Maximum Streamflow Research Articles

Temporal Variability of Hydroclimatic Extremes: A Case Study of Vhembe, uMgungundlovu, and Lejweleputswa District Municipalities in South Africa

The current study investigated hydroclimatic extremes in Vhembe, Lejweleputswa, and uMgungundlovu District Municipalities based on streamflow data from 21 river gauge stations distributed across the study site for the period spanning 1985–2023. Statistical metrics such as the annual mean and maximum streamflow, as well as trends in annual, maximum, seasonal, and high/low flow, were used to evaluate the historical features of streamflow in each of the three district municipalities. Moreover, the Standardized Streamflow Index (SSI) time series computed from streamflow observations at 3- and 6-month accumulation periods were used to assess hydroclimatic extremes, including drought episodes, proportion of wet/dry years and trends in SSI, drought duration (DD), and drought severity (DS). The results indicate that the three district municipalities have experienced localized and varying degrees of streamflow levels and drought conditions. The uMgungundlovu District Municipality in particular has experienced a significant decline in annual and seasonal streamflow as well as an increase in drought conditions during the 38-year period of analysis. This is supported by the negative trends observed in most of the assessed metrics (e.g., annual, maximum, seasonal, low/high flow, and SSI), whereas DD and DS showed positive trends in all the stations, suggesting an increase in prolonged duration and severity of drought. The Lejweleputswa District Municipality depicted positive trends in most of the assessed metrics, suggesting that streamflow increased, whereas drought decreased in the region over the 38-year period of study. Moreover, the Vhembe District Municipality experienced both negative and positive trends, suggesting localized variations in dry and wet conditions. The results presented in this study contribute towards drought monitoring and management efforts in support of policy- and decision-making that aim to uplift water resources management and planning at local and district municipality levels.

Untangling the implications of climate-forcing and human-induced drivers in streamflow variability: the Magdalena River, Colombia

ABSTRACT 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.

Evaluating streamflow and temperature effects on Bull Trout migration and survival with linear spatial capture–recapture models

AbstractObjectiveIn the U.S. Pacific Northwest, climate change is increasing air temperatures, decreasing warm season (April–September) streamflow, and increasing cool season (October–March) streamflow. Warmer water temperatures may alter conditions for migratory coldwater fishes like the Bull Trout Salvelinus confluentus. Consequently, an understanding of Bull Trout migration and survival is critical for species conservation and restoration. In the Salmon River basin, Idaho, 1992 and 1993 transpired to be two of the most opposing extreme years among the past three decades for warm season water temperature and streamflow. These extremes provided a unique opportunity to retrospectively compare Bull Trout survival and migration under potential climate change scenarios.MethodsWe evaluated prespawning and postspawning migrations and survival of fluvial Bull Trout that were radio‐tagged and tracked from 1992 to 1994. We used a Cormack–Jolly–Seber linear spatial capture–recapture model to simultaneously model the migration and survival of radio‐tagged prespawn (n = 58) and postspawn (n = 23) Bull Trout among weeks and river reaches with streamflow, water temperature, and habitat covariates.ResultMost individual prespawning migrations were similar among tagged fish, whereas postspawn fish adopted multiple migration and overwintering strategies. Movements of prespawn Bull Trout were larger when (1) weekly average daily maximum streamflow increased and (2) weekly average daily maximum water temperature increased. The model estimated that at least 52% of spawners survived to spawning, and mean weekly prespawning apparent survival was higher in the low‐streamflow year (1992) than in the year with higher and more variable streamflow (1993). Survival of 1992–1994 fish during the 38‐week postspawning period was intermediate to that in the prespawning period. Detections of prespawn Bull Trout were generally higher at sites with more complex habitats, less large woody debris, and fewer undercut banks.ConclusionWe found that the prespawn life stage can represent a shorter time frame (14–18 weeks) with increased mortality compared to the longer postspawning period (38 weeks). Bull Trout apparent survival increased with lower streamflow variability, indicating that expected future changes in climate may adversely affect Bull Trout.

