Basin-wide Index Research Articles

Seasonal and Interannual Variability of the Indo-Pacific Warm Pool and its Associated Climate Factors Based on Remote Sensing

With satellite observed Sea Surface Temperature (SST) accumulated for multiple decades, multi-time scale variabilities of the Indo-Pacific Warm Pool are examined and contrasted in this study by separating it into the Indian Ocean sector and the Pacific Ocean sector. Surface size, zonal center, meridional center, maximum SST and mean SST as the practical warm pool properties are chosen to investigate the warm pool variations for the period 1982–2018. On the seasonal time scale, the oscillation of the Indian Warm Pool is found much more vigorous than the Pacific Warm Pool on size and intensity, yet the interannual variabilities of the Indian Warm Pool and the Pacific Warm Pool are comparable. The Indian Warm Pool has weak interannual variations (3–5 years) and the Pacific Warm Pool has mighty interdecadal variations. The size, zonal movement and mean SST of the Indian Ocean Warm Pool (IW) are more accurate to depict the seasonal variability, and for the Pacific Ocean Warm Pool (PW), the size, zonal and meridional movements and maximum SST are more suitable. On the interannual scale, except for the meridional movements, all the other properties of the same basin have similar interannual variation signals. Following the correlation analysis, it turns out that the Indian Ocean basin-wide index (IOBW) is the most important contributor to the variabilities of both sectors. Lead-lag correlation results show that variation of the Pacific Ocean Warm Pool leads the IOBW and variation of the Indian Ocean Warm Pool is synchronous with the IOBW. This indicates that both sectors of the Indo-Pacific Warm Pool are significantly correlated with basin-wide warming or cooling.

Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition

Streamflow is the result of complex climatic and hydrological interactions that are driven by atmosphere–ocean circulation. Teleconnection analysis of streamflow is significant for identifying the atmosphere- and climate-related indices of hydrology series. To mine the physical information of streamflow series more effectively, ensemble empirical mode decomposition (EEMD) was employed to decompose streamflow series into inherent stationary components with different periodic oscillations and a trend. In this study, important climate indices were identified based on the correlation coefficients, which were calculated using cross-correlation. According to the selected indicators, the decomposed and streamflow series were regressed using an artificial neural network (ANN) and support vector regression (SVR). In total, 130 climate phenomenon indices, pre-runoff, and 1–12-month time lags were considered as teleconnection variables. The results show that EEMD can be used to extract the period and trend of streamflow, and that the decomposed hydrology components have much stronger correlations than the original runoff with the climate phenomenon indices. The original streamflow was the most closely correlated with the series from the previous month, which is an autocorrelation. However, more physical information was obtained through the teleconnection of the sub-streamflow series. The periodic oscillations was explained by relatively diverse atmospheric circulation and sea surface temperature indices with different time lags. The low-frequency periodic series were better represented than the high-frequency series by the climate phenomenon indices. In addition, Nino 3.4, Nino 4, the Warm-pool ENSO Index, and the ENSO Modoki Index were representative of the high-frequency component; the mid-frequency signals were sensitive to the Solar Flux Index, Total Sunspot Number Index, Pacific Decadal Oscillation Index, and Southern Oscillation Index; the low-frequency series were influenced by the Atlantic Multi-decadal Oscillation Index, North African Subtropical High Area Index, North Atlantic Triple Index, etc.; and the Indian Ocean Basin-Wide Index, Atlantic Multi-decadal Oscillation Index, Western Pacific Warm Pool Strength Index, and East Pacific 850 mb Trade Wind Index expressed well the long-term trend of monthly streamflow. Meanwhile, the regression results of the decomposed series obtained by the ANN and SVR exhibited better statistical performance than those of the original series, especially for the EEMD-SVR.

