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Topics - Pankaj Dey

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16


The University of Texas at Austin is recruiting new or recent Ph.D. scientists for a postdoctoral fellowship position focused on groundwater/surface interactions and urban watershed evolution and how these processes can be captured in integrated hydrologic models. This position is part of a campus-wide research initiative on future statewide resilience with support from multiple research and academic units across UT Austin. This position will focus on water resources in upland and urban centers and ways to improve our process understanding and parameterization for future projections.

Position: Groundwater/Surface Water Interactions and Urban Watershed Evolution

We are interested in outstanding postdoctoral fellowship applicants with the following qualifications:

    1. Formal training in Earth sciences or engineering; numerical modeling experience highly valued.
    2. Research experience in surface and near-surface hydrology, geochemistry, soil physics, hyporeic environments in upland and urban settings, and/or ecohydrology
    3. Computational proficiency across an array of tools and platforms and processes is preferred.
    4. Experience in running simulations in parallel or in HPC environments is a plus.
    5. Practical experience in applied problem solving is a plus.

The position will support a new broad research initiative at the University of Texas at Austin called Planet Texas 2050 (https://bridgingbarriers.utexas.edu/planet-texas-2050/). The goals of Planet Texas 2050 are to improve our understanding of the synergistic impacts of climate change and rapid population growth on availability and equitable distribution of Texas’ vital resources, especially water resources and particularly surface water. Currently, river systems of interest include the Brazos and Colorado Rivers in central Texas and their tributaries, but other systems are vital too. Underpinning these research goals is the need to connect hydrologic data and understanding to an integrated modeling platform that will help build scenarios for Texas’ 21st century urban centers. The successful candidate is expected to lead in data integration and field data collection with a focus on central Texas, and then to integrate these data into other components of the hydrologic cycle, including groundwater and soil moisture.  We anticipate significant interest on the parts of state agencies, industries, and municipalities in Texas who will rely on stable future water supplies. As part of the Planet Texas 2050 initiative, the candidate will have substantial opportunities to collaborate with existing and new faculty, researchers, and students across the UT Austin campus and beyond.

Austin is often on the list of top places to live in the U.S. The Jackson School of Geosciences is one of the top ranked programs in the US for geosciences, and #8 in computer sciences. The Texas Advanced Computing Center designs and operates some of the world's most powerful computing resources and technologies to enable discoveries that advance science and society. This position is grant funded and has the potential to continue depending on funding and the potential to transition into a permanent Research-track position at UT Austin.

Please send a resume/CV, a short expression of interest and names of 3 references to:

Michael Young, Bureau of Economic Geology (michael.young@beg.utexas.edu)

Jay Banner, Department of Geological Sciences (banner@jsg.utexas.edu);

Suzanne Pierce, Texas Advanced Computing Center (spierce@tacc.utexas.edu)

Review of applicants will begin on May 1, 2019 and continue until the position is filled. This position will be based in the Texas Advanced Computing Center and in the Department of Geological Sciences on UT Austin’s main campus. The University of Texas at Austin is an equal employment opportunity/affirmative action employer. All positions are security sensitive, and conviction verification is conducted on applicants selected.

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The absence of aggregated uncertainty measures restricts the assessment of uncertainty in hydrological simulation. In this work, a new composite uncertainty measure is developed to evaluate the complex behaviors of uncertainty existing in hydrological simulation. The composite uncertainty measure is constructed based on a framework, which includes three steps: (1) identification of behavioral measures by analyzing the pairwise correlations among different measures and removing high correlations; (2) weight assignment by means of a new hierarchical weight assembly (HWA) approach incorporating the intra-class and inter-class weights; (3) construction of a composite uncertainty measure through incorporating multiple properties of the measure matrix. The framework and the composite uncertainty measure are demonstrated by case studies in uncertainty assessment for hydrological simulation. Results indicate that the framework is efficient to generate a composite uncertainty index (denoted as CUI) and the new measure CUI is competent for uncertainty evaluation. Besides, the HWA approach performs well in weighting, which can characterize subjective and objective properties of the information matrix. The achievement of this work provides promising insights into the performance comparison of uncertainty analysis approaches, the selection of proper cut-off threshold in the GLUE method, and the guidance of reasonable uncertainty assessment in a range of environmental modelling.

