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 Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmet Irvem ◽  
Mustafa Ozbuldu
Keyword(s):  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
The West ◽  
Data Set ◽  
Precipitation Product ◽  
Distance Weighting ◽  
Areal Precipitation ◽  
Average Annual Precipitation ◽  
Inverse Distance

Use of the satellite and reanalysis precipitation products, as supplementary data sources, are steadily rising for hydrometeorological applications, especially in data-sparse areas. However, the accuracy of these data sets is often lacking, especially in Turkey. This study evaluates the accuracy of satellite precipitation product (TRMM 3B42V7) and reanalysis precipitation product (NCEP-CFSR) against rain gauge observations for the 1998–2010 periods. Average annual precipitation for the 25 basins in Turkey was calculated using rain gauge precipitation data from 225 stations. The inverse distance weighting (IDW) method was used to calculate areal precipitation for each basin using GIS. According to the results of statistical analysis, the coefficient of determination for the TRMM product gave satisfactory results (R2 > 0.88). However, R2 for the CFSR data set ranges from 0.35 for the Eastern Black Sea basin to 0.93 for the West Mediterranean basin. RMSE was calculated to be 95.679 mm and 128.097 mm for the TRMM and CFSR data, respectively. The NSE results of TRMM data showed very good performance for 6 basins, while the PBias value showed very good performance for 7 basins. The NSE results of CFSR data showed very good performance for 3 basins, while the PBias value showed very good performance for 6 basins.

scholarly journals Atmospheric Deposition Effects on Agricultural Soil Acidification State — Key Study: Krupanj Municipality

2014 ◽  
Vol 40 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Dragan Čakmak ◽  
Jelena Beloica ◽  
Veljko Perović ◽  
Ratko Kadović ◽  
Vesna Mrvić ◽  
...  
Keyword(s):  
Soil Acidification ◽  
Interpolation Method ◽  
Natural Processes ◽  
Target Loading ◽  
Distance Weighting ◽  
The Status ◽  
Inverse Distance ◽  
Sustainable Productivity

Abstract Acidification, as a form of soil degradation is a process that leads to permanent reduction in the quality of soil as the most important natural resource. The process of soil acidification, which in the first place implies a reduction in soil pH, can be caused by natural processes, but also considerably accelerated by the anthropogenic influence of excessive S and N emissions, uncontrolled deforestation, and intensive agricultural processes. Critical loads, i.e. the upper limit of harmful depositions (primarily of S and N) which will not cause damages to the ecosystem, were determined in Europe under the auspices of the Executive Committee of the CLRTAP in 1980. These values represent the basic indicators of ecosystem stability to the process of acidification. This paper defines the status of acidification for the period up to 2100 in relation to the long term critical and target loading of soil with S and N on the territory of Krupanj municipality by applying the VSD model. The Inverse Distance Weighting (IDW) geostatistic module was used as the interpolation method. Land management, particularly in areas susceptible to acidification, needs to be focused on well-balanced agriculture and use of crops/seedlings to achieve the optimum land use and sustainable productivity for the projected 100-year period.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

scholarly journals Development and evaluation of an extended inverse distance weighting method for streamflow estimation at an ungauged site

2015 ◽  
Vol 47 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Muhammad Waseem ◽  
Muhammad Ajmal ◽  
Ungtae Kim ◽  
Tae-Woong Kim
Keyword(s):  
Weighted Average ◽  
Inverse Distance Weighting ◽  
General Idea ◽  
Coefficient Of Determination ◽  
Weighting Method ◽  
Ungauged Sites ◽  
Distance Weighting ◽  
Leave One Out ◽  
Mean Square Errors ◽  
Inverse Distance

In spatial interpolation, one of the most widely used deterministic methods is the inverse distance weighting (IDW) technique. The general idea of IDW is primarily based on the hypothesis that the attribute value of an ungauged site is the weighted average of the known attribute values within the neighborhood, and the ‘weights’ are merely associated with the horizontal distances between the gauged and ungauged sites. However, here we propose an extended version of IDW (hereafter, called the EIDW method) to provide ‘alternative weights’ based on the blended geographical and physiographical spaces for estimation of streamflow percentiles at ungauged sites. Based on the leave-one-out cross-validation procedure, the coefficient of determination had a value of 0.77 and 0.82 for the proposed EIDW models, M1 and M2, respectively, with low root mean square errors. Moreover, after investigating the relationship between the prediction efficiency and the distance decay parameter (C), the better performance of the M1 and M2 resulted at C = 2. Furthermore, the results of this study show that the EIDW could be considered as a constructive way forward to provide more accurate and consistent results in comparison to the traditional IDW or the dimension reduction technique-based IDW.

scholarly journals Determination of the best interpolation method in estimating the concen-tration of environmental air pollutants in Tehran city in 2015

2019 ◽  
Author(s):  
Akbar Eslami ◽  
Seyed Mehdi Ghasemi
Keyword(s):  
Interpolation Method ◽  
Ambient Air ◽  
Air Concentration ◽  
Pearson Coefficient ◽  
Distance Weighted ◽  
Kolmogorov Smirnov ◽  
Environmental Air ◽  
Inverse Distance ◽  
Better Than

