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Highlights • Water loss of the Chari River discharge is evaluated using a statistical approach. • The contribution of climate variability and human activities is distinguished. • Previously the contribution of human activities has been underestimated seriously. Abstract Lake Chad was once the sixth largest lake in the world, with a surface area of about 25,000 km2 in the early 1960s. However, it has shrunk to almost only one-twentieth of its maximum area by now. As the biggest river of the Lake Chad basin, the Chari/Logone River contributes approximately 90% of Lake Chad's water input. Consequently, several previous studies have focused on the Chari/Logone River system to investigate the causes of Lake Chad's shrinkage. However, due to the lack of authoritative irrigation and natural runoff observations, it is usually difficult to explain the relative contribution of human activities and climate variability. In this paper, unlike the hydrological modeling approaches used in previous studies, a conceptual and statistical approach was presented to investigate discharge variations and the causes leading to such variations. Specifically, the runoff change caused by climate variability (Δ Q climate ) was estimated using Budyko analysis, while that caused by human activities (Δ Q human ) was estimated using regression analysis. The accuracy of estimation was evaluated through cross validation and comparison with previous studies. The comparison suggests that due to the lack of natural runoff and irrigation withdrawal observations, the runoff simulation produced by previous hydrological models suffers from large errors. The water loss of the Chari/Logone River discharge is dominated by human activities rather than climate variability. On annual average scale, the total water loss caused by climate variability and anthropogenic activities is 16.76 km3. The relative contribution of Δ Q climate and Δ Q human is 26.83% and 73.17%, respectively. The role of human activities is particularly obvious in recent ten years from 2003 to 2013. Due to the increase of rainfall and irrigation withdrawals, the relative contribution of human activities accounts for more than 80% of the total water loss. [ABSTRACT FROM AUTHOR]

The subject of the present paper is the flood situation in the German part of the Elbe river. For this, no (international) agreement could be reached till now. Problems occur due to missing, non-plausible or uncertain flow data and due to non-stationary hydrological conditions in the basin caused by climatological variability and human impact (diking and installation of reservoirs). Regarding this complex hydrological situation a systematic procedure consisting of existing hydrological tools was developed. In a harmonised and complementary structure this procedure mainly includes the consistency analysis of flow data, statistical analyses of the flow process, especially flood statistics and their regionalisation, stochastic simulations of the flow process in the basin and flow routing. Therewith a stepwise increasingly reliable, representative, detailed and funded knowledge of the flow process may be achieved. Till now complete longitudinal sections of statistical flood parameters along the German part of the watercourse were determined for a sufficiently long period (1964–1995) best reflecting the present situation. Moreover, approximative flood statistics could be derived for a longer period (1936–1995), which may be regarded as representative for the variable hydrological conditions during the 20th century.In addition to this fundamental study the tools were applied in order to contribute to a decision support with respect to the presently discussed measures of dike shifting. The analyses revealed that a significant impact of these measures—even the impact of all measures together—on flood peaks may be achieved only if control systems at the sites of the retention spaces are used.Finally, the paper considers options of a further utilisation of the hydrological analysis results by the definition of interfaces to other disciplines. [Copyright &y& Elsevier]

The assessment for impacts of climate variability and human activities on suspended sediment yield (SSY) change has long been a question of great interest. However, the sediment generation processes are sophisticated with high nonlinearity and great uncertainty, which give rise to extreme complexity for SSY change assessment in Newtonian approach. Consequently, few approaches can be simply but widely applied to decompose impacts of climatic variability and human activities on SSY change. Thus, it is an urgent need to develop advanced methods that are simple and robust. Since that the Newtonian approach is hardly achievable due to limitation of either observations or knowledge of mechanisms, there have been repeated calls to capture the hydrologic system in Darwinian approach for hydrological change prediction or explanation. As streamflow is the carrier of suspended sediment, SSY change are thus documented in changes of sediment concentrated flow and suspended sediment concentration – water discharge ( C - Q ) relationships. By deduced corollaries, a differential equation of sediment discharge change was derived to explicitly decompose impacts of climate variability and human activities in Darwinian hydrology. Besides, a new form of sediment rating curves was proposed and curved as C - Q relationships and probability distribution of sediment concentrated flow. River sediment flux can be revealed by this representation, which simply elucidates mechanism of SSY generation covering a range of time scales from finer than rainfall-event to long term. By the new sediment rating curves, the differential equation was partly solved using a segmentation algorithm proposed and validated in this paper, and then was submitted to water balance framework expressed by Budyko-type equation. Thus, for catchment management, hydrologists can obtain explicit explanation of how climate variation and human activities propagate through landscape and result in sediment discharge change. The differential equation is simple and robust for widely application in sediment discharge change assessment, as only discrete data of precipitation, potential evaporation and C - Q observed at gauging stations are required. [ABSTRACT FROM AUTHOR]

Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called “residual chronologies”) can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period. [ABSTRACT FROM AUTHOR]

• Use calibration period that closely resembles future climate conditions. • Conceptual rainfall-runoff models are less transferable under drier conditions. • Certain catchment characteristics result in limited model transferability. • Rainfall-runoff dynamics play the most significant role in model transferability. • A strategy to routinely test model robustness under various climate conditions. Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. [ABSTRACT FROM AUTHOR]

Summary Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500–5000 km 2 ) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index ( I M ) and temperature variability index ( I TV ), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity ( I M < 80%), I M is a better predictor of model performance improvement than I TV ; whereas for catchments with high moisture homogeneity ( I M > 80%), I TV is a better predictor of performance improvement than I M . Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow). [ABSTRACT FROM AUTHOR]

Summary: The Mekong River Basin in South East Asia has experienced in recent decades large inter-annual variations between large floods and severe droughts. The general perception is that climate variability has increased in the region. In this paper our aim was to examine the role of El Niño-Southern Oscillation (ENSO) on these recent climate variations. To achieve our aim, we analysed the influence of ENSO on precipitation (over period 1981–2005) and discharge (1910–2008) by using spatial GIS analyses and statistical methods, such as linear correlations, spectral analysis and stochastic regression models. We found that the Mekong’s hydrology was significantly influenced by ENSO, particularly in the decay year of ENSO events. The influence was strongest in the southern parts of the basin. The precipitation and discharge decreased (increased) during El Niño (La Niña) and the annual flood period was shorter (longer) during El Niño (La Niña). We found also decadal variations in the ENSO-discharge relationship. Our findings indicate high potential for developing prediction methods for ENSO induced hydrological anomalies. These findings may significantly improve the possibility of preparing for extreme hydrological events, either drought or exceptional floods, and thus potentially improve water resources management in the basin. [ABSTRACT FROM AUTHOR]

Summary: The focus of this paper is to understand the multi-decadal oscillatory component of variability in the Okavango River system, in southwestern Africa, and its potential evolution through the 21st century under climate change scenarios. Statistical analyses and hydrological modelling are used to show that the observed multi-decadal wet and dry phases in the Okavango River and Delta result from multi-decadal oscillations in rainfall, which are likely to be related to processes of internal variability in the climate system, rather than external natural or anthropogenic forcing. Analyses of changes in this aspect of variability under projected climate change scenarios are based on data from a multi-model ensemble of 19 General Circulation Models, which are used to drive hydrological models of the Okavango River and Delta. Projections for the 21st century indicate a progressive shift towards drier conditions attributed to the influence of increasing temperatures on water balance. It is, however, highly likely that multi-decadal oscillations, possibly of similar magnitude to that of 20th century, will be superimposed on the overall trend. These may periodically offset or amplify the mean drying trend. This effect should be accounted for in water and catchment management and climate change adaptation strategies. [Copyright &y& Elsevier]

Summary: Miyun Reservoir is the main raw water source for Beijing’s domestic water supply. Flow discharge to Miyun Reservoir decreased drastically over a 50-year period, from 1956 to 2005, and had seriously affected Beijing’s water supply. Climate variability and human activity had been identified as the two main reasons for the decrease in flow. Here, climate variability refers to changes in precipitation and temperature. Impact of human activity includes direct withdrawal of water (referred to as “direct abstraction” in this paper) from the river (primary) or groundwater and indirect impact due to man-made changes in land use and vegetation in the upstream of the reservoir. According to the historical record, “direct abstraction” from the upstream of the reservoir has increased significantly since 1984. The study period was split into two sub-periods, from 1956 to 1983 and from 1984 to 2005. Based on the historical record, annual runoff in the upstream catchment of Miyun Reservoir (i.e., inflow into the reservoir) had decreased from 90.3mm to 41.8mm for the two sub-periods, a decrease of 48.5mm. Over the same period, average annual “direct abstraction” increased from 2.2mm to 13.4mm, an increase of 11.2mm. The latter accounted for 23% (11.2mm/48.5mm) of the decrease in inflow into the reservoir. This study utilised: (1) a distributed hydrological model (geomorphology-based hydrological model, or GBHM) and (2) a climate elasticity model to conduct a quantitative assessment of the impact of climate variability and the indirect impact of human activity on the inflow into the reservoir. Simulation results of GBHM and the climate elasticity model showed that climate impact was accountable for about 55% and 51% of the decrease in reservoir inflow, respectively. The indirect impact of human activity (mainly man-made land use and vegetation changes) accounted for 18% of the decrease in reservoir inflow. [Copyright &y& Elsevier]

Summary: Recent research points to changing frequency and intensity of heavy rainfall events and consequently, flood flows in Northeastern Illinois. Flood frequency modeling is a key component of managing floodwaters in this highly populated metropolitan area, which includes Chicago and surrounding urban areas, and has enormous economic and environmental significance. In this research, 100-year 24-h precipitation totals in Northeastern Illinois were quantified using the L-moments method with 1948–2004 hourly precipitation data at six stations in the region for comparison with published results from the US Weather Bureau Technical Paper No. 40 (TP-40), Illinois State Water Survey Bulletin 70 (Bulletin 70) and National Oceanic and Atmospheric Administration Atlas-14 (NOAA-14). Sensitivity analyses were conducted to examine the effects of various factors on 100-year, 24-h precipitation at Aurora College station, in particular the effects of selecting different periods of the precipitation record, different regions, and different underlying distributions. The sensitivity analyses used 1900–2004 daily precipitation data at 12 stations in the region. Finally, the HEC-HMS rainfall-runoff model was used to illustrate relative impacts of changing estimates of design precipitation on flood peaks at 12 small watersheds in the region. It was demonstrated that the oldest source, TP-40, produced significantly smaller 100-year, 24-h rainfall totals, than Bulletin 70, NOAA-14, and the current study. It was also shown that the variability in design rainfall calculated based on different 50-year records (nearly 200%) was much larger than those based on the choice of underlying statistical distribution (50%), or the selection of region (25%). The average relative increase in hydrologic peaks exceeded that of rainfall peaks. This could partly be explained by the non-linear nature of the hydrologic system. [Copyright &y& Elsevier]

Summary: The Danjiangkou reservoir lies in the upper Hanjiang basin and is the source of water for the middle route of the South-to-North Water Diversion Project (SNWDP) in China. Any significant change in the magnitude or timing of runoff from the Danjiangkou reservoir induced by changes in climatic variables would have significant implications for the economic prosperity of the area in the Hanjiang basin as well as for the South-to-North Water Diversion Project. In this paper the following issues are investigated: (1) Temporal trends of annual and seasonal precipitation and temperature from 1951 to 2003 in the Hanjiang basin are analyzed using the Mann–Kendall and the linear regression methods; spatial distributions of precipitation and temperature are interpolated by the inverse distance weighted interpolation method. (2) Temporal trends of runoff, precipitation and temperature from 1951 to 2003 in the Danjiangkou reservoir, an upper stream basin of the Hanjiang River, are further tested. (3) To assess the impact of climate change on water resources and predict the future runoff change in the Danjiangkou reservoir basin, a two-parameter water balance model is used to simulate the hydrological response for the climate change predicted by GCMs for the region for the period of 2021–2050. The results indicate that (1) at the α =0.05 significance level precipitation in the Hanjiang basin has no trend, but the temperature in the same region has significant upward trends in most parts of the Hanjiang basin. (2) The mean annual, spring, and winter runoffs in the Danjiangkou reservoir basin have decreasing trends. (3) The results simulated for the period 2021–2050 show that runoff of the Danjiangkou reservoir would increase in all the seasons, mainly in response to the predicted precipitation increase in the region. Sensitivity analysis shows that a 1°C and 2°C increase in temperature would reduce the mean annual runoff to about 3.5% and 7%, respectively. A decrease/increase of the mean monthly precipitation of 20% and 10% would decrease/increase the mean annual runoff to about 30% and 15%, respectively. The results of this study provide a scientific reference not only for assessing the impact of the climate change on water resources and the flood prevention in the Hanjiang basin, but also for dimensioning the middle route of the SNWDP in China. [Copyright &y& Elsevier]

Summary: Frequency analysis is a technique of fitting a probability distribution to a series of observations for defining the probabilities of future occurrences of some events of interest, e.g., an estimate of a flood magnitude corresponding to a chosen risk of failure. The use of this technique has played an important role in engineering practice. The assumptions of independence and stationarity are necessary conditions to proceed with such analyses. However, in the context of climate change, it is possible that these assumptions would no longer hold and the results of conventional frequency analysis would become doubtful. Under such circumstances, it is important that recourse be made to other suitable approaches which incorporate non-independence and non-stationarity of hydro-meteorological extremes. In paper, a brief review of the current approaches to these issues is presented. Approaches that remove the effect of serial dependence to obtain independent observations are presented and the ones that involve kernel or wavelet-based probability density estimation are only discussed for frequency analysis of dependent observations. For frequency analysis of non-stationary observations, the reviewed approaches include the extremal, r-largest, peaks-over-threshold, time-varying moments, pooled flood frequency analysis, local likelihood and quantile regression. Synthesis of the reviewed approaches is presented, issues related to establishing non-stationary behavior are discussed and future challenges in frequency analysis of hydro-meteorological extremes are discussed as well. Extensions to some of the reviewed approaches and new avenues of research have been identified for further development and improvement. It is also concluded that, to be most useful, non-stationarity considerations be incorporated into new risk assessment frameworks. [Copyright &y& Elsevier]

The existence of an ice cover has important effects on the streamflow conditions as well as on the heat transfer between water bodies and the overlying atmosphere. This paper investigates the effects of climate variability on the termination of the ice season in the Baltic region. In particular, trends and fluctuations observed in the cryophenological records from this region are analyzed in detail, searching for possible connections with the North Atlantic Oscillation (NAO). The NAO seems to affect mostly the late-winter temperature (January–March) with a significant impact also on the mid-spring (April–May) period, when the air temperature is strongly correlated to the ice dates. A regional analysis shows the existence in the series of winter temperature (JFM) of the same fluctuations as the winter NAO. The same components can be found in the cryophenological records and (partly) in the series of spring temperature. Nevertheless, both ice phenology and spring temperature show the existence of a very well defined trend that is not detectable in the series of winter NAO at time scales of a century or longer. This leads to the conclusion that winter NAO has still a weak, though significant, effect on the regime of spring temperature in the Baltic region and explains the most significant fluctuating components embedded in the cryophenological records. However it is argued that other climatic forcings, related to CO2-induced regional and global warming, acting at the end of the ice season, are able to induce pronounced trends in the regime of spring temperature and have an important impact on the cryosphere leading to the earlier occurrence of ice break-up observed in the last several decades. [Copyright &y& Elsevier]

• Time trend- and precipitation-informed models are tested. • 31 gauging stations with 55 years of data were used. • Precipitation-informed models outperform time trend-informed models. • Tested models yielded significantly different flood quantiles. • The best-fitting model is to some extent related to the flow regime. Estimation of reliable design discharges under variable climate is a key challenge for today's engineers. Therefore, researchers are intensively exploring different alternative approaches in order to improve standard methods for design discharge estimation. Paper investigates the performance of time-invariant, time trend- and precipitation-informed models based on generalized extreme value (GEV) distribution for 31 Slovenian discharge gauging stations with data availability from 1961 until 2015. Different rainfall durations are used as covariates in the case of precipitation-informed models. The selected catchments are located in different climate regions and characterized by five flow regimes. The results indicate that in most cases precipitation-informed models gave better fit to the measured data comparing to time-invariant and time trend-informed models. Relative differences in the design discharge estimations associated with 10- and 100-year return periods using time trend- and precipitation-informed models compared to time-invariant model were up to 60%. Additionally, the results indicate that identified best-fitting model of individual gauging station can to some extent be related to its flow regime. [ABSTRACT FROM AUTHOR]

• Disparity analysis of two and multi parametric models is performed with 8 Budyko based equations. • The study is carried out over 19 catchments across India. • Higher discrepancy in percentage contributions values for the case of multi-parameter model. Water availability is crucial for sustaining the development and even existence of human civilization. Identifying major sources of its variability is of paramount importance. The climate elasticity method provides a suitable platform to quantify the relative influences of climatic variables and anthropogenic stresses using Budyko hypothesis to the changes in runoff but judicious selection of Budyko based equations with relevant runoff elasticities is vital. In this paper, comparative study is carried out using climate elasticity approach in 19 catchments across India, to evaluate the disparity among runoff elasticities and percentage contributions of climatic variables (precipitation, maximum and minimum temperature, wind speed, sunshine duration, and relative humidity) and anthropogenic stress in runoff alterations. Among climatic parameters, precipitation has shown the maximum influence in 16 catchments. In 15 catchments, maximum temperature has higher relative contribution than minimum temperature. In addition, anthropogenic influence is higher in 9 catchments for Two-parameter approach (precipitation and potential evapotranspiration) whereas it has higher impact in 10 catchments as per Multi-parameter (including evapotranspiration elasticities) approach. Decomposing evapotranspiration elasticity to five climatic variables has been proven to be unproductive as it has produced more disparity among the percentage contribution values. The standard deviation values in contributions are more in the case of the Multi-parameter model. Thus, it is pragmatic to adopt Multi-parameter model that constitutes runoff elasticities to different climatic variables, when assignment demands the individual influences of these variables on hydrology. Adding more parameters into the framework introduces more error in the assessment of impacts of climatic variabilities. [ABSTRACT FROM AUTHOR]