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The Arabian Sea (AS), a basin in the North Indian Ocean (NIO), possesses strong coastal upwelling in its eastern and western margins. The spatio-temporal variability of upwelling over Somalia, Oman and the southwest coast of India is analysed using observed datasets. The spectral analysis of upwelling indices highlights that the semi-annual and annual frequencies were dominant by two orders of magnitude compared with the inter-annual variability over these upwelling zones. The inter-annual variability of coastal upwelling was demarcated with variations in the causative forcing of upwelling associated with the positive modes of Indian Ocean Dipole (pIOD), El-Niño Southern Oscillation (El-Niño) and combined pIOD and El-Niño events over the southwest coast of India (SWCI) and Somalia. Along SWCI, the sea surface temperature and meridional wind stress weakened leading to warming associated with the suppression of upwelling during El-Niño, pIOD and combined events. Along Somalia, the upwelling favourable wind stress showed slight weakening due to pIOD and strong decline due to El-Niño and combined modes. Along Oman, the upwelling favourable wind stress indicated negligible changes due to pIOD, an increase due to El-Niño and a slight decline due to combined modes. [ABSTRACT FROM AUTHOR]

A comprehensive study was conducted to analyse the impact of different drivers on streamflow downstream of the Magdalena River (MR), Colombia, between 1971 and 2020. The study considered El Niño Southern Oscillation (ENSO) events as a climate-forcing driver and evaporation from the reservoir network as a human-induced driver. Streamflow data was linked to previously categorized ENSO events, then standardized and classified based on date and year to evaluate inter- and intra-annual changes in streamflow. Daily evaporation losses were calculated using meteorological records and the aerodynamic method. The study also grouped 10-year periods of evaporation and streamflow to assess their ranges, rates, and water volumes. The results showed a decrease in means, volume, and maximum streamflow, while evaporation and minimum streamflow increased, especially during positive ENSOs. These changes in streamflow could have a significant impact on river-dominated dependent ecosystems, such as the MR downstream wetland complex, putting these ecosystems at risk. [ABSTRACT FROM AUTHOR]

The La Plata basin is one of the largest river basins in the world, draining about a fifth of the South American continent. It is a system of great hydrological significance, sensitive to extreme events, and also has the most economic development in the region. This work analyses the total water storage (TWS) changes over the La Plata basin from the GRACE (Gravity Recovery And Climate Experiment) mission and compares it with TRMM (Tropical Rainfall Measuring Mission) rainfall data. Subsequently, a study was conducted on the main sub-basins for the period 2003 to 2017 to identify regional-scale variations in TWS. Precipitation and GLDAS (Global Land Data Assimilation System) runoff data was evaluated in these areas, along with their connections to ENSO (El Niño Southern Oscillation) episodes. The results suggest that the majority of TWS variations are associated with extreme climatic events, such as the 2009 drought, and are linked to ENSO periods. The statistical analysis indicates a significant correlation between severe Niño/Niña episodes and changes in TWS in the sub-basins. [ABSTRACT FROM AUTHOR]

This study employs a panel local projections (LP) model to analyze the influence of El Niño (EN), La Niña (LN) Southern Oscillation (ENSO) on real equity returns based on twenty economies, collectively accounting for 80.2% of global output. We demonstrate that an ENSO shock has a delayed and oscillatory effect on equity returns, where the most significant impact is positive (negative) during an EN (LN) phase. We further analyze the climate state by each of the twenty economies. The literature shows that the effect of an ENSO shock on economic growth can vary across different economies. A main finding is that we show a near uniform result similar to the panel LP model, which we posit is driven in part by strong interconnections of financial markets on a global scale. These findings offer valuable insights for policymakers and investors. Highlights We estimate a panel and economy-specific climate ENSO switching via LP model. Twenty economies representing 80.2% of global output are analyzed. ENSO shock has a delayed and oscillatory effect on equity returns. Most significant impact of an ENSO shock on equity returns is positive (negative) during an EN (LN) phase. [ABSTRACT FROM AUTHOR]

East African coastal waters in the tropical western Indian Ocean experience strong seasonality which varies yearly, leading to the establishment of a prominent interannual Indian Ocean Dipole (IOD). This has a significant influence on regional and global socio-economic, climatic and human development. Sea surface temperature (SST) variability in these waters and its association with ocean–atmosphere feedbacks and internal subsurface ocean dynamics in relation to the IOD is the subject of this study. The research used reference simulation accompanied with sensitivity simulations with forcings from higher frequency variabilities against climatological signals of ocean–atmosphere or internal subsurface ocean dynamics from 1980 to 2007. Wind forcing with higher-frequency variabilities was applied in all simulations. Cooling and warming during pure and positive IOD events leading El Niño events in the region is caused by a combination of both surface heat fluxes associated with ocean–atmosphere feedbacks, and internal subsurface ocean dynamics, from July and peaking in August and October, respectively. Such processes also dominate the warming (cooling) in the region during pure El Niño (La Niña) events from July to December (July to October) where the SST patterns extend southwards. The warmest (coolest) SST anomalies during positive (negative) IOD events co-occurring with El Niño (La Niña) events stay longer than other events, being characterised by bimodal peaking in August and December. Such SST patterns are significantly forced with ocean–atmosphere feedbacks that might be associated with Walker circulation driving links between the Indian and Pacific oceans; however, the peaking in August might be enhanced by small ocean dynamics off the Somali coast, probably owing to the existing upwelling systems during these conditions. [ABSTRACT FROM AUTHOR]

Banding has historically been the most efficient method of marking individuals to gather large datasets on bird movement, especially in seabirds which spend a large proportion of their life-cycle at sea. The Australian Bird and Bat Banding Scheme has gathered such long-term records, including for the Australasian Gannet (Morus serrator). Between 1954 and 2022, a total of 12,583 bands were deployed at seven breeding sites in Australia, of which 522 were recovered dead. An additional 325 individuals from four banding sites in New Zealand were recovered along the Australian coastline. The effects of broadscale environmental indices on movement (inferred from banding recoveries) during the post-breeding period were investigated at two colonies with the most banding effort (Pope's Eye, Lawrence Rocks). Individuals >1 year old and those from Pope's Eye were recovered closer to their banding site. There were negative effects of current-year Southern Oscillation Index (SOI) and 1-year lagged Southern Annular Mode (SAM) on recovery distances for Lawrence Rocks individuals, while 2-year lagged SAM positively and negatively affected recovery distance and direction, respectively, for Pope's Eye individuals. The number of chicks banded (index of chick production) at Pope's Eye was negatively influenced by 1-year lagged SAM and 2-year lagged SOI, while the likelihood of recoveries of first-year individuals was positively influenced by 1-year lagged SAM. The results of the present study revealed a potential effect of environmental conditions on post-breeding movement with seemingly contrasting effects between colonies, and on reproduction, highlighting the usefulness of long-term banding and recovery efforts. [ABSTRACT FROM AUTHOR]

