scholarly journals Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging

2020 ◽  
Vol 12 (16) ◽  
pp. 2547 ◽  
Author(s):  
Wei Zhang ◽  
Dan Liu ◽  
Shengjie Zheng ◽  
Shuya Liu ◽  
Hugo A. Loáiciga ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Absolute Error ◽  
Weighted Regression ◽  
Distribution Model ◽  
Monthly Precipitation ◽  
Regression Kriging ◽  
Time Step ◽  
Predictive Skill ◽  
Precipitation Model ◽  
Meteorological Modeling

High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work’s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results.

scholarly journals Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies

2021 ◽  
Vol 13 (6) ◽  
pp. 3270
Author(s):  
Li Gao ◽  
Mingjing Huang ◽  
Wuping Zhang ◽  
Lei Qiao ◽  
Guofang Wang ◽  
...  
Keyword(s):  
Soil Nutrients ◽  
Geographically Weighted Regression ◽  
Prediction Accuracy ◽  
Hybrid Methods ◽  
Interpolation Method ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Weighted Regression ◽  
Available Phosphorus ◽  
Regression Kriging

Soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) are important indicators of soil fertility when undertaking a quality evaluation. Obtaining a high-precision spatial distribution map of soil nutrients is of great significance for the differentiated management of nutrient resources and reducing non-point source pollution. However, the spatial heterogeneity of soil nutrients lead to uncertainty in the modeling process. To determine the best interpolation method, terrain, climate, and vegetation factors were used as auxiliary variables to participate in the investigation of soil nutrient spatial modeling in the present study. We used the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and accuracy (Acc) of a dataset to comprehensively compare the performance of four different geospatial techniques: ordinary kriging (OK), regression kriging (RK), geographically weighted regression kriging (GWRK), and multiscale geographically weighted regression kriging (MGWRK). The results showed that the hybrid methods (RK, GWRK, and MGWRK) could improve the prediction accuracy to a certain extent when the residuals were spatially correlated; however, this improvement was not significant. The new MGWRK model has certain advantages in reducing the overall residual level, but it failed to achieve the desired accuracy. Considering the cost of modeling, the OK method still provides an interpolation method with a relatively simple analysis process and relatively reliable results. Therefore, it may be more beneficial to design soil sampling rationally and obtain higher-quality auxiliary variable data than to seek complex statistical methods to improve spatial prediction accuracy. This research provides a reference for the spatial mapping of soil nutrients at the farmland scale.

Tendencias del cambio climático en la Demarcación Hidrográfica de Manabí

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Campos Cedeño Antonio Fermín ◽  
Mendoza Álava Junior Orlando
Keyword(s):  
El Niño ◽  
El Nino ◽  
Technical Information ◽  
Temporal Distribution ◽  
Temporal Analysis ◽  
Botanical Garden ◽  
Monthly Precipitation ◽  
Water Network ◽  
Monthly Distribution ◽  
Index Terms

Abstract— The Manabí Hydrographic Demarcation (DHM) is characterized as the only one that does not receive input from Andes Mountains, therefore, its water network is fed exclusively by the rainfall that occurs in the rainy season and that the warm current of El Niño plays a fundamental role in its production. In order to have technical information, important for the planning, control and development of the water resources of the DHM, in this research is made a temporal analysis of the monthly precipitation for 55 years, period 1963-2017. The National Institute of Hydrology and Meteorology of Ecuador (INAMHI) in station M005, located in the Botanical Garden of the Technical University of Manabí (Universidad Técnica de Manabí) in Portoviejo, obtained these records. An analysis is made of the monthly and annual patterns, establishing that the El Niño events that occurred in 1983, 1997 and 1998, have set guidelines for the change in rainwater production at the intensity and temporal distribution levels, increasing the months of drought, while the levels of rainfall increase, concentrating in fewer months, basically in February and March. This is a situation that increases the water deficit especially when there is not enough infrastructure of hydraulic works for the storage and regulation of runoff.   Index Terms— Hydrology, rainfall, monthly distribution, annually distribution, climate change, El Niño phenomenon

Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

2017 ◽  
Vol 20 ◽  
pp. 76-91 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Yuanfang Huang ◽  
Shiwen Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Regression Kriging

scholarly journals Precipitation Atlas for Germany (GePrA)