Link: https://www.mdpi.com/2073-4441/11/4/812

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A single framework, unifying, extending, and improving a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific 'parent' Gaussian process Papalexiou (2018) <doi:10.1016/j.advwatres.2018.02.013>

Simulate rainfall, streamflow, wind or relative humidity in seconds. Just choose the probability distribution and correlations of the time series you want to generate and it'll do the rest

Link to Vignette: https://cran.r-project.org/web/packages/CoSMoS/vignettes/vignette.html

19
Interesting information / Time of steady climate change
« on: April 22, 2019, 09:39:01 AM »
In a future where greenhouse gas concentrations have been stabilized, it is expected that the rate of global warming will decrease to a steady and slower rate than that observed in the 21st century. We also expect that the time of arriving at this steady, slow state of warming will have regional differences with some locations steadying sooner than others. Here we examine the time it takes to arrive at this steady state of warming and probe the regional differences in this arrival time. Do to so, we make use of a collection of climate models all run under identical emissions scenario where the concentrations of greenhouse gases increase throughout the 21st century and are then held constant for the following two centuries. We observe regional differences in the time it takes to arrive at a slower, steady state of warming. In particular, the Arctic and tropics are the last to arrive at a steady warming rate. Because the climate signal has been found to first emerge in the tropics, this work suggests that low latitudes will experience the longest duration of rapid warming.

Link: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018GL081704

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Interesting information / ENSO bimodality and extremes
« on: April 21, 2019, 09:25:34 PM »
Abstract

Tropical sea surface temperature (SST) and winds vary on a wide range of timescales, and have a substantial impact on weather and climate across the globe. Here we study the variability of SST and zonal wind during El Niño‐Southern Oscillation (ENSO) between 1982 and 2014. We focus on changes in extreme statistics using higher‐order moments of SST and zonal winds. We find that ENSO characteristics exhibit bimodal distributions and fat tails with extreme warm and cold temperatures in 1982‐1999, but not during 2000‐2014. The changes in the distributions coincide with changes in the intensity of ENSO events and the phase of the Interdecadal Pacific Oscillation (IPO). We also find that the strongest Easterly Wind Bursts occur during extreme El Niños and not during La Niñas. Maps of SST kurtosis can serve as a diagnostic for the thermocline feedback mechanism responsible for the differences in ENSO diversity between the two periods.

Key Points

    Large changes in higher order moments of ENSO variability in the historical record
    Strong bimodality in El Niño SST linked to westerly wind bursts
    Strongest easterly wind bursts occur during the extreme El Niño, not La Niña, years

Link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019GL082270

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Atlantic Zonal Mode (AZM) and Indian summer monsoon rainfall (ISMR) are known to have an inverse relationship, which means that the cold (warm) phases of AZM result in strong (weak) ISMR. Here, we report that the inverse relationship between AZM and ISMR has significantly strengthened in recent decades. The cause of this strengthening relationship has been investigated. We find a robust increase in interannual variability of Sea Surface Temperature (SST) over the eastern tropical Atlantic Ocean in recent decades, which implies an increase in the number of strong AZM events towards the end of the 20th century. The increase in strong AZM events alters the large‐scale monsoon circulation over the Indian subcontinent by enhancing the Kelvin wave response into the Indian Ocean, leading to an enhanced AZM‐ISMR teleconnection. This demands a better representation of the AZM‐ISMR teleconnection in climate models for improving seasonal monsoon prediction in a warming world.
Link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019GL082379

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One of the most enduring and important questions for hydrology is how water input in the form of precipitation is partitioned among evapotranspiration, runoff, groundwater recharge, and storage of moisture in the soil. We quantified how precipitation was partitioned at a semiarid savanna site in Arizona, USA, with 13 years of data. We found that almost all of the precipitation goes into evapotranspiration with only a small of runoff and negligible recharge. Contrary to expectations, we saw significant, episodic carryover of soil moisture from the summer/fall growing season to the subsequent springtime when the plants awake from winter dormancy and extract the stored moisture. These comprehensive, long‐term measurements support expectations about the overriding importance of ET in semiarid watersheds' water balance and reveal a surprising degree of interseasonal water storage.

Link: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR023477

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Interesting information / Deficit Irrigation Toolbox
« on: April 16, 2019, 09:25:44 PM »
Deficit Irrigation Toolbox (DIT) is an open-source software to simulate and maximize crop-water productivity of deficit irrigation systems. Written in Matlab language, the toolbox allows you to perform the complex analysis of crop yield response to climate change, soil variability, and water management practices.

The current DIT version includes:

   1. Interfaces to crop models: Simple Soil-Water Balance, AquaCrop-OS .

   2. 9 irrigation strategies including rainfed, soil-moisture based, constant, and optimized open-loop and closed-loop irrigation scheduling strategies.

   3. Advanced features like site-specific crop-water production functions, parallel computation, and a probabilistic framework for the analysis of climate and soil variability on crop yield.