Predicting biological conditions for small headwater streams in the Chesapeake Bay watershed

A primary goal for Chesapeake Bay watershed restoration is to improve stream health and function in 10% of stream miles by 2025. Predictive spatial modeling of stream conditions, when accurate, is one method to fill gaps in monitoring coverage and estimate baseline conditions for restoration goals. Predictive modeling can also monitor progress as additional data become available. We developed a random forests model to predict biological condition of small streams (<200 km2 in drainage) in the Chesapeake Bay watershed. Biological condition was measured with the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI), a stream macroinvertebrate index. Our goal was to predict biological condition in all unsurveyed small streams present in a 1:24,000 scale catchment layer as a 2004–2008 baseline. We reclassified the 5-category Chessie BIBI ratings into two categories, poor and fair/good, to align with management goals of the Chesapeake Bay Program. The model included 12 geospatial predictor variables including measures on spatial location, bioregion, land cover, soil, precipitation, and number of dams in local catchments. We trained the model with a random 75% subset of Chessie BIBI data (n = 1449), and used the remaining 25% of Chessie BIBI data (n = 484) as test data. The model performed well, correctly predicting 72% of samples in training data and 73% of samples in test data, but model accuracy varied among bioregions. We performed uncertainty analyses by adding bands of either ±0.05 or ±0.10 BIBI units to the cutoff between poor and fair/good. These uncertainty analyses resulted in 14.5% (±0.05 band) and 24.8% (±0.10 band) of samples in test data being classified as in uncertain condition. For 95,877 small stream reaches in the Chesapeake Bay watershed, the model predicted 64% in fair/good condition, the ±0.05 uncertainty analyses predicted 57% in fair/good condition, and the ±0.10 uncertainty analysis predicted 50% in fair/good condition. These reported values have different implications for the number of improved stream miles required to meet the goal of improving 10%. Incorporating uncertainty provides an assessment of model strength as well as confidence in predictions. We, therefore, suggest increased reporting of uncertainty in studies that spatially predict stream conditions.

Geomorphology-based index for detecting minimal flood stages in arid alluvial streams

Abstract. Identification of a geomorphic index to represent lower thresholds for minor flows in ephemeral, alluvial streams in arid environments is an essential step as a precursor for reliable flash flood hazard estimations and establishing flood warning systems. An index, termed Alluvial wadi Flood Incipient Geomorphologic Index (AFIG), is presented. Analysis of data from an extensive field survey in the arid ephemeral streams in southern and eastern Israel was conducted to investigate the AFIG and the control over its value across the region. During the survey we identified distinguishable flow marks in the lower parts of streams' banks, such as niches, vegetation line, and change in bank material, which are indicative of low flows. The cross-sectional characteristics of the AFIG were studied in relationship with contributing drainage basin characteristics such as lithology, topography, and precipitation. Drainage area and hardness of the exposed lithology (presented as a basin-wide index) are the preferred descriptors to be used in estimating a specific AFIG in unsurveyed sites. Analyses of discharge records from seven hydrometric stations indicate that the recurrence interval of the determined AFIG is equal to or more frequent than 0.5 yr.

Ecosystem effects of the Atlantic Multidecadal Oscillation

Multidecadal variability in the Atlantic Ocean and its importance to the Earth's climate system has been the subject of study in the physical oceanography field for decades. Only recently, however, has the importance of this variability, termed the Atlantic Multidecadal Oscillation or AMO, been recognized by ecologists as an important factor influencing ecosystem state. A growing body of literature suggests that AMO-related fluctuations are associated with shifts in ecological boundaries, primary productivity, and a number of ecologically and economically important coastal and marine populations across the Atlantic basin. Although the AMO is a basin-wide index of SST, the drivers of ecosystem change encompass more than temperature anomalies and the mode of action differs within each ecosystem. A common theme in assessing ecosystem change indicates that fluctuations in water masses and circulation patterns drive shifts in ecosystem states, but the magnitude and rate of change is dependent on the physical characteristics of the region. Because of the wide ranging geographic effects of the AMO, and considering its multidecadal nature, a more complete understanding of its causes and effects would allow scientists and managers to more effectively inform ecosystem-based management across the Atlantic Basin.

