Occurrence Of Low Flows Research Articles

Mississippi River low-flows: context, causes, and future projections

The Mississippi River represents a major commercial waterway, and periods of anomalously low river levels disrupt riverine transport. These low-flow events occur periodically, with a recent event in the fall of 2022 slowing barge traffic and generating sharp increases in riverine transportation costs. Here we combine instrumental river gage observations from the lower Mississippi River with output from the Community Earth System Model v2 Large Ensemble (LENS2) to evaluate historical trends and future projections of Mississippi River low streamflow extremes, place the 2022 low-flow event in a broader temporal context, and assess the hydroclimatic mechanisms that mediate the occurrence of low-flows. We show that the severity and duration of low-flow events gradually decreased between 1950 and 1980 coincident with the establishment of artificial reservoirs. In the context of the last ∼70 years, the 2022 low-flow event was less severe in terms of stage or discharge minima than other low-flow events of the mid- and late-20th century. Model simulations from the LENS2 dataset show that, under a moderate-high emissions scenario (SSP3-7.0), the severity and duration of low-flow events is projected to decrease through to the end of the 21st century. Finally, we use the large sample size afforded by the LENS2 dataset to show that low-flow events on the Mississippi River are associated with cold tropical Pacific forcing (i.e. La Niña conditions), providing support for the hypothesis that the El Niño-Southern Oscillation plays a critical role in mediating Mississippi River discharge extremes. We anticipate that our findings describing the trends in and hydroclimatic mechanisms of Mississippi River low-flow occurrence will aid water resource managers to reduce the negative impacts of low water levels on riverine transport.

Variability of Annual and Monthly Streamflow Droughts over the Southeastern United States

Understanding the patterns of streamflow drought frequency and intensity is critical in defining potential environmental and societal impacts on processes associated with surface water resources; however, analysis of these processes is often limited to the availability of data. The objective of this study is to quantify the annual and monthly variability of low flow river conditions over the Southeastern United States (US) using National Water Model (NWM) retrospective simulations (v2.1), which provide streamflow estimates at a high spatial density. The data were used to calculate sums of outflow deficit volumes at annual and monthly scales, from which the autocorrelation functions (ACF), partial autocorrelation functions (PACF) and the Hurst exponent (H) were calculated to quantify low flow patterns. The ACF/PACF approach is used for examining the seasonal and multiannual variation of extreme events, while the Hurst exponent in turn allows for classification of “process memory”, distinguishing multi-seasonal processes from white noise processes. The results showed diverse spatial and temporal patterns of low flow occurrence across the Southeast US study area, with some locations indicating a strong seasonal dependence. These locations are characterized by a longer temporal cycle, whereby low flows were arranged in series of several to dozens of years, after which they did not occur for a period of similar length. In these rivers, H was in the range 0.8 (+/−0.15), which implies a stronger relation with groundwater during dry periods. In other river segments within the study region the probability of low flows appeared random, determined by H oscillating around the values for white noise (0.5 +/−0.15). The initial assessment of spatial clusters of the low flow parameters suggests no strict relationships, although a link to geologic characteristics and aquifer depth was noticed. At monthly scales, low flow occurrence followed precipitation patterns, with streamflow droughts first occurring in the Carolinas and along the Gulf Coast around May and then progressing upstream, reaching maxima around October for central parts of Mississippi, Alabama and Georgia. The relations for both annual and monthly scales are better represented with PACF, for which statistically significant lags were found in around 75% of stream nodes, while ACF explains on average only 20% of cases, indicating that streamflow droughts in the region occur in regular patterns (e.g., seasonal). This repeatability is of greater importance to defining patterns of extreme hydrologic events than the occurrence of high magnitude random events. The results of the research provide useful information about the spatial and temporal patterns of low flow occurrence across the Southeast US, and verify that the NWM retrospective data are able to differentiate the time processes for the occurrence of low flows.

Contrasting changes in hydrological processes of the Volta River basin under global warming

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100). Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water–energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.

Extreme Low Flow Estimation under Climate Change

Climate change’s impact on water availability has been widely studied, including its impact on very rare values quantified by return levels using the statistical extreme value theory. However, the application of this theory to estimate extreme low flows is barely justified due to a large temporal dependency and a physically highly bounded lower tail. One possible way of overcoming this difficulty is to simulate a very large sample of river flow time series consistent with the observations or the climate projections in order to enable empirical rare percentile estimations. In this paper, such an approach based on simulation is developed and tested for a small mountainous watershed in the French Alps. A bivariate generator of daily temperature and rainfall, developed in collaboration with Paris-Saclay University and based on hidden Markov models, is used to produce a large number of temperature and rainfall time series, further provided as input to a hydrological model to produce a similarly large sample of river flow time series. This sample is statistically analyzed in terms of low flow occurrence and intensity. This framework is adapted to the analysis of both current climate conditions and projected future climate. To study historical low flow situations, the bivariate temperature and rainfall model is fitted to the observed time series while bias-adjusted climate model outputs are used to calibrate the generator for the projections. The approach seems promising and could be further improved for use in more specific studies dedicated to the climate change impact on local low flow situations.

