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.

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 radar-gauge merging methods for quantitative precipitation estimates

2008 ◽  
Vol 5 (5) ◽  
pp. 2975-3003 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Radar Observations ◽  
Geostatistical Methods ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

scholarly journals Technical note on probabilistic assessment of one-step-ahead rainfall variation by Split Markov Process

2011 ◽  
Vol 8 (1) ◽  
pp. 189-206
Author(s):  
R. Maity ◽  
D. Prasad
Keyword(s):  
Markov Process ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Technical Note ◽  
The State ◽  
Contour Plot ◽  
Probabilistic Assessment ◽  
Time Step ◽  
Rainfall Variation ◽  
One Step

Abstract. In this paper, Split Markov Process (SMP) is developed to assess one-step-ahead variation of daily rainfall at a rain gauge station. SMP is an advancement of general Markov Process (MP) and specially developed for probabilistic assessment of change in daily rainfall magnitude. The approach is based on a first-order Markov chain to simulate daily rainfall variation at a point through state/sub-state Transitional Probability Matrix (TPM). The state/sub-state TPM is based on the historical transitions from a particular state to a particular sub-state, which is the basic difference between SMP and general MP. In MP, the transition from a particular state to another state is investigated. However, in SMP, the daily rainfall magnitude is categorized into different states and change in magnitude from one temporal step to another is categorized into different sub-states for the probabilistic assessment of rainfall variation. The cumulative state/sub-state TPM is represented in a contour plot at different probability levels. The developed cumulative state/sub-state TPM is used to assess the possible range of rainfall in next time step, in a probabilistic sense. Application of SMP is investigated for daily rainfall at Khandwa station in the Nimar district of Madhya Pradesh, India. Eighty years of daily monsoon rainfall is used to develop the state/sub-state TPM and twenty years data is used to investigate its performance. It is observed that the predicted range of daily rainfall captures the actual observed rainfall with few exceptions. Overall, the assessed range, particularly the upper limit, provides a quantification possible extreme value in the next time step, which is very useful information to tackle the extreme events, such flooding, water logging etc.

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2009 ◽  
Vol 13 (2) ◽  
pp. 195-203 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Network Density ◽  
Radar Observations ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.

scholarly journals A Novel Method for Interpolating Daily Station Rainfall Data Using a Stochastic Lattice Model

2020 ◽  
Vol 21 (5) ◽  
pp. 909-933
Author(s):  
Boualem Khouider ◽  
C. T. Sabeerali ◽  
R. S. Ajayamohan ◽  
V. Praveen ◽  
Andrew J. Majda ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Lattice Model ◽  
Climate Model ◽  
Interpolation Method ◽  
Daily Rainfall ◽  
India Meteorological Department ◽  
Grid Cell ◽  
Rain Gauge ◽  
Distance Weighting ◽  
The World

AbstractRain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.

scholarly journals Radiological Assessment of Indoor Radon and Thoron Concentrations and Indoor Radon Map of Dwellings in Mashhad, Iran

2020 ◽  
Vol 18 (1) ◽  
pp. 141
Author(s):  
Mohammademad Adelikhah ◽  
Amin Shahrokhi ◽  
Morteza Imani ◽  
Stanislaw Chalupnik ◽  
Tibor Kovács
Keyword(s):  
Limit Of Detection ◽  
Indoor Radon ◽  
Absolute Error ◽  
Soil Gas ◽  
Indoor Radon Concentration ◽  
Soil Gas Radon ◽  
Distance Weighting ◽  
The City ◽  
Monitoring Devices ◽  
Radon Concentrations

A comprehensive study was carried out to measure indoor radon/thoron concentrations in 78 dwellings and soil-gas radon in the city of Mashhad, Iran during two seasons, using two common radon monitoring devices (NRPB and RADUET). In the winter, indoor radon concentrations measured between 75 ± 11 to 376 ± 24 Bq·m−3 (mean: 150 ± 19 Bq m−3), whereas indoor thoron concentrations ranged from below the Lower Limit of Detection (LLD) to 166 ± 10 Bq·m−3 (mean: 66 ± 8 Bq m−3), while radon and thoron concentrations in summer fell between 50 ± 11 and 305 ± 24 Bq·m−3 (mean 115 ± 18 Bq m−3) and from below the LLD to 122 ± 10 Bq m−3 (mean 48 ± 6 Bq·m−3), respectively. The annual average effective dose was estimated to be 3.7 ± 0.5 mSv yr−1. The soil-gas radon concentrations fell within the range from 1.07 ± 0.28 to 8.02 ± 0.65 kBq·m−3 (mean 3.07 ± 1.09 kBq·m−3). Finally, indoor radon maps were generated by ArcGIS software over a grid of 1 × 1 km2 using three different interpolation techniques. In grid cells where no data was observed, the arithmetic mean was used to predict a mean indoor radon concentration. Accordingly, inverse distance weighting (IDW) was proven to be more suitable for predicting mean indoor radon concentrations due to the lower mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, the radiation health risk due to the residential exposure to radon and indoor gamma radiation exposure was also assessed.

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

scholarly journals Daily precipitation records over mainland Spain and the Balearic Islands

2013 ◽  
Vol 13 (10) ◽  
pp. 2483-2491 ◽  
Author(s):  
C. Ramis ◽  
V. Homar ◽  
A. Amengual ◽  
R. Romero ◽  
S. Alonso
Keyword(s):  
Extreme Events ◽  
Extreme Values ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Balearic Islands ◽  
Precipitation Event ◽  
Similar Distribution ◽  
Major Interest ◽  
Precipitation Records ◽  
Different Characteristics

Abstract. Understanding the spatial distribution of extreme precipitations is of major interest in order to improve our knowledge of the climate of a region and its relationship with society. These analyses inevitably require the use of directly observed values to account for the actual extreme amounts rather than analyzed gridded values. A study of daily rainfall extremes observed over mainland Spain and the Balearic Islands is performed by using records from 8135 rain gauge stations from the Spanish Weather Agency (AEMET). Results show that the heaviest daily precipitations have been observed mainly on the coastal Mediterranean zone from Gibraltar to the Pyrenees. In particular, a record value of 817 mm was recorded in the Valencia region in 1987. The current map of daily records in Spain, which updates the pioneering work of the Spanish meteorologist Font, shows similar distribution of extreme events but with notably higher amounts. Generalized extreme values distributions fit the Mediterranean and Atlantic rain gauge measurements and shows the different characteristics of the extreme daily precipitations in both regions. We identify the most extreme events (above 500 mm per day) and provide a brief description of a typical meteorological situation in which these damaging events occur. An analysis of the low-level circulation patterns producing such extremes – by means of simple indices such as NAO, WeMOi and IBEI – confirms the relevance of local flows in the generation of either Mediterranean or Atlantic episodes. WeMOi, and even more IBEI, are good discriminants of the region affected by the record precipitation event.

scholarly journals Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

2018 ◽  
Vol 10 (12) ◽  
pp. 1879 ◽  
Author(s):  
Véronique Michot ◽  
Daniel Vila ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Josyane Ronchail ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Spatial Coverage ◽  
Rainfall Regimes ◽  
Trmm 3B42 ◽  
Good Agreement

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

scholarly journals TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

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