Recent Posts

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Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes, and to support operational water resources management. Conceptual models, where catchment dynamics are approximated using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g. a reservoir) to spatially distributed configurations (e.g. nested sub-catchments), with the ability to customize all individual model elements. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations, and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. SuperflexPy is available as open-source code, and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work. A comprehensive documentation is available online and provided as supplementary material to this paper.
Paper: https://gmd.copernicus.org/preprints/gmd-2020-409/gmd-2020-409.pdf
Supplement: https://gmd.copernicus.org/preprints/gmd-2020-409/gmd-2020-409-supplement.pdf
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Interesting information / A fast method to measure the evaporation rate
« Last post by Shubham Goswami on December 08, 2020, 10:30:35 AM »
The paper presents a unique way to quickly measure the evaporation rate of water. The time duration of the measurement varied between 2.5 and 6 min; this temporal resolution opens new opportunities in the area of plant sciences. The effect of varying atmospheric conditions, including wind speed and Sun-like radiation, were also studied. They also proposed a reduced-order mathematical model for evaporation, whose results agreed reasonably well with those measured in the experiments. The device is easy-to-use, simple to construct, and portable. Considering these benefits, the proposed device can become an integral part of the weather stations and will improve the analysis of the real-time energy distribution at the earth’s surface.

https://doi.org/10.1016/j.jhydrol.2020.125642
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Interesting information / A rational performance criterion for hydrological model
« Last post by Pankaj Dey on November 22, 2020, 12:51:16 AM »
Performance criteria are essential for hydrological model identification or its parameters estimation. The Kling-Gupta efficiency (KGE), which combines the three components of Nash-Sutcliffe efficiency (NSE) of model errors (i.e. correlation, bias, ratio of variances or coefficients of variation) in a more balanced way, has been widely used for calibration and evaluation hydrological models in recent years. However, the KGE does not take a reference forecasts or simulation into account and still underestimates of variability of flow time series when optimizing its value for hydrological model. In this study, we propose another performance criterion as an efficiency measure through reformulating the previous three components of NSE. Moreover, the distribution function of the new criterion was also derived to analyze uncertainties of the new criterion, which is originated from the distinction between the theoretical or population statistic and its corresponding sampling properties. The proposed criterion was tested by calibrating the “abcd” and XAJ hourly hydrological models at monthly and hourly time scales data for two different case study basins. Evaluation of the results of the case study clearly demonstrates the overall better or comparable model performances from the proposed criterion. The analysis of the uncertainties of the new criterion based on its distribution probability function suggests a rational approach to distinguish between the probabilistic properties and behavior of the theoretical statistics and the rather different sampling properties of estimators of those statistics when computed from data.https://doi.org/10.1016/j.jhydrol.2020.125488
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Interesting information / Unanswered questions on the Budyko framework
« Last post by Pankaj Dey on November 05, 2020, 09:17:04 AM »
Landscapes and their hydrology are complex and sui generis. As a result, few theories exist that (without calibration) usefully describe or predict catchment-scale hydrological behavior [Beven, 2000; Sivapalan, 2005]. The Budyko hypothesis [Budyko, 1951; 1974] is a rare exception: its simple parameterization of how aridity (the ratio of long-term mean precipitation to long-term mean potential evapotranspiration) controls the long-term mean partitioning of precipitation into streamflow and evapotranspiration captures the behavior of many catchments around the world. In recent years, the Budyko framework has increasingly being used to interpret and predict (often non-stationary) water balances. While uses of the framework have become diverse and widespread, they are typically founded in common principles that rely on largely untested assumptions and strongly relate to questions for which no clear answers exist. Therefore, we believe that answering several basic questions around the Budyko framework can strengthen (or invalidate) many old, recent, and future applications. We realize that similar questions have been contemplated by others, but we hope that presenting them in the following manner may prove useful.
https://doi.org/10.1002/hyp.13958
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Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.
https://doi.org/10.1016/j.rse.2020.112162
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Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information.
 https://doi.org/10.1002/wat2.1487
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Interesting information / IMDLIB - A Python Package for handling IMD datasets
« Last post by Pankaj Dey on October 15, 2020, 07:02:12 PM »
IMDLIB is a python package to download and handle binary grided data from India Meteorological Department (IMD). For more information about the IMD datasets, follow the following link: http://imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html
Link to tutorial: https://pratiman-91.github.io/2020/10/05/IMDLIB.html
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Interesting information / FUSE: Framework for Understanding Structural Errors
« Last post by Pankaj Dey on October 11, 2020, 12:29:12 PM »
This is a source code repository for the Framework for Understanding Structural Errors or FUSE. FUSE is modular modelling framework which enables the generation of a myriad of conceptual hydrological models by recombining elements from commonly-used models. Running a hydrological model means making a wide range of decisions, which will influence the simulations in different ways and to different extents. Our goal with FUSE is enable users to be in charge of these decisions, so that they can understand their effects, and thereby, develop and use better models.
FUSE was build from scratch to be modular, it offers several options for each important modelling decision and enables the addition of new modules. In contrast, most traditional hydrological models rely on a single model structure (most processes are simulated by a single set of equations). FUSE modularity makes it easier to i) understand differences between models, ii) run a large ensemble of models, iii) capture the spatial variability of hydrological processes and iv) develop and improve hydrological models in a coordinated fashion across the community.
 New features FUSE initial implementation (FUSE1) is described in Clark et al. (WRR, 2008). The implementation provided here (which will become FUSE2) was created with users in mind and significantly increases the usability and range of applicability of the original version. In particular, it involves 5 main additional features:
 
  • an interface enabling the use of the different FUSE modes (default, calibration, regionalisation),
  • a distributed mode enabling FUSE to run on a grid whilst efficiently managing memory,
  • all the input, output and parameter files are now NetCDF files to improve reproducibility,
  • a calibration mode based on the shuffled complex evolution algorithm (Duan et al., WRR, 1992),
  • a snow module described in Henn et al. (WRR, 2015).
Manual Instructions to compile the code provided in this repository and to run FUSE are provided in FUSE manual.
 License FUSE is distributed under the GNU Public License Version 3. For details see the file LICENSE in the FUSE root directory or visit the online version.


https://github.com/naddor/fuse/tree/develop
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Abstract. Over past decades, a lot of global land-cover products have been released, however, these is still lack of a global land-cover map with fine classification system and spatial resolution simultaneously. In this study, a novel global 30-m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5° × 5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010, and that GLC_FCS30-2015 achieved the best overall accuracy of 82.5% against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products generated in this paper is available at https://doi.org/10.5281/zenodo.3986871 (Liu et al., 2020).
Link: https://essd.copernicus.org/preprints/essd-2020-182/
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High temporal resolution meteorology and soil physics observations from INCOMPASS land surface stations in India, 2016 to 2018

The dataset contains time series observations of meteorological and soil physics variables logged at one minute time resolution at three Land Surface Stations in India. The three INCOMPASS Land Surface Stations were located at: (1) agricultural land in Southern Karnataka (Berambadi); (2) the University of Agricultural Sciences in Dharwad in northern Karnataka; and (3) a semi-natural grassland at the Indian Institute of Technology in Kanpur (IITK), Uttar Pradesh.

Observations were collected under the Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) Project between January 2016 and January 2019.

Link to dataset: https://catalogue.ceh.ac.uk/documents/c5e72461-c61f-4800-8bbf-95c85f74c416
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