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Messages - Pankaj Dey

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31
The zone of deep air convection and heavy precipitation in the Earth’s tropics, known as the intertropical convergence zone (ITCZ), is characterized by seasonal southward and northward movement, following Sun’s radiation, as well as long-term meridional shifts, which greatly affect water availability in many regions around the world. This study proposes and applies a new probabilistic framework to track the ITCZ by jointly considering multiple physical variables to define its location. It also allows for detailed analysis of the intra-annual dynamics in all longitudes of the globe, while being computationally efficient and flexible in its implementation. We reveal a statistically significant southward trend in the location of the ITCZ over the central Pacific.


Link to paper: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL079865

32
Interesting information / M_Map: A Mapping Package for MATLAB
« on: December 02, 2018, 10:29:27 PM »
M_Map is a set of mapping tools written for Matlab (it also works under Octave). M_Map includes:
 
  • Routines to project data in 19 different projections (and determine inverse mappings), using spherical and ellipsoidal earth-models.
  • A grid generation routine to make nice axes with limits either in lat/long terms or in planar X/Y terms.
  • A coastline database (with 1/4 degree resolution).
  • A global elevation database (1 degree resolution).
  • Hooks into freely available high-resolution coastline and bathymetry databases.
  • Other useful stuff.
Link: https://www.eoas.ubc.ca/~rich/map.html

33
Hydrological sciences / Energy, rain, and the future
« on: December 01, 2018, 06:26:00 PM »
The intensification of precipitation over land owing to rising atmospheric temperature is expected to increase in the 21st century, but there are still many details about why and how much that need to be better understood. Richardson et al. used a suite of climate models to analyze the atmospheric energy budget and its effects on the fast (due to atmospheric components like carbon dioxide and sulfate) and slow (due to surface temperature changes) drivers of precipitation change. They discuss likely future changes over land and sea and the causes of the past and projected trends and find that the increase may become clearly observable by around 2050.
Link to paper: https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-17-0240.1

34
We’ve all heard it before: “Yeah, but the climate has ALWAYS changed.”
Oh, really? Well, this timeline of Earth’s average temperature shows just how much we’ve influenced the climate. This epic webcomic was created by Randall Munroe, the artist behind xkcd, one of our favorite places for simplifying complicated scientific concepts.
It’s pretty long, but bear with us.
Link: https://grist.org/science/when-someone-tells-you-the-climate-is-always-changing-show-them-this-cartoon/amp/?__twitter_impression=true

35
Hydrological sciences / hddtools: Hydrological Data Discovery Tools in R
« on: November 25, 2018, 11:13:58 AM »
hddtools stands for Hydrological Data Discovery Tools. This R package is an open source project designed to facilitate access to a variety of online open data sources relevant for hydrologists and, in general, environmental scientists and practitioners.
This typically implies the download of a metadata catalogue, selection of information needed, a formal request for dataset(s), de-compression, conversion, manual filtering and parsing. All those operations are made more efficient by re-usable functions.
Depending on the data license, functions can provide offline and/or online modes. When redistribution is allowed, for instance, a copy of the dataset is cached within the package and updated twice a year. This is the fastest option and also allows offline use of package's functions. When re-distribution is not allowed, only online mode is provided.
Link to manual: https://cran.r-project.org/web/packages/hddtools/hddtools.pdf

36
High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001–2014 during Indian monsoon seasons (June–September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR.

Link to paper: https://www.nature.com/articles/sdata2018264

Link to data: https://figshare.com/articles/High-resolution_gridded_soil_moisture_and_soil_temperature_datasets_for_the_Indian_monsoon_region/6884657

37
We conduct critical reviews on the experimental and theoretical methodologies on soil water density to identify their limitations, flaws, and uncertainties. We synthesize some recent findings on intermolecular forces, interfacial interactions, and soil water retention mechanisms to clarify molecular-scale physicochemical mechanisms governing the soil water density. We propose a unified model to quantify soil water density variation.


Link: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018RG000597

38
Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.


Link: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018RG000598

39
Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.


Link: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR022643

40
The Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. There are many advantages to large samples, but researchers using statistical inference must be aware of the p-value problem associated with them. In very large samples, p-values go quickly to zero, and solely relying on p-values can lead the researcher to claim support for results of no practical significance. In a survey of large sample IS research, we found that a significant number of papers rely on a low p-value and the sign of a regression coefficient alone to support their hypotheses. This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay. We believe that addressing the p-value problem will increase the credibility of large sample IS research as well as provide more insights for readers.