In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the "If-then" rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. [ABSTRACT FROM AUTHOR]

This study reassesses the spatiotemporal variability of and changes in extreme precipitation events (EPEs) and examines the response of soil moisture and water table depth to EPEs in Argentina's core crop region. Standardized nonparametric indices are applied to precipitation, soil moisture, and groundwater. The temporal variability of EPEs exhibits a decadal cycle and inter-annual modes with dominant frequencies between 2.5 and 4 years related to El Niño Southern Oscillation periodicities. The soil moisture and water table depth respond to precipitation variability replicating both decadal and inter-annual EPE variability. The response of soil moisture to EPEs attenuates in time as soil depth increases. Water table depth responds to EPEs with an average delay of 10 months. In recent decades, soil moisture increases (water table depth becomes shallower) rapidly when reacting to excess precipitation, while soil moisture decreases (water table depth deepens) slowly during and after drought events. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are filamentary regions of high-water vapour flux in the lower troposphere that contribute significantly to poleward moisture movement in mid-latitude regions. Key characteristics (frequency, duration, and intensity) of ARs have been explored to recognize the regions vulnerable to AR-flood. To investigate the association of ARs with large-scale climate oscillations (LSCOs), precipitation extremes (PEs) maximum 1-day precipitation (Rx1day), maximum consecutive 5-day precipitation (Rx5day), precipitation amount from very wet days (R95pTOT) are explored in a non-stationary framework of generalized extreme value distribution, taking the Arctic Oscillation, North Atlantic Oscillation, El Niño Southern Oscillation, and Pacific Decadal Oscillation (PDO) as covariates. In almost 30% of regions around the globe, May-June-July-August-September (MJJAS) season PDO was found to be the relatively most influential covariate for capturing PEs. The west coast of North America and of Europe, southernmost South America, central East Asia, New Zealand, and Australia have been identified as the most critical regions associated with AR linked with PE-associated LSCOs. [ABSTRACT FROM AUTHOR]

India's climate is semi-arid, and most of the people in this country depend on agriculture. In this article, we present the long-term relationships of the satellite-derived Normalized Difference Vegetation Index (NDVI) with the rainfall, land surface temperature (LST), surface soil moisture (SSM), and the groundwater storage (GWS) during the Kharif (June–October) season of the monsoon core region (MCR) of India from the year 2000 to 2018. Results show that the NDVI could capture the stages of crop phenology and that it undergoes interannual variability characterized by the El Niño Southern Oscillation. A strong dependence of NDVI on SSM and GWS is evidenced by the statistically significant correlation coefficients of +0.89 and +0.88, respectively. A substantial negative correlation coefficient of −0.91 is observed between NDVI and LST. Wavelet coherence spectrum suggests the in-phase relation of NDVI with rainfall, SSM, GWS, and anti-phase connection with LST, with weaker correlations from 2004 to 2008. The results of this study attain significance since the studies on NDVI and hydrologic variables are much less than those on NDVI and climate variables over the MCR of India. [ABSTRACT FROM AUTHOR]

Earth surface longwave radiation (SLR), including downward (DLR), upward (ULR), and net longwave radiation (NLR), significantly impacts the surface radiation budget and global climate evolution. However, the spatiotemporal variation in SLR remains poorly understood. In this study, three satellite products (GLASS-MODIS V40, GLASS-AVHRR, and CERES-SYN) and three reanalysis datasets (ERA5, MERRA-2, and GLDAS) were validated using ground measurements from 288 sites at seven observation networks. The mean biases and root mean square errors of the monthly DLR (ULR, NLR) estimates from the six products were −6.36 (−3.56, −2.86) Wm-2 and 16.63 (14.33, 13.38) Wm-2, respectively. Large differences in the spatial distribution of the SLR were mainly observed at high-latitude, high-altitude and desert/barren-covered regions. Large interannual variability was detected at high latitudes. GLASS-AVHRR and ERA5 better captured the long-term variability in DLR and ULR, whereas GLASS-AVHRR and MERRA-2 better detected trends in NLR. An increasing trend in DLR and ULR was observed between 1982 and 2015, followed by a decreasing trend from 2016 to 2021; the NLR flux did not exhibit a significant trend. Overall, the GLASS-AVHRR and ERA5 SLR estimates were more accurate and stable than those of the other products in accuracy, spatiotemporal distribution, and trend analysis. [ABSTRACT FROM AUTHOR]

In southern Africa, drier conditions are more pronounced during the El Niño Southern Oscillation (ENSO) years, triggering wildfire activity and extreme drought conditions which, individually or together, lead to loss of crop productivity, deaths of livestock and wildlife, famine, degraded ecosystems, water quality and quantity. However, the fire characteristics in relation to the emissions from biomass burning and surface properties are only examined to a limited extent in the literature, especially in Africa, where anthropogenic activities largely determine the fire activity. This study uses the available data from multi-source remote sensing platforms to (1) analyse the spatial distribution of wildfires and associated emissions during strong El Niño (2015/2016) and La Niña (2010/2011) phases in southern Africa, and (2) examine the effects of the severe El Niño and La Niña years on the relationship between the emission parameters, vegetation parameters and climatic parameters. Generally, the results suggest more emissions from the wildfire in the El Niño phase than that of the La Niña. Overall, the Pearson’s correlation clearly shows the influence and the relationship between the climate parameters themselves and also with emission parameters. [ABSTRACT FROM AUTHOR]

