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The Turpan basin is one of the most arid and water-insecure regions in China. Turpan Basin has a continental desert climate as it is located in central Eurasia and surrounded by mountains on all sides, with the lowest elevation of 154.5 m below sea level. Its rivers and water availability are mainly supplied by mountain snowmelt through surface streams and the Karez system. To assess the impact of climate change on streamflow in the Turpan basin, this study examined the long-term trends and change points of hydro-meteorological variables and explored the plausible correlation between them at annual and seasonal scales. A set of non-parametric statistical tests was applied to analyze the trend, its magnitude, and the change point of the hydro-meteorological variables from 1959 to 2012 in the Turpan basin. Results showed that the annual mean temperature had a significant increasing trend with an average warming rate of 0.26 °C/decade. Annual precipitation did not show a consistent upward or downward trend across the basin; however, there was a significant increase in winter precipitation. The increasing rates of streamflow at Alagou and Meiyaogou hydrological stations were the highest in the summer (0.42 and 0.18 m3/s per decade, respectively), which might increase the risk of flooding. The change points of various meteorological variables and streamflow can be generally found in the 1980s, in which temperature, precipitation, relative humidity, and streamflow had abrupt changes concurrently in 1986 and had respectively increased by 7.2, 41.3, 5.7, and 18.3% after 1986. In general, the strength of the relationship between annual streamflow and precipitation (0.34) was greater than that between streamflow and temperature (0.16). The significant increase in temperature and agriculture in the basin in recent decades indicates the importance of reducing unnecessary soil evaporation in agriculture to preserve water resources under climate change and using innovative technology to preserve soil quality under increasing evapotranspiration and irrigation. This study provided valuable information for understanding the changing properties of the streamflow in the basin and insights for better-integrated water resources management planning that focuses on mitigating flood disasters and soil degradation and improving irrigation efficiency. [ABSTRACT FROM AUTHOR]

Teleconnection patterns are one of the key features to understanding high‐frequency natural climate variability. The North Sea‐Caspian Pattern (NCP) was identified as a middle tropospheric dipole and its hydroclimatological implications have been substantially restricted to the Eastern Mediterranean region. Thus, the hydroclimatological influences of the NCP in the Euro‐Mediterranean region were investigated via a comparative approach with dominant tropospheric teleconnections in the Eurasian region and synoptic features such as ridge‐trough positioning and strength. By using high‐resolution ERA5 reanalysis data, cross‐correlations between indexes, anticorrelations at 500 hPa and composite anomaly maps for seasonally representative months were produced to understand the working mechanism of the NCP. Comparisons included the East Atlantic/Western Russian (EAWR) pattern, a rotated principal component analysis (RPCA) variant of NCP which utilizes pole‐based representation. Analysis revealed that the NCP was correlated well with the Mediterranean trough displacement and with the strength of the East Asian trough. Climate anomalies indicated by the NCP were greater and more spatially consistent compared to other teleconnections. The NCP also showed higher contrasts of temperature and precipitation than the EAWR based on the composite anomaly maps. In conclusion, the NCP explained climate variability in all seasons linking remote centres of action within Eurasia's east and west extremes. [ABSTRACT FROM AUTHOR]

Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e. positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage anomaly (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R ^2 –0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-climatological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LST _max ) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSA _min ). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LST _max and TWSA < TWSA _min ) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydro-climatological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.