Link to the paper: https://pdfs.semanticscholar.org/262b/854628d8e2b073816935d82b5095e1703977.pdf

42
Despite evidence of increasing precipitation extremes, corresponding evidence for increases in flooding remains elusive. If anything, flood magnitudes are decreasing despite widespread claims by the climate community that if precipitation extremes increase, floods must also. In this commentary we suggest reasons why increases in extreme rainfall are not resulting in corresponding increases in flooding. Among the possible mechanisms responsible, we identify decreases in antecedent soil moisture, decreasing storm extent, and decreases in snowmelt. We argue that understanding the link between changes in precipitation and changes in flooding is a grand challenge for the hydrologic community, and, is deserving of increased attention.


Link: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023749?af=R

43
Fractures in porous media have been documented extensively. However, they are often omitted from groundwater flow and mass transport models due to a lack of data on fracture hydraulic properties and the computational burden of simulating fractures explicitly in large model domains. We present a MATLAB toolbox, FracKfinder, that automates HydroGeoSphere (HGS), a variably‐saturated, control volume finite‐element model, to simulate an ensemble of discrete fracture network (DFN) flow experiments on a single cubic model mesh containing a stochastically‐generated fracture network. Because DFN simulations in HGS can simulate flow in both a porous media and a fracture domain, this toolbox computes tensors for both the matrix and fractures of a porous medium. Each model in the ensemble represents a different orientation of the hydraulic gradient, thus minimizing the likelihood that a single hydraulic gradient orientation will dominate the tensor computation. Linear regression on matrices containing the computed 3‐D hydraulic conductivity (K) values from each rotation of the hydraulic gradient is used to compute the K tensors. This approach shows that the hydraulic behavior of fracture networks can be simulated where fracture hydraulic data are limited. Simulation of a bromide tracer experiment using K tensors computed with FracKfinder in HydroGeoSphere demonstrates good agreement with a previous large‐column, laboratory study. The toolbox provides a potential pathway to upscale groundwater flow and mass transport processes in fractured media to larger scales.


Link: https://onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12837

44
Hydrological sciences / Global Streamflow Characteristics Dataset
« on: November 03, 2018, 11:33:33 AM »

The Global Streamflow Characteristics Dataset (GSCD) consists of global maps of 17 streamflow characteristics, such as baseflow index, runoff coefficient, and flow percentiles, providing information about runoff behavior for the entire land surface including ungauged regions. The maps are unique in that they were derived using a data-driven (top down) approach based on streamflow observations from thousands of catchments around the globe, rather than using a physically-based (bottom up) process model. For more information, see the following open-access papers:


Beck, H.E., A.I.J.M. van Dijk, A. de Roo (2015): Global maps of streamflow characteristics based on observations from several thousand catchments. Journal of Hydrometeorology 16(4), 1478–1501.


Beck, H.E., A.I.J.M. van Dijk, D.G. Miralles, R.A.M. de Jeu, L.A. Bruijnzeel, T.R. McVicar, J. Schellekens (2013): Global patterns in baseflow index and recession based on streamflow observations from 3394 catchments. Water Resources Research 49(12), 7843–7863.


Link to data: https://www.dropbox.com/s/hf0ue9czgf2tfdb/GSCD_v1.9.zip?dl=0


Link to webpage: http://www.gloh2o.org/gscd/

45
Heatwaves are extended periods of unusually high temperatures with significant societal and environmental impacts. Despite their significance, there is not a generalized definition for heatwaves. In this paper, we introduce a multi-method global heatwave and warm-spell data record and analysis toolbox (named GHWR). In addition to a comprehensive long-term global data record of heatwaves, GHWR allows processing and extracting heatwave records for any location efficiently. We use traditional constant temperature threshold methods, as well as spatially and temporally localized threshold approaches to identify heatwaves. GHWR includes binary (0/1) occurrence records of heatwaves/warm-spells, and annual summary files with detailed information on their frequency, duration, magnitude and amplitude. GHWR also introduces the standardized heat index (SHI) as a generalized statistical metric to identify heatwave/warm-spells. SHI has direct association with the probability distribution function of long-term daily temperatures for any given calendar day and spatial grid. Finally, GHWR offers a unique opportunity for users to select the type of heatwave/warm-spell information from a plethora of methods based on their needs and applications.


Link: https://www.nature.com/articles/sdata2018206#data-citations

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