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El Niño Southern Oscillation (ENSO),the periodic fluctuation of sea surface temperature(SST)- atmospheric pressure over the tropical Pacific ocean, have great influence on the climate all around the world.Many studies reveal that there is direct relationship between Indian monsoon and ENSO. The current study is
performed to understand the impact of...
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... Niño, La Niña and normal years selected based on these ONI values during the study period are shown in Table 1. Annual rainfall values for the period 2000-2013 were plotted and variability of rainfall during ENSO years and normal years were studied (Figure 3). It is obtained that all El-Niño years are associated with lower rainfall than the preceding normal years and all La Niña years are associated with higher rainfall values. ...
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Citations
... [13], oceanic Niño index(ONI) [10], southern oscillation index(SOI) [14], sea surface temperature(SST) index [29], ocean heat content(OHC) index [15], wind index [4] and so on. Among them, ONI is the most commonly used indicator of ENSO events in the ocean [7], which represents the three-month running mean of SST anomalies in the east-central tropical Pacific between 5 • S -5 • N and 170 • W -120 • W, namely Niño3.4 region (See Fig. 1). ...
... FTA and STA denote the temporal attention is changed to the simultaneous use of feature channel and time channel attention and the simultaneous use of spatial channel and time channel attention in the Best results are in bold and R 2 will be negative when the model fitting is worse than average Table 4 also compares the various number of SU, including N = 7, 8, 9, 10, 11 and 12, respectively. As can be seen in the table, a smaller number (7,8) clearly decreases the score, which illustrates that N is small and the model has not extracted the information sufficiently. A larger number of layers Spatiotemporal semantic network for ENSO forecasting over long time horizon (10,11,12) also produces a decrease in the score, suggesting redundancy in information, and increasing the number of N also lengthens the model execution time. ...
El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models.
... According to Rishma and Jasima (2015), Wunna watershed has spatially varied runoff throughout the sub-basin and it was stressed the watershed for higher runoff. Wunna watershed is preferentially known for its wetlands. ...
Modeling of hydrological processes plays a vital role in water resources planning and integrated watershed management. Hydrological models have been developed to estimate the streamflow and sediment yield at the sub-basin scale the using the radar precipitation data. In the present investigation, the Soil and Water Assessment (SWAT) model was configured using radar precipitation data of Tropical Rainfall Measuring Mission (TRMM). This setup model was implemented for three watersheds of different hydrological and climatic conditions in India. The observed streamflow and total suspended solids (TSS) in the watershed outlet were used to execute simulation of setup model. The sequential uncertainty fitting technique (SUFI-2) in SWAT-CUP was adopted for sensitivity analysis, calibration, and validation of SWAT model components. Performance of the developed model was assessed by Nash–Sutcliffe model efficiency (NSE) and coefficient of determination (R2). The result of the streamflow simulation obtained at Mahanadi, Bharathpuzha, and Wunna watershed show NSE values of 0.78, 0.70, and 0.63 while the R2 values were 0.81, 0.60, and 0.63 respectively. During streamflow, the model was well simulated for Mahanadi and Bharathpuzha watershed and insignificantly for the Wunna watershed, compared to others. During the simulation of TSS, It was only Mahanadi that was well simulated. Higher runoff and sediment yield were observed in Wunna and Mahanadi, respectively, and insignificant response was observed compared to others in Bharathpuzha. The calibrated parameters and obtained results could be implemented for proper land management practices and watershed management.
... Validation of spatially distributed rain product is required for accurate assessment of hydrological process that will provide information about the performance of various distributed rain products. Rishma et al. (2015) studied the impact of ENSO on rainfall and runoff using rain gauge rainfall over the Venna river; it was found that runoff was higher in the southern region and ENSO events are closely related to in both rainfall and runoff. Das et al. (2018) studied the evaluation of potential climate change impact on monsoon flows over Wain ganga river basin using future response climate model and found that future streamflow will increase with higher temperature. ...
