scholarly journals Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard

2020 ◽  
Vol 12 (15) ◽  
pp. 2436
Author(s):  
Noa Ohana-Levi ◽  
Kyle Knipper ◽  
William P. Kustas ◽  
Martha C. Anderson ◽  
Yishai Netzer ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Soil Type ◽  
Irrigation Management ◽  
Spatial Association ◽  
Environmental Modeling ◽  
Topographic Wetness Index ◽  
Modeling Framework ◽  
Error Matrix ◽  
Management Zones

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.

scholarly journals Irrigation Management Scale and Water Application Method to Improve Yield and Water Productivity of Field-Grown Strawberries

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 286 ◽  
Author(s):  
Guillaume Létourneau ◽  
Jean Caron
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Water Productivity ◽  
Irrigation Management ◽  
Richards Equation ◽  
Water Application ◽  
Experimental Site ◽  
Management Zones ◽  
Rock Fragments ◽  
Application Method

Improvements in water productivity are of primary importance for maintaining agricultural productivity and sustainability. Water potential-based irrigation management has proven effective for this purpose with many different crops, including strawberries. However, problems related to spatial variability of soil properties and irrigation efficiency were reported when applying this management method to strawberries in soils with rock fragments. In this study, a field-scale experiment was performed to evaluate the impacts of three irrigation management scales and a pulsed water application method on strawberry yield and water productivity. An analytical solution to Richards’ equation was also used to establish critical soil water potentials for this crop and evaluate the effects of the variability in the soil properties. Results showed that spatial variability of soil properties at the experimental site was important but not enough to influence crop response to irrigation practices. The studied properties did not present any spatial structure that could allow establishing specific management zones. A four-fold reduction in the size of the irrigation management zones had no effect on yield and increased the water applications. Pulsed application led to significant yield (22%) and water productivity (36%) increases compared with the standard water application method used by the producer at the experimental site.

Soil spatial variability characterization: Delineating index-based management zones in salt-affected agroecosystem of India

2021 ◽  
Vol 296 ◽  
pp. 113243
Author(s):  
Arijit Barman ◽  
Parvender Sheoran ◽  
Rajender Kumar Yadav ◽  
Ramesh Abhishek ◽  
Raman Sharma ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Management Zones ◽  
Soil Spatial Variability

The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE

2021 ◽  
Author(s):  
Peter H. Verburg ◽  
Žiga Malek ◽  
Sean P. Goodwin ◽  
Cecilia Zagaria
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
Land Use Changes ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Regional Modelling ◽  
Model Application ◽  
Climatic Regions ◽  
Single Region

The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model application, which then allocates land use change across different regions. This facilitates integration with the Integrated Economic-Environmental Modeling (IEEM) Platform for subnational assessments and increases the efficiency of the IEEM and Ecosystem Services Modeling (IEEMESM) workflow. Multi-regional modelling is particularly useful in larger and diverse countries, where we can expect different spatial distributions in land use changes in different regions: regions of different levels of achieved socio-economic development, regions with different topographies (flat vs. mountainous), or different climatic regions (dry vs humid) within a same country. Accounting for such regional differences also facilitates developing ecosystem services models that consider region specific biophysical characteristics. This manual, and the data that is provided with it, demonstrates multi-regional land use change modeling using the country of Colombia as an example. The user will learn how to prepare the data for the model application, and how the multi-regional run differs from a single-region simulation.

scholarly journals Generative modeling of single-cell population time series for inferring cell differentiation landscapes

2020 ◽  
Author(s):  
Grace H.T. Yeo ◽  
Sachit D. Saksena ◽  
David K. Gifford
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Differentiation ◽  
Single Cell ◽  
Cell Fate ◽  
Lineage Tracing ◽  
Model Framework ◽  
Modeling Framework ◽  
Generative Modeling ◽  
Model Complex

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.

