Long-term rainfall trends analysis under climate change, particularly in developing countries where rainfed agriculture is substantial, is vigorous to evaluate rainfall variability brought changes and propose possible adaptation measures. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. The result revealed that the performance of different products varies with climate. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson-Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research-University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Moreover, this methodology can be also applied to new variables, regions and problems, as there are no physical restrictions on the used predictors nor predictands.The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Our results highlight the suitability of data driven models to simulate storm surge maximum levels, and prove the methodology is appropriate for finding a well performing atmospheric predictor that is able for reconstruct these values. For the Kling–Gupta Efficiency (KGE incorporating 3 sub-metrics: correlation, bias term and variability term), which is the metric used to rank the models, the average value is 0.82. Results show very good performance for the best atmospheric predictor and statistical model, providing average values of 0.88 for the Pearson correlation coefficient and 4.3 cm for the root mean squared error metric (RMSE) (the average value for the RMSE in the 99% percentile is 8.2 cm). Finally, the storm surge daily maxima are reconstructed with the different statistical models along the entire coast, using the best performing predictor. Firstly, several atmospheric predictors are utilized that incorporate different variables, time lags and spatial domains, using 3 statistical models, in a selected number of locations in New Zealand, to find the combination that optimizes the reconstruction. This study explores the use of data driven methods as an alternative to numerical models to reconstruct the daily storm surge maximum levels along the entire coast of New Zealand. Therefore, the performance of a live prediction system based on such methods will likely be subject to a trade-off between prediction accuracy, prediction speed and cost. Because local inundation is strongly modulated by the local shape of the coastline and the bathymetric slope, accurate storm surge predictions using traditional numerical models require the use of very fine grids and are hence resource intensive. In conjunction with tides, storm surge is one major driver of coastal flooding associated with storm events.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |