On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe

2021 ◽  
Author(s):  
Jannis Hoch ◽  
Edwin Sutanudjaja ◽  
Rens van Beek ◽  
Marc Bierkens
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
The Novel ◽  
Data Set ◽  
Meteorological Forcing ◽  
Recent Developments ◽  
Terrestrial Water ◽  
Multiple Challenges ◽  
Global Data ◽  
Development Application

<p>Developing and applying hyper-resolution models over larger extents has long been a quest in hydrological sciences. With the recent developments of global-scale yet fine data sets and advances in computational power, achieving this goal becomes increasingly feasible.</p><p>We here present the development, application, and results of the novel 1 km version of PCR-GLOBWB for the period 1981 until 2020. Even though employing global data sets only, we developed, ran, and evaluated the 1 km model for the continent Europe only. In comparison to past versions of PCR-GLOBWB, input data was replaced with sufficiently fine data, for example the recent SoilGrids and MERIT-DEM data. Preliminary results indicate an improvement of model outcome when evaluating simulated discharge, evaporation, and terrestrial water storage.</p><p>Additionally, we aim to answer the question to what extent developing hyper-resolution models is actually needed of whether the run times could be saved by using hyper-resolution state-of-the-art meteorological forcing. Therefore, the relative importance of model resolution and forcing resolution was cross-compared. To that end, the ERA5-Land data set was employed at different resolutions, matching the model resolutions at 1 km, 10 km, and 50 km.</p><p>Despite multiple challenges still lying ahead before achieve true hyper-resolution, this application of a 1 km model across an entire continent can form the basis for the next steps to be taken.</p>

scholarly journals Near-real-time adjusted reanalysis forcing data for hydrology

2018 ◽  
Vol 22 (2) ◽  
pp. 989-1000 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Global Scale ◽  
Reanalysis Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set ◽  
Global Data Sets ◽  
Global Data

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

scholarly journals Global modelling of continental water storage changes – sensitivity to different climate data sets

2007 ◽  
Vol 11 ◽  
pp. 63-68 ◽  
Author(s):  
K. Fiedler ◽  
P. Döll
Keyword(s):  
Water Storage ◽  
Global Scale ◽  
Data Sets ◽  
Precipitation Data ◽  
Climate Data ◽  
Satellite Mission ◽  
Full Data ◽  
Data Set ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Since 2002, the GRACE satellite mission provides estimates of the Earth's dynamic gravity field with unprecedented accuracy. Differences between monthly gravity fields contain a clear hydrological signal due to continental water storage changes. In order to evaluate GRACE results, the state-of-the-art WaterGAP Global Hydrological Model (WGHM) is applied to calculate terrestrial water storage changes on a global scale. WGHM is driven by different climate data sets to analyse especially the influence of different precipitation data on calculated water storage. The data sets used are the CRU TS 2.1 climate data set, the GPCC Full Data Product for precipitation and data from the ECMWF integrated forecast system. A simple approach for precipitation correction is introduced. WGHM results are then compared with GRACE data. The use of different precipitation data sets leads to considerable differences in computed water storage change for a large number of river basins. Comparing model results with GRACE observations shows a good spatial correlation and also a good agreement in phase. However, seasonal variations of water storage as derived from GRACE tend to be significantly larger than those computed by WGHM, regardless of which climate data set is used.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

Migration with reduced artifacts from internal multiple reflections

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. A25-A29
Author(s):  
Lele Zhang
Keyword(s):  
Seismic Reflection ◽  
Computational Cost ◽  
Data Sets ◽  
Square Root ◽  
Multiple Reflections ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recent Developments ◽  
Multiple Elimination

Migration of seismic reflection data leads to artifacts due to the presence of internal multiple reflections. Recent developments have shown that these artifacts can be avoided using Marchenko redatuming or Marchenko multiple elimination. These are powerful concepts, but their implementation comes at a considerable computational cost. We have derived a scheme to image the subsurface of the medium with significantly reduced computational cost and artifacts. This scheme is based on the projected Marchenko equations. The measured reflection response is required as input, and a data set with primary reflections and nonphysical primary reflections is created. Original and retrieved data sets are migrated, and the migration images are multiplied with each other, after which the square root is taken to give the artifact-reduced image. We showed the underlying theory and introduced the effectiveness of this scheme with a 2D numerical example.

scholarly journals Sustainability in Data and Food

2019 ◽  
Vol 1 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Dean Allemang
Keyword(s):  
Food Production ◽  
Global Scale ◽  
Future Research ◽  
World Population ◽  
Data Sets ◽  
Current State ◽  
The World ◽  
Production And Distribution ◽  
Data Ecosystem ◽  
Global Data

As the world population continues to increase, world food production is not keeping up. This means that to continue to feed the world, we will need to optimize the production and utilization of food around the globe. Optimization of a process on a global scale requires massive data. Agriculture is no exception, but also brings its own unique issues, based on how wide spread agricultural data are, and the wide variety of data that is relevant to optimization of food production and supply. This suggests that we need a global data ecosystem for agriculture and nutrition. Such an ecosystem already exists to some extent, made up of data sets, metadata sets and even search engines that help to locate and utilize data sets. A key concept behind this is sustainability—how do we sustain our data sets, so that we can sustain our production and distribution of food? In order to make this vision a reality, we need to navigate the challenges for sustainable data management on a global scale. Starting from the current state of practice, how do we move forward to a practice in which we make use of global data to have an impact on world hunger? In particular, how do we find, collect and manage the data? How can this be effectively deployed to improve practice in the field? And how can we make sure that these practices are leading to the global goals of improving production, distribution and sustainability of the global food supply? These questions cannot be answered yet, but they are the focus of ongoing and future research to be published in this journal and elsewhere.