Global hydro-climatic trends are ambiguous, challenging the management of water resources. This challenge is addressed in the current study by investigating the impacts of hydro-climatic trends and upstream water management on hydropower generation at the Bagré dam. Modified Mann–Kendall trend test, Standard Normal Homogeneity Test and Pettit test were applied to some selected hydro-climatic variables for the trend and the change year detection, whereas the relationship between upstream dam management, hydro-climatic variables and hydropower were assessed through the Spearman correlation. The results revealed an annual positive trend for all hydro-climatic variables except for water level, lake evaporation and outflow. The break years observed in hydropower generation (2002) and inflow (2006) were mainly due to the construction of the Ziga dam in 2000 and its management change in 2005, respectively. The study also showed that hydropower generation declines each May (−30.36 MWh) and June (−16.82 MWh) due to the significant increase in irrigation withdrawals (1.94 hm3 in May and 0.67 hm3 in June). The results of this study highlighted the non-linearity in the relationship between hydropower generation and hydro-climatic variables as none of the correlation coefficients (apart from turbine) are very strong (>0.8). As many human activities occurred in the basin, further research should be focused on the use of semi-distributed models to assess the impacts of water-use and land-use change on hydropower generation. HIGHLIGHTS All hydro-climatic variables increased significantly except water level, lake evaporation and outflow.; The construction of the Ziga dam in 2000 led to a significant increase in annual hydropower generation at Bagré dam from 2002.; The change in management of the Ziga dam in 2005 led to a significant increase in annual inflow at Bagré dam from 2006.; All the months of the year experienced an increasing trend of hydropower generation except May and June.; The decline of hydropower generation in May and June is the consequence of the increase of water withdrawals for irrigation.;

The Turpan basin is one of the most arid and water-insecure regions in China. Turpan Basin has a continental desert climate as it is located in central Eurasia and surrounded by mountains on all sides, with the lowest elevation of 154.5 m below sea level. Its rivers and water availability are mainly supplied by mountain snowmelt through surface streams and the Karez system. To assess the impact of climate change on streamflow in the Turpan basin, this study examined the long-term trends and change points of hydro-meteorological variables and explored the plausible correlation between them at annual and seasonal scales. A set of non-parametric statistical tests was applied to analyze the trend, its magnitude, and the change point of the hydro-meteorological variables from 1959 to 2012 in the Turpan basin. Results showed that the annual mean temperature had a significant increasing trend with an average warming rate of 0.26 °C/decade. Annual precipitation did not show a consistent upward or downward trend across the basin; however, there was a significant increase in winter precipitation. The increasing rates of streamflow at Alagou and Meiyaogou hydrological stations were the highest in the summer (0.42 and 0.18 m3/s per decade, respectively), which might increase the risk of flooding. The change points of various meteorological variables and streamflow can be generally found in the 1980s, in which temperature, precipitation, relative humidity, and streamflow had abrupt changes concurrently in 1986 and had respectively increased by 7.2, 41.3, 5.7, and 18.3% after 1986. In general, the strength of the relationship between annual streamflow and precipitation (0.34) was greater than that between streamflow and temperature (0.16). The significant increase in temperature and agriculture in the basin in recent decades indicates the importance of reducing unnecessary soil evaporation in agriculture to preserve water resources under climate change and using innovative technology to preserve soil quality under increasing evapotranspiration and irrigation. This study provided valuable information for understanding the changing properties of the streamflow in the basin and insights for better-integrated water resources management planning that focuses on mitigating flood disasters and soil degradation and improving irrigation efficiency.

Hydro-climatic (HC) models have complex environments due to the integration of hydrological processes and climate indices for the assessment of historical and future scenarios. The approximation of HC models leads to a major uncertainty in the selection of optimal methods for processing, enhancement, and assessment. The present work developed a User-Friendly Interface (UI) in the R programming platform to enhance the geospatial HC models using machine learning concepts. Here, UI complies with various technologies together to perform consistently with input control, processing, and visualization. To validate this interface, a snow-dominated alpine watershed was selected. The results showed that, (a) UI assisted to downscale of the future climatic data into finer resolution, (b) boosted the efficiency of the geospatial model by adaptive random forest regression with NSE = 0.92 and 0.84, respectively. Moreover, UI designed to apply for different geospatial optimization problems which assist academicians, scientists, decision-makers, planners, and stakeholders, etc. [ABSTRACT FROM AUTHOR]