This research aims to present a composite approach to characterizing the intra-annual and interannual variability in inlet throat width and understanding its mechanism (how, when, and why it varies). The techniques used include remote sensing imagery analysis, spectral and cross-spectral analyses, correlation analyses, and random forest variable importance. The Da Dien River mouth (DDRM), which is located in southern Central Vietnam and experiences a strong annual signal of climate regimes, was selected as a case study to demonstrate the applicability of the approach. The results show that the narrowing/widening of the DDRM throat width is highly dynamic and variable under the significant dominant influence of wave height, tide and river flow, which seasonally and interannually vary according to the monsoon regime, El Niño/Southern Oscillation and sunspot number variations (better correlation with ENSO events). The overall throat width decreases during El Niño/the positive QBO phase and increases during La Niña periods/the negative QBO phase. The difference in river flow magnitudes is the main cause for the constriction/expansion of the DDRM throat width. The successful application of the study approach to the DDRM case study demonstrates its usefulness and ability to be applied to other case studies in tropical monsoon regions. [ABSTRACT FROM AUTHOR]

The complex process of water storage change in the Yangtze River Basin (YRB) includes strong feedbacks and connections with global climate change. This study adopted Global Positioning System (GPS) observations and Gravity Recovery and Climate Experiment (GRACE) and Follow-On (FO) data from 2002 to 2021, along with Global Land Data Assimilation System (GLDAS) model outputs and Satellite Altimetry data, to analyse changes in hydrological mass in the YRB. Singular Spectrum Analysis (SSA) was used fill the missing data of GRACE/GRACE-FO from 2017 to 2018. GRACE/GRACE-FO and GLDAS derived terrestrial water storage (TWS) show different spatial distributions in the different river sections of YRB, and the long-term positive trend is due to increased mass, mainly in the late upstream and middle-lower reaches. Principal component analysis (PCA) was used to separate the common-mode components (CMC) in the YRB's continuous GPS network, and it was found several of these periodic oscillation signals are connected to hydrology. In addition, it was discovered that changes in hydrological mass in the YRB are influenced by precipitation and temperature and TWS anomalies may be associated with El Niño/Southern Oscillation (ENSO) events. These findings increase our understanding of the complex climate and dynamic hydrological processes of YRB. [ABSTRACT FROM AUTHOR]

The Upper Blue Nile Basin (UBNB) is the main flow contributor to the downstream river. Given the competing demands, we characterize the water balance by statistical analysis of past observations and data assimilation models. UBNB runoff shows minimal trend (1% of variance) and a large annual cycle (69%) governed by the equatorial trough and Hadley circulation. The diurnal cycle linked to local heat fluxes contrasts with modulation by the slower Pacific El Niño Southern Oscillation and faster Indian Dipole. Using daily measurements over ~25 years, we found that the inflow to Lake Tana (average 48 m3/s) obtains a linear regression fit of r2 = 76% with the discharge at Khartoum (average 1380 m3/s) at a lag of +6 days, implying that the small catchment conveys the hydro-climate signal to the entire Nile Basin. The inflow-induced turbidity in Lake Tana was studied using changes in colour detected by satellite. Land-use changes in the UBNB were quantified by satellite infra-red temperature and aerosol density. Trends in vegetation temperatures showed a + 0.2°C/year rise in agricultural zones. Ensemble model-projected trends in runoff are upward and may keep pace with the growing demand for Nile water. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly impact bird species through changes to breeding and survival. Malleefowl (Leipoa ocellata) are threatened mound-building birds that persist in uncleared mallee and shrubland vegetation in semi-arid parts of southern Australia. Our aim was to understand the relationship between environmental factors and mound breeding activity (a proxy for population trends) to identify likely climate change impacts and possible proactive management actions. We compared annual activity at grids that encompassed groups of mounds at six sites in South Australia for up to 24 years with a range of environmental and habitat variables, focussing on variables predicted to change with a warming climate. Mound activity declined significantly over the study period at five of the six sites. Activity was positively associated with increased cumulative rain in the previous 2 years, lower average maximum temperatures in the winter, higher-than-average Southern Oscillation Index (i.e. more rain) 2 years before breeding and greater winter vegetation cover. These results suggest that moisture and vegetation cover are important for higher breeding activity. Climate change is predicted to lead to drier conditions and more frequent fires in our study region, suggesting that Malleefowl populations will continue to decline. We urge conservation practitioners to minimise climate change impacts through implementing proactive management actions that increase habitat quality for Malleefowl: fire management to reduce the scale of fire events and controlling introduced and overabundant native herbivores to preserve vegetation cover, retain soil moisture, increase food resources and protect from temperature extremes. [ABSTRACT FROM AUTHOR]

This study investigates the indirect effect of large-scale climate oscillations and the corresponding teleconnection with lake water level (WL) oscillations. For this, the effect of the Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO) on the Lake Urmia WL during 1966–2016 is investigated using cross-correlation, cross-wavelet (XWT), wavelet-coherence (CWT), and nonlinear multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models. Based on the XWT and CWT analyses, a temporal phase of WL leading by the SOI and an anti-phase of WL-NAO linkage within 11 years is evident. The models also depict a long-term persistence and effectiveness of the conditional covariance on both SOI–WL and NAO–WL links. It is concluded that the SOI and NAO have no immediate impact on the WL while the magnitude of the impact is exacerbated after the year 2000; however, the establishment of the SOI–WL link is found to be more relevant than that of the NAO–WL link. [ABSTRACT FROM AUTHOR]

The aim of the present work is to analyse the variations of Sea Surface Temperature (SST) and Anomaly of Sea Surface Temperature (ASST) using more than thirty years of satellite data. We studied if these variations have possible ocean-atmosphere connections with the Southern Annular Mode (SAM), the Southern Oscillation Index (SOI), and the Oceanic Niño Index (ONI) in a crucial region for fisheries off the Patagonian Shelf. For the analysis, we used SST data collected from 1985 to 2019 with Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. The two databases were merged to obtain 35 years of continuous SST data. The study area was divided into three zones to present different oceanographic and bathymetric characteristics. These zones are located in the gulf, in the middle of the continental shelf, and at the shelf break. For each of the zones, the monthly SST climatology and the ASST series are shown. To define their periodicity, monthly ASST data were analysed spectrally by Fast Fourier Transform (FFT) and Wavelet Transform. Cross-Wavelet Transform (XWT) was applied to analyse the co-variance of ASST with respect to climate indices. Our results show that the isotherm distribution seems to be separated by the 64º W meridian. To the east of 64º W, it follows an oblique direction from southwest to northeast; to the west of 64º W, a more complex behaviour of the SST field seems to be associated with the coastal dynamics. The ASST series shows that the values of negative anomalies are higher than those of the positive ones. Nevertheless, positive ASSTs are more extreme and persistent in time than the negative ones. The ASST wavelet diagrams denote a common and non-stationary periodicity in the three zones, with statistically significant power spectra in the intra-seasonal (9-month) and inter-annual (24-month and 4-year) time scales. There are clearly common features in the wavelet power of the time series related to ASST and climate forcing. The SAM exhibited the closest relationship with ASST in the three zones. The SAM and ASST indices denote significant power peaks in the 2-year and 4- to 6-year bands. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new distribution that allows us to skew any symmetric distribution which will be referred to as the deflation-inflation distribution (DID). We discuss some properties of the DID such as moments, stochastic representation, log-concavity. Also, we obtain Fisher information matrix and derive the maximum likelihood estimator of the parameters. We conduct a simulation study and use the proposed distribution to fit two data sets namely the southern oscillation index data set and the wind speed data set. We compare our findings to those based on the skewed p-generalized normal distribution as well as Azzlaini's skew normal distribution and conclude that the DID fits both data set better then the other two approaches. [ABSTRACT FROM AUTHOR]

