Assessing the utility of climate variability information in streamflow forecasting

2021 ◽  
Author(s):  
Prem Lal Patel ◽  
Priyank Sharma ◽  
Ramesh Teegavarapu
Keyword(s):  
Climate Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Input Parameter ◽  
Statistical Error ◽  
Linear Process ◽  
Linear Regression Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Time Step

<p>The prediction of total and peak streamflows are essential for effective management of water resources systems. A data-driven approach, Model Tree (MT), is applied to predict daily streamflows for a tropical river basin in India. The Tapi River drains a total area of 65,225 km<sup>2</sup>, wherein more than 20 million people are directly or indirectly dependent on it for their water and food requirements. The MT approach executes piece-wise linearization of a non-linear process for the input parameter space and develops linear regression models for each sub-space. The large-scale oceanic-atmospheric oscillations, such as El Niño-Southern Oscillation (ENSO), exert considerable influence on the hydroclimatic conditions across the globe. Based on the Oceanic Niño Index, the warm and cool phases of ENSO are identified as El Niño and La Niña, respectively. It is found that the El Niño and La Niña are associated with drier and wetter than normal conditions respectively across the Tapi basin. Hence, the hypothesis that incorporation of climate variability information would help in enhancing the predictive performance of the model is being tested. A daily-time step model for streamflow prediction is developed considering various hydrometerological inputs observed for the period 1975-2013 to predict streamflows at the catchment outlet. Additionally, two separate models, viz., El Niño- and La Niña-specific models, are developed considering the observed variables corresponding to these phases, and their skill of prediction with respect to the overall model is evaluated. The evaluation of the developed models is further carried out through a suite of statistical error and performance indices, and inferences are drawn.</p>

scholarly journals Synchronous combined effects of fishing and climate within a demersal community

2012 ◽  
Vol 70 (2) ◽  
pp. 319-328 ◽  
Author(s):  
Antoni Quetglas ◽  
Francesc Ordines ◽  
Manuel Hidalgo ◽  
Sebastià Monserrat ◽  
Susana Ruiz ◽  
...  
Keyword(s):  
Climate Variability ◽  
Age Structure ◽  
Large Scale ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Early Period ◽  
Combined Effects ◽  
Population Fluctuations ◽  
Fishing Activity ◽  
The Mediterranean

Abstract Quetglas, A., Ordines, F., Hidalgo, M., Monserrat, S., Ruiz, S., Amores, Á., Moranta, J., and Massutí, E. 2013. Synchronous combined effects of fishing and climate within a demersal community. – ICES Journal of Marine Science, 70: 319–328. Accumulating evidence shows that fishing exploitation and environmental variables can synergistically affect the population dynamics of exploited populations. Here, we document an interaction between fishing impact and climate variability that triggered a synchronic response in the population fluctuations of six exploited species in the Mediterranean from 1965–2008. Throughout this period, the fishing activity experienced a sharp increase in fishing effort, which caused all stocks to shift from an early period of underexploitation to a later period of overexploitation. This change altered the population resilience of the stocks and brought about an increase in the sensitivity of its dynamics to climate variability. Landings increased exponentially when underexploited but displayed an oscillatory behaviour once overexploited. Climatic indices, related to the Mediterranean mesoscale hydrography and large-scale north Atlantic climatic variability, seemed to affect the species with broader age structure and longer lifespan, while the global-scale El Niño Southern Oscillation index (ENSO) positively influenced the population abundances of species with a narrow age structure and short lifespan. The species affected by ENSO preferentially inhabit the continental shelf, suggesting that Mediterranean shelf ecosystems are sensitive to the hydroclimatic variability linked to global climate.

scholarly journals The Influence of Large-Scale Climate Variability on Winter Maximum Daily Precipitation over North America

2010 ◽  
Vol 23 (11) ◽  
pp. 2902-2915 ◽  
Author(s):  
Xuebin Zhang ◽  
Jiafeng Wang ◽  
Francis W. Zwiers ◽  
Pavel Ya Groisman
Keyword(s):  
North America ◽  
Climate Variability ◽  
El Niño ◽  
Extreme Precipitation ◽  
Large Scale ◽  
El Nino ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
Winter Season ◽  
The North

Abstract The generalized extreme value (GEV) distribution is fitted to winter season daily maximum precipitation over North America, with indices representing El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the North Atlantic Oscillation (NAO) as predictors. It was found that ENSO and PDO have spatially consistent and statistically significant influences on extreme precipitation, while the influence of NAO is regional and is not field significant. The spatial pattern of extreme precipitation response to large-scale climate variability is similar to that of total precipitation but somewhat weaker in terms of statistical significance. An El Niño condition or high phase of PDO corresponds to a substantially increased likelihood of extreme precipitation over a vast region of southern North America but a decreased likelihood of extreme precipitation in the north, especially in the Great Plains and Canadian prairies and the Great Lakes/Ohio River valley.

scholarly journals Influence of Modes of Climate Variability on Global Temperature Extremes

2008 ◽  
Vol 21 (15) ◽  
pp. 3872-3889 ◽  
Author(s):  
Jesse Kenyon ◽  
Gabriele C. Hegerl
Keyword(s):  
North America ◽  
Climate Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Temperature Extremes ◽  
Modes Of Variability ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation ◽  
Modes Of Climate Variability

Abstract The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Niño–Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwide land areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Niño–Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Likewise, the influence of Pacific interdecadal variability is strongest in the Northern Hemisphere, especially around the Pacific region and North America, but it extends to the Southern Hemisphere. The North Atlantic Oscillation has a strong continent-wide effect for Eurasia, with a clear but weaker effect over North America. Modes of variability influence the shape of the daily temperature distribution beyond a simple shift, often affecting cold and warm extremes and sometimes daytime and nighttime temperatures differently. Therefore, for reliable attribution of changes in extremes as well as prediction of future changes, changes in modes of variability need to be accounted for.

scholarly journals Large-scale Climate Variability Footprint in Water Levels of Alluvial Aquifers Across Iran

2021 ◽  
Author(s):  
Abolfazl Rezaei
Keyword(s):  
Climate Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Groundwater Resources ◽  
Water Levels ◽  
Climate Indices ◽  
Frequency Bands ◽  
Groundwater Levels ◽  
Aquifer Storage ◽  
Atmospheric Circulations

Abstract The ability to predict 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 proposes a potentially effective source of longer term forecasting capability. In this study, as a first national-scale assessment, we use the continues wavelet transform, global power spectrum, and wavelet coherence analyses to quantify the controls of the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (ENSO) over the representative groundwater levels of the 24 principal aquifers, scattered across different 14 climate zones of Iran. The results demonstrate that aquifer storage variations are partially controlled by annual to interdecadal climate variability and are not solely a function of pumping variations. Moreover, teleconnections are observed to be both frequency and time specific. The significant coherence patterns between the climate indices and groundwater levels are observed at five frequency bands of the annual (~1-yr), interannual (2-4- and 4-6-yr), decadal (8-12-yr), and interdecadal (14-18yr), consistent with the dominant modes of climate indices. AMO’s strong footprint is observed at interdecadal and annual modes of groundwater levels while PDO’s highest imprint is seen in interannual, decadal, and interdecadal modes. The highest controlling influence of ENSO is observed across the decadal and interannual modes whereas the NAO’s footprint is marked at annual and interdecadal frequency bands. Further, it is observed that the groundwater variability being higher modulated by a combination of large-scale atmospheric circulations rather than each individual index. The decadal and interdecadal oscillation modes constitute 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 where the groundwater variability is observed to be more correlated with lower frequent climate circulations such as PDO and AMO, rather than ENSO and NAO. Finally, it is found that the data length can significantly affect the teleconnections if the time series are not contemporaneous and only one value of coherence/correlation is computed for each particular series instead of separate computations for different frequency bands and different time spans.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Increasing Frequency in Off-Season Tropical Cyclones and its relation to Climate Variability and Change

2020 ◽  
Author(s):  
José J. Hernandez Ayala ◽  
Rafael Méndez-Tejeda
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Surface Temperature ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Linear Regression Models ◽  
Climate Variability And Change ◽  
West Pacific ◽  
Change Factors ◽  
Increasing Trends

Abstract. This article analyzes the relationship between off-season tropical cyclone (TC) frequency and climate variability and change for the Pacific and Atlantic Ocean basins. TC track data was used to extract the off-season storms for the 1900–2019 period. TC counts were aggregated by decade and the number of storms for the first six decades (pre-satellite era) was adjusted. Mann-Kendall non-parametric tests were used to identify trends in decadal TC counts and multiple linear regression models (MRL) were used to test if climatic variability or climate change factors explained the trends in off-season storms. MRL stepwise procedures were implemented to identify the climate variability and change factors that explained most of the variability in off-season TC frequency. A total of 713 TCs were identified as occurring earlier or later than their peak seasons, most during the month of May and in the West Pacific and South Pacific basins. The East Pacific (EP), North Atlantic (NA) and West Pacific (WP) basins exhibit significant increasing trends in decadal off-season TC frequency. MRL results show that trends in sea surface temperature, global mean surface temperature, and cloud cover explain most of the increasing trend in decadal off-season TC counts in the EP, NA, and WP basins. Stepwise MLR results also identified climate change variables as the dominant forces behind increasing trends in off-season TC decadal counts, yet they also showed that climate variability factors like El Niño-Southern Oscillation, the Atlantic Multidecadal Oscillation, and the Interdecadal Pacific Oscillation also account for a portion of the variability.

