Using oceanic‐atmospheric oscillations for long lead time streamflow forecasting

Estimating long‐lead time precipitation under the stress of increased climatic variability is a challenging task in the field of hydrology. A modified Support Vector Machine (SVM) based framework is proposed to estimate annual precipitation using oceanic‐atmospheric oscillations. Oceanic‐atmospheric oscillations, consisting of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) for a period of 1900–2008, are used to generate annual precipitation estimates with a 1 year lead time. The SVM model is applied to 17 climate divisions encompassing the Colorado River Basin in the western United States. The overall results revealed that the annual precipitation in the Colorado River Basin is significantly influenced by oceanic‐atmospheric oscillations. The long‐term precipitation predictions for the Upper Colorado River Basin can be successfully obtained using a combination of PDO, NAO, and AMO indices, whereas coupling AMO and ENSO results in improved precipitation predictions for the Lower Colorado River Basin. The results also show that the SVM model provides better precipitation estimates compared to the Artificial Neural Network and Multivariate Linear Regression models. The annual precipitation estimates obtained using the modified SVM modeling framework may assist water managers in statistically understanding the hydrologic response in relation to large scale climate patterns within the Colorado River Basin.

Teleconnections between El Niño/Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) and seasonal precipitation regimes over the Yangtze River basin have been analysed based on the rotated empirical orthogonal functions. Results show that ENSO is the leading driver of seasonal precipitation variability over the Yangtze River basin, and the spring precipitation has been influenced by the PDO and ENSO, the summer and autumn precipitation has been influenced by the ENSO and IOD, the winter precipitation has been influenced by the ENSO, IOD and NAO. Furthermore, changes for the seasonal occurrence and intensity of wet days linked to the ENSO, NAO, IOD and PDO indices have also been investigated to discover which is the dominant mechanism driving seasonal precipitation changes. And results indicated that the influences of ENSO, NAO, IOD and PDO on the seasonal occurrence and intensity of precipitation events are complex, such as that the negative PDO event at the same year tends to increase the spring occurrence of precipitation events in the southwestern part of the Yangtze River basin while the positive ENSO event a year earlier tends to increase the spring intensity of precipitation events in the east part of the Yangtze River basin.

Groundwater level response in U.S. principal aquifers to ENSO, NAO, PDO, and AMO

The ability to predict the future variability of groundwater resources in time and space is of critical importance in society’s adaptation to climate variability and change. Periodic control of large-scale ocean-atmospheric circulations on groundwater levels serves as a potentially effective source for relatively long-term forecasting. In this study, as a first national-scale assessment, we use the continuous wavelet transform, global power spectrum, and wavelet coherence analyses to quantify the linkage between the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (ENSO) and the representative groundwater levels of the 24 principal aquifers, scattered across 14 different climate zones of Iran. Aquifer storage variations are found to be partially derived from annual to interdecadal climate variability and not solely a function of pumping variability. Moreover, teleconnections are observed to be both frequency and time-specific. The significant coherence patterns between the climate indices and groundwater levels are concentrated at five frequency bands of the annual (~ 1 year), short-interannual (2–4 years), medium-interannual (4–6 years), decadal (8–12 years), and interdecadal (14–18 years), consistent with the dominant oscillations of climate indices. AMO’s strong linkage to groundwater variability is found to be at interdecadal and annual modes of groundwater levels while PDO’s highest imprint is concentrated in interannual, decadal, and interdecadal periodicities. Unlike ENSO for which the highest modulating influence is found to be across the decadal and interannual modes, the NAO’s footprint in aquifers is marked at annual and interdecadal frequency bands. Findings further show that the groundwater variability is driven primarily by a combination of multiple large-scale atmospheric circulations rather than a single atmospheric circulation index. The decadal and interdecadal oscillation modes are the dominant modes in Iranian aquifers. Findings also mark the unsaturated zone contribution in damping and lagging of the climate variability modes, particularly for the higher-frequency indices of ENSO and NAO so that groundwater variability is more correlated with those climate circulations that have lower frequency such as PDO and AMO. Finally, the data length is found to have a significant effect on the teleconnections if the time series are not contemporaneous and for each particular series, only one value of coherence/correlation is computed instead of separate computations for different frequency bands and different time spans.

Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the forecast opportunity for the streamflow. Prediction of future water availability accurately and reliably is a key step for successful water resource management in the arid regions. Four popular ocean-atmospheric indices were used in this study for annual streamflow volume prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven model based on Bayesian learning approach was used as a prediction model. The model was applied to four unimpaired stream gages in Utah that spatially covers the state from north to south. Different models were developed based on the combinations of oscillation indices in the input. A total of 60 years (1950-2009) of data were used for the analysis. The model was trained on 50 years of data (1950-1999) and tested on 10 years of data (2000-2009). The best combination of oscillation indices and the lead-time were identified for each gage which was used to develop the prediction model. The predicted flow had reasonable agreement with the actual annual flow volume. The sensitivity analysis shows that the PDO and ENSO have relatively stronger effect compared to other oscillation indices in Utah. The prediction results from the MVRVM were compared with the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM performed relatively better.

We investigate the potential impact of Stratospheric Aerosol Intervention (SAI) on the spatiotemporal behavior of large-scale climate teleconnection patterns represented by the North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), El Niño/Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO) indices using simulations from the Community Earth System Models (CESM1 and CESM2). The leading Empirical Orthogonal Function of sea surface temperature (SST) anomalies indicates that greenhouse gas forcing is accompanied by increases in variance across both the North Atlantic (i.e., AMO) and North Pacific (i.e., PDO) and a decrease over the tropical Pacific (i.e., ENSO); however, SAI effectively reverses these global warming-imposed changes. The projected spatial patterns of SST anomaly related to ENSO show no significant change under either global warming or SAI. In contrast, the spatial anomaly patterns pertaining to AMO (i.e., in the North Atlantic) and PDO (i.e., in the North Pacific) changes under global warming are effectively suppressed by SAI. For AMO, the low contrast between the cold-tongue pattern and its surroundings in the North Atlantic, predicted under global warming, is restored under SAI scenarios to similar patterns as in the historical period. The frequencies of El Niño and La Niña episodes increase with greenhouse gas emissions in the models, while SAI tends to compensate for them. All climate indices’ dominant modes of inter-annual variability are projected to be preserved in both warming and SAI scenarios. However, the dominant decadal and interdecadal variability mode changes induced by global warming are exacerbated by SAI, particularly in the Atlantic-based AMO.

<p>The North Atlantic Oscillation (NAO) index describes the hemispheric meridional oscillation of atmospheric masses above near Iceland and the subtropical Atlantic regions. In specific, NAO index indicates the differences in atmospheric pressure patterns between the two regions. Strong positive NAO index values relate to significant pressure differences, exposing US East and Northern Europe to warmer weather conditions, and Southern Europe to colder weather conditions. Negative NAO index values indicate weaker pressure differences, exposing US East and Northern Europe to cold weather, and Southern Europe to warm weather. A significant portion of the Atlantic sector climate is associated with NAO index and its variability. Historically, NAO index values were in a positive trend between 1970s and 1980s. Highest positive values were reported in the early 1990s. By that time, it was suggested that NAO index positive trends contributed significantly to the global warming signal. Most recently, research outputs from climate model predictions suggest that NAO index values will be more at the positive phase as a result of strong global warming signal. In a warmer climate the overall number of storms is predicted to decrease but storms will be more intense. However, more research is needed to understand variability trends in NAO index and to what extent they might be attributed to climate change impacts. Hence referred in here as NAO natural and non-natural, or else, climate change related variability trends. This study investigated the NAO variability trends by use of Singular Spectrum Analysis (SSA) and SSA based Entropy index. SSA is a statistical mechanics tool used to study the non-linear behavioral characteristics in complex geophysical, meteorological and climatic systems, monitored by time series data. The main objective of this analysis is to reveal the evolution of the NAO index dynamical system and convey information about the changing dynamics of the system. By use of SSA Entropy based index, the chaotic behavior of NAO index is studied. An SSA entropy based chaotic descriptor might entail information of the non-natural variability trend for NAO index values. Also the same descriptor might prove capable of defining a historical milestone of when this NAO variability trend started changing in an unpredictable- non natural- way owed to climate change forcing factors. NAO Index data are extracted from Climatic Research Unit, University of East Anglia from 1979-2018. SSA Phase space reconstruction by method of delays has been applied first to characterize the statistical and chaotic behavior of NAO patterns, by calculating variability and inconsistency descriptors. Phase space reconstruction allows analyzing time series data within the dynamics systems theory context. Following that, reconstructed attractor from the NAO observed time series allowed to build an approximation of the unknown observed states. Results revealed a highly variable and inconsistent behavior in NAO patterns over time. SSA Entropy based index investigation is currently underway to further understand the nature of inconsistency revealed in NAO patterns. Further research is expected to establish wind and wave storm patterns connections with NAO index patterns, through transfer dynamics concepts.</p>

