scholarly journals Toward improved parameterization of a macro-scale hydrologic model in a discontinuous permafrost boreal forest ecosystem

2017 ◽  
Author(s):  
Abraham Endalamaw ◽  
W. Robert Bolton ◽  
Jessica M. Young-Robertson ◽  
Don Morton ◽  
Laryy Hinzman ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Vegetation Cover ◽  
Large Scale ◽  
Small Scale ◽  
Hydrological Models ◽  
Landscape Model ◽  
Coarse Resolution ◽  
Fine Resolution ◽  
Resolution Dataset ◽  
Meso Scale

Abstract. Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. However, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse spatial resolution datasets used in land surface modeling poised a new challenge in simulating the spatially distributed and basin integrated processes since these datasets do not adequately represent the small-scale hydrologic, thermal and ecological heterogeneity. The goal of this study is to improve the prediction capacity of meso-scale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and fine resolution landscape modeling in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) and one that is permafrost-dominated (HighP). The fine resolution landscape model used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the fine resolution landscape model than the coarse resolution datasets. Parameters derived from coarse resolution dataset and from the fine resolution landscape model are implemented into the Variable Infiltration Capacity (VIC) meso-scale hydrological model to simulate runoff, evapotranspiration (ET) and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows with similar accuracy in both sub-basins compared to the parameterization based on coarse resolution dataset. On average, small-scale parameterization improves the total runoff simulation approximately by up to 50 % in the LowP sub-basin and 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed small-scale landscape model can be used to improve the performance of meso-scale hydrological models in the Alaskan sub-arctic watersheds.

scholarly journals Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

2017 ◽  
Vol 21 (9) ◽  
pp. 4663-4680 ◽  
Author(s):  
Abraham Endalamaw ◽  
W. Robert Bolton ◽  
Jessica M. Young-Robertson ◽  
Don Morton ◽  
Larry Hinzman ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Soil Properties ◽  
Vegetation Cover ◽  
Large Scale ◽  
Hydrological Processes ◽  
Parameterization Scheme ◽  
Small Scale ◽  
Hydrological Models ◽  
Parameterization Method ◽  
Coarse Resolution

Abstract. Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.

scholarly journals Downscaling Satellite and Reanalysis Precipitation Products Using Attention-Based Deep Convolutional Neural Nets

2020 ◽  
Vol 2 ◽  
Author(s):  
Alexander Y. Sun ◽  
Guoqiang Tang
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Model Performance ◽  
Data Retrieval ◽  
Neural Nets ◽  
Coarse Resolution ◽  
Central Texas ◽  
Local Water ◽  
Fine Resolution

High-quality and high-resolution precipitation products are critically important to many hydrological applications. Advances in satellite remote sensing instruments and data retrieval algorithms continue to improve the quality of the operational precipitation products. However, most satellite products existing today are still too coarse to be ingested for local water management and planning purposes. Recent advances in deep learning algorithms enable the fusion of multi-source, high-dimensional data for statistical learning. In this study, we investigated the efficacy of an attention-based, deep convolutional neural network (AU-Net) for learning spatial and temporal mappings from coarse-resolution to fine-resolution precipitation products. The skills of AU-Net models, developed using combinations of static and dynamic predictors, were evaluated over a 3 × 3° study area in Central Texas, U.S., a region known for its complex precipitation patterns and low predictability. Three coarse-resolution satellite/reanalysis precipitation products, ERA5-Land (0.1°), TRMM (0.25°), and IMERG (0.1°), are used as part of the inputs, while the predictand is the 1-km PRISM data. Auxiliary predictors include elevation, vegetation index, and air temperature. The study period includes 18 years of data (2001–2018) at the monthly scale for training, validation, and testing. Results show that the trained AU-Net models achieve different degrees of success in downscaling the baseline coarse-resolution products, depending on the total precipitation, the accuracy of large-scale patterns captured by the baseline products, and the amount of information transferable from predictors. Higher precipitation rate tends to affect AU-Net model performance negatively. Use of the attention mechanism in the AU-Net models allows for infilling of multiscale features and generation of sharper images. Correction using gauge data, if there is any, can further improve the results significantly.

scholarly journals Preliminary step towards comparing large scale features in surface wind field between ERA-Interim re-analysis and a higher resolution simulation

2013 ◽  
Vol 10 (1) ◽  
pp. 113-117
Author(s):  
A. Boilley ◽  
T. Ranchin ◽  
A. Ghennioui
Keyword(s):  
Large Scale ◽  
Surface Wind ◽  
Weather Research And Forecasting ◽  
Small Scale ◽  
Forecasting Model ◽  
Preliminary Step ◽  
The Mean ◽  
Surface Wind Field ◽  
Resolution Dataset ◽  
Resolution Simulation