Recent intensification of tropical climate variability in the Indian Ocean

Coral records from a range of sites extend the index of the Indian Ocean Dipole back to 1846. Indian Ocean Dipole events increased in strength and frequency in the twentieth century, coincident with the development of direct feedbacks with the Asian Monsoon. The interplay of the El Nino Southern Oscillation, Asian monsoon and Indian Ocean Dipole (IOD)1,2,3 drives climatic extremes in and around the Indian Ocean. Historical4,5 and proxy6,7,8,9 records reveal changes in the behaviour of the El Nino Southern Oscillation and the Asian monsoon over recent decades10,11,12. However, reliable instrumental records of the IOD cover only the past 50 years1,3, and there is no consensus on long-term variability of the IOD or its possible response to greenhouse gas forcing13. Here we use a suite of coral oxygen-isotope records to reconstruct a basin-wide index of IOD behaviour since AD 1846. Our record reveals an increase in the frequency and strength of IOD events during the twentieth century, which is associated with enhanced seasonal upwelling in the eastern Indian Ocean. Although the El Nino Southern Oscillation has historically influenced the variability of both the IOD and the Asian monsoon3,8,10, we find that the recent intensification of the IOD coincides with the development of direct, positive IOD–monsoon feedbacks. We suggest that projected greenhouse warming may lead to a redistribution of rainfall across the Indian Ocean and a growing interdependence between the IOD and Asian monsoon precipitation variability.

Application of the Wetland Fish Index to Northern Great Lakes Marshes with Emphasis on Georgian Bay Coastal Wetlands

The wetland fish index (WFI), a published indicator of wetland condition that ranks wetlands based on tolerance of fish species to degraded water-quality conditions, had been developed with data from 40 wetlands located exclusively in the southern portion of the Great Lakes basin (Erie, Ontario, and Michigan). No data had been included from wetlands of the northern Great Lakes (Superior and Huron) and especially those of eastern and northern Georgian Bay, where many wetlands are still unaffected by human activities. We demonstrate why application of the WFI for the lower lakes (WFI Lower) can yield biased scores when applied to data for upper lakes wetlands. We then develop a basin-wide index to include data from 60 other coastal wetlands located in the northern portion of the basin, including 32 from Georgian Bay. Inclusion of northern sites in development of a basin-wide WFI (WFI Basin) still produced index scores that were positively correlated with water-quality conditions as indicated by water quality index scores. We explain why use of the basin-wide WFI is better than one developed specifically for upper lakes (WFI Upper). Overall, WFI Basin scores were higher in the northern lakes (Superior 3.49, Georgian Bay 3.67, Huron 3.62) than in the southern lakes (Michigan 3.33, Erie 3.12, Ontario 3.09). WFI scores are only minimally affected by inter-annual variation, which allows for its use for long-term monitoring. We recommend that the WFIBasin be used when managers need to manage at a scale across the entire Great Lakes basin.

Developing biological indicators from diverse data: The Potomac Basin-wide Index of Benthic Integrity (B-IBI)

Abstract A Basin-wide Index of Benthic Integrity (B-IBI) was developed to uniformly assess aquatic ecosystem health of non-tidal, wadeable streams in the multi-jurisdictional Potomac River basin (US). Multiple datasets were merged and used to identify stream classes and discriminating biological metrics. The index (aggregated metric scores) accurately identified 95% of impaired sites. A jackknife cross-validation procedure confirmed the accuracy of the B-IBI. B-IBI assessments generally compare favorably to basin states’ assessments derived from the same data. A habitat quality matrix which includes an indicator of anthropogenic alterations and disturbances is recommended. The Potomac B-IBI is more useful than existing state-specific indexes for stream health comparisons across jurisdictional boundaries and basin-wide. The Potomac B-IBI can improve understanding of water quality issues in the basin and enhance the abilities of water quality managers to make well-informed decisions concerning the basin's non-tidal waters.