Impact and Trends of Climate Change Analysis of Streamflow at Various Gauging Stations in the Jhelum River, Jammu & Kashmir

Abstract: The planet Earth's water resources are involved in the biggest matter movement in the Earth's system, the hydrological cycle, which is infinitely repeated. Given that water is the fundamental component of the planet's life support system, it is critical to comprehend how current and anticipated climate change is affecting water availability and supplies. Stream flow trend analysis is crucial for flood forecasting and hydrologic drought assessment-related research, in addition to the study of water resource management. This study examined the long-term variations in stream flow at several gauging stations in the Jhelum River of Jammu and Kashmir . The Modified Mann-Kendall Test was used to analyze trends (MMKT). Sen's Slope Estimator (SSE) was employed in order to quantify trend. Trend analysis was performed using long-term time series data of the monthly and annual maximum stream flows at 10 gauging stations. The streamflow annual timeseries was not visible. any discernible upward or downward trends at any gauging point. Nonetheless, it was discovered that every noteworthy trend discovered at several gauging sites over the course of several seasons was declining in character. Throughout July, six gauging sites showed tendencies of declining streamflow. However, in addition to helping with regional hydrologic studies, this kind of river water flow research would also be helpful for studying the occurrence of floods and hydrologic droughts.

Earlier emergence of winter-spring maximum streamflow across Poland, 1981–2020

Earlier emergence of winter-spring maximum streamflow across Poland, 1981–2020

Regional Flood Frequency Analysis in the Doce River Basin and Coastal Regions in Espírito Santo State, Brazil – Evaluation of the Performance of L-Moments Approach

Objective: To evaluate the performance of the Index-Flood methods using L-Moments when analyzing the regional flood frequency. Theoretical benchmark: The maximum stream flows estimation is relevant for projects and reduction of flood impacts. However, hydrologic regionalization methods are usually employed due to the absence of maximum stream flow data. In this context, it is relevant to understand the methods performance of regional analysis available. Method: Regional flood frequency analisys in the Doce River basin and in basins located predominantly in the state of Espírito Santo by the Index-Flood methods, using L-Moments, direct multiple regression and Dimensionless Curve. Results and conclusion: The Index-Flood method using L-Moments presented better results than the other methods of regionalization evaluated. Changes in stream flow series and configuration of homogeneous regions did not alter significantly the results obtained with the Index-Flood method using L-Moments. Implications of research: Improvement of the process of regional analysis of flood frequency. Originality/value: Consistent analysis of the frequency of occurrence of floods, a recurrent socio-environmental problem aggravated in the context of climate change, constitutes a relevant advance for the management of water resources and has the potential to assist in the establishment of public policies aimed at territorial planning.

СВЕРХДОЛГОСРОЧНЫЙ ПРОГНОЗ СТОКА РЕК РОССИИ

The possibility of extended predictions of the Russian river streamflow is considered based on dynamic approach, in which the HBV-96 water-balance runoff formation model is used jointly with the extended ensemble meteorological forecast obtained with the INM5 model. Twelve river basins located in different climatic and physiographic zones of Russia were selected for analysis. The average annual and average monthly discharges, as well as the annual maximum streamflow, were predicted with a lead time of 1-5 years. The test on the reanalysis data for the period from 1980 to 2020 has shown that the applied dynamic approach makes it possible to adequately assess possible interannual fluctuations in the streamflow and its intraannual distribution. The ensemble of forecasts of the annual and maximum streamflow for the period 2023-2026 obtained using the HBV-96 and INM5 models is consistent with the data on the water regime of the analyzed rivers.

Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy.

The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.

Hydrological response under CMIP6 climate projection in Astore River Basin, Pakistan

