Indian Forum for Water Adroit

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Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research.

https://www.sciencedirect.com/science/article/pii/S0168192318303873
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Interesting information / How much can forests fight climate change?
« Last post by Pankaj Dey on January 17, 2019, 05:49:42 PM »
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Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930–1960 CE responded positively to temperature (20.8 ± 3.7 Mio km2; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km2; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant (P < 0.001) decrease in temperature response at cold-dry sites between 1930–1960 and 1960–1990 CE, and the total temperature-limited area shrunk by −8.7 ± 0.6 Mio km2. Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.

http://advances.sciencemag.org/content/5/1/eaat4313
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 Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, and these reflect a diverse range of methods to calculate it. As such, a comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of evaporation (or evapotranspiration – sum of transpiration and soil evaporation) that the actual ecosystem would attain if it evaporates at maximal rate. We use eddy-covariance measurements from the FLUXNET2015 database, covering eleven different biomes, to parameterize and inter-compare the most widely used Ep methods and to uncover their relative performance. For each site, we isolate the days for which ecosystems can be considered as unstressed based on both an energy balance approach and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method calibrated per biome consistently performs best, with a mean correlation of 0.93, unbiased RMSE of 0.56mmday−1, and bias of −0.02mmday−1 against in situ measurements of unstressed evaporation. A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches relates largely to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. Contrastingly, the biome-specific parameters required for the simple radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate Ep and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.

Link:  https://www.hydrol-earth-syst-sci-discuss.net/hess-2018-470/
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Data / GFPLAIN250m, a global high-resolution dataset of Earth’s floodplains
« Last post by Subir Paul on January 16, 2019, 09:23:49 PM »
Identifying floodplain boundaries is of paramount importance for earth, environmental and socioeconomic studies addressing riverine risk and resource management. However, to date, a global floodplain delineation using a homogeneous procedure has not been constructed. In this paper, we present the first, comprehensive, high-resolution, gridded dataset of Earth’s floodplains at 250-m resolution (GFPLAIN250m). We use the Shuttle Radar Topography Mission (SRTM) digital terrain model and set of terrain analysis procedures for geomorphic floodplain delineations. The elevation data are processed by a fast geospatial tool for floodplain mapping available for download at https://github.com/fnardi/GFPLAIN. The GFPLAIN250m dataset can support many applications, including flood hazard mapping, habitat restoration, development studies, and the analysis of human-flood interactions. To test the GFPLAIN250m dataset, we perform a consistency analysis with floodplain delineations derived by flood hazard modelling studies in Europe.

https://www.nature.com/articles/sdata2018309?fbclid=IwAR3MHI8XxKnlhlBjiEHOm-roCgMULRh9EdDAmfw_8lqm90aiBzhYHyX8GX0
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FYI

Dear Colleagues,


You are cordially invited to submit an abstract to our AOGS 2019, 16th Annual Meeting (28th July to 2nd August 2019 in Singapore) session on

"impacts and Consequences of Changing Climate and Landuse on Hydrology"  [HS16]

 Session Details (HS16): http://www.asiaoceania.org/aogs2019/public.asp?page=sessionList.htm

The goal of this session is to contribute to the discussions on the consequences of climate change and its impact on  hydrological  extremes. In this session, studies addressing but not limited to the following questions are welcome:

-What advances have we made in study related to Impact of changing climate and land use on hydrology?

-what are the bottlenecks that hinder decision makeing taking decisions based on Impact of changing climate and land use on hydrology studies?.

-Case studies of local to regional scale about Impact of changing climate and land use on Hydrology

- what are the current and future challenges in handling of climate change and land use change data and their uncertainty for the forcing of hydrological models?

-what advances have we made in dealing with uncertainty associated in study of Impact of changing climate and land use on hydrology and how useful are they to decision makers?

Please feel free to forward this invitation to anyone who might be interested in this session.

Last date of Abstract Submission is 12th February 2019.

Warm Regards,

Convenors


http://www.asiaoceania.org/aogs2019/public.asp?page=funding.htm
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The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.

Paper: https://www.nature.com/articles/sdata2018296

Portal Link: https://chrsdata.eng.uci.edu/
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Dear Colleagues,


You are cordially invited to submit an abstract to our AOGS 2019, 16th Annual Meeting (28th July to 2nd August 2019 in Singapore) session on

" Hydrologic Extremes in a Changing Climate" 


 Session Details (HS10): http://www.asiaoceania.org/aogs2019/public.asp?page=sessionList.htm

The goal of this session is to contribute to the discussions on the consequences of climate change and its impact on  hydrological  extremes. In this session, studies addressing but not limited to the following questions are welcome:

i. Surface and Ground water management under climate change


ii. Variations in extreme hydrological events and possible attributing factors


iii. Present methodologies adopted, limitations and advances


iv. Stationarity/ Non-stationarity factors


v. Case studies on climate change impact assessment at local/ basin/ regional scales


vi. Flood and Drought analyses


vii. Streamflow assessment and Reservoir operation


viii. Future climate and hydrology


ix. Change in spatio-temporal pattern of precipitation


x. Adaptation and Mitigation strategies


xi. Policy making


xii. Remote sensing applications

Please feel free to forward this invitation to anyone who might be interested in this session.

Last date of Abstract Submission is 12th February 2019.


Warm Regards,

Convenors
image.png

http://www.asiaoceania.org/aogs2019/public.asp?page=funding.htm
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Hydrological sciences / CRAN Task View: Hydrological Data and Modeling
« Last post by Pankaj Dey on January 12, 2019, 10:34:17 AM »
This Task View contains information about packages broadly relevant to hydrology , defined as the movement, distribution and quality of water and water resources over a broad spatial scale of landscapes. Packages are broadly grouped according to their function; however, many have functionality that spans multiple categories. We also highlight other, existing resources that have related functions - for example, statistical analysis or spatial data processing.

Link: https://cran.r-project.org/web/views/Hydrology.html
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Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global asseSMent of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.

Link: https://www.mdpi.com/2072-4292/11/1/95
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