scholarly journals Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 830
Author(s):  
Gabriele Buttafuoco ◽  
Massimo Conforti
Keyword(s):  
Spatial Distribution ◽  
Ordinary Kriging ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Block Kriging ◽  
Kriging With External Drift ◽  
Elevation Data ◽  
Support Size ◽  
Ordinary Cokriging ◽  
External Drift

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates.

scholarly journals Spatial Interpolation for Real-Time Rainfall Field Estimation in Areas with Complex Topography

2013 ◽  
Vol 14 (1) ◽  
pp. 85-104 ◽  
Author(s):  
M. C. Rogelis ◽  
M. G. F. Werner
Keyword(s):  
Real Time ◽  
Ordinary Kriging ◽  
Kriging With External Drift ◽  
Time Operation ◽  
Spatially Distributed ◽  
Interpolation Procedure ◽  
Real Time Operation ◽  
Distributed Hydrological Models ◽  
Gaussian Simulation ◽  
External Drift

Abstract For many hydrological applications interpolation of point rainfall measurements is needed. One such application is flood early warning, particularly where spatially distributed hydrological models are used. Operation in real time poses challenges to the interpolation procedure, as this should then both be automatic and efficiently provide robust interpolation of gauged data. The differences in performance of ordinary kriging, universal kriging, and kriging with external drift with individual and pooled variograms were assessed for 139 daily datasets with significant precipitation in a study area in Bogotá, Colombia. Interpolators were compared using the percentage of variability explained and the root-mean-square error found in cross validation, aiming at identifying a procedure for real-time interpolation. The results showed that interpolators using pooled variograms provide a performance comparable to when the interpolators were applied to the storms individually, showing that they can be used successfully for interpolation in real-time operation in the study area. The analysis identified limitations in the use of kriging with external drift. Only when the adjusted R2 between the secondary variables and precipitation is higher than the percentage of variability explained found in ordinary kriging, then kriging with external drift provided a consistent improvement. This interpolator was found to give a lower performance in all other cases. The distribution of precipitation over basins of interest for each of the storms, derived through sampling rainfall fields generated through conditional Gaussian simulation, shows that, while differences between the interpolators may appear to be significant, the variability of the precipitation volume is less significant.

scholarly journals Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal

2021 ◽  
Author(s):  
Alexandru Antal ◽  
Pedro M. P. Guerreiro ◽  
Sorin Cheval
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Polynomial Interpolation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Distribution ◽  
Interpolation Methods ◽  
Mainland Portugal ◽  
The Mean ◽  
Spatial Interpolation Methods

Abstract Precipitation has a strong and constant impact on different economic sectors, environment, and social activities all over the world. An increasing interest for monitoring and estimating the precipitation characteristics can be claimed in the last decades. However, in some areas the ground-based network is still sparse and the spatial data coverage insufficiently addresses the needs. In the last decades, different interpolation methods provide an efficient response for describing the spatial distribution of precipitation. In this study, we compare the performance of seven interpolation methods used for retrieving the mean annual precipitation over the mainland Portugal, as follows: local polynomial interpolation (LPI), global polynomial interpolation (GPI), radial basis function (RBF), inverse distance weighted (IDW), ordinary cokriging (OCK), universal cokriging (UCK) and empirical Bayesian kriging regression (EBKR). We generate the mean annual precipitation distribution using data from 128 rain gauge stations covering the period 1991 to 2000. The interpolation results were evaluated using cross-validation techniques and the performance of each method was evaluated using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (R) and Taylor diagram. The results indicate that EBKR performs the best spatial distribution. In order to determine the accuracy of spatial distribution generated by the spatial interpolation methods, we calculate the prediction standard error (PSE). The PSE result of EBKR prediction over mainland Portugal increases form south to north.

scholarly journals Mapping monthly rainfall data in Galicia (NW Spain) using inverse distances and geostatistical methods

2007 ◽  
Vol 10 ◽  
pp. 51-57 ◽  
Author(s):  
J. M. Mirás-Avalos ◽  
A. Paz-González ◽  
E. Vidal-Vázquez ◽  
P. Sande-Fouz
Keyword(s):  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Conditional Simulation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Nw Spain ◽  
Data Set ◽  
Kriging With External Drift ◽  
Interpolation Techniques ◽  
External Drift

