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Messages - Pankaj Dey

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Retrieves data and estimates unmeasured flows of water through the urban network. Any city may be modeled with preassembled data, but data for US cities can be gathered via web services using this package and dependencies 'geoknife' and 'dataRetrieval'.

Link to tutorial:

Link to Manual:


The question of when we may be able to detect the influence of climate change on UK rainfall extremes is important from a planning perspective, providing a timescale for necessary climate change adaptation measures. Short-duration intense rainfall is responsible for flash flooding, and several studies have suggested an amplified response to warming for rainfall extremes on hourly and sub-hourly timescales. However, there are very few studies examining the detection of changes in sub-daily rainfall. This is due to the high cost of very high-resolution (kilometre-scale) climate models needed to capture hourly rainfall extremes, and to a lack of sufficiently long high-quality sub-daily observational records. Results here using output from a 1.5km climate model over the southern UK indicate that changes in 10-minute and hourly precipitation emerge before changes in daily precipitation. In particular, model results suggest detection times for short-duration rainfall intensity in the 2040s in winter and 2080s in summer, which are respectively 5-10 years and decades earlier than for daily extremes. Results from a new quality-controlled observational dataset of hourly rainfall over the UK do not show a similar difference between daily and hourly trends. Natural variability appears to dominate current observed trends (including an increase in the intensity of heavy summer rainfall over the last 30 years), with some suggestion of larger daily than hourly trends for recent decades. The expectation of the reverse, namely larger trends for short-duration rainfall, as the signature of underlying climate change has potentially important implications for detection and attribution studies.


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Data / New Map of Worldwide Croplands Supports Food and Water Security
« on: January 19, 2018, 11:18:47 AM »

India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world.A new map was released today detailing croplands worldwide in the highest resolution yet, helping to ensure global food and water security in a sustainable way.
The map establishes that there are 1.87 billion hectares of croplands in the world, which is 15 to 20 percent—or 250 to 350 million hectares (Mha)—higher than former assessments. The change is due to more detailed understanding of large areas that were never mapped before or were inaccurately mapped as non-croplands.
Earlier studies showed either China or the United States as having the highest net cropland area, but this study shows that India ranks first, with 179.8 Mha (9.6 percent of the global net cropland area). Second is the United States with 167.8 Mha (8.9 percent), China with 165.2 Mha (8.8 percent) and Russia with 155.8 Mha (8.3 percent). Statistics of every country in the world can be viewed in an interactive map.
South Asia and Europe can be considered agricultural capitals of the world due to the percentage of croplands of the total geographic area. Croplands make up more than 80 percent of Moldova, San Marino and Hungary; between 70 and 80 percent of Denmark, Ukraine, Ireland and Bangladesh; and 60 to 70 percent of the Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czech Republic, Italy and India. For comparison, the United States and China each have 18 percent croplands.
The study was led by the USGS and is part of the Global Food Security-Support Analysis Data @ 30-m (GFSAD30) Project. The map is built primarily from Landsat satellite imagery with 30-meter resolution, which is the highest spatial resolution of any global agricultural dataset.

Link to the maps:

Announcements / WMO Fellowship Programme
« on: January 18, 2018, 02:53:44 PM »

The aim of the WMO Fellowship Programme is to support the education and training of qualified and suitable candidates, particularly from Least Developed and Developing Countries and Small Island Developing States. Please see the website for general information on WMO Fellowship Programme:

WMO fellowship opportunities related to hydrology and water resources are available for the following institutions: Hohai University (Nanjing, China), Leibniz Universität (Hannover, Germany), UNESCO-IHE, Institute for Water Education (Delft, Netherlands), Russian State Hydrometeorological University (St. Petersburg, Russia), Indian Institute of Technology - Department of Hydrology (Roorkee, India), Universidad Nacional del Litoral (Santa Fe, Argentina).

The deadline for general nomination to WMO is 28 February 2018. Please note that any nomination should be endorsed by the Permanent Representative of the candidate's country of origin with WMO. For detailed information:

The year 2015 was special for climate scientists, particularly for the El Niño Southern Oscillation (ENSO) research community, as a major El Niño finally materialized after a long pause since the 1997/1998 extreme El Niño. It was scientifically exciting since, due to the short observational record, our knowledge of an extreme El Niño has been based only on the 1982/1983 and 1997/1998 events. The 2015/2016 El Niño was marked by many environmental disasters that are consistent with what is expected for an extreme El Niño. Considering the dramatic impacts of extreme El Niño, and the risk of a potential increase in frequency of ENSO extremes under greenhouse warming, it is timely to evaluate how the recent event fits into our understanding of ENSO extremes. Here we provide a review of ENSO, its nature and dynamics, and through analysis of various observed key variables, we outline the processes that characterize its extremes. The 2015/2016 El Niño brings a useful perspective into the state of understanding of these events and highlights areas for future research. While the 2015/2016 El Niño is characteristically distinct from the 1982/1983 and 1997/1998 events, it still can be considered as the first extreme El Niño of the 21st century. Its extremity can be attributed in part to unusually warm condition in 2014 and to long-term background warming. In effect, this study provides a list of physically meaningful indices that are straightforward to compute for identifying and tracking extreme ENSO events in observations and climate models.


High-impact review papers describe and synthesize the current state of the art, the open questions and controversies, and provide ideas for future investigations. They are written not only for a specific scientific discipline but also for the broader Earth and space science community. They not only summarize the literature, but they also create a framework from which to understand the progress, problems, and connections between different communities, observations, models, and approaches. Here we describe how to write a high-impact review paper, and why you should consider writing one for Reviews of Geophysics.