Simulating low flows over a heterogeneous landscape in southeastern Poland

AbstractThis paper presents a scheme describing low flow formation processes in areas with different environmental conditions, including the impact of the selection and explanatory power of predictors for a probabilistic model based on the Logit model. The research was carried out using 29 daily streamflow gauges located in the Lublin region of southeastern Poland for the hydrological period 1976–2018. Analysis resulted in two distinct low flow schemes. In the lowland rivers, low flows occur during the warm season and are related to evaporation exceeding precipitation. In the upland rivers, hydrogeological factors related to water levels in the local Cretaceous aquifers determine the occurrence of low flows. This differentiation affects the quality of the predictive models. For lowland rivers, models based on the climatic water balance with a monthly shift have a better fit, while these models used for upland rivers are characterized by an approximately 10% decrease in accuracy. For upland rivers, the combined CtHt models without shifts produce the best model fit. The generalized precision of the Logit models is around 80%–90%.

Changing Low Flow and Streamflow Drought Seasonality in Central European Headwaters

In the context of the ongoing climate warming in Europe, the seasonality and magnitudes of low flows and streamflow droughts are expected to change in the future. Increasing temperature and evaporation rates, stagnating precipitation amounts and decreasing snow cover will probably further intensify the summer streamflow deficits. This study analyzed the long-term variability and seasonality of low flows and streamflow droughts in fifteen headwater catchments of three regions within Central Europe. To quantify the changes in the low flow regime of selected catchments during the 1968–2019 period, we applied the R package lfstat for computing the seasonality ratio (SR), the seasonality index (SI), mean annual minima, as well as for the detection of streamflow drought events along with deficit volumes. Trend analysis of summer minimum discharges was performed using the Mann–Kendall test. Our results showed a substantial increase in the proportion of summer low flows during the analyzed period, accompanied with an apparent shift in the average date of low flow occurrence towards the start of the year. The most pronounced seasonality shifts were found predominantly in catchments with the mean altitude 800–1000 m.a.s.l. in all study regions. In contrast, the regime of low flows in catchments with terrain above 1000 m.a.s.l. remained nearly stable throughout the 1968–2019 period. Moreover, the analysis of mean summer minimum discharges indicated a much-diversified pattern in behavior of long-term trends than it might have been expected. The findings of this study may help identify the potentially most vulnerable near-natural headwater catchments facing worsening summer water scarcity.

Impact of trends in river discharge and ocean tides on water level dynamics in the Pearl River Delta

The spectrum of tidal and subtidal water level variations in river deltas responds to river discharge variation, ocean tides, and human activities of many kinds. It remains a contemporary challenge to identify the main sources of changes in tidal dynamics in deltas, because of nonlinear interactions between tides and the river discharge in a changing setting. Understanding the main forcing factors controlling the evolution of mean water levels and the associated amplitudes and phases of tidal constituents can help to understand the causes of floods and the occurrence of low flows hindering navigation. Here, a nonstationary harmonic analysis tool (NS_TIDE) is applied to hydrological data from 14 stations in the Pearl River Delta (PRD) spanning the period 1961–2012. The water levels and main tidal constituent properties are decomposed into contributions of external forcing by river discharges and ocean tides, providing insight into the nonstationary tidal-fluvial processes. Significant temporal trends in mean water levels and tidal properties are observed in the PRD. Results indicate that there is spatial variability in the response of mean water levels and tidal properties to river discharge variation in the delta. The abrupt changes in bathymetry in the delta due to intensive sand excavation are likely responsible for the observed spatial variations in tidal response, which reduce the flood-dominant tidal asymmetry in this area.

Impact of climate change on streamflow timing (case study: Guilan Province)

The timing of streamflow characteristics is important for water resources management and agricultural irrigation. Using daily discharge data from 37 gaging stations, located in Guilan Province, Iran, for the period 2002–2016, this study investigated nine streamflow timing measures and tested their trends by the Mann-Kendall non-parametric test at the 90% significance level. The nine measures included Q5%, Q10%, Q50%, Q90%, and Q95%, date corresponding to the center of fall volume, and dates corresponding to centers of spring and winter volumes. Results revealed that the median values of low flow timing slopes (Q5% and Q10%) had significant negative trends. Q5% had the steepest downward trends, implying that low flows originating from groundwater occurred earlier than in the past. In contrast, high flow timings (Q90% and Q95%) showed upward trends. Also, Q50% exhibited upward trends for all 37 stations. The trends suggest that climate change has altered the river flow regime. Among the nine measures, the date corresponding to the center of fall volume (FCV) and the dates corresponding to the centers of winter/spring volumes (WSCV) had the most significant downward trends. Trends in the spring freshet were mainly significantly negative in the northwest. However, trends in the pulse date were negative mainly in the west. The occurrence of low flows became earlier in time, and the length of dry periods within a year became longer.