The importance of providing multi satellite-based chlorophyll-a in an ocean model for accurate representation of penetrative shortwave radiation is demonstrated in this study using Modular Ocean Model. It questions the traditional usage of chlorophyll-a climatology in models. The model sensitivity experiments Chl_IA (with interannually varying multi satellite-based chlorophyll-a) and Chl_Clim (with chlorophyll-a climatology) during 1998–2016 reveals the importance of using the interannual chlorophyll. The maximum difference between the two chlorophyll-a products is found during the peak phase of strong El Niño (January-March 1998 and 2016) in the Niño1 + 2 region, allowing 5–11 W m−2 more shortwave radiation to penetrate below 25 m in Chl_IA, effectively warming the subsurface temperature and cooling the sea surface temperature (SST) by ~0.4°C in the Niño1 + 2 region. The reduction in SST bias (warm) by 0.5°C, deepening of mixed layer and isothermal layer, enhancement of upwelling, reduction of upper ocean stability with realistic distribution of upper ocean heat content are some of the major improvements in Chl_IA. The interannual chlorophyll-a variability in fact rectifies the warm (cold) bias in surface (subsurface) temperature commonly found in ocean models especially in the eastern Pacific region during the peak phase of strong El Niño. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature is a critical physical attribute of coastal marine habitats. Remote sensing from satellite is the most widely used approach for monitoring the stress on coral reef ecosystems on large scale. Regional coral bleaching monitoring framework has been applied in five major Indian coral reef regions to investigate the threats. Degradation of coral reefs is a major environmental problem worldwide. ENSO (El Niño Southern Oscillation) event is one of the extreme climate change event, which elevate Sea Surface Temperatures (SSTs) of tropical oceans. This warming of SST increases the level of thermal stress on coral reefs. Coral reefs are the most sensitive ecosystem of all coastal ecosystems due to temperature change and exhibit bleaching when SST exceeds their normal summer maxima and remain high for more than 28 days. SST data from NOAA OISST v2 over period from 1982 to 2018 (37 years) were used for this study. Coral bleaching indices have been observed over five major Indian coral reef regions. Bleaching Threshold (BT), Positive SST Anomaly (PA) and Degree Heating Week (DHW) are commonly used indices for calculating thermal stress on the coral reefs computed from satellite-derived SST data. The BT value for each regions based on the long-term SST data analysis and found different. These thermal stress analyses over India were found close with the recorded mass coral bleaching events during 1998, 2010 and 2016. In addition, this study included calculating the thermal stress over India during Mass Coral Bleaching years. [ABSTRACT FROM AUTHOR]

This study monitored the isotopic compositions of precipitation in Guilin as well as the influence of El Niño–Southern Oscillation (ENSO) on the isotope ratios and water vapour sources from 2015 to 2016. The results indicate that the lower isotope values of precipitation from ocean water vapour source are affected by long transport distance and fractionation during summer and autumn. In contrast, the isotope values of winter and spring precipitation are affected by continental air masses and their evaporation sources yielding higher values. The intercepts of the local meteoric water line in Guilin are larger than those of the global meteoric water line, which is typical for subtropical monsoon climate. During the El Niño event, development of anomalous anticyclonic circulation enhances the northbound transport over the western Pacific and brings abundant water vapour to the southern part of China. During El Niño event prevailing period, precipitations exhibit a lower δ18O value and low d (deuterium excess) value, indicating that the 2015/2016 ENSO event had a significant effect on the precipitation distribution, precipitation amount, and isotope ratios in regional precipitation. [ABSTRACT FROM AUTHOR]

El Niño is the warm phase of ENSO when ocean temperatures are warmer and precipitation is greater than normal in the area spanning the central to eastern Pacific Ocean. The El Niño-Southern Oscillation (or ENSO) is a climate pattern defined by sea surface temperature and precipitation departures from normal across the equatorial Pacific Ocean that can influence weather and climate patterns across the United States and around the world. With AI seemingly making its way into every facet of our lives at lightspeed, it should come as no surprise that researchers are testing its ability to help predict the weather. [Extracted from the article]

We use the computational capabilities of Google Earth Engine (GEE) to access and process different geospatial databases in order to generate a multiyear characterization of changes in rivers planforms. We develop the necessary code in GEE to delimit rivers at different scales using Landsat (Land Remote-Sensing Satellite (System)) and Sentinel images. We implement three different methods for delimiting rivers based on satellite information: the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index (AWEI). We apply these methods to annual mosaics generated for each source of information, and estimate rivers centerlines based on a thinning method. With the centerline and river planform delimitations, we automatically estimate three geomorphological indicators: mean width, connectivity and sinuosity. We apply our tool to analyse several reaches along eight representative Colombian rivers that exhibit different planforms and flow regimes, some of them correlated with the Southern Oscillation Index (SOI). [ABSTRACT FROM AUTHOR]