In-situ rain gauge stations are not adequately covering the total area of Upper Benue basin in Nigeria. An accurate gridded precipitation dataset is therefore required for climate study, hydrological modelling and water resource management. For this purpose, three candidates of global, gauge-based, daily, gridded precipitation products were evaluated and compared with the monitoring data from eight stations spanning 25 years period from 1982 to 2006. The three products are Climate Research Unit (CRU), the Climate Prediction Centre (CPC), and the Global Precipitation Climatology Centre (GPCC). The evaluations covered spatial analyses and statistical validations for daily, monthly, seasonal, and extreme rainfall data. Results show that all the three datasets captured well the spatial rainfall characteristics and were able to replicate the rainfall gradient and orographic conditions of the study area, thoughthe GPCC dataset outperformed the others. Line plots and scatter plots were used to compare the gridded datasets with the observation data formonthly data. The GPCC and CRU datasets are both better than that of the CPC. We also used the mean absolute error, the mean bias error, the correlation coefficient, and the modified index of agreement to compare daily data, and found that the GPCC set isthe most agreeable with the observation and the CPC set is the least. The daily gridded data products were also compared using probability distribution similarity, with the in-situ data by KS and AD tests. We found the distributions of GPCC and CRU datasets to follow the same distribution as that of the rain gauge data but that of the CPC does not. The results of the extreme events namely annual maximum numbers of consecutive dry-, wet-day, and total rainfall depth show that the dataset of GPCC is the most compatible with the observation and the CPC set is again the least. The most suitable daily ground-based gridded precipitation dataset for hydrological study and modelling in the Upper Benue basin is therefore the GPCC.

In-situ rain gauge stations are not adequately covering the total area of Upper Benue basin in Nigeria. An accurate gridded precipitation dataset is therefore required for climate study, hydrological modelling and water resource management. For this purpose, three candidates of global, gauge-based, daily, gridded precipitation products were evaluated and compared with the monitoring data from eight stations spanning 25 years period from 1982 to 2006. The three products are Climate Research Unit (CRU), the Climate Prediction Centre (CPC), and the Global Precipitation Climatology Centre (GPCC). The evaluations covered spatial analyses and statistical validations for daily, monthly, seasonal, and extreme rainfall data. Results show that all the three datasets captured well the spatial rainfall characteristics and were able to replicate the rainfall gradient and orographic conditions of the study area, though the GPCC dataset outperformed the others. Line plots and scatter plots were used to compare the gridded datasets with the observation data for monthly data. The GPCC and CRU datasets are both better than that of the CPC. We also used the mean absolute error, the mean bias error, the correlation coefficient, and the modified index of agreement to compare daily data, and found that the GPCC set is the most agreeable with the observation and the CPC set is the least. The daily gridded data products were also compared using probability distribution similarity, with the in-situ data by KS and AD tests. We found the distributions of GPCC and CRU datasets to follow the same distribution as that of the rain gauge data but that of the CPC does not. The results of the extreme events namely annual maximum numbers of consecutive dry-, wet-day, and total rainfall depth show that the dataset of GPCC is the most compatible with the observation and the CPC set is again the least. The most suitable daily ground-based gridded precipitation dataset for hydrological study and modelling in the Upper Benue basin is therefore the GPCC. [ABSTRACT FROM AUTHOR]

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State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively.

Over the last few decades as hydrologists have slowly raised their line of sight above the watershed boundary, it has become increasingly recognised that what happens in the atmosphere, as a major source of moisture for the terrestrial branch of the hydrological cycle, can strongly influence river dynamics at a range of spatial and temporal scales. Notwithstanding this, there is still a tendency for some in the river research community to restrict their gaze to the river channel or floodplain. However, Geoff Petts, the person to which this special issue is dedicated, understood well and widely encouraged a holistic view of river catchment processes. This included an acknowledgment of the role of climate, in its broadest sense, in shaping what happens within and without the river channel. The purpose of this paper therefore is to offer a broad overview of the role of some aspects of climate science in advancing knowledge in river research. Topics to be addressed include the role of climate in influencing river flow regimes, a consideration of the large‐scale climate mechanisms that drive hydrological variability within river basins at interannual to decadal timescales and atmospheric rivers and their link to surface hydrology. In reviewing these topics, a number of key knowledge gaps have emerged including attributing the causes of river flow regime changes to any one particular cause, the nonstationary and asymmetric forcing of river regimes by modes of climate variability and establishing links between atmospheric rivers, and terrestrial river channel processes, fluvial habitats, and ecological change. [ABSTRACT FROM AUTHOR]

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale.

Runoff from Arctic rivers constitutes a major freshwater influx to the Arctic Ocean. In these nival-dominated river systems, the majority of annual discharge is released during the spring snowmelt period. The circulation regime of the salinity-stratified Arctic Ocean is connected to global earth–ocean dynamics through thermohaline circulation; hence, variability in freshwater input from the Arctic flowing rivers has important implications for the global climate system. Daily discharge data from each of the four largest Arctic-draining river watersheds (Mackenzie, Ob, Lena and Yenisei; herein referred to as MOLY) are analyzed to identify historic changes in the magnitude and timing of freshwater input to the Arctic Ocean with emphasis on the spring freshet. Results show that the total freshwater influx to the Arctic Ocean increased by 89 km3/decade, amounting to a 14% increase during the 30-year period from 1980 to 2009. A distinct shift towards earlier melt timing is also indicated by proportional increases in fall, winter and spring discharges (by 2.5%, 1.3% and 2.5% respectively) followed by a decrease (by 5.8%) in summer discharge as a percentage of the mean annual flow. This seasonal increase in discharge and earlier pulse onset dates indicates a general shift towards a flatter, broad-based hydrograph with earlier peak discharges. The study also reveals that the increasing trend in freshwater discharge to the Arctic Ocean is not solely due to increased spring freshet discharge, but is a combination of increases in all seasons except that of the summer.

A baseflow separation methodology combining the outcomes of the flow-duration curve and the digital filtering algorithms to cope with the restrictions of traditional procedures has been assessed. Using this methodology as well as the monitored and simulated hydro-climatological data, the baseflow annual variations due to climate change and human-induced activities were determined. The outcomes show that the long-term baseflow index at the upstream sub-basin is nearly half of that at the downstream from October to April, whereas they are close to each other for the remaining months. Some of the groundwater reacts to precipitation and an evident rise in the groundwater contribution was detected for the hydrological years 1998-2001 and 2006-2008. The contrary was recorded for1987. The water released from the reservoir in the dry periods led to distinctions in the detected baseflow index between the pre-damming and post-damming periods of the river. [ABSTRACT FROM AUTHOR]

We present an approach for evaluating catchment-scale hydro-meteorological impacts of projected climate change based on the atmospheric circulation patterns (ACPs) of a region. Our approach is motivated by the conjecture that GCMs are especially good at simulating the atmospheric circulation patterns that control moisture transport, and which can be expected to change in response to global warming. In support of this, we show (for the late 20th century) that GCMs provide much better simulations of ACPs than those of precipitation amount for the Upper Guadiana Basin in central Spain. For the same period, four of the twenty GCMs participating in the most recent (5th) IPCC Assessment provide quite accurate representations of the spatial patterns of mean sea level pressure, the frequency distribution of ACP type, the ‘number of rainy days per month’, and the daily ‘probability of rain’ (they also reproduce the trend of ‘wet day amount’, though not the actual magnitudes). A consequent analysis of projected trends and changes in hydro-climatic ACPology between the late 20th and 21st Centuries indicates that (1) actual changes appear to be occurring faster than predicted by the models, and (2) for two greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5) the expected decline in precipitation volume is associated mainly with a few specific ACPs (primarily directional flows from the Atlantic Ocean and Cantabric Sea), and with decreasing probability of rain (linked to increasing temperatures) rather than wet day amount. Our approach is a potentially more insightful alternative for catchment-scale climate impacts assessments than the common approach of statistical downscaling and bias correction. [ABSTRACT FROM AUTHOR]

