scholarly journals Basin‐scale runoff prediction: An E nsemble K alman F ilter framework based on global hydrometeorological data sets

2015 ◽  
Vol 51 (10) ◽  
pp. 8450-8475 ◽  
Author(s):  
Christof Lorenz ◽  
Mohammad J. Tourian ◽  
Balaji Devaraju ◽  
Nico Sneeuw ◽  
Harald Kunstmann
Keyword(s):  
Data Sets ◽  
Basin Scale ◽  
Runoff Prediction

Analysis of the comprehensiveness of AIS data sets: application to the underwater noise modelling at basin scale

2019 ◽  
Author(s):  
Benjamin Ollivier ◽  
Florent Le Courtois ◽  
G. Bazile Kinda ◽  
Catherine Ratsivalaka ◽  
Olivier Sarzeaud ◽  
...  
Keyword(s):  
Data Sets ◽  
Underwater Noise ◽  
Basin Scale

scholarly journals Role of regression model selection and station distribution on the estimation of oceanic anthropogenic carbon change by eMLR

2012 ◽  
Vol 9 (10) ◽  
pp. 14589-14638 ◽  
Author(s):  
Y. Plancherel ◽  
K. B. Rodgers ◽  
R. M. Key ◽  
A. R. Jacobson ◽  
J. L. Sarmiento
Keyword(s):  
Model Selection ◽  
Regression Model ◽  
North Atlantic ◽  
Data Sets ◽  
Linear Regression Models ◽  
The North ◽  
Basin Scale ◽  
Anthropogenic Carbon ◽  
Hydrographic Features ◽  
Carbon Change

Abstract. Differencing predictions of linear regression models generated from hydrographic data collected at different times (the eMLR method) was proposed as a means of quantifying the dominant patterns of change in oceanic anthropogenic carbon in the context of sparse data sets subject to natural variability. The ability of eMLR to recover the anthropogenic carbon signal in the North Atlantic was tested using a global circulation and biogeochemistry model. Basin-scale applications of eMLR on horizontal layers can estimate the change in anthropogenic carbon inventory with an accuracy typically better than 10%. Regression model selection influences the distribution of the recovered anthropogenic carbon change signal. The systematic use of statistically optimum regression formulae does not produce the best estimates of anthropogenic carbon change if the distribution of the station locations emphasizes hydrographic features differently in time. Additional factors, such as a balanced station distribution and vertical continuity of the regression formulae should be considered to guide model selection. Accurate results are obtained when multiple formulae are used throughout the water column. Different formulae can yield results of similar quality. The fact that good results are obtained in the hydrographically complex North Atlantic suggests that eMLR can produce accurate estimates in other basins.

scholarly journals The assessment of temperature and salinity sampling strategies in the Mediterranean Sea: idealized and real cases

Ocean Science ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 97-112 ◽  
Author(s):  
F. Raicich
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Regional Scale ◽  
Optimal Interpolation ◽  
Data Sets ◽  
Sampling Strategies ◽  
Argo Data ◽  
Basin Scale ◽  
The Mediterranean ◽  
The Impact

Abstract. Temperature and salinity sampling strategies are studied and compared by means of the Observing System Simulation Experiment technique in order to assess their usefulness for data assimilation in the framework of the Mediterranean Forecasting System. Their impact in a Mediterranean General Circulation Model is quantified in numerical twin experiments via bivariate data assimilation of temperature and salinity profiles in summer and winter conditions, using the optimal interpolation algorithm implemented in the System for Ocean Forecasting and Analysis. The data impact is quantified by the error reduction in the assimilation run relative to the free run. The sampling strategies studied here include various combinations of temperature and salinity profiles collected along Volunteer Observing Ship (VOS) tracks, by Mediterranean Multi-sensor Moored Arrays (M3A), a Glider and ARGO floating profilers. Idealized sampling strategies involving VOS data allow to recognize the impact of individual tracks. As a result, the most effective tracks are those crossing regions characterized by high mesoscale variability and the presence of frontal structures between water masses. Sampling strategies adopted in summer–autumn 2004 and winter 2005 are studied to assess the impact of VOS and ARGO data in real conditions. The combination of all available data allows to achieve up to 30% error reductions. ARGO data produce a small impact when alone, but represent the only continuous coverage of the basin and are useful as a complement to VOS data sets. Localized data sets, as those obtained by M3As and the Glider, seem to have an almost negligible impact in the basin-scale assessment, and are expected to be more effective at regional scale.