This paper seeks to understand the perceptions of local communities to climate variability and change in relation to observed climatic data. A perception survey, historical timeline and analysis of sea level and meteorological data from the study area were used to achieve this objective. As perceptions regarding different types of climate stimuli are likely to trigger response actions, this study highlights the importance of integrating local perceptions into planning for adaptation. The study suggests that climate change is happening in Zanzibar and is set to intensify as it is directly linked to regional and global events such as ENSO. The perceptions of local communities regarding variability in rainfall, temperature and sea level are highly influenced by the relationship between these elements and their livelihood activities, namely fishing, farming and seaweed farming; their direct experience of shifts in weather patterns; and their recall of past climate. While variability and changes were recognized, the majority of respondents linked this to God's doing, and most had little exposure to the phenomenon of global climate change. Furthermore, while local people's perceptions of rainfall and temperature changes showed some relationship with observed data, their perceptions on sea level rise did not. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of the 2015 El Niño-induced drought on household consumption in Ethiopia. A difference-in-difference method was used to compare consumption changes over time in a group unaffected by the drought to the changes in a group affected by the drought. By using the ESS household-level consumption aggregate data, we find that the 2015 drought reduces affected household's annual consumption by 8%, and the reduction was largely driven by changes in the lower tails of the consumption distribution. Overall, we find a significant consumption decline due to the 2015 drought, and much of the decline has been experienced among the consumption poor, indicating shock resilience inequality among rural households. [ABSTRACT FROM AUTHOR]

In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions. [ABSTRACT FROM AUTHOR]

The mid-latitude coastal area at the western South-Atlantic out of the tropics is under the combined effect of different atmospheric circulation patterns at different temporal scales, which can be shown by the isotope composition of precipitation. This pattern effect is more changing and complex than that for the well-studied tropical areas, the isotope studies being an interesting proxy for identifying major processes. This study is focused on the isotope composition of precipitation at a mid-latitude zone, in the western South-Atlantic coastal area of Argentina. δ18O and d-excess were analysed in a data series of 14 years, obtained from the integration of three neighbouring rain collectors at 38°S. A seasonality is observed in both parameters, but with some differences in the extreme months. δ18O showed a seasonality according to the temperature effect, but with a displacement of high values to spring months. Significant linear links between δ18O and Southern Annular Mode (SAM) index and Southern Oscillation Index (SOI) were recognised for the summer and spring seasons, respectively. [ABSTRACT FROM AUTHOR]

Tree rings are widely used to reconstruct past climates in regions where observational records of climate are short. In this study, we developed a 294 years-long (1718–2011 CE) ring-width chronology of the Himalayan Silver fir (Abies spectabilis (D.Don, Spach)) from Humla district in western Nepal to reconstruct winter minimum temperature for the remote region of central Himalaya where instrumental records are limited to past three or four decades. Ring-width chronology of the Himalayan Silver fir showed strongest and significant positive correlation with minimum winter temperature prior to the growing season. Based on this relationship, we reconstructed the winter season (previous October–current February) minimum temperature for western Nepal covering the period of 1780–2008 CE. Our reconstruction showed that winter minimum temperature is continuously increasing since the early twentieth century with unprecedented rapid warming in the latter half. The cold episodes in the reconstruction coincided with the major volcanic eruptions in the Northern Hemisphere and tropical regions. The spectral analysis using Multi-Taper Method revealed that the winter temperature in the north-western Himalaya has short- to medium-term periodicities of 2–3, 5.8–6.2, 7.9–8.2, 39–46 and 56–73 years, which suggest possible teleconnections with ENSO (El-Nino Southern Oscillation) and AMO (Atlantic Multidecadal Oscillation). [ABSTRACT FROM AUTHOR]

The Northern China Plain is China's traditional and important agricultural production base, where climate change in the past has significantly affected China in many aspects such as national agricultural policies, ecological construction and foreign trade exchanges. High-resolution paleoclimatology research reveals that the precipitation, relative humidity, and drought index in this area have a significantly variability in the past few centuries, and spectral analysis shows that each time series has an obviously short period coinciding with the El Niño-Southern Oscillation (ENSO) cycle. In this study, we summarized that ENSO signals in hydroclimate variations of northern China are probably from the Indian water vapor partly. In addition, the possible impact of hydroclimate variations in northern China on social and cultural events are briefly discussed considering historical records. [ABSTRACT FROM AUTHOR]

Stronger intensity and frequent arrival of the El Niño- Southern Oscillation (ENSO) caused varied temperature and wildly varying precipitation with drought and flood conditions that threaten paddy production. Using the Pooled Mean Group estimation method, this study investigates the long run and short run relationship of ENSO impact on Southeast Asia paddy production from 1961 to 2014 with Oceanic Niño Index, Niño 3.4 sea surface temperature anomalies, Southern Oscillation Index and Multivariate ENSO Index. This study also investigates four different ENSO intensities with Multivariate ENSO Index to determine their long run and short run impact on paddy production. The findings reveal that the ENSO effect is exclusively captured by Multivariate ENSO Index among four climate change variables. Furthermore, ENSO arrival during the paddy planting season caused a paddy reduction in the long run. Specifically, the arrival of a moderate La Niña during the paddy planting season has a greater impact on paddy reduction in the long run compared to a strong El Niño. In addition, moderate and strong La Niña caused higher paddy production in the short run. Further research can be carried out by including spatial and temporal ENSO impacts on paddy planting season with single cultivation method. [ABSTRACT FROM AUTHOR]

This study provides a spatio-temporal analysis of the great floods that occurred in South America in 1983 using hydrometeorological data and outputs from a continental-scale hydrological-hydrodynamic model. In the extreme year 1983, there were three main flooding periods (February, June and July) in many South American river basins, such as the Araguaia, Tocantins, São Francisco, Uruguay, La Plata and its tributaries, resulting in high discharge of the Paraguay River for many months. Depth–area–duration curves show that 3-day precipitation events in northern regions of South America were among the largest 15 events in the period 1980–2015 but only for specific locations, whereas in southern areas, the most extreme events in the same period were for larger durations (≥7-day precipitation). Modelled total export of water volume to the oceans indicates that rivers draining to the South Atlantic reached an anomaly of 3.7 during 1983, followed by 1998 (1.9) and 1992 (1.1), all of them corresponding to El Niño years. [ABSTRACT FROM AUTHOR]