ABSTRACT Since human activities and ecosystem health require adequate, reliable water supplies, hydro-climatic variability and extremes pose serious threats to society and the environment. Previous studies have shown that the Canadian Prairies normally experience considerable hydro-climatic variability, including periodic droughts and excessive moisture conditions, which are mainly caused by mid-tropospheric circulations that disrupt expected precipitation and temperature patterns. However, no investigations have specifically focused on both past and future hydro-climatology over watersheds within the region. Evaluation of the Standardized Precipitation Evapotranspiration Index reveals considerable inter-annual and decadal-scale variability over the study regions of the Oldman and Swift Current Creek Watersheds, with no discernible trends during the last ∼100 years. There is also an indication of increased variability since the mid-1980s. Assessment of the 500 hPa circulation patterns associated with identified hydro-climatic extremes shows that major droughts are related with higher frequencies of distinctive ridging patterns over the Prairie region, and lower incidences of zonal/troughing patterns (the former significantly increasing over the last 60 years). Excessive moisture conditions have opposite patterns. Projections from two (a drier/warmer and wetter/cooler) Regional Climate Models indicate an uncertain future in the selected watersheds ranging from a substantial increase in drought with a higher degree of inter-annual variability, to relatively no change from current conditions. Furthermore, future changes to key atmospheric circulations suggest that those patterns associated with extreme dry conditions will continue in the future and in some cases, increase in frequency. Results from this analysis have improved the understanding of historical hydro-climatic extremes in western Canada and have provided insight into potential future occurrences of these extremes as driven by changes to key surface climate and synoptic-scale atmospheric circulation patterns. [ABSTRACT FROM AUTHOR]

Climate change is expected to substantially influence precipitation amounts and distribution. To improve simulations of extreme rainfall events, we analyzed the performance of different convection and microphysics parameterizations of the WRF (Weather Research and Forecasting) model at very high horizontal resolutions (12, 4 and 1 km). Our study focused on the eastern Mediterranean climate change hot-spot. Five extreme rainfall events over Cyprus were identified from observations and were dynamically downscaled from the ERA-Interim (EI) dataset with WRF. We applied an objective ranking scheme, using a 1-km gridded observational dataset over Cyprus and six different performance metrics, to investigate the skill of the WRF configurations. We evaluated the rainfall timing and amounts for the different resolutions, and discussed the observational uncertainty over the particular extreme events by comparing three gridded precipitation datasets ( E -OBS, APHRODITE and CHIRPS). Simulations with WRF capture rainfall over the eastern Mediterranean reasonably well for three of the five selected extreme events. For these three cases, the WRF simulations improved the ERA-Interim data, which strongly underestimate the rainfall extremes over Cyprus. The best model performance is obtained for the January 1989 event, simulated with an average bias of 4% and a modified Nash-Sutcliff of 0.72 for the 5-member ensemble of the 1-km simulations. We found overall added value for the convection-permitting simulations, especially over regions of high-elevation. Interestingly, for some cases the intermediate 4-km nest was found to outperform the 1-km simulations for low-elevation coastal parts of Cyprus. Finally, we identified significant and inconsistent discrepancies between the three, state of the art, gridded precipitation datasets for the tested events, highlighting the observational uncertainty in the region. [ABSTRACT FROM AUTHOR]

Evidence for climate change impacts on the hydro-climatology of Japan is plentiful. The objective of the present study was to evaluate the impacts of possible future climate change scenarios on the hydro-climatology of the upper Ishikari River basin, Hokkaido, Japan. The Soil and Water Assessment Tool was set up, calibrated, and validated for the hydrological modeling of the study area. The Statistical DownScaling Model version 4.2 was used to downscale the large-scale Hadley Centre Climate Model 3 Global Circulation Model A2 and B2 scenarios data into finer scale resolution. After model calibration and testing of the downscaling procedure, the SDSM-downscaled climate outputs were used as an input to run the calibrated SWAT model for the three future periods: 2030s (2020-2039), 2060s (2050-2069), and 2090s (2080-2099). The period 1981-2000 was taken as the baseline period against which comparison was made. Results showed that the average annual maximum temperature might increase by 1.80 and 2.01, 3.41 and 3.12, and 5.69 and 3.76 °C, the average annual minimum temperature might increase by 1.41 and 1.49, 2.60 and 2.34, and 4.20 and 2.93 °C, and the average annual precipitation might decrease by 5.78 and 8.08, 10.18 and 12.89, and 17.92 and 11.23% in 2030s, 2060s, and 2090s for A2a and B2a emission scenarios, respectively. The annual mean streamflow may increase for the all three future periods except the 2090s under the A2a scenario. Among them, the largest increase is possibly observed in the 2030s for A2a scenario, up to approximately 7.56%. Uncertainties were found within the GCM, the downscaling method, and the hydrological model itself, which were probably enlarged because only one single GCM (HaDCM3) was used in this study. [ABSTRACT FROM AUTHOR]

