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Messages - Pankaj Dey

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The relationship between rainfall variability and economic growth is complex, and tends to be significant in economies like India where agriculture plays a major role in economic output and food security. This paper seeks to provide insight into this relationship using Indian state-level economic and rainfall data from 1961 to 2012. We examine all 15 Indian states with populations exceeding 20m as of 2000, totalling 920m people, about 12% of the global population. Physical and human geography vary greatly between, and even within, these states, reflecting the global range of water security challenges and providing an analogue for a range of global economic development and environmental conditions. We identify three patterns of interdependence between rainfall variability and economic growth: i) Continuous Correlation of rainfall and economic growth rates, ii) Decayed Correlation from a significant to an insignificant relationship, and iii) Never Correlated i.e. no significant observable correlation between rainfall and growth. Sensitivity to rainfall variability is somewhat less in wetter states. Investment in irrigation infrastructure has helped states to reduce their economic sensitivity to rainfall variability, with three of the four states that have Decayed Correlation of growth with rainfall having the highest percentage expansion in irrigated areas of the 15 states. Greater use of groundwater supplies (rather than surface water) does not, however, appear to influence the sensitivity of economic growth to rainfall variability. The relationship between rainfall-growth correlation and long term income is complex; states which are correlated generally appear to be growing faster than states which are not correlated, but that growth is occurring from a lower per capita income level. Finally, confirming national trends for India, the paper does not find that economic diversification away from agriculture has reduced economic sensitivity to rainfall variability. The observation that growth in economically-diversified states can still be dependent on rainfall invites further research into the ways in which rainfall either directly, or through other hydro-climatic variables, influences the general economy.


Interesting information / ArXives of Earth science
« on: March 01, 2018, 10:47:39 AM »
Preprint servers afford a platform for sharing research before peer review. We are pleased that two dedicated preprint servers have opened for the Earth sciences and welcome submissions that have been posted there first.

Link for more details:

Assessing the accuracy of gridded climate datasets is highly relevant to climate-change impact studies, since evaluation, bias-correction and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded datasets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing datasets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given dataset is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989-2008 and includes five gridded daily meteorological datasets: E-OBS, MSWEP, MESAN, APGD and ADIGE. The analysis highlights that APGD and ADIGE, the datasets with highest effective resolution, display similar spatio-temporal precipitation patterns and produce the largest hydrological efficiency indexes. Lower performances are observed for E-OBS, MESAN and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatio-temporal patterns of gridded climate datasets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias-correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded datasets to be used in large-scale hydro-climatic studies.


Theoretically, if the distribution of daily rainfall is known or justifiably assumed, then one could argue, based on extreme value theory, that the distribution of the annual maxima of daily rainfall would resemble one of the three limiting types: (a) type I, known as Gumbel; (b) type II, known as Fréchet; and (c) type III, known as reversed Weibull. Yet, the parent distribution usually is not known and often only records of annual maxima are available. Thus, the question that naturally arises is which one of the three types better describes the annual maxima of daily rainfall. The question is of great importance as the naive adoption of a particular type may lead to serious underestimation or overestimation of the return period assigned to specific rainfall amounts. To answer this question, we analyze the annual maximum daily rainfall of 15,137 records from all over the world, with lengths varying from 40 to 163 years. We fit the generalized extreme value (GEV) distribution, which comprises the three limiting types as special cases for specific values of its shape parameter, and analyze the fitting results focusing on the behavior of the shape parameter. The analysis reveals that (a) the record length strongly affects the estimate of the GEV shape parameter and long records are needed for reliable estimates; (b) when the effect of the record length is corrected, the shape parameter varies in a narrow range; (c) the geographical location of the globe may affect the value of the shape parameter; and (d) the winner of this battle is the Fréchet law.


This article examines the effects of using leaves, something most students see every day and have some familiarity with, as an analogy for the concept of watersheds in an undergraduate water resources engineering course. The ultimate goal of the leaf/watershed analogy and associated instruction is to increase students’ understanding of hydrology principles, which in turn may facilitate better watershed management through increased public awareness, increased adoption of appropriate best management practices, and improved policy decisions. The assessment was performed with junior and senior undergraduate students enrolled in a Water Resource Engineering course. The assessment results showed that overall, students benefitted from the leaf analogy as a tool for learning watersheds. However, this effect varied depending on students’ learning style preferences.

Citation: Anandhi, A., Y. Yang, and M. Hubenthal. 2017. Using Leaves as a Model for Teaching Watershed Concepts in Natural Resources Science and Engineering Programs. Natural Sciences Education 46:170020. doi:10.4195/nse2017.09.0020

The spatial distribution of subsurface parameters such as permeability are increasingly relevant for regional to global climate, land surface and hydrologic models that are integrating groundwater dynamics and interactions. Despite the large fraction of unconsolidated sediments on Earth's surface with a wide range of permeability values, current global, high-resolution permeability maps distinguish solely fine-grained and coarse-grained unconsolidated sediments. Representative permeability values are derived for a wide variety of unconsolidated sediments and applied to a new global map of unconsolidated sediments to produce the first geologically-constrained, two-layer global map of shallower and deeper permeability. The new mean logarithmic permeability of the Earth’ surface is-12.7 + 1.7 m2 being one order of magnitude higher than derived from previous maps which is consistent with the dominance of the coarser sediments. The new dataset will benefit a variety of scientific applications including the next generation of climate, land surface and hydrology models at regional to global scales.


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.


Please follow the link :

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:

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