This study presented multiscale characterization of monthly streamflow time series using Multivariate Empirical Mode Decomposition (MEMD) and developed an innovative approach for streamflow prediction by coupling MEMD with Genetic Programming (GP). Firstly, the possible hydro-climatic teleconnection of monthly streamflows of Mahanadi river basin in India with two large-scale climate oscillations of ElNiño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) is investigated by applying MEMD based Time-Dependent Intrinsic Correlation (TDIC) analysis. The TDIC analysis showed that the association between large-scale climate oscillations and streamflows is not unique always, but both the nature and strength of the association varies with time scales and over the time domain. Based on this finding, the study proposed MEMD-GP coupled approach for streamflow prediction, in which different modes corresponding to different process scales obtained by the MEMD are predicted separately using GP; and summation of these predicted modes provides the monthly streamflow at the station. A statistical performance evaluation based on multiple criteria showed that the proposed approach performs better than the multiple linear regression, M5 model tree and GP models for monthly streamflow prediction including extreme low and high flows, due to its unique capability to include the significant predictors at different time scales. [ABSTRACT FROM AUTHOR]

The palynological sequence in core QZMS from Qizimei Mountain, central China, reflects the vegetation and climate dynamics since 12,900 calibrated years before present (cal yr BP). A palynological analysis demonstrated that the temperate deciduous broadleaved and coniferous mixed forest that was present from 12,900 to 10,600 cal yr BP changed to a deciduous broadleaved and coniferous mixed forest from 10,600 to 10,300 cal yr BP due to climate warming. From 10,300 to 7000 cal yr BP, a subtropical and temperate broadleaved and coniferous mixed forest was present. Then, the climate cooled from 7000 to 3000 cal yr BP. The variations in freshwater algae and Sphagnum (AS) and aquatic herbs (AH) indicate that rainfall increased from the early Holocene to 6890 cal yr BP and then gradually decreased. This change in rainfall coincided with changes in the Asian Summer Monsoon (ASM) and rainfall patterns in southern and northern China during different periods. The rainfall pattern was dominated by a shift in the summer rainbelt in the Chinese ASM region, which was mainly driven by orbitally forced changes in insolation and shifts in the El Nino/Southern Oscillation during the Holocene. [ABSTRACT FROM AUTHOR]

Long-term satellite data are used to investigate the variability of ocean surface chlorophyll-a (chl-a) concentration in the Halmahera Sea (HS) under influence of the Australian-Indonesian Monsoon (AIM), the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). In this study, we first analysed the seasonal variability of chl-a, and then examine the relationship between surface chl-a, sea surface temperature (SST), and sea surface wind stress in the area. Our results suggest that prevailing southeasterly winds play a fundamental role in generating chl-a blooms in the HS. Particularly on a seasonal timescale, through the mechanism of Ekman mass transport, strengthening of southeasterly wind stress during the Southeast Monsoon season (June – August) produces enhanced chl-a concentrations associated with ocean surface cooling in the area of study. On the other hand, the chl-a bloom completely diminishes during the Northwest Monsoon season (December – February) due to weakening of wind stress and Ekman transport. On an interannual timescale, sea level pressure and wind stress are coherent with ENSO and IOD phases. During El Niño and positive IOD events (La Niña and negative IOD events), both sea level pressure and wind stress greatly increase (decrease) over the HS. These conditions cause an anomaly in southerly (northerly) wind stress, which is favourable to an enhancement (reduction) of the chl-a concentration in the region. This study demonstrates that sea level pressure and wind stress are the critical factors in determining the magnitude of chl-a bloom in the HS. [ABSTRACT FROM AUTHOR]

Leading patterns of observed seasonal extreme and mean streamflow on the Korean peninsula were estimated using an empirical orthogonal teleconnection (EOT) technique. In addition, statistical correlations on a seasonal basis were calculated using correlation and regression analyses between the leading streamflow patterns and various climate indices based on atmospheric–ocean circulation. The spatio-temporal patterns of the leading EOT modes for extreme and mean streamflow indicate an upstream mode for the Han River, with increasing trends in summer, and a downstream mode for the Nakdong River, with oscillations mainly on inter-decadal time scales in winter. The tropical ENSO (El Niño Southern Oscillation) forcing for both extreme and mean streamflow is coherently associated with summer to winter streamflow patterns. The western North Pacific monsoon has a negative correlation with winter streamflow variability, and tropical cyclone indices also exhibit significant positive correlation with autumn streamflow. Leading patterns of autumn and winter streamflow time series show predictability up to two seasons in advance from the Pacific sea-surface temperatures. [ABSTRACT FROM AUTHOR]

We review and test an ecological paradigm that asserts that alpine invertebrate communities may shift upslope with climate warming. Our model couples the end of summer snow line (EOSS) elevation with invertebrate populations in New Zealand's Southern Alps, using a forty-year data set, from fifty index glaciers. We show the snow line has risen an average 3.7 m a-1. This is equivalent to raising alpine isotherms by almost 150 m and presents alpine biotic populations with four possible scenarios: upslope tracking, stasis, horizontal dispersal, or local adaptation. We characterize the alpine invertebrate biota (AIB) and present two case studies that show that high-elevation taxa have tracked the snow line within a narrow range (<20 m), whereas lower elevation taxa have potentially shifted by tens of meters. Relationships between the EOSS and Southern Oscillation Index (SOI) are investigated because precipitation and temperature influence snow line elevation by 25 percent. We also highlight the utility of invertebrates for monitoring climate change impacts on alpine ecosystems with a proposal for alpine climate monitoring units (CMUs), complementing an existing network of ecological management units (EMUs). We include an annotated list of New Zealand alpine invertebrates as potential indicators of climate change. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) phase state is reported to drive interannual variability in sea temperatures along South Africa's south coast through its influence on wind-induced upwelling processes. Whether ENSO drives the intensity of localised, abrupt, intermittent upwelling is less well known. To explore this relationship, we used an index of localised, extreme (>2 °C anomaly), intermittent upwelling intensity, derived from in situ sea temperature data within the Tsitsikamma National Park Marine Protected Area, and quantified the relationship between annual cumulative upwelling intensities (1991–2013) with an annual ENSO index, namely the Southern Oscillation Index. We found that ENSO phase state modulates the cumulative intensity of extreme intermittent upwelling events during an annual period, with more and greater events during La Niña phases compared with El Niño phases. Furthermore, these extreme upwelling events have increased with time along South Africa's south coast as ENSO phase state becomes more intense and variable. Our findings support the emerging notion that the biological effects of climate change may be manifested through increased environmental variability rather than long-term mean environmental changes as ENSO is predicted to remain the dominant driver of local climate patterns in the future. [ABSTRACT FROM AUTHOR]