Large-scale assessments of the vulnerability of electric infrastructure are usually performed for a baseline water year or a specific period of drought. This approach does not provide insights into the full distribution of stress on the grid across the diversity of historic climate events. In this paper we estimate the Western US grid stress distribution as a function of inter-annual variability in regional water availability. We softly couple an integrated water model (climate, hydrology, routing, water resources management, and socioeconomic water demand models) into an electricity production cost model and simulate electricity generation and delivery of power for combinations of 30 years of historical water availability data. Results indicate a clear correlation between grid vulnerability (unmet electricity services) for the month of August, and annual water availability. There is a 21% chance of insufficient generation (system threshold) and a 3% chance that at least 6% of the electricity demand cannot be met in August. Better knowledge of the probability distribution of the risk exposure of the electricity system due to water constraints could improve power system planning. Deeper understanding of the impacts of regional variability in water availability on the reliability of the grid could help develop tradeoff strategies. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper reviews applications of hybrid wavelet–AI models in hydro-climatology. [•] Efficiency of hybrid models regarding processes and model type were investigated. [•] Survey shows wavelet pre-processor capability to enhance AI models performance. [•] Organized information about wavelet–AI models can show the future research pass. [ABSTRACT FROM AUTHOR]

Because of their significant public health impact, waterborne Cryptosporidium and Giardia have been monitored in surface water in order to assess microbial quality of water bodies used for drinking water production and/or for recreational purposes. In this context, sampling strategy is of key importance and should be representative enough to appropriately assess the related microbial risk. This, however, requires sound knowledge on the behaviour of both pathogens in water. In the present study, the spatial and temporal distribution of Cryptosporidium and Giardia was explored in the rural Upper-Sûre watershed used for drinking water production in Luxembourg. By subdividing it into three compartments including (i) sub-catchments, (ii) the Sûre River fed by the sub-catchments and (iii) the Upper-Sûre reservoir fed by the Sûre River, parasite distribution was assessed using sampling designs adapted to the hydro-dynamic characteristics of the respective compartments. Results highlighted the high spatial and temporal variability in parasite distribution at watershed scale, as well as the prevalence of Giardia over Cryptosporidium. Besides land use features and catchment characteristics, hydro-climatology appeared to be a major driver of parasite behaviour in the watershed. It introduced a seasonal trend in their occurrence, highest densities being detected during the wet season. Peaks of contamination triggered out by rainfall-induced runoff were further observed in the three compartments. In the Sûre River, Cryptosporidium and Giardia fluxes peaked at 109 and 1010 (oo)cysts.d−1, respectively, and were discharged into the drinking water reservoir, where they underwent a 2 to 3 log10 removal rate. Despite this, parasite fluxes entering the drinking water treatment plant were still high (106 to 107 (oo)cysts.d−1) and stressed on the need for improved watershed management upstream the water treatment barrier. The catchment-wide analysis described here constitutes a valuable tool for assessment of catchment microbial dynamics, especially within the framework of Water Safety Plans. [ABSTRACT FROM AUTHOR]

State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively. [ABSTRACT FROM AUTHOR]

This study deals with the historical hydro-climatological watershed characteristics of the Litani basin located in Lebanon, east of the Mediterranean Sea, during 1900-2008. The basin is divided into two distinct sub-basins. The Upper Litani Basin (ULB) is mostly dominated by mountainous geophysical features and the Lower Litani Basin (LLB) is closer to the Mediterranean coast. Monthly and annual temperature and precipitation data were generated from different sources, including global gridded data, satellite data, and local station data. Because long-term runoff records were not available, previously generated monthly synthetic runoff data were employed. Temperature and precipitation trends were investigated using the Mann-Kendall and Sen Slope nonparametric trend tests. The mean annual and monthly runoff were subsequently correlated with temperature and precipitation variations using a multiple linear regression approach. The results show that between 1900 and 2008, the whole Litani basin, including both sub-basins, experienced a drying trend without a significant change in temperature. However, within the period of 1970-2008, the whole basin grew notably warmer in all seasons, without being wetter; this climatic pattern was reflected in the LLB but not in the ULB, which was only slightly warmer, but also slightly wetter in this period. In addition, the results revealed distinctive seasonal and annual correlations between temperature and precipitation changes and the basin's runoff. However, runoff for both the ULB and the LLB was shown to more frequently correlate with precipitation than with temperature. Positive correlations were found between runoff and precipitation during winter and wet seasons, and a negative correlation was detected during winter between temperature and runoff variations of the ULB. Hence, the continuation of increasing temperature and decreasing precipitation trends may pose a threat to future water resources in the Litani as a whole, and the ULB in particular. [ABSTRACT FROM AUTHOR]