Abstract. The weather research and forecasting model (WRF) is initialized with ERA-Interim re-analysis to produce a higher resolution dataset over a one month period. WRF is supposed to introduce small scale information and maintain the mean large scale features. A detailed spectral analysis is performed to verify this statement. It reveals that consistency in large-scale features is not achieved for wavelengths greater than or equal to 6° even with the activation of spectral nudging.

scholarly journals Small scale spatial heterogeneity of soil respiration in an old growth temperate deciduous forest

2009 ◽  
Vol 6 (5) ◽  
pp. 9977-10005 ◽  
Author(s):  
A. Jordan ◽  
G. Jurasinski ◽  
S. Glatzel
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Spatial Heterogeneity ◽  
Large Scale ◽  
Stand Structure ◽  
Small Scale ◽  
Sampling Point ◽  
Old Growth ◽  
Old Growth Forest

Abstract. The large scale spatial heterogeneity of soil respiration caused by differences in site conditions is quite well understood. However, comparably little is known about the micro scale heterogeneity within forest ecosystems on homogeneous soils. Forest age, soil texture, topographic position, micro topography and stand structure may influence soil respiration considerably within short distance. In the present study within site spatial heterogeneity of soil respiration has been evaluated. To do so, an improvement of available techniques for interpolating soil respiration data via kriging was undertaken. Soil respiration was measured with closed chambers biweekly from April 2005 to April 2006 using a nested design (a set of stratified random plots, supplemented by 2 small and 2 large nested groupings) in an unmanaged, beech dominated old growth forest in Central Germany (Hainich, Thuringia). A second exclusive randomized design was established in August 2005 and continually sampled biweekly until July 2007. The average soil respiration values from the random plots were standardized by modeling soil respiration data at defined soil temperature and soil moisture values. By comparing sampling points as well as by comparing kriging results based on various sampling point densities, we found that the exclusion of local outliers was of great importance for the reliability of the estimated fluxes. Most of this information would have been missed without the nested groupings. The extrapolation results slightly improved when additional parameters like soil temperature and soil moisture were included in the extrapolation procedure. Semivariograms solely calculated from soil respiration data show a broad variety of autocorrelation distances (ranges) from a few centimeters up to a few tens of meters. The combination of randomly distributed plots with nested groupings plus the inclusion of additional relevant parameters like soil temperature and soil moisture data permits an improved estimation of the range of soil respiration, which is a prerequisite for reliable interpolated maps of soil respiration.

scholarly journals Modelling Small-scale Storage Interventions in Semi-Arid India at the Basin Scale

2021 ◽  
Author(s):  
Robyn Horan ◽  
Pawan Wable ◽  
Veena Srinivasan ◽  
Helen Baron ◽  
Virginie D. J. Keller ◽  
...  
Keyword(s):  
Large Scale ◽  
Water Security ◽  
Step Change ◽  
Small Scale ◽  
Hydrological Models ◽  
Check Dams ◽  
Basin Scale ◽  
Scale Modelling ◽  
Local Water ◽  
The Impact

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

Northern Hemisphere Extratropical Turbulent Heat Fluxes in ASRv2 and Global Reanalyses

2019 ◽  
Vol 32 (7) ◽  
pp. 2145-2166 ◽  
Author(s):  
Flavio Justino ◽  
Aaron B. Wilson ◽  
David H. Bromwich ◽  
Alvaro Avila ◽  
Le-Sheng Bai ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Vegetation Cover ◽  
Large Scale ◽  
Intraseasonal Variability ◽  
Horizontal Resolution ◽  
Heat Fluxes ◽  
Small Scale ◽  
Turbulent Heat ◽  
Area Index ◽  
Wind Speeds

Abstract Large-scale objectively analyzed gridded products and satellite estimates of sensible (H) and latent (LE) heat fluxes over the extratropical Northern Hemisphere are compared to those derived from the regional Arctic System Reanalysis version 2 (ASRv2) and a selection of current-generation global reanalyses. Differences in H and LE among the reanalyses are strongly linked to the wind speed magnitudes and vegetation cover. Specifically, ASRv2 wind speeds match closely with observations over the northern oceans, leading to an improved representation of H compared to the global reanalyses. Comparison of evaporative fraction shows that the global reanalyses are characterized by a similar H and LE partitioning from April through September, and therefore exhibit weak intraseasonal variability. However, the higher horizontal resolution and weekly modification of the vegetation cover based on satellite data in ASRv2 provides an improved snow–albedo feedback related to changes in the leaf area index. Hence, ASRv2 better captures the small-scale processes associated with day-to-day vegetation feedbacks with particular improvements to the H over land. All of the reanalyses provide realistic dominant hemispheric patterns of H and LE and the locations of maximum and minimum fluxes, but they differ greatly with respect to magnitude. This is especially true for LE over oceanic regions. Therefore, uncertainties in heat fluxes remain that may be alleviated in reanalyses through improved representation of physical processes and enhanced assimilation of observations.