The spatially distributed rainfall datasets are the most appropriate input for distributed/semi-distributed hydrological model, and there is a less probability of uncertainty in this model. The output of rain gauge results in a high probability of uncertainty in models. The focus of the study is to check the feasibility of different rainfall datasets in hydrological modeling. In this current study, satellite-based four rainfall datasets are adopted. The available high resolution, Tropical Rainfall Measuring Mission (TRMM), and National Centers for Environmental Prediction (NCEP) analysis derived Climate Forecast System Reanalysis (CFSR), The Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) and Global Precipitation Climatology Centre (GPCP) rainfall datasets were used to evaluate the performance of the SWAT model. The performance evaluation of the SWAT model was checked with four rainfall datasets using the coefficient of determination (R²) and the coefficient of Nash-Sitcliff efficiency (NSE). It was found that TRMM, GPCP and APHRODITE rainfall datasets could be an alternative source, where APHRODITE is not suitable for the hydrological process because of weak %wet days. The rainfall data sets are used to forecast accurate surface runoff.
... 3 4 region 12 , that is, the ONI is the three-month-moving-average of the Niño . 3 4 index. ...
El Niño-Southern Oscillation (ENSO), which is one of the main drivers of Earth’s inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of ENSO is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Niño 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Niño 3.4 index and SOI mix many low- and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. Therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (EEMD-TCN) hybrid approach, which decomposes the highly variable Niño 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Niño 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984–2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Niño 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Niño 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and EEMD-LSTM methods showed that the EEMD-TCN provides the best overall prediction of both the Niño 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. This result means that the TCN approach performs well in the advance prediction of ENSO and will be of great guiding significance in studying it.
... ElNiño Southern Oscillation (ENSO), the periodic fluctuation in sea surface temperature (SST) and air pressure of the overlying atmosphere across the equatorial Pacific Ocean, often changes the oceanic and atmospheric conditions with irregular periods and amplitude (Cobb et al., 2003) in many regions of the world. The teleconnections between ENSO and regional variables such as precipitation, t e m pe r a t u r e , e va p o r at i o n a nd p r e s s ur e h a ve b e e n documented in various studies over different regions of the world (Ropelewski and Halpert, 1987; Dettingter and Diaz,2000;Rishma et al., 2015). ...
Remote sensing and satellite images have been used to explore the response of rainfall and
vegetation density in Venna river basin of Maharashtra, India to ENSO events selected based on the
Oceanic Niño Index (ONI). Mean rainfall of different ENSO and normal events during 2000-2013 and
anomaly maps of rainfall was generated.Study revealed that El Niño events introduced drier conditions
while LaNiña caused wetter conditions. Change in vegetation studied for crop and non-crop seasons
using MODIS NDVI data indicated that the year to year variability of rainfall and vegetation coincides with
ENSO events
Global climate change has stressed the water availability scenario across the globe. As far as India is concerned, still about 60% of agriculture is rainfed, and global climate change has huge impact on its sustainability. Accurate information on the probable changes in monsoon patterns and availability of water resource is a must for sustainable rainfed agriculture. Proper monitoring and timely anticipation of spatial impacts also assume greater importance in water resource management. Drastic changes in the global environment have compelled us to increase the awareness amongst masses, especially related to and affected by water availability to convert our efforts into higher rate of success. Environmental management could be achieved only through monitoring of the changes in environment and making everybody aware of it. Geoinformatics is the potential technology for generating baseline data for monitoring of environmental parameters pertaining to several environmental changes. Remote sensing technology with various satellites collecting information from the space at different spectral, spatial and temporal resolutions is being widely used for extracting information related to many baseline parameters like vegetation, crops, forests, water resources, urban changes, rain water harvesting, etc. Geographic information system (GIS) has the capability to generate spatial digital data for projecting the parametric information at various levels of environmental monitoring. There is need to exploit the full potential of geoinformatics for timely monitoring and management of the water resources. In today’s changing global climate, visualization of water resource parameters and management of water resources could be effectively achieved through application of geoinformatics. The chapter discusses applications of geoinformatics in various aspects of water resources monitoring, especially with reference to climate change.
