scholarly journals Spatial Behavior of Soil Erodibility in the La Villa River Basin, Panama

2021 ◽  
pp. 309-315
Author(s):  
Lwonel Agudo Martínez ◽  
José Villarreal Núñez ◽  
Jhon Villalaz Pérez ◽  
Iván Ramos Zachrisson
Keyword(s):  
Organic Matter ◽  
River Basin ◽  
Land Surface ◽  
Interpolation Method ◽  
Fine Sand ◽  
Complete Information ◽  
Spatial Behavior ◽  
Soil Erodibility ◽  
Spatial Analyst ◽  
Inverse Distance

Introduction: Soil erodibility is an important factor in understanding the erosion that takes place in a territory. This is a parameter that can behave erratically in small spaces, but that describes a trend in larger spaces. Aim: Determine the K factor of soil erodibility in the La Villa-Panama river basin. Place and Duration of Study: La Villa River Basin-Azuero Peninsula, Panama. 2010-2012. Methodology: 98 points of the La Villa river watershed were sampled. Factor K was calculated using the adaptation to the sol-erodibility nomogram. The percentage of organic matter, structure class (in the field), permeability (combination permeameter) and the percentages of sand, silt and very fine sand (Bouyoucos method) were determined. To obtain the most complete information possible on the distribution of erodibility, a superficial interpolation of the point values ​​corresponding to the soil samples taken was carried out. The software used was Arcview 3.3 and the Spatial Analyst extension. The interpolation method was IDW (Inverse Distance Weight). The erodibility values ​​were categorized into seven intervals in such a way that it was possible to observe the differences on the map. Results: The erodibility values ​​were influenced by the content of organic matter and coarse particles (percentage of sand and silt + very fine sand) of the soil. In the province of Herrera, 86% of the land surface and 76% in the province of Los Santos presents susceptibility to erosion in the ranges of 0.032 to 0.043 Ton ha h ha-1 Mj-1 mm-1. Conclusion: The results indicate that 80% of the soils of the La Villa river basin present a moderately high erodibility factor, with the highest values ​​being registered in the upper middle zone.

scholarly journals MÉTODOS DE INTERPOLAÇÕES DE DADOS OBTIDOS POR SENSORES DE AGRICULTURA DE PRECISÃO

Nativa ◽  
2018 ◽  
Vol 6 (6) ◽  
pp. 681
Author(s):  
Emanuele Helmann Nunes ◽  
Thiago Martins Machado ◽  
Étore Francisco Reynaldo ◽  
Cassiano Spaziani Pereira
Keyword(s):  
Electrical Conductivity ◽  
Organic Matter ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Distance Method ◽  
Normal Distance ◽  
Contact Sensor ◽  
Variographic Analysis ◽  
The City ◽  
Inverse Distance

Os sensores que medem as características do solo em campo são importantes ferramentas para o manejo da agricultura de precisão, entre eles, destaca-se o sensor de contato, que mede a condutividade elétrica (CE), matéria orgânica (MO) e potencial hidrogeniônico (pH) do solo. Objetivou-se avaliar os erros dos métodos de interpolação por krigagem, inverso da distância e normal da distância a partir de dados de CE, MO e pH do solo. O experimento foi realizado no município de Candói – PR, onde foram amostradas duas áreas, os dados foram coletados por sensor de contato, o qual foi conFigurado para uma coleta de 150 pontos por hectare para a condutividade elétrica e a matéria orgânica, e para potencial hidrogeniônico a frequência de coleta foi de 15 pontos por hectare, o equipamento foi acoplado em um trator operado a uma velocidade de 8 km h-1 com passadas paralelas de 20 m. Realizaram-se análises variográficas, validação cruzada e elaboração de mapas. Os menores erros de interpolação ou “jack knifing” para CE, MO e pH foram apresentados pelo método de interpolação inverso da distância, para o talhão T2, e no talhão T1 o método da Krigagem obteve os menores erros para o pH. Concluiu-se que distância das amostragens foi adequada e a krigagem e o inverso da distância foram mais eficientes que o normal da distância. Verificou-se que quanto maior a potência de elevação, tanto para o método do inverso da distância quanto para normal da distância, os erros aumentam e também o grau de contagiosidade.Palavras-chave: geoestatística, sensor Veris, variabilidade espacial. METHODS OF DATA INTERPOLATIONS OBTAINED BY PRECISION AGRICULTURE SENSORS ABSTRACT:The sensors that measure soil characteristics in the field are important tools for the management of precision agriculture, among them the contact sensor, which measures the electrical conductivity (EC), organic matter (OM) and hydrogenation potential (pH) of the soil. The objective of this study was to evaluate the errors of the interpolation methods by kriging, inverse distance and normal distance from the data of EC, MO and soil pH. The experiment was carried out in the city of Candói - PR, where two areas were sampled, the data were collected by contact sensor, which was configured for a collection of 150 points per hectare for electrical conductivity and organic matter, and for potential the collection frequency was 15 points per hectare, the equipment was coupled in a tractor operated at a speed of 8 km h-1 with parallel passes of 20 m. Variographic analysis, cross-validation and mapping were performed. The smallest interpolation errors or jack knifing for CE, MO and pH were presented by the inverse distance interpolation method for the T2 field, and in T1 field the Kriging method obtained the lowest errors for pH. It was concluded that distance from the samplings was adequate and the kriging and the inverse of the distance were more efficient than the normal distance. It was verified that the higher the elevation power, both for the inverse distance and the normal distance method, the errors increase and also the degree of contagiousness.Keywords: geostatistics, Veris sensor, spatial variability.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