The discharge variability of the main rivers that drain the Guyana Shield is analysed over the last 50 years using cross-wavelet, coherence and composite analysis involving oceanic and atmospheric variables. We highlight the overall hydro-climatological homogeneity of this region that allowed us to focus on the longest discharge time series available. Therefore, a wavelet cross-analysis was carried out between monthly and seasonal Maroni River discharge at the Langa Tabiki station and selected climate indices. This confirms a strong relationship between the hydrology of the Guyana Shield and the Pacific sea-surface temperature (SST) fluctuations. There is evidence of intermittent influence, of between inter-annual and near decadal scales, of the Atlantic SST fluctuations, in particular around 1970 and 1990. Finally, we show that the links between oceanic regions and high discharge in the rivers of Guyana are realized through the reinforcement of the Walker and Hadley cells between the Amazon and the adjacent oceans and through decreased trade winds and monsoon flux that favour the persistence of humidity over the Guyana Shield. Editor Z.W. Kundzewicz; Associate editor D. Hughes Citation Labat, D., Espinoza, J.-C., Ronchail, J., Cochonneau, G., de Oliveira, E., Doudou, J.C. and Guyot, J.-L., 2012. Fluctuations in the monthly discharge of Guyana Shield rivers, related to Pacific and Atlantic climate variability. Hydrological Sciences Journal, 57 (6), 1081–1091. [ABSTRACT FROM AUTHOR]

Abstract: Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg–Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others. [Copyright &y& Elsevier]

The hydro-climatic variability of the Colombian Andes associated with El Niño-Southern Oscillation (ENSO) is reviewed using records of rainfall, river discharges, soil moisture, and a vegetation index (NDVI) as a surrogate for evapotranspiration. Anomalies in the components of the surface water balance during both phases of ENSO are quantified in terms of their sign, timing, and magnitude. During El Niño (La Niña), the region experiences negative (positive) anomalies in rainfall, river discharges (average and extremes), soil moisture, and NDVI. ENSO's effects are phase-locked to the seasonal cycle, being stronger during December-February, and weaker during March-May. Besides, rainfall and river discharges anomalies show that the ENSO signal exhibits a westerly wave-like propagation, being stronger (weaker) and earlier (later) over the western (eastern) Andes. Soil moisture anomalies are land-cover type dependant, but overall they are enhanced by ENSO, showing very low values during El Niño (mainly during dry seasons), but saturation values during La Niña. A suite of large-scale and regional mechanisms cooperating at the ocean-atmosphere-land system are reviewed to explaining the identified hydro-climatic anomalies. This review contributes to an understanding of the hydro-climatic framework of a region identified as the most critical hotspot for biodiversity on Earth, and constitutes a wake-up call for scientists and policy-makers alike, to take actions and mobilize resources and minds to prevent the further destruction of the region's valuable hydrologic and biodiversity resources and ecosystems. It also sheds lights towards the implementation of strategies and adaptation plans to coping with threats from global environmental change. [ABSTRACT FROM AUTHOR]

This paper discusses four areas where, in the author's view, statistical methods are misused in reporting results of hydro-climatological research. They are: (i) the use of the same data set both to suggest a hypothesis (commonly of trend over time) and to test it; (ii) failure to use an appropriate significance level for tests in which a number of hypotheses are tested, even when the data sets used are mutually uncorrelated; (iii) failure to account for spatial correlations between variables, whether these are explanatory or response variables; and (iv) exaggerated importance given to statistical tests of significance, in particular to the 5% and 1% significance levels. Citation Clarke, R. T. (2010) On the (mis)use of statistical methods in hydro-climatological research. Hydrol. Sci. J. 55(2), 139-144. [ABSTRACT FROM AUTHOR]

