Recent Posts

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Hydrological sciences / Where is the bottom of a watershed ?
« Last post by Pankaj Dey on February 20, 2020, 06:57:55 PM »
Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988; McDonnell, 2017; Perrault, 1674). With growing interest in groundwater‐surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed “bottom” is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as ‘deep’ varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where ‘deep’ flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology.
https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR026010
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Hydrological sciences / A global canopy water content product from AVHRR/Metop
« Last post by Pankaj Dey on February 20, 2020, 11:10:19 AM »
Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related to canopy structure. An accuracy assessment of the EPS/AVHRR CWC indicated a close agreement with multi-temporal ground data from SMAPVEX16 in Canada and Dahra in Senegal, with RMSE of 0.19 kg m−2 and 0.078 kg m−2 respectively. Particularly, when the Normalized Difference Infrared Index (NDII) was included the algorithm was better constrained in semi-arid regions and saturation effects were mitigated in dense canopies. An analysis of spatial scale effects shows the mean bias error in CWC retrievals remains below 0.001 kg m−2 when spatial resolutions ranging from 20 m to 1 km are considered. The present study further evaluates the consistency of the LSA-SAF product with respect to the Simplified Level 2 Product Prototype Processor (SL2P) product, and demonstrates its applicability at different spatio-temporal resolutions using optical data from MSI/Sentinel-2 and MODIS/Terra & Aqua. Results suggest that the LSA-SAF EPS/AVHRR algorithm is robust, agrees with the CWC dynamics observed in available ground data, and is also applicable to data from other sensors. We conclude that the EPS/AVHRR CWC product is a promising tool for monitoring vegetation water status at regional and global scales.

https://www.sciencedirect.com/science/article/pii/S0924271620300411
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Hydrological sciences / Learning the Physics of Pattern Format ion from Images
« Last post by Pankaj Dey on February 15, 2020, 11:14:31 AM »
Using a framework of partial differential equation-constrained optimization, we demonstrate that multiple constitutive relations can be extracted simultaneously from a small set of images of pattern formation. Examples include state-dependent properties in phase-field models, such as the diffusivity, kinetic prefactor, free energy, and direct correlation function, given only the general form of the Cahn-Hilliard equation, Allen-Cahn equation, or dynamical density functional theory (phase-field crystal model). Constraints can be added based on physical arguments to accelerate convergence and avoid spurious results. Reconstruction of the free energy functional, which contains nonlinear dependence on the state variable and differential or convolutional operators, opens the possibility of learning nonequilibrium thermodynamics from only a few snapshots of the dynamics.
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.060201
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The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products.
https://doi.org/10.1016/j.rse.2020.111697
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Hydrological sciences / The ECOSTRESS spectral library version 1.0
« Last post by Pankaj Dey on February 14, 2020, 04:57:52 PM »
In June 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission was launched to measure plant temperatures and better understand how they respond to stress. While the ECOSTRESS mission delivers imagery with ~60 m spatial resolution, it is often useful to have spectra at the leaf level in order to explain variability seen at the pixel level. As it was originally titled, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library version 2.0 has been expanded to support ECOSTRESS studies by including major additions of laboratory measured vegetation and non-photosynthetic vegetation (NPV) spectra. The library now contains 541 leaf visible shortwave infrared (VIS/SWIR) spectra, 472 leaf thermal infrared (TIR) spectra, and 51 NPV VIS/SWIR and TIR spectra. Previously, the library primarily contained VSWIR and TIR laboratory spectra of minerals, rocks, and man-made materials. This new library, containing over 3000 spectra, was renamed the ECOSTRESS spectral library version 1.0 and is publicly available (http://speclib.jpl.nasa.gov). It should be noted that as with the prior versions of the library, the VSWIR and TIR measurements were made with separate instruments with different calibration sources. Care should be taken when combining the data into a seamless spectrum to cover the entire spectral range. The ECOSTRESS spectral library provides a comprehensive collection of natural and man-made laboratory collected spectra covering the wavelength range of 0.35–15.4 μm.

https://www.sciencedirect.com/science/article/pii/S0034425719302081

https://speclib.jpl.nasa.gov/
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Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.
https://www.nature.com/articles/s41467-020-14411-z#Sec18
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Post your question/information / Re: Quantile Mapping for bias correction
« Last post by NARESHAADHI on January 29, 2020, 08:18:05 AM »
Ok sir

Thank you
With regards
Aadhi Naresh
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Post your question/information / Re: Quantile Mapping for bias correction
« Last post by Sat Kumar Tomer on January 25, 2020, 11:32:47 AM »
Yes, there is a bias correction function in the library.
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Post your question/information / Re: Quantile Mapping for bias correction
« Last post by NARESHAADHI on January 25, 2020, 09:45:26 AM »
Dear Sat kumar sir,

Thanks for the reply. Can we apply this quantile mapping for bias correction.
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Post your question/information / Re: Quantile Mapping for bias correction
« Last post by Sat Kumar Tomer on January 23, 2020, 02:06:07 PM »
You can try ambhas python library.
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