scholarly journals Spatiotemporal Evolution of Landscape Ecological Risk Based on Geomorphological Regionalization during 1980–2017: A Case Study of Shaanxi Province, China

2020 ◽  
Vol 12 (3) ◽  
pp. 941
Author(s):  
Di Liu ◽  
Hai Chen ◽  
Hang Zhang ◽  
Tianwei Geng ◽  
Qinqin Shi
Keyword(s):  
River Basin ◽  
Ecological Risk ◽  
Land Surface ◽  
Risk Model ◽  
Shaanxi Province ◽  
Regional Differentiation ◽  
Ecological Risks ◽  
Han River ◽  
Han River Basin ◽  
Landscape Ecological

Land surface elements, such as land use, are in constant change and dynamically balanced, driving changes in global ecological processes and forming the regional differentiation of surface landscapes, which causes many ecological risks under multiple sources of stress. The landscape pattern index can quickly identify the disturbance caused by the vulnerability of the ecosystem itself, thus providing an effective method to support the spatial heterogeneity of landscape ecological risk. A landscape ecological risk model based on the degree of interference and fragility was constructed and spatiotemporal differentiation of risk between 1980 and 2017 in Shaanxi Province was analyzed. The spatiotemporal migration of risk was demonstrated from the perspective of geomorphological regionalization and risk gravity. Several conclusions were drawn: The risk of Shaanxi Province first increased and then decreased, at the same time, the spatial differentiation of landscape ecological risk was very significant. The ecological risk presented a significant positive correlation but the degree of autocorrelation decreased. The risk of the Qinba Mountains was low and the risk of the Guanzhong Plain and Han River basin was high. The risk of Loess Plateau and sandstorm transition zone decreased greatly and their risk gravities shifted to the southwest. The gravity of the Guanzhong Plain and Qinling Mountains had a northward trend, while the gravity of the Han River basin and Daba Mountains shifted to the southeast. In the analysis of typical regions, there were different relationships between morphological indicators and risk indexes under different geomorphological features. The appropriate engineering measures and landscape management for different geomorphological regionalization were suggested for effective reduction of ecological risks.

Hydroclimatic Response of Watersheds to Urban Intensity: An Observational and Modeling-Based Analysis for the White River Basin, Indiana

2010 ◽  
Vol 11 (1) ◽  
pp. 122-138 ◽  
Author(s):  
Guoxiang Yang ◽  
Laura C. Bowling ◽  
Keith A. Cherkauer ◽  
Bryan C. Pijanowski ◽  
Dev Niyogi
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Great Influence ◽  
Flow Distribution ◽  
Surface Characteristics ◽  
Infiltration Capacity ◽  
Vic Model ◽  
White River ◽  
Urban Model ◽  
Streamflow Regime