The article presents a study that investigates the climate features in large-scale areas affecting the inter-annual hydrologic variability of streams that flow into Upper Klamath Lake (UKL) in Oregon. It notes that developing statistical models for the prediction of spring and summer seasonal streamflow volumes for the lake which are accurate, is difficult due to complex hydrology and high levels of variability in topography and climatology. Large-scale climate indices including the Southern Oscillation Index, Pacific North American Pattern, and the revised Trans-Niño Index (TNI) are evaluated for their ability in explaining inter-annual variability of hydrologic inputs into UKL.

Summary: Freshwater ice affects several physical, chemical, and biological processes in cold regions. Its duration and break-up also has numerous economic implications ranging from transportation, to the occurrence and severity of ice-jam flooding. Recent evidence indicates a shortening of the freshwater-ice season over much of Canada with the reduction being mainly attributable to earlier break-ups. These trends match those in surface air temperature during the last 50 years. Several studies have shown significant relationships between Canadian temperature and large-scale atmospheric and oceanic oscillations (i.e. teleconnections) particularly, during the cold season. However, no investigations have analyzed relationships between several teleconnection indices and historical freshwater-ice durations over Canada. This paper examines the impacts of El Niño/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Pacific North American (PNA) pattern, the North Pacific (NP) index, the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO) on Canadian freshwater-ice break/freeze-up dates from 1950–1999. Composite and correlation analyses reveal strongest links between the Pacific-related PDO, PNA, NP, and ENSO indices and ice dates over western Canada. Lakes have stronger and more spatially coherent results than rivers, while break-up dates have higher correlations as compared to freeze-up. The NAO/AO results are less coherent with no discernible impacts over any region of the country. Results from this analysis improve the understanding of relationships between large-scale atmospheric and oceanic oscillations and past freshwater-ice durations over Canada. They also provide insight into future regional changes to lake and river ice given projected changes to teleconnection patterns. [Copyright &y& Elsevier]

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale. [ABSTRACT FROM AUTHOR]

Runoff from Arctic rivers constitutes a major freshwater influx to the Arctic Ocean. In these nival-dominated river systems, the majority of annual discharge is released during the spring snowmelt period. The circulation regime of the salinity-stratified Arctic Ocean is connected to global earth–ocean dynamics through thermohaline circulation; hence, variability in freshwater input from the Arctic flowing rivers has important implications for the global climate system. Daily discharge data from each of the four largest Arctic-draining river watersheds (Mackenzie, Ob, Lena and Yenisei; herein referred to as MOLY) are analyzed to identify historic changes in the magnitude and timing of freshwater input to the Arctic Ocean with emphasis on the spring freshet. Results show that the total freshwater influx to the Arctic Ocean increased by 89 km3/decade, amounting to a 14% increase during the 30-year period from 1980 to 2009. A distinct shift towards earlier melt timing is also indicated by proportional increases in fall, winter and spring discharges (by 2.5%, 1.3% and 2.5% respectively) followed by a decrease (by 5.8%) in summer discharge as a percentage of the mean annual flow. This seasonal increase in discharge and earlier pulse onset dates indicates a general shift towards a flatter, broad-based hydrograph with earlier peak discharges. The study also reveals that the increasing trend in freshwater discharge to the Arctic Ocean is not solely due to increased spring freshet discharge, but is a combination of increases in all seasons except that of the summer. [ABSTRACT FROM AUTHOR]

Wetlands require an inventory on the entire national territory as they represent a social issue. A regional approach based on medium spatial resolution satellite images Landsat is a good scale investigation. However, it is necessary to have a proper methodological approach to ensure a consistent result on the whole territory as well as to overcome the identification difficulties related to the protean nature of the geographical object wetland. To do this we propose to maximize the potential of identifying wetlands selecting representative images of a hydro-climatological situation which is particularly favorable to the realization land submergence, witnessing the presence of a wetland. In this regard the more favorable hydro-climatological period is detected by the statistical method of CUSUM. Next, a hybrid method using selected images and exogenous data is developed. It leads to highlight three wetlands levels.