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 A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method

2016 ◽  
Vol 538 ◽  
pp. 82-95 ◽  
Author(s):  
Wenbin Liu ◽  
Lei Wang ◽  
Jing Zhou ◽  
Yanzhong Li ◽  
Fubao Sun ◽  
...  
Keyword(s):  
Water Balance ◽  
Balance Method ◽  
Water Balance Method ◽  
Basin Scale

Estimating the water balance and uncertainty bounds in a highly groundwater-dependent and data-scarce areas: An example for the upper Citarum basin

2021 ◽  
Author(s):  
Steven Reinaldo Rusli ◽  
Albrecht Weerts ◽  
Victor Bense
Keyword(s):  
Water Balance ◽  
Water Storage ◽  
Total Water ◽  
Groundwater Abstraction ◽  
Water Balance Components ◽  
Groundwater Storage ◽  
Actual Evaporation ◽  
Basin Scale ◽  
Basin Water ◽  
Storage Change

<p>In this study, we estimate the water balance components of a highly groundwater-dependent and hydrological data-scarce basin of the upper reaches of the Citarum river in West Java, Indonesia. Firstly, we estimate the groundwater abstraction volumes based on population size and a review of literature (0.57mm/day). Estimates of other components like rainfall, actual evaporation, discharge, and total water storage changes are derived from global datasets and are simulated using a distributed hydrological wflow_sbm model which yields additional estimates of discharge, actual evaporation, and total water storage change. We compare each basin water balance estimate as well as quantify the uncertainty of some of the components using the Extended Triple Collocation (ETC) method.</p><p>The ETC application on four different rainfall estimates suggests a preference of using the CHIRPS product as the input to the water balance components estimates as it delivers the highest r<sup>2</sup>  and the lowest RMSE compared to three other sources. From the different data sources and results of the distributed hydrological modeling using CHIRPS as rainfall forcing, we estimate a positive groundwater storage change between 0.12 mm/day - 0.60 mm/day. These results are in agreement with groundwater storage change estimates based upon GRACE gravimetric satellite data, averaged at 0.25 mm/day. The positive groundwater storage change suggests sufficient groundwater recharge occurs compensating for groundwater abstraction. This conclusion seems in agreement with the observation since 2005, although measured in different magnitudes. To validate and narrow the estimated ranges of the basin water storage changes, a devoted groundwater model is necessary to be developed. The result shall also aid in assessing the current and future basin-scale groundwater level changes to support operational water management and policy in the Upper Citarum basin.</p>

scholarly journals VikNGS: A C ++ Variant Integration Kit for Next Generation Sequencing Association Analysis

2019 ◽  
Author(s):  
Zeynep Baskurt ◽  
Scott Mastromatteo ◽  
Jiafen Gong ◽  
Richard F Wintle ◽  
Stephen W Scherer ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Association ◽  
Association Analysis ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Combining Data ◽  
Generation Sequencing

Abstract Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools. Availability The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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

Distribution and magnitude of geologic carbon sinks: a water balance study of the Chaotian River basin, Guilin, China

2015 ◽  
Vol 74 (2) ◽  
pp. 913-920
Author(s):  
Liang Li ◽  
Jianhua Cao ◽  
Fen Huang ◽  
Pei Wang ◽  
Yi Liang
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Carbon Sinks ◽  
Balance Study

Image Inpainting for Missing Values in Observational Climate Datasets Using Partial Convolutions in a cuDNN

2020 ◽  
Author(s):  
Christopher Kadow ◽  
David Hall ◽  
Uwe Ulbrich
Keyword(s):  
Missing Values ◽  
Coupled Model ◽  
Image Inpainting ◽  
Climate Science ◽  
Data Sets ◽  
Learning Technology ◽  
Climate Change Research ◽  
The Past ◽  
Combining Data ◽  
High Degree

<p>Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that machine learning technology can be applied to refill these missing climate values in observational datasets. We found that the technology of image inpainting using partial convolutions in a CUDA accelerated deep neural network can be trained by large Earth system model experiments from NOAA reanalysis (20CR) and the Coupled Model Intercomparison Project phase 5 (CMIP5). The derived deep neural networks are capable to independently refill added missing values of these experiments. The analysis shows a very high degree of reconstruction even in the cross-reconstruction of the trained networks on the other dataset. The network reconstruction reaches a better evaluation than other typical methods in climate science. In the end we will show the new reconstructed observational dataset HadCRUT4 and discuss further investigations.</p>

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