Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India

Abstract

The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.

Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

“…The default hyper parameter values implemented in the current study were obtained from Kolluru et al . (2020b) and are represented in Table S2. The 13 MLAs implemented in the current study are…”

“…Recently, a study by Kolluru et al . (2020b) merged multiple SPPs with multiple MLAs. However, the study was constrained to a medium‐sized watershed and hence the outcomes cannot be extrapolated for other basins having contrast climatic conditions.…”

Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India

“…ese statistics will help us to comprehend the hydrological consequences of sources of errors in SPP [71][72][73]. For computing POD and FAR, a threshold of 1 mm/ day was implemented in the study as mentioned in [7,20,74,75]. e categorical metrics were computed either for the entire time series or after segregating for different rainfall regimes.…”

“…Reviews related to the SPPs evaluation through various hydrological framework models can also be found in different climatic and geographical regions [9][10][11][12]. Most of these studies focused on either analyzing a single rainfall productʼs performance in hydrological modeling or evaluating the efficiency of a few rainfall products in runoff simulations, thus restricting their analysis to specific products [7,[13][14][15][16][17][18][19][20]. Most of the studies have not considered reanalysis products during their evaluation or have not recalibrated each rainfall dataset, thus missing to differentiate the in situ corrected and uncorrected dataset efficiencies [15,[21][22][23][24].…”

“…Further, the SPPs produced from satellite sensors are increasingly considered for hydrological modeling because of their long-term, consistent, and continuous data availability in mountainous and hilly regions [31][32][33]. However, a recent study conducted in [20] in different climatic regions of India concluded that the precipitation datasets failed in detecting rainfall in the tropical region. is mandates the testing of SPPs performance by employing statistical and hydrological techniques in a mountainous tropical river basin.…”