Abstract. In this paper, results from three different interpolation techniques based on Geostatistics (ordinary kriging, kriging with external drift and conditional simulation) and one deterministic method (inverse distances) for mapping total monthly rainfall are compared. The study data set comprised total monthly rainfall from 1998 till 2001 corresponding to a maximum of 121 meteorological stations irregularly distributed in the region of Galicia (NW Spain). Furthermore, a raster Geographic Information System (GIS) was used for spatial interpolation with a 500×500 m grid digital elevation model. Inverse distance technique was appropriate for a rapid estimation of the rainfall at the studied scale. In order to apply geostatistical interpolation techniques, a spatial dependence analysis was performed; rainfall spatial dependence was observed in 33 out of 48 months analysed, the rest of the rainfall data sets presented a random behaviour. Different values of the semivariogram parameters caused the smoothing in the maps obtained by ordinary kriging. Kriging with external drift results were according to former studies which showed the influence of topography. Conditional simulation is considered to give more realistic results; however, this consideration must be confirmed with new data.

scholarly journals Elevation Dependency of Precipitation over Southern Slope of Central Himalaya

Jalawaayu ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Shankar Sharma ◽  
Nitesh Khadka ◽  
Bikash Nepal ◽  
Shravan Kumar Ghimire ◽  
Nirajan Luintel ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Precipitation Amount ◽  
Winter Season ◽  
Annual Precipitation ◽  
Spatial And Temporal Variation ◽  
Mean Annual Precipitation ◽  
Eastern Region ◽  
Monthly Precipitation ◽  
Monsoon Precipitation ◽  
Post Monsoon

Precipitation plays vital roles in the global water cycle, knowledge of the spatial and temporal variation of the precipitation is essential to understanding extreme environmental phenomena such as floods, landslides, and drought. In this paper, the integrated characteristics of precipitation during 1980–2016 over Nepal along with the seasonal elevation dependency of precipitation were examined for three different regions over the country using Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. The spatial distribution of mean annual precipitation varies significantly with the highest (lowest) precipitation of ~5500 (~100) mm/year in the Arun valley (Manang and Mustang). The precipitation regime of the country is determined by the contribution of the monthly precipitation amount with distinct spatial gradients between the eastern and the western sides during pre-monsoon, post-monsoon, and winter seasons. On the contrary, the spatial distribution of monsoon precipitation tends to more heterogeneous with visible differences between the lowland, midland, and highlands as similar to the annual one. Further, elevation dependency of seasonal precipitation revealed that the winter and post-monsoon precipitation distribution in western and central are very similar, whereas post-monsoon precipitation was found slightly higher than winter season in the eastern region. The highest precipitation areas in eastern and central region are located between 2000-2500 m, which is between 500 and 1000 m in the western region of the country. Overall, the pre-monsoon, summer monsoon and annual precipitation increases gradually with elevation upto 2500 m and then decreases with increasing elevation, whereas winter and post-monsoon precipitation are almost identical to each elevation interval of 500 m.

Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

2014 ◽  
Vol 51 (1) ◽  
pp. 43-55 ◽  
Author(s):  
Bardia Bayat ◽  
Mohsen Nasseri ◽  
Gholamreza Naser
Keyword(s):  
Maximum Entropy ◽  
Ordinary Kriging ◽  
Cluster Method ◽  
Annual Precipitation ◽  
Cluster Center ◽  
Bayesian Maximum Entropy ◽  
Mean Annual Precipitation ◽  
Secondary Variable

The main purpose of this research is to investigate spatial variations of mean annual precipitation in a watershed. As a case study, the research focused on the Namak Lake watershed in Iran. Literature provides various techniques for studying spatial patterns of precipitation in a watershed. These techniques often require a large dataset. On the other hand, nonuniform data distribution in a watershed can reduce the accuracy and reliability of the predictions. To overcome these problems, this research applied the cluster method coupled with ordinary Kriging and Bayesian maximum entropy techniques. An estimated point was modified based on the distance from the point to the cluster center. The research considered elevation variations as a secondary variable. A cross-validation technique was used for evaluating the results of mean annual precipitations. The research compared the results of ordinary Kriging and Bayesian maximum entropy methods with and without the application of the clustering method. The research concluded that the cluster-based method can estimate the dynamics of long-term mean annual precipitation more reliably and accurately. The research also revealed more informative results for the cluster-based method.

scholarly journals Downscaling of Satellite OPEMW Surface Rain Intensity Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1763 ◽  
Author(s):  
Angela Cersosimo ◽  
Salvatore Larosa ◽  
Filomena Romano ◽  
Domenico Cimini ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Ordinary Kriging ◽  
Second Generation ◽  
Slope Aspect ◽  
Rain Intensity ◽  
Mean Values ◽  
Kriging With External Drift ◽  
Infrared Imager ◽  
Precipitation Estimation ◽  
Fine Resolution ◽  
External Drift

This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.

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