Introduction: Air pollution is one of the important issues in developing coun-tries, due to increased population and industrialization. In this research, the spatial distribution of ambient air concentration such as CO, NO2, SO2, PM2.5, PM10, O3 and Air quality Index (AQI) in Tehran city in 2015 were evaluated using different deterministic ( inverse distance weighted, local polynomial, global polynomial, radial basis functions) and geostatistical (Kriging, Cokrig-ing) methods.   Materials and methods: Root Mean Square Error (RMSE) and Mean Error (ME) using cross-evaluation methods were used to control the accuracy of the interpolation. To find the secondary variables in the cokriging method, the Pearson coefficient of each pollutant was calculated with another pollutant.   Results: The Kolmogorov-Smirnov test showed that all data followed normal distribution. Also the results indicated that in most cases, geostatistical meth-ods were the best methods to estimate ambient air concentration. Finally, after selecting the best interpolation method, the zoning map of the pollutant was drawn with ArcGIS.   Conclusion: The results of 71 methods showed that in most cases, the geosta-tistical method is better than the deterministic method

scholarly journals AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

2021 ◽  
Vol V-4-2021 ◽  
pp. 83-88
Author(s):  
M. Zhou ◽  
K. Li ◽  
M. Pan ◽  
J. Chen ◽  
C. Li ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Meteorological Parameter ◽  
Atmospheric Correction ◽  
Absolute Error ◽  
Lapse Rate ◽  
Interpolation Methods ◽  
Spatial Interpolation Method ◽  
Meteorological Elements ◽  
Inverse Distance

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

scholarly journals Geostatistical Merging of a Single-Polarized X-Band Weather Radar and a Sparse Rain Gauge Network over an Urban Catchment

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 496 ◽  
Author(s):  
Ibrahim Seck ◽  
Joël Van Baelen
Keyword(s):  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
X Band ◽  
Distance Weighting ◽  
Field Bias ◽  
The Mean ◽  
Inverse Distance

Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy.

Suitable Proportion Sample of Holdout Validation for Spatial Rainfall Interpolation in Surrounding the Makassar Strait

2020 ◽  
Vol 33 (2) ◽  
pp. 219-232
Author(s):  
Giarno Giarno ◽  
Muhammad Pramono Hadi ◽  
Slamet Suprayogi ◽  
Sigit Heru Murti
Keyword(s):  
Sample Selection ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Rainfall Variation ◽  
Accuracy Comparison ◽  
Distance Weighting ◽  
Holdout Validation ◽  
Sample Ratio ◽  
Modelling Data

Spatial rainfall interpolation requires a number of suitable validation samples to maintain accuracy. Generally, the larger the areas which can be predicted, the better the interpolation. In addition, the data used for validation should be separated from the modelling data. Moreover, the number of samples determine optimally proportion the independent sites. The objective of this study is to determine the optimal sample ratio for holdout validation in interpolation methods; the Makassar Strait was chosen as the study location because of its daily rainfall variation. The accuracy of the sample selection is tested using correlation, root mean square error (RMSE), mean absolute error (MAE) and the indicators of contingency tables. The results show that accuracy depends on the ratio of the modelling data. Therefore, the more extensive the data used for interpolation, the better the accuracy. Otherwise, if the rain gauge data is separated according to province, there will be a variation in accuracy in the portion of independent samples. For rainfall interpolation, it is recommended to use a minimum 75% of data sites to maintain accuracy. Comparison between kriging and inverse distance weighting or IDW methods indicates that IDW is better. Moreover, rainfall characteristics affect the accuracy and portion of the independent sample.

Impacts of Mid-Rainy Season Rainfall on Runoff into the Chao Phraya River, Thailand

2013 ◽  
Vol 8 (3) ◽  
pp. 397-405 ◽  
Author(s):  
Shunji Kotsuki ◽  
◽  
Kenji Tanaka ◽  
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Heavy Rainfall ◽  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Necessary Condition ◽  
Surface Model ◽  
Chao Phraya River ◽  
June July

In Chao Phraya River basin, the runoff at the middle basin (Nakhon Sawan station: C.2 point) is important for the prevention of lower basin floods. Through analyzing 1980 to 2011 runoff and rain gauge data and performing numerical calculations using a hydrological land surface model, this study will describe a condition that causes massive floods at the C.2 point. The main conclusions are the following: (1) In 2011, precipitation exceeding the average by about 40% caused naturalized runoff +125% (+29 billion m3) that in an average year. The massive 2011 flood would have been difficult to prevent even if the operation of the Bhumibol Dam and Sirikit Dam had been appropriate. (2) In 1980, 1995, and 2006, precipitation exceeding the average by about 10% caused naturalized runoff exceeding that of the average year by 50 to 75%. The runoff rate in the Chao Phraya River basin is about 20%, and characteristically a minor increase in precipitation results in a considerable amount of runoff. (3) There are natural flood years, which have higher than average precipitation that causes massive floods, and there are non-natural flood years, which have high precipitation but nomassive floods. In natural flood years, the precipitation in June, July, and August is higher than that in the average years, and the total water storage capacity is brought close to saturation in September. Due to this, in addition to base runoff, surface runoff increases. (4) The coefficient of the determination of observed runoff from August to October is 0.6481 for rainfall from June to August and 0.5276 for rainfall from August to October. Heavy rainfall in June, July and August has the effect of bringing the soil close to saturation, which is a necessary condition for massive flooding. Massive flooding results if this necessary condition is met and there is heavy rainfall in September and October. This finding is also supported by a high coefficient of determination of 0.7260 between rainfall in May, June, July, August, September, and October and naturalized runoff in August, September, and October.

Sign in / Sign up

Export Citation Format

Share Document