scholarly journals Evaluating topographic wetness indices across central New York agricultural landscapes

2013 ◽  
Vol 10 (11) ◽  
pp. 14041-14093 ◽  
Author(s):  
B. P. Buchanan ◽  
M. Fleming ◽  
R. L. Schneider ◽  
B. K. Richards ◽  
J. Archibald ◽  
...  
Keyword(s):  
New York ◽  
Soil Moisture ◽  
Soil Properties ◽  
Flow Direction ◽  
Field Studies ◽  
Agricultural Landscapes ◽  
Mixed Effects Modeling ◽  
Digital Elevation ◽  
Low Pass ◽  
Elevation Model

Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWI) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches in the US. We calculated TWIs using over 400 unique formulations that considered different: Digital Elevation Model (DEM) resolution (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA; each field was visited 5–8 times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) LiDAR-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved the correlations.

scholarly journals Evaluating topographic wetness indices across central New York agricultural landscapes

2014 ◽  
Vol 18 (8) ◽  
pp. 3279-3299 ◽  
Author(s):  
B. P. Buchanan ◽  
M. Fleming ◽  
R. L. Schneider ◽  
B. K. Richards ◽  
J. Archibald ◽  
...  
Keyword(s):  
New York ◽  
Soil Moisture ◽  
Soil Properties ◽  
Flow Direction ◽  
Field Studies ◽  
Agricultural Landscapes ◽  
Mixed Effects Modeling ◽  
Digital Elevation ◽  
Low Pass ◽  
Elevation Model

Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches – especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) lidar-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved correlations.

scholarly journals Topography impact on nutrition content in soil and yield

2008 ◽  
Vol 54 (No. 6) ◽  
pp. 255-261 ◽  
Author(s):  
J. Kumhálová ◽  
Š. Matějková ◽  
M. Fifernová ◽  
J. Lipavský ◽  
F. Kumhála
Keyword(s):  
Soil Properties ◽  
Flow Direction ◽  
Yield Data ◽  
Flow Accumulation ◽  
Accumulation Model ◽  
Soil Nutrition ◽  
Experimental Field ◽  
Growing Seasons ◽  
Digital Elevation ◽  
Elevation Model

The main aim of this study was to determine the dependence of yield and selected soil properties on topography of the experimental field by using topographical data (elevation, slope and flow accumulation). The topography and yield data were obtained from a yield monitor for combine harvester, and soil properties data were taken from sampling points of our experimental field. Initially, the topographical parameters of elevation and slope were estimated and then the Digital Elevation Model (DEM) grid was created. On the basis of field slope the flow direction model and the flow accumulation model were created. The flow accumulation model, elevation and slope were then compared with the yield and content of nitrogen and organic carbon in soil in the years 2004, 2005 and 2006 in relation to the sum of precipitation and temperatures in crop growing seasons of these years. The correlation analysis of all previously mentioned elements was calculated and statistical evaluation proved a significant dependence of yield and soil nutrition content on flow accumulation. For the wettest evaluated year the correlation coefficient 0.25 was calculated, for the driest year it was 0.62.

scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

scholarly journals Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2160
Author(s):  
Daniel Kibirige ◽  
Endre Dobos
Keyword(s):  
Soil Moisture ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Kriging ◽  
Environmental Covariates ◽  
Observatory Data ◽  
Digital Elevation ◽  
Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

scholarly journals Electrical resistivity and spatial variation in agriculture terraces: statistical correlation between ert and flow direction algorithms

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
J. Fernandes ◽  
C. Bateira ◽  
A. Costa ◽  
B. Fonseca ◽  
R. Moura
Keyword(s):  
Electrical Resistivity ◽  
Standard Deviation ◽  
Spatial Variation ◽  
Soil Depth ◽  
Flow Direction ◽  
Statistical Correlation ◽  
Average Value ◽  
Digital Elevation ◽  
Elevation Model ◽  
Soil Electrical Resistivity

AbstractThe construction of earthen embankment terraces in the Douro Region raises a set of problems related to hydrological processes. The main objective of this study is the evaluation of the spatial variation of electrical resistivity in agriculture terraces at Douro valley (Portugal). To achieve this objective, two variables are analysed, the soil electrical resistivity and the flow direction algorithm. In a field survey we recorded 13 electrical resistivity profiles. The contributing area was calculated with the algorithms D∞ (Deterministic Infinity Flow) and MFD (Multiple Flow Direction) and the results are the base of the internal runoff modelling, both supported by the digital elevation model with a spatial resolution of 1m2. A correlation between the spatial variation of the soil electrical resistivity represented by the standard deviation of the electrical resistivity for each profile and the average value of the contributing area coincident with each profile was established. The electrical resistivity standard deviation seems to be moderately well correlated according to the D∞ algorithm at about 1m of depth, and it has a good correlation at 1,5m to 2m of depth with the MFD algorithm. Taken together, the results show a significant positive statistical correlation between the electrical resistivity standard deviation and the contributing areas (MFD and D∞) depending on the soil depth.

scholarly journals Inundation Model Using UAV-derived Digital Elevation Data and PCRaster Dynamic Model in An Excessive Rainfall Event

2018 ◽  
Vol 2 (2) ◽  
pp. 152-159
Author(s):  
Dwi Setyo Aji ◽  
Warsini Handayani ◽  
Retnadi Heru Jatmiko ◽  
Agung Kurniawan
Keyword(s):  
Flow Direction ◽  
Extreme Weather ◽  
Rainfall Data ◽  
Digital Elevation ◽  
Hourly Data ◽  
Elevation Data ◽  
Additional Water ◽  
Flowing Material ◽  
Elevation Model ◽  
Uav Image

Extreme weather reportedly occurred on 28th November 2017 caused by a cyclone called Cempaka. Categorized as extreme weather since this event triggered an excessive rainfall reaching 246.8 mm in a 24-hour. Consequently, some areas in Yogyakarta Special Region are inundated. This research attempts to model the inundation of excessive rainfall using GIS software, PCRaster. The study area is concentrated in Selopamioro and Sriharjo, where Opak and Oyo rivers meet. Elevation model and rainfall data are used as the principal data to model the inundation. Elevation model is derived from the Unmanned Aerial Vehicle (UAV)  image, while, the rainfall data of a-24-hour hourly data from the Meteorological Agency is also used as an input. The elevation model works as a flow direction model and the rainfall amount plays as the flowing material. The original states of water of the river are not considered, thus the study merely describes how the certain amount of rainfall adds to the level height of terrain and modeled for 24 hours. The result maps are the area that experience of a-24-hour high intensity of rainfall. The study depicts the additional water level caused by rainfall and the concentration of excessive rainfall in the study area. This information is beneficial in order to alarm a similar future event.  

scholarly journals Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

2020 ◽  
Vol 12 (23) ◽  
pp. 3916
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Qiyue Liu ◽  
Gengke Wang ◽  
Tao Yu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Maximum Entropy ◽  
Near Infrared ◽  
Bayesian Maximum Entropy ◽  
General Knowledge ◽  
Combined Approach ◽  
Soft Data ◽  
Digital Elevation ◽  
Source Data ◽  
Elevation Model

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.

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