This study aims at investigating the intradecadal and decadal signals of the sea level using 25 years of altimetry data around Australia. We have used the multivariable spectral analysis to extract six periodic signals at the 95% confidence level from altimetry-derived sea-level time series in the study area. They are signals with periods of 1, 1.5, 3, 4.3, 5.7 and 11.17 years, which can also be detected in the estimated power spectra from climate indices of the Interdecadal Pacific Oscillation, Multivariate ENSO Index, and Pacific Decadal Oscillation. A parametric model including the detected periodic signals is used to estimate sea-level trends. The determined trends in the area are in a good agreement with recent studies that consider effects of climate indices through a multivariate regression model. The advantage of our model is to present more descriptive explanation of the sea level signals around Australia in terms of periodicity and spatial variability. [ABSTRACT FROM AUTHOR]

The influence of the El Niño Southern Oscillation (ENSO) phenomenon on monthly mean river flows of 12 rivers in the extreme south of South America in the 20th century is analysed. The original dataset of each river is divided into two subsets, i.e. warm ENSO events or El Niño, and cold ENSO events or La Niña. The elements of the subsets are composites of 24 consecutive months, from January of the year when the ENSO event begins to December of the following year. The ENSO signal is analysed by comparing the monthly mean value of each subset to the long-term monthly mean. The results reveal that, in general, monthly mean El Niño (La Niña) river flows are predominantly larger (smaller) than the long-term monthly mean in the rivers studied. The anomalies are more evident during the second half of the year in which the event starts and the first months of the following year. [ABSTRACT FROM AUTHOR]

Flood modelling inputs used to create flood hazard maps are normally based on the assumption of data stationarity for flood frequency analysis. However, changes in the behaviour of climate systems can lead to nonstationarity in flood series. Here, we develop flood hazard maps for Ho Chi Minh City, Vietnam, under nonstationary conditions using extreme value analysis, a coupled 1D-2D model and high-resolution topographical data derived from LiDAR (Light Detection and Ranging) data. Our findings indicate that ENSO (El Niño Southern Oscillation) and PDO (Pacific Decadal Oscillation) influence the magnitude and frequency of extreme rainfall, while global sea-level rise causes nonstationarity in local sea levels, having an impact on flood risk. The detailed flood hazard maps show that areas of high flood potential are located along river banks, with 0.60 km2 of the study area being unsafe for people, vehicles and buildings (H5 zone) under a 100-year return period scenario. [ABSTRACT FROM AUTHOR]

Satellite and ground-based observations are used to explore the composite oceanic-atmospheric link known as the El Niño/La Niña Southern Oscillation (ENSO) phenomenon, which is closely associated with extreme weather events (e.g. heat waves, tornadoes, floods, and droughts), incidence of epidemic diseases (e.g. malaria), severe coral bleaching, etc. The ENSO temporal evolution depends on the energy exchange within the coupled ocean/atmosphere system. Its cycle has an average period of about 4 years, but there is considerable modulation of it from several sources and this is not yet fully understood. In this article, we attempted to explore the intrinsic features of the Best ENSO Index (BEI) from 1870 to 2017. Studying the distribution that characterizes BEI increments, the asymptotic power-law scaling was revealed in their extreme fluctuations. Additionally, in order to study the evolution of BEI data over time, the detrended fluctuation analysis was used, which showed positive long-range correlations of power-law type and multifractal behaviour. These results aim to give a better insight into the global signature of ENSO evolution, considering both the continuous natural interactions taking place between the oceans and the atmosphere and anthropogenic effects. Furthermore, the results obtained could be employed to elucidate the development of more accurate advanced modelling of ocean-atmosphere interactions, thereby improving climate change projections. [ABSTRACT FROM AUTHOR]

The archaeology of Bald Rock 1, Bald Rock 2 and Bald Rock 3 at the sandstone outcrop of Maliwawa has established ∼25,000 years of Indigenous occupation in the Wellington Range, northwestern Arnhem Land. Flaked stone artefacts were found from the beginning of the sequence, with ground-edge axes, pounding and grinding technology and ochre recovered from deposits dating from the Last Glacial Maximum (LGM) to the recent contact period. Maliwawa was occupied during the LGM and other major regional environmental changes arising from post-glacial sea level rise and stabilisation along with the climatic variability of the Indonesian Australian Summer Monsoon (IASM) and El Niño Southern Oscillation (ENSO), supporting models that define Arnhem Land as a refugium. Lithic assemblages are represented by a quartz and quartzite flake abundance technological strategy, with an unusual lack of stone points observed, although other typical Arnhem Land Holocene retouched lithics are present. Raw material diversity in the late Holocene, alongside a variety of emergent pan-Arnhem Land rock art styles in the Wellington Range, supports the proposition of increasing exchange between Indigenous groups. These changes in the archaeological record signal the expansion of cultural systems throughout western Arnhem Land, documented historically and archaeologically, at the time of culture contact. [ABSTRACT FROM AUTHOR]

This study reports the improvement in the predictability of circulation and precipitation associated with monsoon intraseasonal oscillations (MISO) when the initial state is produced by assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and water vapour profiles in Weather Research Forecast (WRF) model. Two separate simulations are carried out for nine years (2003 to 2011) . In the first simulation, forcing is fromNational Centers for Environmental Prediction (NCEP, CTRL) and in the second, apart from NCEP forcing, AIRS temperature and moisture profiles are assimilated (ASSIM). Ten active and break cases are identified from each simulation. Three dimensional temperature states of identified active and break cases are perturbed using twin perturbation method and carried out predictability tests. Analysis reveals that the limit of predictability of low level zonal wind is improved by four (three) days during active (break) phase. Similarly the predictability of upper level zonal wind (precipitation) is enhanced by four (two) and two (four) days respectively during active and break phases. This suggests that the initial state using AIRS observations could enhance predictability limit of MISOs in WRF. More realistic baroclinic response and better representation of vertical state of atmosphere associated with monsoon enhance the predictability of circulation and rainfall. [ABSTRACT FROM PUBLISHER]