Floods in the middle reaches of the Yangtze River threaten thousands of million people, causing casualties and economic loss. Yet, the prediction of floods in this region is still challenging. To better understand the floods in this region, we investigate the interdecadal-interannual rainfall variation of the flood season (April–September) in Hunan province. The relationship between the rainfall and the Pacific decadal oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and El Niño-Southern Oscillation (ENSO) are also analyzed. The results show that the precipitation in the flood seasons shows an interdecadal oscillation with a period of about 20 years, which is caused by the joint effect of the PDO and AMO. When the PDO and AMO are in the same phase, the corresponding flood season is characterized by more precipitation, and conversely, it is less precipitation. Further analyses show that in the year after El Niño, when the PDO and AMO are both in the positive phase, it is favorable for the west Pacific subtropical high (WPSH) to be stronger and more southward than normal. Such circulation anomaly is conducive to the water vapor transport to the southern China, and as a result there is more precipitation in Hunan. When the PDO and AMO are both in the negative phase, the WPSH is weaker than normal, but the India-Burma trough is obviously stronger, which is also favorable for the southwesterly water vapor transport to the southern China. However, in the next year of the La Niña year, regardless of the phase combination of the PDO and AMO, the southern coast of China are controlled by a negative geopotential height anomaly and the WPSH retreats to the sea, which is not conducive to the northward transport of water vapor, and the precipitation in Hunan is less than normal. But if only the cold SST background in the previous stage is considered (without reaching the standard of a La Niña event),is more precipitation in most of the Hunan Province. Therefore, at the interannual scale, the PDO and AMO also have a modulating effect on the precipitation signal. However, the interannual-scale ENSO signal has a greater influence on the precipitation in Hunan flood seasons. Our results will give implications for the predications of floods in Hunan.

Pacific and Atlantic Ocean influence on the spatiotemporal variability of heavy precipitation in the western United States

Interannual to Decadal Rainfall Variability over Greater Horn of Africa: Non-Stationary Teleconnections, Physical Mechanisms, and Predictability

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

Effect of ENSO modulation by decadal and multi-decadal climatic oscillations on contiguous United States streamflows

Freshwater delivery is an important factor determining estuarine character and health and may be influenced by large-scale climate oscillations. Variability in freshwater delivery (precipitation and discharge) to the Altamaha River estuary (GA, USA) was examined in relation to indices for several climate signals: the Bermuda High Index (BHI), the Southern Oscillation Index (SOI), the Improved El Nino Modoki Index (IEMI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Pacific/North American Pattern (PNA). Discharge to this estuary has been linked to key ecosystem properties (e.g., salinity regime, water residence time, nutrient inputs, and marsh processes), so understanding how climate patterns affect precipitation and river discharge will help elucidate how the estuarine ecosystem may respond to climate changes. Precipitation patterns in the Altamaha River watershed were described using empirical orthogonal functions (EOFs) of the combined multidecadal time series of precipitation at 14 stations. The first EOF (67 % of the variance) was spatially uniform, the second EOF (11 %) showed a spatial gradient along the long axis of the watershed (NW–SE), and the third EOF (6 %) showed a NE–SW pattern. We compared the principal components (PCs) associated with these EOFs, monthly standardized anomalies of Altamaha River discharge at the gauge closest to the estuary, and the climate indices. Complex, seasonally alternating patterns emerged. The BHI was correlated with June–January discharge and precipitation PC 1. The SOI was correlated with January–April discharge and precipitation PC 2, and also weakly correlated with PC 1 in November–December. The AMO was correlated with river discharge and precipitation PC 3 mainly in December–February and June. The correlation patterns of precipitation PCs with PDO and PNA were similar to those with SOI, but weaker. There were no consistent relationships with two NAO indices or IEMI. Connections between climate signals and estimates of nutrient loading were consistent with the connections to discharge. The occurrence of tropical storms in the region was strongly related to the BHI but not to the other climate indices, possibly representing the influence of storm tracking more than the rate of storm formation. Comparison with the literature suggests that the patterns found may be typical of southeastern USA estuaries but are likely to be different from those outside the region.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.