scholarly journals Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

2020 ◽  
Author(s):  
Marko Kallio ◽  
Joseph H. A. Guillaume ◽  
Vili Virkki ◽  
Matti Kummu ◽  
Kirsi Virrantaus
Keyword(s):  
Land Surface ◽  
Model Averaging ◽  
R Package ◽  
Observation Data ◽  
Areal Interpolation ◽  
Multiple Input ◽  
Minimal Data ◽  
Intercomparison Project ◽  
Improved Performance ◽  
Area Unit

Abstract. An increasing number of different types of hydrological, land surface, and rainfall-runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scale are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library hydrostreamer v1.0 which aims to improve the usability of existing runoff products by addressing the Modifiable Area Unit Problem, and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes 1) interpolation from source zones to target zones, 2) river routing, and 3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products, and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible – intuitive with minimal data requirements – and minimizes need for calibration. We tested the performance of hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ under MIT licence.

scholarly journals Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

2021 ◽  
Vol 14 (8) ◽  
pp. 5155-5181
Author(s):  
Marko Kallio ◽  
Joseph H. A. Guillaume ◽  
Vili Virkki ◽  
Matti Kummu ◽  
Kirsi Virrantaus
Keyword(s):  
Land Surface ◽  
Model Averaging ◽  
R Package ◽  
Observation Data ◽  
Areal Interpolation ◽  
Multiple Input ◽  
Minimal Data ◽  
Intercomparison Project ◽  
Improved Performance ◽  
Area Unit

Abstract. An increasing number of different types of hydrological, land surface, and rainfall–runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scales are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library Hydrostreamer v1.0, which aims to improve the usability of existing runoff products by addressing the modifiable area unit problem and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes (1) interpolation from source zones to target zones, (2) river routing, and (3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible – intuitive with minimal data requirements – and minimises the need for calibration. We tested the performance of Hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ (last access: 5 May 2021) under the MIT licence.

scholarly journals On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 92 ◽  
Author(s):  
Marius Appel ◽  
Edzer Pebesma
Keyword(s):  
Land Surface ◽  
Method Development ◽  
Satellite Image ◽  
Original Data ◽  
R Package ◽  
Observation Data ◽  
Data Cubes ◽  
On Demand ◽  
Temperature And Precipitation ◽  
Data Products

Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed.

scholarly journals Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

2020 ◽  
Vol 3 (1) ◽  
pp. 78
Author(s):  
Francis Oloo ◽  
Godwin Murithi ◽  
Charlynne Jepkosgei
Keyword(s):  
Land Cover ◽  
Metropolitan Area ◽  
Land Surface ◽  
Urban Forest ◽  
Urban Forests ◽  
Earth Observation ◽  
Tree Cover ◽  
Observation Data ◽  
Vegetation Health ◽  
Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

scholarly journals CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project

2021 ◽  
Author(s):  
Binghao Jia ◽  
Longhuan Wang ◽  
Yan Wang ◽  
Ruichao Li ◽  
Xin Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Grid Point ◽  
Global Ocean ◽  
Surface Model ◽  
Past Century ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Intercomparison Project

AbstractThe datasets of the five Land-offline Model Intercomparison Project (LMIP) experiments using the Chinese Academy of Sciences Land Surface Model (CAS-LSM) of CAS Flexible Global-Ocean-Atmosphere-Land System Model Grid-point version 3 (CAS FGOALS-g3) are presented in this study. These experiments were forced by five global meteorological forcing datasets, which contributed to the framework of the Land Surface Snow and Soil Moisture Model Intercomparison Project (LS3MIP) of CMIP6. These datasets have been released on the Earth System Grid Federation node. In this paper, the basic descriptions of the CAS-LSM and the five LMIP experiments are shown. The performance of the soil moisture, snow, and land-atmosphere energy fluxes was preliminarily validated using satellite-based observations. Results show that their mean states, spatial patterns, and seasonal variations can be reproduced well by the five LMIP simulations. It suggests that these datasets can be used to investigate the evolutionary mechanisms of the global water and energy cycles during the past century.

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

2021 ◽  
Vol 13 (4) ◽  
pp. 680
Author(s):  
Lei Wang ◽  
Wen Zhuo ◽  
Zhifang Pei ◽  
Xingyuan Tong ◽  
Wei Han ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arabian Peninsula ◽  
Vegetation Coverage ◽  
Observation Data ◽  
Desert Locust ◽  
Meteorological Observations ◽  
Earth Observation Data ◽  
Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.

scholarly journals Benchmarking and Process Diagnostics of Land Models

2018 ◽  
Vol 19 (11) ◽  
pp. 1835-1852 ◽  
Author(s):  
Grey S. Nearing ◽  
Benjamin L. Ruddell ◽  
Martyn P. Clark ◽  
Bart Nijssen ◽  
Christa Peters-Lidard
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Process Diagnostics ◽  
Surface Energy Fluxes ◽  
Structural Error ◽  
Intercomparison Project ◽  
Parameter Values ◽  
Process Level

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

scholarly journals The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

scholarly journals An ensemble of AMIP simulations with prescribed land surface temperatures

2018 ◽  
Author(s):  
Duncan Ackerley ◽  
Robin Chadwick ◽  
Dietmar Dommenget ◽  
Paola Petrelli
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
General Circulation ◽  
Global Climate ◽  
General Circulation Models ◽  
Solar Constant ◽  
Surface Temperatures ◽  
Co2 Concentrations ◽  
Land Surface Temperatures ◽  
Intercomparison Project

Abstract. General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon-dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature and, soil temperature and mositure) under five different configurations (AMIP, AMIP with uniform 4 K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3 %). Then, the land surface temperatures from the free-land experiments are used to perform a set of “AMIP-prescribed land” (PL) simulations, which are evaluated against their free-land counterparts. The PL simulations agree well with the free-land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free-land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP-prescribed land experiments.

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