We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Link to paper:
Link to data:

Announcements / NOAA Climate & Global Change Postdoctoral Fellowships
« on: January 14, 2018, 04:30:13 PM »
NOAA Climate & Global Change Postdoctoral Fellowships - CALL FOR APPLICATIONS - Deadline: 6 April 2018 -

« on: January 10, 2018, 11:01:52 AM »
Here are some recommendations for making scientific graphics which help your audience understand your data as easily as possible. Your graphics should be striking, readily understandable, should avoid distorting the data (unless you really mean to), and be safe for those who are colourblind. Remember, there are no really “right” or “wrong” palettes (OK, maybe a few wrong ones), but studying a few simple rules and examples will help you communicate only what you intend.

Please find the link to the blog:

This class teaches students the basic theory, concepts, and numerical implementation of model-data synthesis approaches including linear/nonlinear regression (topic 1), global optimization (topic 2), multiple criteria optimization (topic 3), Bayesian analysis (topic 4), data assimilation (topic 5), model averaging (topic 6), likelihood-free inference/diagnostic model evaluation (topic 7) and informal Bayesian methods (topic 8). The different concepts are discussed in class and these methods are then used for hypothesis testing to enhance information extraction from data and improve process understanding of the system of interest. Students are given eight different homeworks that cover the material discussed in class.

Link to youtube videos:

Interesting information / Wet Soils Elevate Nighttime Temperatures
« on: January 06, 2018, 11:44:56 AM »

Soil moisture can elevate overnight temperatures, offsetting daytime cooling, especially over areas of strong land-atmosphere interactions.

Using a conceptual model of the surface energy budget, Cheruy et al. [2017] demonstrate a noteworthy negative nocturnal feedback between soil moisture and temperature that is particularly strong in so-called “hot-spot” regions of land-atmosphere coupling. The negative feedback operates through the effect of water on the thermal inertia of the soil: dry soils can fluctuate in temperature much more readily than wet soils. Monsoon regions and transition zones between arid and humid climates have large day-to-day variability of the thermal inertia, which mainly affects variability of nighttime minimum temperatures. Here, positive soil-moisture anomalies induce cooler daytime temperatures through increased evaporative cooling, a well-known phenomenon. However, at night the higher heat capacity and thermal inertia of the wetter soil strongly prevents nocturnal cooling. The opposite situation arises for anomalously dry soils: increased daytime maximum temperatures but lower nighttime minimum temperatures.

Citation: Cheruy, F., Dufresne, J. L., Aït Mesbah, S., Grandpeix, J. Y., & Wang, F. [2017]. Role of soil thermal inertia in surface temperature and soil moisture-temperature feedback. Journal of Advances in Modeling Earth Systems, 9.

Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle.



In this paper, we present a comprehensive review of the data sources and estimation methods of 30
currently available global precipitation datasets, including gauge-based, satellite-related, and reanalysis
datasets. We analyzed the discrepancies between the datasets at daily to annual timescales and found large
differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual
precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis
datasets had a larger degree of variability than the other types of datasets. The degree of variability in
precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found
in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the
variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at
higher latitudes. The reliability of precipitation datasets is mainly limited by the number and spatial coverage
of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described
limit the capability of the products for climate monitoring, attribution, and model validation.

Link to the paper:

Interesting information / Building Confidence in Hydrologic Models
« on: December 08, 2017, 05:19:31 PM »
The Science

Understanding water availability and quality for large-scale surface and groundwater systems requires simulation. Scientists have developed many numerical models to address these simulation needs. How do these models differ in their portrayal of these water-based systems? To answer that question, seven different modeling teams from the United States and Europe exercised their models to develop a common set of benchmarks. With the benchmarks, they can better understand how each of the models agrees and differs.

The Impact

Intercomparison benchmark challenges build confidence in the choice of model used to answer a specific scientific question. The challenges also illuminate the implications of model choice. How? They force modeling teams to know the strengths and weaknesses of their own and competing models. This understanding leads to more reliable simulations and improves integrated hydrologic modeling.


Following up on a first integrated hydrologic model intercomparison project several years ago, seven teams of modelers, including two teams supported by the Interoperable Design for Extreme-scale Application Software (IDEAS) project, participated in a second intercomparison project. Teams met at a workshop in Bonn, Germany, and designed a series of three model intercomparison benchmark challenges. The challenges focused on different aspects of integrated hydrology, including a hillslope-scale catchment, subsurface structural inclusions and layering, and a field study of hydrology on a small ditch with simple but data-informed topography. Parameters were standardized, but each team used their own model, including differences in model physics, coupling, and algorithms. Results were collected, stimulating detailed conversations to explain similarities and differences across the suite of models. While each of the models share a common underlying core capability, they are focused on different applications and scales, and have their own strengths and weaknesses. This type of effort leads to improvement in all the codes. It also improves the modeling community’s understanding of simulating integrated surface and groundwater systems hydrology.Publications

S. Kollet, M. Sulis, R.M. Maxwell, C. Paniconi, M. Putti, G. Bertoldi, E.T. Coon, E. Cordano, S. Endrizzi, E. Kikinzon, E. Mouche, C. Mugler, Y.J. Park, J.C. Refsgaard, S. Stisen, and E. Sudicky, “The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks.” Water Resources Research 53, 867-890 (2017). [DOI: 10.1002/2016WR019191]

Link :

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