other locations and scales, 2) it underperforms against multivariate data not used during calibration, and 3) simulated fluxes do not fulfill the flux-matching closure condition [1] if compared across scales. These shortcomings lead to parameter fields exhibiting artifacts and sharp discontinuities over space (not seamless) [2] and thus, to a poor spatial representation of water fluxes and states.&#160;Existing terrestrial water cycle observations have spatial supports ranging from few hundred square meters to hundred square kilometers. Currently, most of the existing LSM/HMs are not able to assimilate simultaneously these observations because they do not have scale-invariant parameterizations. Preliminary tests at continental scale indicate that nested multiscale simulations are possible only if the model exhibits a scale-invariant parameterization [3]. In mHM [4], this capability is provided via the multiscale parameter regionalization (MPR) technique [1].</p><p>In this study, transfer-function parameters for mHM are estimated with 5500 GRDC streamflow time series, tens of FLUXNET evapotranspiration products, and the terrestrial total water storage anomaly (GRACE). This parameter estimation problem at global-scale requires a powerful supercomputer (JUWELS) [5] and the usage of recently implemented and extremely efficient parallelized algorithms [6].&#160;The daily reconstructed high-resolution hydrologic simulations (0.25&#176;) since 1950 reveal that the use of the MPR technique improves the overall model efficiency (compared to other global models [7]) and allows us to identify locations of consistent changes in hydrologic variables responding to long-term climate variability. The median of the NSE for the uncalibrated mHM model over the selected GRDC stations reaches a value of 0.40 for daily streamflow. Models reported in Beck et al. [7] exhibit a mean value of -0.09! This indicates the great potential of the proposed method.&#160;Comparison of terrestrial water storage (TWS) of GRACE against mHM simulations reveals hotspots of weaker model performance in regions where the water balance closure error is large.&#160;</p><p><strong>References</strong></p><p>[1] https://doi.org/10.1029/2008WR007327<br>[2] https://doi.org/10.5194/hess-21-4323-2017<br>[3] https://doi.org/10.1175/JHM-D-15-0054.1<br>[4] www.ufz.de/mhm<br>[5] http://www.fz-juelich.de/ias/jsc/juwels<br>[6] https://meetingorganizer.copernicus.org/EGU2019/EGU2019-8129-1.pdf<br>[7] https://doi.org/10.1002/2015WR018247</p>
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