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 737
Author(s):  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Monthly Precipitation ◽  
New Approach ◽  
Very High Spatial Resolution

A new approach for modeling daily precipitation (RR) at very high spatial resolution (25 m × 25 m) was introduced. It was used to develop the Precipitation Atlas for Germany (GePrA). GePrA is based on 2357 RR time series measured in the period 1981–2018. It provides monthly percentiles (p) of the large-scale RR patterns which were mapped by a thin plate spline interpolation (TPS). A least-squares boosting (LSBoost) approach and orographic predictor variables (PV) were applied to integrate the small-scale precipitation variability in GePrA. Then, a Weibull distribution (Wei) was fitted to RRp. It was found that the mean monthly sum of RR ( R R ¯ s u m ) is highest in July (84 mm) and lowest in April (49 mm). A great dependency of RR on the elevation (ε) was found and quantified. Model validation at 425 stations showed a mean coefficient of determination (R2) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months. The high spatial resolution, including the effects of the local orography, make GePrA a valuable tool for various applications. Since GePrA does not only describe R R ¯ s u m , but also the entire monthly precipitation distributions, the results of this study enable the seasonal differentiation between dry and wet period at small scales.

Estimation of the Parameters in a Dynamical Marine Ecosystem Model Based on the Adjoint Assimilation

2013 ◽  
Vol 321-324 ◽  
pp. 2419-2423
Author(s):  
Xiao Yan Li ◽  
Chun Hui Wang ◽  
Xian Qing Lv
Keyword(s):  
Marine Ecosystem ◽  
Absolute Error ◽  
Ecosystem Model ◽  
Global Scale ◽  
Time Step ◽  
Marine Ecosystem Model ◽  
Maximum Uptake Rate ◽  
Influence Radius ◽  
Adjoint Assimilation ◽  
Spatially Varying

By utilizing spatial biological parameterizations, the adjoint variational method was applied to a 3D marine ecosystem model (NPZD-type) and its adjoint model which were built on global scale based on climatological environment and data. When the spatially varying Vm (maximum uptake rate of nutrient by phytoplankton) was estimated alone, we discussed how would the distribution schemes of spatial parameterization and influence radius affected the results. The reduced cost function (RCF), the mean absolute error (MAE) of phytoplankton in the surface layer, and the relative error (RE) of Vm between given and simulated values decreased obviously. The influence of time step was studied then and we found that the assimilation recovery would not be more successful with a smaller time step of 3 hours compared with 6 hours.

COMPUTER MODELLING OF THE OXFORDIAN DEPOSITIONAL SYSTEMS IN THE KENDREW TROUGH, DAMPIER SUB-BASIN

2001 ◽  
Vol 41 (1) ◽  
pp. 463 ◽  
Author(s):  
K. Liu ◽  
C.M. Griffiths ◽  
C.P. Dyt
Keyword(s):  
Computer Modelling ◽  
Temporal Distribution ◽  
Sedimentary Facies ◽  
Sediment Supply ◽  
Three Dimensions ◽  
Spatial And Temporal Distribution ◽  
Time Step ◽  
Sea Level Fluctuations ◽  
Basin Scale ◽  
Depositional Systems

A 3D depositional modelling program, SEDSIM, was used to model the various depositional systems operating in the Kendrew Trough, Dampier Sub-basin during a two million year period of the Oxfordian. The simulation covers an area of 40 km by 100 km, from the Goodwyn Field in the southwest to the Lambert Field in the northeast, covering the Rankin Trend, Kendrew Trough, Madeleine Trend and part of the Lewis Trough. The simulation started from the Jurassic main unconformity (156.7 Ma) forward to 154.7 Ma using a spatial resolution of 1 km and a time step of 5 ka.The 3D model from the simulation quantitatively mimics the interaction of the palaeogeographic setting, sediment supply, sea level fluctuations, tectonic movement and palaeo-oceanographic setting in three dimensions, to simulate the spatial and temporal distribution of sedimentary facies. The model identified five Oxfordian leads within the Kendrew Trough, including two major slope and basin-floor fan systems, a shelfal-shoreface system, a deltaic system, and a submarine channel system.The study has shown that 3D depositional models produced by SEDSIM are not only able to depict the spatial and temporal distribution of depositional systems on a basin scale, but are also capable of making useful contributions to the understanding of play fairway and lead development.

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