scholarly journals Global validation of improved SCIAMACHY scientific ozone limb data using ozonesonde measurements

2015 ◽  
Vol 8 (5) ◽  
pp. 4817-4858
Author(s):  
J. Jia ◽  
A. Rozanov ◽  
A. Ladstätter-Weißenmayer ◽  
J. P. Burrows
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
Vertical Profiles ◽  
Data Set ◽  
Latitude Range ◽  
Ozone Profile ◽  
The Tropics ◽  
Relative Differences ◽  
Significant Underestimation ◽  
Good Agreement

Abstract. In this manuscript, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the upcoming V3.0, are extensively compared with ozone sonde data from the WOUDC database. The comparisons are made on a global scale from 2003 to 2011, involving 61 sonde stations. The retrieval processors used to generate V2.9 and V3.0 data sets are briefly introduced. The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20–30 km is in good agreement with ground based measurements with less than 5% relative differences in the latitude range of 90° S–40° N (with exception of the tropical Pacific region where an overestimation of more than 10% is observed), which corresponds to less than 5 DU partial column differences. In the tropics the differences are within 3%. However, this data set shows a significant underestimation northwards of 40° N (up to ~15%). The newly developed V3.0 data set reduces this bias to below 10% while maintaining a good agreement southwards of 40° N with slightly increased relative differences of up to 5% in the tropics.

scholarly journals RNAseq in the mosquito maxillary palp: a little antennal RNA goes a long way

2015 ◽  
Author(s):  
David C. Rinker ◽  
Xiaofan Zhou ◽  
Ronald Jason Pitts ◽  
Patrick L. Jones ◽  
Antonis Rokas ◽  
...  
Keyword(s):  
Anopheles Gambiae ◽  
Strong Evidence ◽  
Maxillary Palp ◽  
Data Sets ◽  
The Novel ◽  
Cross Contamination ◽  
Data Set ◽  
Insect Olfaction ◽  
Data Support ◽  
Rnaseq Data

A comparative transcriptomic study of mosquito olfactory tissues recently published in BMC Genomics (Hodges et al., 2014) reported several novel findings that have broad implications for the field of insect olfaction. In this brief commentary, we outline why the conclusions of Hodges et al. are problematic under the current models of insect olfaction and then contrast their findings with those of other RNAseq based studies of mosquito olfactory tissues. We also generated a new RNAseq data set from the maxillary palp of Anopheles gambiae in an effort to replicate the novel results of Hodges et al. but were unable to reproduce their results. Instead, our new RNAseq data support the more straightforward explanation that the novel findings of Hodges et al. were a consequence of contamination by antennal RNA. In summary, we find strong evidence to suggest that the conclusions of Hodges et al were spurious, and that at least some of their RNAseq data sets were irrevocably compromised by cross-contamination between samples.

scholarly journals Near real-time adjusted reanalysis forcing data for hydrology

2017 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Arctic Region ◽  
Analysis Data ◽  
Global Scale ◽  
Current Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set

Abstract. Updating climatological forcing data to near current data are compelling for impact modelling, e.g. to update model simulations or to simulate recent extreme events. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of daily resolution data at a global scale has previously been solved by adjusting re-analysis data global gridded observations. However, existing data sets of this type have been produced for a fixed past time period, determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between present and the end of the data set. Further, hydrological forecasts require initialisations of the current state of the snow, soil, lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present a method named GFD (Global Forcing Data) to combine different data sets in order to produce near real-time updated hydrological forcing data that are compatible with the products covering the climatological period. GFD resembles the already established WFDEI method (Weedon et al., 2014) closely, but uses updated climatological observations, and for the near real-time it uses interim products that apply similar methods. This allows GFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the GFD method and different produced data sets, which are evaluated against the WFDEI data set, as well as with hydrological simulations with the HYPE model over Europe and the Arctic region. We show that GFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data, although less well for the last two months of the updating cycle. For real-time updates until the current day, extending GFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

Intercomparisons of CSR GRACE-FO Solutions with External Data Sets

2020 ◽  
Author(s):  
Mark Tamisiea ◽  
Benjamin Krichman ◽  
Himanshu Save ◽  
Srinivas Bettadpur ◽  
Zhigui Kang ◽  
...  
Keyword(s):  
Deep Ocean ◽  
Data Sets ◽  
Ocean Bottom ◽  
Data Set ◽  
The North ◽  
External Data ◽  
Low Degree ◽  
Terrestrial Water ◽  
Land Data Assimilation

<p>To assess the quality of the CSR solutions, we compare results against external data sets that have contemporaneous availability.  These evaluations fall into three categories: changes in terrestrial water storage against data from the North American and Global Land Data Assimilation Systems, variations in ocean bottom pressure against data from the Deep Ocean Assessment of Tsunami Network, and estimates of the low degree and order Stokes coefficients compared against those inferred from satellite laser ranging observations (i.e. the CSR monthly 5x5 gravity harmonics from the MEaSUREs project).   As the mission provides a unique measurement of mass changes in the Earth system, evaluation of the new solutions against other data sets and models is challenging.  Thus, we primarily focus on relative agreement with these data set with the GRACE-FO solutions in relation to the historic agreement of the data sets with the GRACE solutions.</p>

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