In this paper, the  effects  of the El Niño-Southern Oscillation (ENSO) on the annual maximum flood (AMF) and volume over threshold (VOT) in two major neighbouring river basins in southwest Iran are investigated. The basins are located upstream of the Dez and Karun-I dams and cover over 40 000 km2in total area. The effects of ENSO on the frequency, magnitude and severity (frequency times magnitude) of flood characteristics over the March–April period were analysed. ENSO indices were also correlated with both AMF and VOT. The results indicate that, in the Dez and Karun basins, the El Niño phenomenon intensifies March–April floods compared with neutral conditions. The opposite is true in La Niña conditions. The degree of the effect is more intense in the El Niño period. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) is characterized based on the date the events are mature. Their time lag defined relative to the central value of successive intervals of 4 years length, e.g. 01/1868–01/1872, 01/1872–01/1876 …, 01/1996–01/2000 … affects their evolution and,for a given amplitude, their variability. It specifies the dynamics of the quadrennial Quasi-Stationary Wave (QSW) in the tropical Pacific since ENSO always occurs at the end of the eastward phase propagation of that QSW. A third of events are unlagged with very low variability, SST anomalies being nearly concomitant between the extreme eastern and the central-eastern Pacific. A third of events are weakly lagged, in phase with the annual QSW, whose variability is much greater. Several months may elapse between the maximum SST anomalies east of the basin and along its equatorial central part. The last third of the events exhibits considerable variability, whether they are out of phase with the annual QSW or strongly lagged. The SST anomaly between 5°N and 20°N plays a key role in the maturation of the events out of phase. The events in phase (10% of the total population) are characterized by a negative SST anomaly in the central-eastern Pacific that reverses just before the maturation stage of ENSO. Sea water temperature 125 m deep in the central-eastern Pacific carries the earliest relevant information with a lead time of one year for forecasting the amplitude of unlagged ENSO while reporting how SST anomalies will develop until ENSO is fully developed. Besides, long-term forecast of the resumption of La Niña can be achieved accurately when weakly lagged events in phase with the annual QSW occur. The well differentiated typology of events vs. their time lag is the best clue to prove the leading role of the quadrennial QSW in the genesis of ENSO, while the related dynamic of the atmosphere ensues. [ABSTRACT FROM PUBLISHER]

Rainfall prediction has been one of the most challenging problems around the globe and has significance in many fields of science and technology. A Bayesian approach is used for monthly mean rainfall prediction at 21 stations in Assam, India. The inter-station rainfall dependencies/independencies are represented using Bayesian network (BN) structure and five atmospheric variables which include, Temperature, Relative Humidity, Wind Speed, Cloud Cover, and Southern Oscillation Index are used as predictors. Two different BN structure learning algorithms, K2 and Markov Chain Monte Carlo (MCMC) algorithm, are used. Thirteen different models are developed using different combinations of five predictors. K2 algorithm outperformed MCMC algorithm for all combinations. A combination of Temperature, Cloud cover, and Wind speed performed best for K2 algorithm giving 91.27% correct predictions, whereas a combination containing all the atmospheric variables performed best for MCMC algorithm giving 88.56% correct predictions. Thirteen stations out of 21 stations have accuracy above 90% for K2 algorithm, whereas only eight stations have accuracy above 90% for MCMC algorithm. Stations in the western part of the state performed better than stations in the other parts of the state. [ABSTRACT FROM AUTHOR]

Lake Urmia, the second largest hyper-saline lake on Earth, has recently experienced a dramatic water-level reduction. This could have been caused by climate change or other anthropogenic effects; however it could also be a part of natural climate variability. To explore the hydroclimate variability effect on the lake water level, two teleconnections were considered for analysis: the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO). Spectral and coherency analyses were used to recognize the frequency components and the relationship between the teleconnections and the lake water-level fluctuations. The results show that the recent water level fall and the rise of water level in 1994–1999 cannot be explained by the natural periodic behavior of Lake Urmia. The inter-decadal water-level oscillations are coherent with NAO and SOI components of such oscillations. The anthropogenic effect on the lake water level was also explored using non-parametric trend analysis. The results show that the lake water level has a positive trend between 1966 and 1995, but the trend is not significant at a 95% confidence level. However, the trend between 1995 and 2009 is negative and it is significant at a 99% confidence interval. This study shows that the very recent fall in water level is due to anthropogenic impacts rather than natural variability.Editor Z.W. Kundzewicz; Associate editor not assigned [ABSTRACT FROM AUTHOR]

A new method known as Unobserved Components–Dynamic Harmonic Regression (UC-DHR) was applied to a 39-year record of rainfall and streamflow for three sub-catchments of the Sarukawa Experimental Watershed in southwestern Japan. Some 25% of the timber was harvested from one of the sub-catchments in May–July 1982 and the objective was to quantify the magnitude of this effect relative to the effects of climate cycles (e.g. Southern Oscillation Index). The observed effects of inter-annual climate cycles (i.e. 0.89–1.36 mm/d) were seen to be comparable (i.e. 0.70–1.17 mm/d) to the effects of harvesting 25% of the standing timber. This result underlines the importance of always quantifying the effect of climate on streamflow response when harvesting impacts are studied.Editor D. Koutsoyiannis; Associate editor T. Okruszko [ABSTRACT FROM AUTHOR]

Since the performance of hydrological models relies on numerous factors, the selection of an appropriate modeling approach for hydrological study has always been a crucial issue. The major objective of this research is to demonstrate that data-driven models such as the Adaptive Neuro-Fuzzy Inference system (ANFIS) are more suitable in a region where spatially distributed precipitation datasets are not available. Since precipitation has a teleconnection with the El Niño Southern Oscillation (ENSO) in different parts of the world, the sea surface temperatures (SSTs) and sea level pressures (SLPs) of the equatorial Pacific can be expected to act as surrogates for the precipitation if there are insufficient raingauge stations in the watershed. Moreover, in contrast to conceptual and physically-based models, data driven models can incorporate SST and SLP in their input vectors, and hence additional forcing of SST with precipitation has been experimented with in past studies. Therefore, our second objective is to test whether the additional forcing of SST and SLP will improve the hydrologic simulation. For this, various ANFIS models for the winter season were developed considering 10 raingauge stations situated at various locations in the watershed. Rainfall from each raingauge station was considered in the ANFIS model one at a time with and without SST/SLP. The results show that the performance of the ANFIS model improved with the additional fusion of SST and SLP, especially when a raingauge station from a remote location was considered. However, this improvement was observed when the analysis was primarily focused on the winter season which is a period with a strong ENSO signal. Editor D. Koutsoyiannis Associate editor L. See [ABSTRACT FROM AUTHOR]