A Coarse Grid Correction to Domain Decomposition Based Preconditioners for Meso-Scale Simulation of Concrete

2012 ◽  
Vol 204-208 ◽  
pp. 4683-4687 ◽  
Author(s):  
Jian Ping Wu ◽  
Jun Qiang Song ◽  
Wei Min Zhang ◽  
Huai Fa Ma
Keyword(s):  
Linear System ◽  
Domain Decomposition ◽  
Linear Systems ◽  
Large Scale ◽  
Coarse Grid ◽  
Small Scale ◽  
Sparse Linear Systems ◽  
Incomplete Factorization ◽  
Coarse Grid Correction ◽  
Meso Scale

Meso-scale simulation is one of the important ways to study dynamic behaviors of concrete materials, while most of the simulation time is used to solve the sparse linear systems. Because the discrete grid is three dimensional and is of large scale, iterations are the best solutions. But the convergence depends on the distribution of the eigenvalues of the coefficient matrix, to make the eigenvalues distributed more closely each other, it is required to adopt preconditioning techniques. In this paper, with the characteristics of the sparse linear systems considered, there provides a coarse grid correction algorithm, which is based on domain decomposition preconditioners and aggregation of sub-domains, with each aggregated into a single super-node. A linear system with small scale size is formed, which contains the global information and the solution is used to correct the solution components of the original auxiliary linear system. For incomplete factorization preconditioner parallelized with block Jacobi, classic additive Schwarz, and factors combination techniques, the experiments show that the presented algorithm can improve the convergence rate and the efficiency.

Small-Scale Science to Large-Scale Profession

2000 ◽  
Vol 45 (4) ◽  
pp. 396-398
Author(s):  
Roger Smith
Keyword(s):  
Large Scale ◽  
Small Scale

Development of Physics Learning Module Based on Guided Inquiry with Light Wave Material in Class XI of Senior High School

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Evi Rahmawati ◽  
Irnin Agustina Dwi Astuti ◽  
N Nurhayati
Keyword(s):  
Large Scale ◽  
Learning Strategy ◽  
Student Response ◽  
Average Score ◽  
Small Scale ◽  
Design Development ◽  
Teacher Response ◽  
Student Responses ◽  
Analysis Design ◽  
Scientific Skills

IPA Integrated is a place for students to study themselves and the surrounding environment applied in daily life. Integrated IPA Learning provides a direct experience to students through the use and development of scientific skills and attitudes. The importance of integrated IPA requires to pack learning well, integrated IPA integration with the preparation of modules combined with learning strategy can maximize the learning process in school. In SMP 209 Jakarta, the value of the integrated IPA is obtained from 34 students there are 10 students completed and 24 students are not complete because they get the value below the KKM of 68. This research is a development study with the development model of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The use of KPS-based integrated IPA modules (Science Process sSkills) on the theme of rainbow phenomenon obtained by media expert validation results with an average score of 84.38%, average material expert 82.18%, average linguist 75.37%. So the average of all aspects obtained by 80.55% is worth using and tested to students. The results of the teacher response obtained 88.69% value with excellent criteria. Student responses on a small scale acquired an average score of 85.19% with highly agreed criteria and on the large-scale student response gained a yield of 86.44% with very agreed criteria. So the module can be concluded receiving a good response by the teacher and students.

Planetary gentrification and urban (re)development

2019 ◽  
Vol 61 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Loretta Lees
Keyword(s):  
Low Income ◽  
Policy Making ◽  
Large Scale ◽  
Urban Redevelopment ◽  
Urban Transformation ◽  
Small Scale ◽  
Income Groups ◽  
Low Income Groups

Abstract Gentrification is no-longer, if it ever was, a small scale process of urban transformation. Gentrification globally is more often practised as large scale urban redevelopment. It is state-led or state-induced. The results are clear – the displacement and disenfranchisement of low income groups in favour of wealthier in-movers. So, why has gentrification come to dominate policy making worldwide and what can be done about it?

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