Importance of maximum snow accumulation for summer low flows in humid catchments

Abstract. Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6–9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle- and lower-elevation catchments with a decrease of minimum discharge by 2–5 % per 10 % decrease of maximum SWE. For higher- and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70–90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil–Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments.

Occurrence and magnitude of low flows for Canadian rivers: an ecozone approach

Low-flow occurrence and magnitude have been documented for Canada using the National Ecological Framework. The low flow database is composed of the 7-day low flow with 2-year return period (7Q2) values for 453 natural flow hydrometric stations with record lengths of at least 30 years; drainage areas ranged from 10 to 30 000 km2. Occurrence zones corresponding to predominant season for annual low flows are associated with ecozones. The ecozone scale was found to be suitable for regional analysis for several ecozones. For some ecozones there were insufficient data for regional analysis and for others finer resolution is required. Regional regression equations were developed for estimating 7Q2 in terms of area for ecozones containing at least 20 stations. The results of this work will help practitioners to identify the season of low flow occurrence and the appropriate method of analysis, and provide a means of estimating 7Q2 for ungauged sites for some ecozones.

Low flow analysis of the lower Drava River

Understanding the regime and the characteristics of low streamflows is of vital importance in several aspects. It is essential for the effective planning, designing, constructing, maintaining, using and managing different water management systems and structures. In addition, frequent running and assessing of estimates of low stream-flow statistics are especially important when different aspects of water quality are considered. This paper attempts to present the results of a stochastic analysis of the River Drava low flow from the gauging station, Donji Miholjac [located at rkm 77+700]. Currently, almost all specialists apply the truncation method in low-flows analysis. Taking this into consideration, it is possible to accept the definition of a low streamflow, as a period when the analysed characteristics are either, equal to or lower than the truncation level of drought. The same method has been applied in this analysis. The calculating method applied takes into account all the essential components of the afore-mentioned process. This includes a number of elements, such as the deficit, duration or the time of the occurrence of low flows, the number of times, the maximum deficit and the maximum duration of the low flows in the analysed time period. Moreover, this paper determines computational values for deficits and for the duration of low flow in different return periods.

Modelling Future Streamflow Extremes — Floods and Low Flows in Georgia Basin, British Columbia

The Georgia Basin is one of the most hydrologically complex areas of Canada. Variations in temperature, precipitation and elevation influence the amount and form of water that drives streamflow in its rivers and streams. Climate change could have major regional effects on air temperature, precipitation, evapotranspiration, and ultimately runoff. In previous work, zones of homogenous hydrologic processes were delineated within the basin. Watersheds were separated into three types: rainfall-driven streams, snowmelt-driven streams, and hybrid (mixed rainfall- and snowmelt-driven) streams. Climate change was shown to have major regional effects on each type of watershed, affecting the amounts and patterns of runoff. In the current study we consider changes in extreme hydrologic events, floods and low flows, in these watersheds. Climate data downscaled from the Canadian Coupled General Circulation Model for future time periods are used as inputs to a hydrologic model optimized for mountain watersheds. The discrepancies between observed and modelled streamflows are examined. While the model reproduces central tendency measures well, there are significant biases in the ability of the models to reproduce extremes. Output from the hydrologic model is used to assess relative changes in the frequency, timing, and magnitude of floods and low flows between present and future (2020, 2050 and 2080) climate scenarios. The models suggest that frequency of floods will increase in all watersheds under the projected climate scenarios. In rainfall-driven streams, flood events increase in number, but not in magnitude. In hybrid streams, winter events occur more often while summer snowmelt flood events occur less often. In snowmelt-driven streams, the magnitude and duration of summer floods increase. Low flows in rainfall-driven streams maintain the same frequency and magnitude but occur over an extended period of time during summer. Hybrid streams show an increase in frequency, a decrease in magnitude, and a shift in time of occurrence of low flows to summer rather than winter. In snowmelt-driven streams, low flow events occur less often largely moderated by increased flow due to an overall increase in winter streamflow in a warmer climate.

The relationship between ENSO cycle and high and low-flow in the upper Yellow River

Firstly, the hydrological and meteorological features of the upper reaches of the Yellow River above Tangnag are analyzed based on observation data, and effects of EI Nino and La Nina events on the high and low flow in the upper Yellow River are discussed. The results show El Nino and La Nina events possess consanguineous relationship with runoff in the upper Yellow River. As a whole, the probability of low flow occurrence in the upper Yellow River is relatively great along with the occurrence of El Nino event. Moreover, the flood in the upper Yellow River occurs frequently with the occurrence of La Nina event. Besides, the results also show dissimilarity of El Nino event occurring time exerts greater impact on high flow and low flow in the upper Yellow River, that is, the probability of drought will be greater in the same year if El Nino event occurs in spring, the high-flow may happen in this year if El Nino occurs in summer or autumn; the longer the continuous period of El Nino is, the lower the runoff in the upper Yellow River is.