Abstract Impervious surface area (ISA) has different surface characteristics from the natural land cover and has great influence on watershed hydrology. To assess the urbanization effects on streamflow regimes, the authors analyzed the U.S. Geological Survey (USGS) streamflow data of 16 small watersheds in the White River [Indiana (IN)] basin. Correlation between hydrologic metrics (flow distribution, daily variation in streamflow, and frequency of high-flow events) and ISA was investigated by employing the nonparametric Mann–Kendall method. Results derived from the 16 watersheds show that urban intensity has a significant effect on all three hydrologic metrics. The Variable Infiltration Capacity (VIC) model was modified to represent ISA in urbanized basins using a bulk parameterization approach. The model was then applied to the White River basin to investigate the potential ability to simulate the water and energy cycle response to urbanization. Correlation analysis for individual VIC grid cells indicates that the VIC urban model was able to reproduce the slope magnitude and mean value of the USGS streamflow metrics. The urban model also reproduced the urban heat island (UHI) seen in the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature products, especially for the grids encompassing the city of Indianapolis, IN. The difference of the hydrologic metrics obtained from the VIC model with and without urban representation indicates that the streamflow regime in the White River has been modified because of urban development. The observed data, together with model analysis, suggested that 3%–5% ISA in a watershed is the detectable threshold, beyond which urbanization effects start to have a statistically significant influence on streamflow regime.

The Metazoan Meiofauna in Its Biogeochemical Environment: The Case of an Antarctic Coastal Sediment

1998 ◽  
Vol 78 (2) ◽  
pp. 411-434 ◽  
Author(s):  
S. Vanhove ◽  
H.J. Lee ◽  
M. Beghyn ◽  
D. Van Gansbeke ◽  
S. Brockington ◽  
...  
Keyword(s):  
Organic Matter ◽  
Standing Stock ◽  
Austral Summer ◽  
Fine Sand ◽  
Coastal Sediment ◽  
Feeding Guild ◽  
Fresh Organic Matter ◽  
Benthic Environment ◽  
Metazoan Meiofauna ◽  
Deposit Feeding

The metazoan meiobenthos was investigated in an Antarctic coastal sediment (Factory Cove, Signy Island, Antarctica). The fine sands contained much higher abundances compared to major sublittoral sediments worldwide. Classified second after Narrangansett Bay (North Atlantic) they reached numbers of 13 × 106ind m-2. The meiofauna was highly abundant in the surface layers, but densities decreased sharply below 2 cm. Vertical profiles mirrored steep gradients of microbiota, chloropigments and organic matter and were coincident with chemical stratification. Spatial patchiness manifested especially in the surface layer. Nematodes dominated (up to 90%), andAponema, Chromctdorita, Diplolaimella, Daptonema, MicrolaimusandNeochromadoraconstituted almost the entire community. Overall, the nematode fauna showed a strong similarity with fine sand communities elsewhere. The dominant trophic strategies were epistrarum and non-selective deposit feeding, but the applied classification for feeding guild structure of the nematodes of Factory Cove is discussed. High standing stock, low diversity and shallow depth distribution may have occurred because of the high nutritive (chlorophyll exceeded lOOOmgm-2and constituted almost 50% of the organic pool) and reductive character of the benthic environment. These observations must have originated from the substantial input of fresh organic matter from phytoplankton and microphytobenthic production, typical for an Antarctic coastal ecosystem during the austral summer.

A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

2021 ◽  
Author(s):  
Yifan Cheng ◽  
Andrew Newman ◽  
Sean Swenson ◽  
David Lawrence ◽  
Anthony Craig ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Land Surface ◽  
Computational Cost ◽  
Reanalysis Data ◽  
Freshwater Ecosystems ◽  
System Model ◽  
Surface Model ◽  
Yukon River ◽  
Yukon River Basin

<p>Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).</p><p>A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.</p><p>In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24<sup>th</sup> degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.</p>

scholarly journals Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

2017 ◽  
Vol 21 (4) ◽  
pp. 2187-2201 ◽  
Author(s):  
Pere Quintana-Seguí ◽  
Marco Turco ◽  
Sixto Herrera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Surface Model ◽  
Low Resolution ◽  
Precipitation Events

Abstract. Offline land surface model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/1980–2013/2014). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate regional climate models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the latter slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.

scholarly journals Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1884 ◽  
Author(s):  
Guojie Wang ◽  
Jian Pan ◽  
Chengcheng Shen ◽  
Shijie Li ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Land Surface ◽  
Yellow River ◽  
Yellow River Basin ◽  
Balance Method ◽  
The Yellow River ◽  
Water Balance Method ◽  
Global Land ◽  
The Yellow River Basin

Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation.

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