scholarly journals Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

2020 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Weiqiang Ling ◽  
Binang Helen Barong ◽  
Prisca Ngetemalah Mbah-Fongkimeh ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Variability ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Spatial Association ◽  
Ordinary Least Squares ◽  
Variable Coefficients ◽  
Weighted Regression ◽  
Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Causes for Large-Scale Hydrographic Changes at the Hawaii Ocean Time Series Station

2008 ◽  
Vol 38 (9) ◽  
pp. 1931-1948 ◽  
Author(s):  
D. Stammer ◽  
S. Park ◽  
A. Köhl ◽  
R. Lukas ◽  
F. Santiago-Mandujano
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Global Ocean ◽  
Modeling Framework ◽  
Decadal Time Scale ◽  
Quasi Biennial Oscillation ◽  
Salinity Field ◽  
Additional Information ◽  
Independent Observations ◽  
Station Aloha

Abstract Results from Estimating the Circulation and Climate of the Ocean (ECCO)–Scripps Institution of Oceanography (SIO) global ocean state estimate, available over the 11-yr period 1992 through 2002, are compared with independent observations available at the Hawaii Ocean time series station ALOHA. The comparison shows that at this position, the estimated temporal variability has some skill in simulating observed ocean variability and that the quality of future syntheses could benefit from additional information available from the Argo network and from the time series observations themselves. On a decadal time scale, the influence radius of the station ALOHA T–S time series covers large parts of the tropical and subtropical Pacific Ocean and reaches even into the Indian Ocean through the Indonesian Throughflow. Estimated changes in sea surface height (SSH) result largely from thermosteric changes; however, nonsteric (barotropic) variations on the order of 1–2 cm also contribute to SSH changes at station ALOHA. Moreover, changes of similar magnitude can be caused by changes in the salinity field because of a quasi-biennial oscillation in the horizontal flow structure and heaving of the mean salinity structure on seasonal and interannual time scales. The adjoint modeling framework confirms westward-propagating Rossby waves (due to wind forcing) and subduction of water-mass anomalies (due to surface buoyancy forcing) as the primary mechanisms leading to observed changes of T–S structures at station ALOHA. Specifically, the analysis identifies surface freshwater fluxes along the wintertime outcrop of intermediate waters as a primary cause for salinity changes at station ALOHA and wind stress forcing east of the station position as another forcing mechanism of salinity variations around the Hawaiian Archipelago.

scholarly journals Development of an ESMF Based Flexible Coupling Application of ADCIRC and WAVEWATCH III for High Fidelity Coastal Inundation Studies

2020 ◽  
Vol 8 (5) ◽  
pp. 308 ◽  
Author(s):  
Saeed Moghimi ◽  
Andre Van der Westhuysen ◽  
Ali Abdolali ◽  
Edward Myers ◽  
Sergey Vinogradov ◽  
...  
Keyword(s):  
System Modeling ◽  
Atlantic Coast ◽  
Storm Track ◽  
Circulation Model ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Earth System Modeling ◽  
Coastal Inundation ◽  
Wavewatch Iii ◽  
Fully Implicit

To enable flexible model coupling in coastal inundation studies, a coupling framework based on the Earth System Modeling Framework (ESMF) and the National Unified Operational Prediction Capability (NUOPC) technologies under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed. The framework is essentially a software wrapper around atmospheric, wave and storm surge models that enables its components communicate seamlessly, and efficiently to run in massively parallel environments. For the first time, we are introducing the flexible coupled application of the ADvanced CIRCulation model (ADCIRC) and unstructured fully implicit WAVEWATCH III including NUOPC compliant caps to read Hurricane Weather Research and Forecasting Model (HWRF) generated forcing fields. We validated the coupled application for a laboratory test and a full scale inundation case of the Hurricane Ike, 2008, on a high resolution mesh covering the whole US Atlantic coast. We showed that how nonlinear interaction between surface waves and total water level results in significant enhancements and progression of the inundation and wave action into land in and around the hurricane landfall region. We also presented that how the maximum wave setup and maximum surge regions may happen at the various times and locations depending on the storm track and geographical properties of the landfall area.

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