Daily runoff prediction using the linear and non-linear models

2017 ◽  
Vol 76 (4) ◽  
pp. 793-805 ◽  
Author(s):  
Alireza Sharifi ◽  
Yagob Dinpashoh ◽  
Rasoul Mirabbasi
Keyword(s):  
Flood Control ◽  
Local Linear Regression ◽  
Support Vector ◽  
Data Sets ◽  
Optimum Length ◽  
Inference System ◽  
Input Combination ◽  
Soft Computing Techniques ◽  
Runoff Prediction ◽  
Testing Data

Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.

scholarly journals Using Gravity Recovery and Climate Experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

2020 ◽  
Vol 24 (4) ◽  
pp. 1763-1779
Author(s):  
Emma L. Robinson ◽  
Douglas B. Clark
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Cold Season ◽  
Surface Model ◽  
Data Sets ◽  
Seasonal Maximum ◽  
Basin Scale ◽  
Gravity Recovery

Abstract. The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold-season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge and is generally higher than that estimated by four commonly used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average, and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold-season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for the undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold-season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

scholarly journals Combining data sets of satellite-retrieved products for basin-scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model

2014 ◽  
Vol 119 (21) ◽  
pp. 12,100-12,116 ◽  
Author(s):  
Simon Munier ◽  
Filipe Aires ◽  
Stefan Schlaffer ◽  
Catherine Prigent ◽  
Fabrice Papa ◽  
...  
Keyword(s):  
Water Balance ◽  
Data Sets ◽  
Balance Study ◽  
Correction Model ◽  
Basin Scale ◽  
Combining Data

scholarly journals Using GRACE to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes

2019 ◽  
Author(s):  
Emma L. Robinson ◽  
Douglas B. Clark
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Cold Season ◽  
Surface Model ◽  
Data Sets ◽  
Seasonal Maximum ◽  
Basin Scale

Abstract. The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly-used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge, and is generally higher than that estimated by four commonly-used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins.

scholarly journals A validation of river routing networks for catchment modelling from small to large scales

2012 ◽  
Vol 44 (5) ◽  
pp. 917-925 ◽  
Author(s):  
Chantal Donnelly ◽  
Jörgen Rosberg ◽  
Kristina Isberg
Keyword(s):  
Catchment Area ◽  
Large Scale ◽  
Global Scale ◽  
Hydrological Modelling ◽  
Data Sets ◽  
Data Set ◽  
Point Of Interest ◽  
Basin Scale ◽  
Scale Modelling ◽  
River Routing

Underpinning all hydrological simulations is an estimate of the catchment area upstream of a point of interest. Locally, the delineation of a catchment and estimation of its area is usually done using fine scale maps and local knowledge, but for large-scale hydrological modelling, particularly continental and global scale modelling, this level of detailed data analysis is not practical. For large-scale hydrological modelling, remotely sensed and hydrologically conditioned river routing networks, such as HYDRO1k and HydroSHEDS, are often used. This study evaluates the accuracy of the accumulated upstream area in each gridpoint given by the networks. This is useful for evaluating the ability of these data sets to delineate catchments of varying scale for use in hydrological models. It is shown that the higher resolution HydroSHEDS data set gives better results than the HYDRO1k data set and that accuracy decreases with decreasing basin scale. In ungauged basins, or where other local catchment area data are not available, the validation made in this study can be used to indicate the likelihood of correctly delineating catchments of different scales using these river routing networks.

Mesozoic tectonostratigraphic evolution of the North Carnarvon Basin unlocked using regional 3D seismic

2016 ◽  
Vol 56 (2) ◽  
pp. 564
Author(s):  
Daniel Bishop ◽  
Megan Halbert ◽  
Katherine Welbourn ◽  
Ben Boterhoven ◽  
Stacey Mansfield ◽  
...  
Keyword(s):  
Seismic Data ◽  
Regional Scale ◽  
Depositional Environments ◽  
Single Channels ◽  
Data Sets ◽  
3D Seismic ◽  
The North ◽  
Basin Scale ◽  
3D Seismic Data ◽  
Carnarvon Basin

Interpretation of regional scale merged 3D seismic data sets covering the North Carnarvon Basin has for the first time enabled a detailed description of Mesozoic stratigraphic and structural features on a basin scale. Isoproportional slicing of the data enables direct interpretation of Triassic depositional environments, including contrasting low-stand and high-stand fluvial channel complexes, marginal marine clastic systems and reef complexes. Channels vary dramatically between sinuous-straight single channels within low net:gross floodplain successions, to broad channel belts within relatively high net:gross fluvial successions. The latter can be traced from the inboard part of the basin to the outer areas of the Exmouth Plateau. 3D visualisation and interpretation has demonstrated the huge variety of structural styles that are present, including basement-involved extensional faults, detached listric fault complexes, polygonal faults, and regional scale vertical strike-slip faults with flower structures. Fault trends include north–south, north–northeast to south–southwest, and northeast–southwest, with deformation events occurring mainly between the Rhaetian and Valanginian. Extensional and compressional deformation has created multiple horsts, three-way fault closures, fold belts and associated four-way anticlinal traps. Wrench tectonics may also explain pock-mark trains with the interpreted transfer of over-pressure from Triassic to Early Cretaceous levels. The use of regional scale merged 3D seismic data sets is now shedding light on tectonostratigraphic features on a basin scale that were previously unrecognised or enigmatic on 2D seismic or local 3D seismic data sets.

Sign in / Sign up

Export Citation Format

Share Document