Development team: Oleksandr Mialyk and Niels Schütze

Link: https://tu-dresden.de/bu/umwelt/hydro/ihm/hydrologie/services/software/DIT

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In India, human population has increased six-fold from 200 million to 1200 million that coupled with economic growth has resulted in significant land use and land cover (LULC) changes during 1880–2010. However, large discrepancies in the existing LULC datasets have hindered our efforts to better understand interactions among human activities, climate systems, and ecosystem in India. In this study, we incorporated high-resolution remote sensing datasets from Resourcesat-1 and historical archives at district (N = 590) and state (N = 30) levels to generate LULC datasets at 5 arc minute resolution during 1880–2010 in India. Results have shown that a significant loss of forests (from 89 million ha to 63 million ha) has occurred during the study period. Interestingly, the deforestation rate was relatively greater under the British rule (1880–1950s) and early decades after independence, and then decreased after the 1980s due to government policies to protect the forests. In contrast to forests, cropland area has increased from 92 million ha to 140.1 million ha during 1880–2010. Greater cropland expansion has occurred during the 1950–1980s that coincided with the period of farm mechanization, electrification, and introduction of high yielding crop varieties as a result of government policies to achieve self-sufficiency in food production. The rate of urbanization was slower during 1880–1940 but significantly increased after the 1950s probably due to rapid increase in population and economic growth in India. Our study provides the most reliable estimations of historical LULC at regional scale in India. This is the first attempt to incorporate newly developed high-resolution remote sensing datasets and inventory archives to reconstruct the time series of LULC records for such a long period in India. The spatial and temporal information on LULC derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of LULC on regional climate, water resources, and biogeochemical cycles in terrestrial ecosystems.

Link: https://www.sciencedirect.com/science/article/pii/S0921818114001283

25
Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations, as well as understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends, and improvements in service applications such as the United States Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi-decadal record of mass variability in the Earth system is within reach.

Link: https://www.nature.com/articles/s41558-019-0456-2

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Since the late 1970s, spaceborne microwave sensors have been providing measurements of radiation emitted by the Earth's surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to vegetation density and its relative water content. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. But, combining multiple sensors into a single product is challenging as systematic differences between input products, e.g. biases, different temporal and spatial resolutions and coverage, need to be overcome.

Here, we present a new series of long-term VOD products, which combine multiple VOD data sets derived from several sensors (SSM/I, TMI, AMSR-E, Windsat, and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our approach to merge the single-sensor VOD products is similar to the one of the ESA CCI Soil Moisture products (Liu et al., 2012; Dorigo et al., 2017): First, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as scaling reference. We apply a new matching technique that scales outliers more robustly than ordinary piece-wise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data, generating a VOD Climate Archive (VODCA).

The characteristics of VODCA are assessed for self-consistency and against other products: spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and both with observations of Leaf Area Index derived from the MODIS instrument as well as Vegetation Continuous Fields from AVHRR instruments. Trend analysis shows that since 1987 there has been a decline in VOD in the tropics and in large parts parts of east-central and north Asia along with a strong increase in India, large parts of Australia, south Africa, southeastern China and central north America. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. In summary, VODCA shows vast potential for monitoring spatio-temporal ecosystem behaviour complementary to existing long-term vegetation products from optical remote sensing.

The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.
Link to the paper: https://www.earth-syst-sci-data-discuss.net/essd-2019-42/

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We present a global dataset of anthropogenic carbon dioxide (CO2) emissions for 343 cities. The dataset builds upon data from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate Action and Reporting (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO2 emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure.
Link to the paper: https://www.nature.com/articles/sdata2018280
Link to the webpage: https://katirg.github.io/GlobalCarbonCities/

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Using a high‐resolution global model with explicit representation of convection, the physical processes involved in the abrupt onset of South Asian summer monsoon are investigated within a moist entropy budget framework. The monsoon onset is a two‐stage transition. During the first stage, which starts around two months before onset, the source terms of column‐integrated moist entropy gradually increase, while the export by the large‐scale circulation slowly strengthens. The second stage is marked by a sudden increase of radiative heating and surface latent heat flux 10 days prior to the onset, followed by abrupt strengthening of large‐scale export of moist entropy. When either cloud‐radiative or wind‐evaporation feedback is disabled in numerical experiments, the monsoon experiences much smoother and weaker onset. The evolution of the system in a gross moist stability plane demonstrates that these positive feedbacks destabilize the system and are responsible for the abruptness of the transition.
Link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019GL082089

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A research work carried out by Subash Yeggina titled A Conceptually Superior Variant of Shepard’s Method with Modified Neighbourhood Selection for Precipitation Interpolation recently published in International Journal of Climatology.

Abstract

The accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, i.e., arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini river basin in South India, and in the state of Kentucky, USA. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site.
 
Link to the paper: https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.6091

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Interesting information / airGR: A R package for hydrological modeling
« on: April 12, 2019, 10:47:59 AM »
airGR is a package which brings into the R software the hydrological modelling tools used and developed at the Catchment Hydrology Research Group of Irstea (France), including the GR rainfall-runoff models and a snowmelt and accumulation model, CemaNeige. Each model core is coded in Fortran to ensure low computational time. The other package functions (i.e. mainly the calibration algorithm and the efficiency criteria calculation) are coded in R.

The airGR package has been designed to fulfill two major requirements: to facilitate the use by non-expert users and to allow flexibility regarding the addition of external criteria, models or calibration algorithms. The names of the functions and their arguments were chosen to this end. airGR also contains basics plotting facilities.

Link to description: https://odelaigue.github.io/airGR/

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