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes. Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes. Coupled Model Intercomparison Project phase 6 (CMIP6) dataset of three GCM’s (BCC-CSM2-MR, INM-CM5-0, and MPI-ESM1-2-HR) with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020–2070. Bias correction method was used to reduce errors. In this study, statistical significance of trends was performed by using the Man- Kendall test. Sen’s estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations. MPI-ESM1-2-HR showed better results with coefficient of determination (COD) ranging from 0.70–0.74 for precipitation and 0.90–0.92 for maximum and minimum temperature at Astore, Rama, and Rattu followed by INM-CM5-0 and BCC-CSM2-MR. University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change. Results revealed that by the end of the 2070s, average annual precipitation is projected to increase up to 26.55% under the SSP1–2.6, 6.91% under SSP2–4.5, and decrease up to 21.62% under the SSP5–8.5. Precipitation also showed considerable variability during summer and winter. The projected temperature showed an increasing trend that may cause melting of glaciers. The projected increase in temperature ranges from - 0.66°C to 0.50°C, 0.9°C to 1.5°C and 1.18°C to 2°C under the scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. Simulated streamflows presented a slight increase by all scenarios. Maximum streamflow was generated under SSP5–8.5 followed by SSP2–4.5 and SSP1–2.6. The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand. The projected streamflows (2020–2070) compared to the control period (1990–2014) showed a reduction of 3%–11%, 2%–9%, and 1%–7% by SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. The results revealed detailed insights into the performance of three GCMs, which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

On the role of climate change in the 2018 flooding event in Kerala

The extreme precipitation during August 2018 in Kerala, India was catastrophic, triggering one of the worst floods in history. There is growing evidence of human-induced climate change in driving hydroclimatic extremes across the globe. However, whether and to what degree the 2018 flooding event in Kerala was influenced by climate change has yet to be fully understood. To this end, we present the first formal attribution analysis of the event, using the probabilistic event attribution (PEA) framework. Three methods using (i) Historical and HistoricalNat runs from CMIP6 (general circulation models-based method), (ii) observed records from 1901–2018 for two periods, split at 1950 (time-slice method) and (iii) observations that are scaled to 1901 and 2018 climates (scaling method), are considered for quantifying the risk ratio (RR) of the event. Using an objective approach, the 2018 precipitation event is defined by the return period of the 4 day cumulative precipitation over the Periyar River Basin (PRB), during 15–18 August, 2018. The subsequent flood event is characterized by the return period of the 1 day maximum streamflow at one of the outlets of the PRB, where maximum impact during the event was reported. The results from multiple methods are consistent, suggesting that the event is exceptionally less likely to have been caused by anthropogenic climate change, with RR for the precipitation and flood events ranging from 0.31 to 0.82 and 0.55 to 0.8, respectively. The role of wet antecedent soil moisture conditions, which is found to be the primary driving factor of floods in the PRB, is also found to be unchanged between simulations with and without climate change. Our results highlight the challenges in unequivocal discerning of the climate change signal on regional hydrological events and emphasize the importance of better consideration of local confounding interventions in PEA studies.

Trend analysis and identification of possible periods of change in the occurrence of extreme streamflow events in a tropical basin

Knowledge of the hydrological patterns of a river basin is important for environmental management in times of scarcity and floods. Among the variables that can influence river streamflow are precipitation and land use, which can intensify the occurrence of extreme events. The objective of this work is to identify trends in the time series of minimum and maximum streamflows, on a monthly and annual scale, in a tropical basin, based on non-parametric techniques. We also aim to analyze the main climatic and anthropic factors that can explain the changes in the rates of streamflow. The Mann-Kendall, modified Mann-Kendall and Pettitt tests were used at different levels of significance, as well as the Theil-Sen slope, to identify the magnitude of the trends found. The results showed that the minimum and maximum streamflows had a decreasing tendency in the dry period of the basin, specifically in October. Changes in the monthly time series occurred mainly in 2012. Precipitation behaved similarly to that observed in streamflows, with significant changes in the dry and rainy seasons, mainly in the north of the basin. Land use and land cover showed little variation in the analyzed period and was not considered a determining factor in streamflow changes. The application of different statistical methods allowed for finding satisfactory results for the behavior of water in the basin, while the analysis of climatic and anthropic factors made it possible to know their possible influences on changes in streamflow trends.