scholarly journals Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptions

2021 ◽  
Author(s):  
Tim Kittel ◽  
Catrin Ciemer ◽  
Nastaran Lotfi ◽  
Thomas Peron ◽  
Francisco Rodrigues ◽  
...  
Keyword(s):  
Climate Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Global Climate ◽  
Surface Air Temperature ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Hierarchical Organization ◽  
Spatio Temporal ◽  
Global Surface

AbstractEpisodically occurring internal (climatic) and external (non-climatic) disruptions of normal climate variability are known to both affect spatio-temporal patterns of global surface air temperatures (SAT) at time-scales between multiple weeks and several years. The magnitude and spatial manifestation of the corresponding effects depend strongly on the specific type of perturbation and may range from weak spatially coherent yet regionally confined trends to a global reorganization of co-variability due to the excitation or inhibition of certain large-scale teleconnectivity patterns. Here, we employ functional climate network analysis to distinguish qualitatively the global climate responses to different phases of the El Niño–Southern Oscillation (ENSO) from those to the three largest volcanic eruptions since the mid-20th century as the two most prominent types of recurrent climate disruptions. Our results confirm that strong ENSO episodes can cause a temporary breakdown of the normal hierarchical organization of the global SAT field, which is characterized by the simultaneous emergence of consistent regional temperature trends and strong teleconnections. By contrast, the most recent strong volcanic eruptions exhibited primarily regional effects rather than triggering additional long-range teleconnections that would not have been present otherwise. By relying on several complementary network characteristics, our results contribute to a better understanding of climate network properties by differentiating between climate variability reorganization mechanisms associated with internal variability versus such triggered by non-climatic abrupt and localized perturbations.

scholarly journals Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2021 ◽  
Vol 13 (5) ◽  
pp. 2001-2023
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

2014 ◽  
Vol 53 (5) ◽  
pp. 1193-1212 ◽  
Author(s):  
Taesam Lee ◽  
Changsam Jeong
Keyword(s):  
Parameter Estimation ◽  
Climate Variability ◽  
South Korea ◽  
Simulation Study ◽  
Extreme Events ◽  
Large Scale ◽  
Southern Oscillation ◽  
Estimation Method ◽  
Parameter Estimation Method

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Niño–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

scholarly journals Climate Variability, Fish, and Fisheries

2006 ◽  
Vol 19 (20) ◽  
pp. 5009-5030 ◽  
Author(s):  
P. Lehodey ◽  
J. Alheit ◽  
M. Barange ◽  
T. Baumgartner ◽  
G. Beaugrand ◽  
...  
Keyword(s):  
Climate Variability ◽  
Time Scales ◽  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Fish Population ◽  
Marine Ecology ◽  
Fish Populations ◽  
Global Ocean ◽  
Close Link

Abstract Fish population variability and fisheries activities are closely linked to weather and climate dynamics. While weather at sea directly affects fishing, environmental variability determines the distribution, migration, and abundance of fish. Fishery science grew up during the last century by integrating knowledge from oceanography, fish biology, marine ecology, and fish population dynamics, largely focused on the great Northern Hemisphere fisheries. During this period, understanding and explaining interannual fish recruitment variability became a major focus for fisheries oceanographers. Yet, the close link between climate and fisheries is best illustrated by the effect of “unexpected” events—that is, nonseasonal, and sometimes catastrophic—on fish exploitation, such as those associated with the El Niño–Southern Oscillation (ENSO). The observation that fish populations fluctuate at decadal time scales and show patterns of synchrony while being geographically separated drew attention to oceanographic processes driven by low-frequency signals, as reflected by indices tracking large-scale climate patterns such as the Pacific decadal oscillation (PDO) and the North Atlantic Oscillation (NAO). This low-frequency variability was first observed in catch fluctuations of small pelagic fish (anchovies and sardines), but similar effects soon emerged for larger fish such as salmon, various groundfish species, and some tuna species. Today, the availability of long time series of observations combined with major scientific advances in sampling and modeling the oceans’ ecosystems allows fisheries science to investigate processes generating variability in abundance, distribution, and dynamics of fish species at daily, decadal, and even centennial scales. These studies are central to the research program of Global Ocean Ecosystems Dynamics (GLOBEC). This review presents examples of relationships between climate variability and fisheries at these different time scales for species covering various marine ecosystems ranging from equatorial to subarctic regions. Some of the known mechanisms linking climate variability and exploited fish populations are described, as well as some leading hypotheses, and their implications for their management and for the modeling of their dynamics. It is concluded with recommendations for collaborative work between climatologists, oceanographers, and fisheries scientists to resolve some of the outstanding problems in the development of sustainable fisheries.

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