Recent streamflow trends across permafrost basins of North America

IntroductionClimate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy.MethodsRecent trends for long-term periods (1990–2021, 1976–2021) in observed minimum, mean, and maximum seasonal and annual streamflow were analyzed for a range of watersheds across North America affected by varying degrees of permafrost coverage.ResultsStreamflow trend analysis revealed that areas affected by permafrost are changing variably over the periods in terms of maximum, mean, and minimum seasonal and annual streamflow. These changes indicate a significant shift occurring in the most recent 46 years towards increasing mean streamflow for the dominant (> 50%) permafrost systems. Meanwhile, minimum streamflow increases for all permafrost-dominant systems and many of the other permafrost-affected systems across the seasons and annual periods considered, with the greatest number of significant changes in streamflow over other metrics. Maximum streamflow is shifting variably with significant increases in the permafrost-dominant systems in winter and fall over longer time periods of analysis. Our analysis suggests that streamflow trends are driven by climate (precipitation, followed by temperature), while variables such as permafrost coverage only appear important in the most recent 32-year period.DiscussionThe increases in streamflow trends observed in this study are reflective of deepening active layers and thawing permafrost, indicating that the entire hydrograph is undergoing change within permafrost-dominant streamflow systems as the Arctic moves towards a warmer future under climate change. Despite the many challenges to understanding changing streamflow in cold regions, there are new products and datasets in development that are increasingly allowing researchers to better understand the patterns of change in Arctic and subarctic systems affected by permafrost, offering a range of new tools, which, along with continued observational records, may help in improved understanding of changing Arctic streamflow patterns.

Implications of changing trends in hydroclimatic and water quality parameters on estuarine habitats in the Gulf Coastal Plain

Florida’s low elevation and geographic location make it particularly vulnerable to climate change effects such as sea level rise, increased intensity and frequency of storm events, and altered precipitation. Climate change is expected to exacerbate hydrological cycling with potential widespread implications for estuarine habitats that thrive under specific salinity regimes. We used historical data from sites in the eastern Gulf Coastal Plain, USA to examine trends and trend variability of several climatic, hydrologic, and estuarine water quality variables which have implications on seagrass and oyster habitat extent in downstream estuarine environments. We analyzed temperature, precipitation, low-flow and high-flow metrics (including the highest or lowest daily, 7-day average, and 30-day average) for each season annually over the period 1985–2020. We also analyzed estuarine water clarity metrics and salinity within waterbody segments of four estuary systems within the study area. Hydroclimate results showed that temperature increased at most sites. While there was variation in streamflow, the overall trend was declining streamflow. Declining trends were observed in most water clarity metrics, indicating improved clarity, especially in winter. Salinity generally declined across the study area. While overall streamflow decreased, main river stems to the estuaries had increasing trends in maximum streamflow characteristics, likely contributing to the decrease in estuarine salinity across the region. These results indicate that trends in streamflow (both magnitude and timing) in the watershed affect downstream estuarine water quality. These results have important implications on seagrass and oyster restoration and management efforts in the region, indicating that it is important to understand changing climatic and hydrologic conditions and how they may impact the estuarine resources.

The increase in streamflows and its impact on the probability of failure by sliding of the Santa Clara HPP dam using Monte Carlo method

Rainfall behavior has been affected worldwide, probably because of climate change. In several cases, this modification generated more intense river streamflows. Risk assessment is essential to check the security of existing dams, and it provides guidance for the design of future hydraulic structures. The risk analysis approach helps new projects to comply with safety criteria, or it is a reliable method to guide the need for safety interventions or repairs and to help in the decision to increase their discharge capacity. A case study of the Santa Clara Hydro-Power Plant was carried out to analyze if there were changes in the design flood and the impact that these variations could have on the dam failure probability. The maximum streamflows determined at the time of the dam construction were compared with the streamflows found in the update of the hydrological studies. Analyzing the results, it was possible to find out there was an increase in streamflow rates. The Monte Carlo method was used to establish a curve correlating the affluent streamflow with the probability of failure, allowing to verify the update of the hydrological study of the project implied an increase in the probability of failure of 18.1% for the millennial flood.

A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting

Extreme streamflow is a key indicator of flood risk, and quantifying the changes in its distribution under non-stationary climate conditions is key to mitigating the impact of flooding events. We propose a non-stationary process mixture model (NPMM) for annual streamflow maxima over the central US (CUS) which uses downscaled climate model precipitation projections to forecast extremal streamflow. Spatial dependence for the model is specified as a convex combination of transformed Gaussian and max-stable processes, indexed by a weight parameter which identifies the asymptotic regime of the process. The weight parameter is modeled as a function of the annual precipitation for each of the two hydrologic regions within the CUS, introducing spatio-temporal non-stationarity within the model. The NPMM is flexible with desirable tail dependence properties, but yields an intractable likelihood. To address this, we embed a neural network within a density regression model which is used to learn a synthetic likelihood function using simulations from the NPMM with different parameter settings. Our model is fitted using observational data for 1972–2021, and inference carried out in a Bayesian framework. The two regions within the CUS are estimated to be in different asymptotic regimes based on the posterior distribution of the weight parameter. Annual streamflow maxima estimates based on global climate models for two representative climate pathway scenarios suggest an overall increase in the frequency and magnitude of extreme streamflow for 2006–2035 compared to the historical period of 1972–2005.

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Overview on the historical series of annual maximum streamflows in Brazil: analysis based on temporal and statistical criteria

Hydrology, as science, refers to the study of the hydrological cycle. This comprises hydrological processes that can be analyzed over time, space, or both together, which, in their turn, are quantitatively described by hydrological variables and represented by historical series. Therefore, it is essential screening historical series to enable properly assessing hydrological processes and their variability. However, this topic has been poorly investigated in the country, despite its remarkable importance for the hydrological engineering field, mainly for at-site and regional statistical hydrology applications. Thus, the aim of the current study is to assess the context of historical series of annual maximum streamflows in Brazil, based on the combination of temporal and statistical criteria. The herein adopted methodology was split into the following stages: collecting streamflow data covering the Brazilian territory; determining the series of annual maximum streamflows; screening the investigated series based on temporal criteria - series determined from 1980 onwards, based on at least 30-year data -; and screening the series based on statistical prerequisites. The analyzed data have shown significant decrease in the number of series at national level, when they were filtered based on the established temporal criterion. However, assumingly, the simultaneous adoption of all 4 statistical prerequisites provided more robustness to the screening process applied to the series of annual maximum streamflows. Moreover, the use of the 4 statistical prerequisites did not imply significantly increased loss of historical series in comparison to the number of series observed when analyzing only the stationarity hypothesis. Thus, results in the current study can encourage professionals to apply greater statistical rigor at the screening stage of historical series adopted in hydrological studies.Keywords: statistical hydrology; hydrological engineering; stationarity; homogeneity; randomness; independence. Panorama de séries históricas de vazões máximas anuais no Brasil: uma análise baseada em premissas estatísticasR E S U M OA hidrologia enquanto ciência está relacionada com o estudo do ciclo hidrológico, o qual é composto por processos hidrológicos que podem ser analisados em função do tempo, do espaço ou de ambos, sendo que os processos hidrológicos são descritos por variáveis hidrológicas, representando uma série histórica. Portanto, a triagem das séries históricas se torna importante para conhecer as ocorrências dos fenômenos hidrológicos e a sua variabilidade. Tendo em vista que este contexto tem sido pouco explorado no país, esse estudo tem por objetivo avaliar o panorama acerca das séries históricas de vazões máximas anuais no Brasil com base na combinação das premissas estatísticas de estacionariedade, homogeneidade, aleatoriedade e independência. A metodologia deste estudo seguiu as seguintes etapas: aquisição de dados de vazão de rios em território brasileiro; constituição das séries de vazões máximas anuais; triagem das séries de acordo com critérios temporais; triagem das séries segundo premissas estatísticas. Com base nos dados analisados, pode-se concluir que há uma grande redução no número de séries a nível nacional quando se filtra essas séries dentro de um critério temporal. No entanto, no que concerne às premissas estatísticas, esse estudo sugere que a consideração simultânea das 4 premissas traz uma maior robustez à triagem das séries de vazões máximas anuais; sendo que, não implica necessariamente em um acréscimo expressivo na perda de séries históricas, quando comparado com o número de séries obtidas após se recorrer unicamente ao uso da premissa de estacionariedade. Desta forma, os resultados encontrados nesse estudo estimulam que os profissionais façam uso de maior rigor estatístico na etapa de triagem das séries históricas para estudos hidrológicos.Palavras-chave: triagem, estacionariedade, homogeneidade, aleatoriedade, independência.

Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales

The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.

Future change in amplitude and timing of high-flow events in a Canadian subarctic watershed

The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.

Controls on Flood Trends Across the United States

AbstractTrends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls.