Extreme Streamflow Research Articles

Yukon River streamflow response to seasonal snow cover changes

AbstractWe used remotely sensed weekly snow water equivalent (SWE) and snow cover extent (SCE) data to investigate streamflow response to seasonal snowcover change over the Yukon watershed. We quantified the seasonal cycles and variations of snowcover (both SWE and SCE) and river streamflow, and identified a clear correspondence of river discharge to seasonal snowcover change. We also examined and compared the weekly mean streamflow with the weekly basin SWE and SCE. The results revealed a strong relation between the streamflow and snowcover change during the spring melt season. This relationship provides a practical procedure of using remotely sensed snowcover information for snowmelt runoff estimation over the large northern watersheds. Analyses of extreme (high/low) streamflow cases (years) and basin snowcover conditions indicate an association of high (low) flood peak with high (low) maximum SWE. Comparative analyses of weekly basin SWE versus SCE, peak snowmelt floods, and climatic variables (temperature and winter precipitation) indicate consistency among basin SWE, SCE, and temperature, but there is some incompatibility between basin SWE and winter precipitation. The inconsistency suggests uncertainties in determination of basin winter snowfall amounts and limitations in applications of the SWE retrieval algorithm over large watersheds/regions with different physical characteristics. Overall, the results of this study demonstrate that the weekly SWE and SCE data/products derived from remote sensing technology are useful in understanding seasonal streamflow changes in the northern regions. Copyright © 2008 John Wiley & Sons, Ltd.

Monitoring Flood and Discharge Variations in the Large Siberian Rivers From a Multi-Satellite Technique

Using a multi-satellite method, employing passive and active microwave along with visible and infrared observations developed to estimate monthly inundation extent at global scale, this study investigates the response of river discharge to seasonal flood change in the large Siberian watersheds. The seasonal cycle and variations of inundation extent over the Ob, the Yenissey, and the Lena basins for the period 1993–2000 show different spatial and temporal behaviors due to different climate and permafrost conditions. Using in-situ discharges collected at the outlets of the three basins, we analyze and quantify the relationships between the river streamflow and the monthly satellite-derived inundation extent during the spring/summer periods. Furthermore, we analyze extreme (high/low) streamflow cases for some years and the associated inundation conditions for the three watersheds and link these cases with other climatic parameters such as the snow water equivalent, temperatures, and precipitation. The results of this study demonstrate that the monthly multi-satellite-derived inundation dataset brings a new useful tool for better understanding both the streamflow processes and the description of the snow-inundation-runoff relations in data scarce areas like the remote Arctic river basins.

Quantifying the Predictability of Winter River Flow in Iberia. Part II: Seasonal Predictability

Abstract The role of the Atlantic summer and autumn SSTs on the predictability of the winter Iberian Peninsula river flows is analyzed during the period 1923–2004. The analysis is based on the results of an interannual predictability experiment, using autoregressive-moving-average (ARMA) models, carried out in the first part of this work. A standard principal component analysis (PCA) was applied to the summer and autumn SST fields for the entire Atlantic Ocean. Then, the association between the resulting principal component (PC) series and the Iberian Peninsula streamflow series was analyzed, in order to use the PC series as additional predictor variables in a seasonal forecasting regression model. Results proved, first, that during autumn, the SST variability in the so-called North Atlantic horseshoe pattern has a statistically significant linear influence in the following winter streamflow values. In particular, the use of this SST information considerably improves the skill of the linear forecast (improvements against climatology range from 61% to 90%) compared to the ARMA-alone model (51%–53%). These improvements are mostly related to the ability of the SST information to provide better estimates of extreme streamflow values. Additionally, results showed that the summer tropical Atlantic and the autumn southwestern Atlantic SST variability have a significant nonlinear influence on the following winter streamflow values. In particular, there is a tendency for negative streamflow anomalies following tropical Atlantic summer negative SST anomalies and following southwestern Atlantic autumn SST positive anomalies. It is finally concluded that the linear interannual predictability of the Iberian Peninsula winter streamflow is greater (two-thirds of the total predictability) than the predictability associated with the previous season autumn SSTs (one-third).

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Abstract. Extreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated with several nonparametric methods, including one method (Mann-Kendall test) for detecting trend, and three methods (Kolmogorov–Smirnov test, Levene's test and quantile test) for detecting changes in probability distribution. It was shown that little change is observed in annual extreme precipitation in terms of various indices, but some significant changes are found in the precipitation processes on a monthly basis, which indicates that when detecting climate changes, besides annual indices, seasonal variations in extreme events should be considered as well. Despite of little change in annual extreme precipitation series, significant changes are detected in several annual extreme flood flow and low-flow series, mainly at the stations along the main channel of Dongjiang River, which are affected significantly by the operation of several major reservoirs. To assess the reliability of the results, the power of three non-parametric methods are assessed by Monte Carlo simulation. The simulation results show that, while all three methods work well for detecting changes in two groups of data with large sample size (e.g., over 200 points in each group) and large differences in distribution parameters (e.g., over 100% increase of scale parameter in Gamma distribution), none of them are powerful enough for small data sets (e.g., less than 100 points) and small distribution parameter difference (e.g., 50% increase of scale parameter in Gamma distribution). The result of the present study raises the concern of the robustness of statistical change-detection methods, shows the necessity of combined use of different methods including both exploratory and quantitative statistical methods, and emphasizes the need of physically sound explanation when applying statistical test methods for detecting changes.

Frequency analysis of rainfall and streamflow extremes accounting for seasonal and climatic partitions

The extreme value distributions of hydrologic variables such as rainfall and streamflow play a key role in the design of water-related infrastructure. The relationship between the intensity, frequency and duration of extreme values is commonly evaluated using observed annual maximum values. This paper considers maximum values that occur within a given season and the relationship between seasonal maxima and annual maxima. Using a climatic index, the seasonal and annual maxima are also partitioned into multiple climatic states so that comparisons of seasonal maxima both within and between climatic states can be made. The distributions of seasonal and annual rainfall maxima are investigated for numerous locations around Australia and a detailed analysis, involving both seasonal and climatic partitions, is given for the daily streamflows of the Murray–Darling Basin, Australia. The results demonstrate significant differences between seasonal distributions within and between climatic states. The application of seasonal extremes to an urban design scenario is illustrated for Scott Creek in the Adelaide Hills, South Australia. The methodology presented can be used in a general setting to gain insight into the relationship between extremes from seasonal and climatic periods to their annual counterparts.

The applicability of power-law frequency statistics to floods

Many natural hazards satisfy power-law (fractal) frequency-size statistics to a good approximation for medium and large events. Examples include earthquakes, volcanic eruptions, asteroid impacts, landslides, and forest fires. A major question is whether this is also true for floods. A number of authors have argued in favor of the applicability of power-law statistics to floods. We discuss these arguments and consider a number of examples, using both instrumental records and paleoflood studies. In our analyses we consider both annual and partial-duration flood series. We argue that use of annual floods for statistical considerations strongly biases the flood-frequency estimates, as in some years, the annual flood will be much smaller than a number of ‘statistically independent’ floods (partial-duration floods) in other years. We examine six USGS hydrologic stations with drainage areas from 41 to 95,300 km 2, representing very different climatic regions and hydrologic conditions, and with periods of records ranging from 74 to 110 water years. Excellent power-law fits to each partial-duration series are found taking Q∼ T α , with Q the discharge associated with the recurrence interval T, and the power-law exponent α ranging from 0.27 to 0.90. We also consider paleoflood estimates for Axehandle Alcove on the Colorado River and Bonza Alcove on the Paria River, and find that that power-law extrapolations based on instrumental partial-duration series for stations in each of these two areas is within a half order of magnitude when compared to their respective paleoflood estimates. Finally, we consider an alternative approach to extreme streamflows that has been proposed, examining the cumulative probability distribution of instrumental daily mean streamflows. We show that this distribution is in good agreement with the power-law correlation found using the partial-duration series.

Use of soft information to describe the relative uncertainty of calibration data in hydrologic models

The impact of inaccurate or unreliable hydrological data on model calibration can be significant. A special case is when calibration data are from different sources with varying accuracy, e.g., missing streamflow measurements that have been supplemented by interpolation or extrapolation. Since the relative accuracy of data is commonly defined subjectively, model calibration with data of varying uncertainty is difficult. In this work, a framework using the method of order of importance is presented to incorporate soft information to describe the relative accuracy of calibration data. A numerical example using synthetic data is presented to demonstrate the validity of the methodology. The applicability of the framework is demonstrated for the Fishtrap Creek Catchment of Washington State, where the short streamflow record with significant gaps was reconstructed using support vector machines (SVM). Incorporation of this educated judgment about the relative accuracy of the calibration data resulted in identification of faulty model calibration as well as errors in the SVM prediction of extreme streamflow events, which would have been undetected otherwise. The methodology was further applied to two management scenarios involving flood frequency analysis and satisfying legally established in‐stream flow requirements.

High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling

The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series. Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll.

Stochastic Modeling of Annual Maximum and Minimum Streamflow of Kelkit Stream

The aim of this study is to determine whether the daily extreme streamflows could be generated by stochastic models. For the study, a linear stochastic model known as either Box-Jenkins or ARIMA was used. A Mann-Kendal nonparametric test was applied to the daily extreme data sequences to examine the existence of trends. This test showed that there was no trend in the daily data sequences. The ARIMA model is referred as ARMA since there was no trend in the data sequences and thus, the non-seasonal differencing operator is equal to zero. By using the graphs of ACF and PACF, alternative ARMA models were determined. The plots of the ACF show that there was no linear dependence between the daily maximum streamflows, whereas a linear dependence was observed between the daily minimum streamflows. Therefore, modeling of daily maximum data sequences was discarded. There are four ARMA models that fulfilled all the diagnostic checks among selected models from the graphs of ACF and PACF for the daily minimum data sequences. But ARMA (1, 0) with a constant was chosen as the best model by considering Schwarz Bayesian Criterion (SBC). In addition, error estimates were also shown that ARMA (1, 0) with a constant was the best model.

Streamflow response to seasonal snow cover extent changes in large Siberian watersheds

This study uses remotely sensed long‐term (1966–1999) weekly snow cover extent data to investigate snowmelt runoff response to seasonal snow cover change in the large Siberian watersheds (Ob, Yenisei, and Lena basins). It quantified the seasonal cycles and variations of snow cover extent and river streamflow and identified a clear correspondence of river streamflow to seasonal snow cover extent change; i.e., an association of low streamflow with high snow cover extent during the cold season and an increase in discharge associated with a decrease of snow cover extent during the melt periods. This study also examined and compared the weekly mean streamflow with the weekly basin snow cover extent for the study period. The results revealed a very strong linkage between the streamflow and snow cover extent change during the spring melt season over the large Siberian watersheds and developed a statistically significant weekly runoff‐snow cover relation. This relation suggests a practical procedure of using remotely sensed snow cover information for snowmelt runoff forecasting over the large northern watersheds. Analyses of extreme (high/low) streamflow cases (years) and the associated snow cover conditions indicate an association of high (low) flood peak with late (early) snowmelt in the Ob and Yenisei basins. Comparisons of snowmelt timing with peak flow show different associations between these two variables among the large Siberian rivers. These results demonstrate that the NOAA weekly snow cover extent data are useful for understanding and predicting streamflow changes in the Arctic regions. Snow cover water equivalent data/products obtained by remote sensing technology and in situ snow observations are currently being examined for what we expect will eventually improve hydrologic forecasts over the large northern watersheds.

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.

Statistics of extremes in hydrology

The statistics of extremes have played an important role in engineering practice for water resources design and management. How recent developments in the statistical theory of extreme values can be applied to improve the rigor of hydrologic applications and to make such analyses more physically meaningful is the central theme of this paper. Such methodological developments primarily relate to maximum likelihood estimation in the presence of covariates, in combination with either the block maxima or peaks over threshold approaches. Topics that are treated include trends in hydrologic extremes, with the anticipated intensification of the hydrologic cycle as part of global climate change. In an attempt to link downscaling (i.e., relating large-scale atmosphere–ocean circulation to smaller-scale hydrologic variables) with the statistics of extremes, statistical downscaling of hydrologic extremes is considered. Future challenges are reviewed, such as the development of more rigorous statistical methodology for regional analysis of extremes, as well as the extension of Bayesian methods to more fully quantify uncertainty in extremal estimation. Examples include precipitation and streamflow extremes, as well as economic damage associated with such extreme events, with consideration of trends and dependence on patterns in atmosphere–ocean circulation (e.g., El Niño phenomenon).

On the extreme streamflow drought analysis

A procedure, based on the distribution of the largest streamflowdrought deficit and on the composite hydrograph recession curve, is proposed for the derivation of the n-year streamflow drought,for the cases for which the assumption of identical deficit and duration frequencies is not valid, for a given river at a given location.The present article is a continuation of the previous work of Zelenhasic on the extremes of low flows. The previous streamflowdrought stochastic model dealt with the situation where theirexists the equality of probability of occurrence of extreme droughtdeficit and extreme drought duration, i.e. the assumption ofidentical deficit and duration frequencies. In practice, thecondition of identical frequencies is often mitigated by the lessstringent condition of approximate frequencies. The present articleanalyzes the situation in which there is no equality of probabilityof occurrence of extreme drought deficit and extreme droughtduration. Or, to be more specific, this method has no constraintssuch as the assumption of identical deficit and duration frequencies. The earlier theoretical explanations of the individualstreamflow drought components of the method of streamflow droughtstochastic process are completely valid in this paper, as well. Themethod has been applied in this paper to data from gaging stationson the Sava River at Srem. Mitrovica, the Drava River at D.Miholjac and the Danube River at Bezdan. The application of theprocedure has shown that the method is simple, reliable and efficient.

Historical evidences of streamflow fluctuations in the Mendoza River, Argentina, and their relationship with ENSO

Documentary sources are used to construct records of high and low flows of the Mendoza River and to relate these to ENSO over the historical period. The historical data are calibrated against measured discharge (ad 1909–1940) and validated by precipitation discharge-series reconstructed from tree-rings (ad 1600–1960). An increase in the number of high-streamflow events since the mid-seventeenth century is accompanied by several intervals with an above-average number of high-streamflow events; some of these intervals are also characterized by an above-average number of low-streamflow events. A considerable number of high-streamflow events do not coincide with ENSO events, which are regarded as a necessary but not sufficient cause of the extreme streamflow variations.

Estimation of the Extreme Flow Distributions by Stochastic Models

The t-year event is a commonly used characteristic to describe the extreme flood peak in hydrological designs. The annual maximum series (AMS) and the partial duration series (PDS) are two basic approaches in flood analyses. In this paper, we first derive the distribution of the maximum extreme or the joint distribution of two or more maximum extremes from historical records based on a stochastic model, and then estimate statistical characteristics, including the t-year event, from the distribution. In addition to the two classical approaches (AMS and PDS), two additional approaches are proposed for estimating the unknown parameters in this paper. The first one uses two or more annual maximums (MAMS) as the sample to estimate the distribution of the maximum extremes. The second one uses multi-variate shock model to estimate the distribution of the maximum extremes for a multi-modal streamflow. The distribution of the extreme streamflow and the associated characteristics in the Bird Creek in Avant, Oklahoma, in the St. Johns River in Deland, Florida, and in the West Walker River in Coleville, California are estimated by using the stochastic model. To investigate further the performance of the estimation, the stochastic models based on AMS, MAMS and PDS related are also applied to the simulated data. The results show that the stochastic model and the related methods are reliable.

Regulated flushing in a gravel-bed river for channel habitat maintenance: A Trinity River fisheries case study

The operation of Trinity and Lewiston Dams on the Trinity River in northern California in the United States, combined with severe watershed erosion, has jeopardized the existence of prime salmonid fisheries. Extreme streamflow depletion and stream sedimentation below Lewiston have resulted in heavy accumulation of coarse sediment on riffle gravel and filling of streambed pools, causing the destruction of spawning, nursery, and overwintering habitat for prized chinook salmon (Salmo gairdnerii) and steelhead trout (Oncorhynchus tschawytscha). Proposals to restore and maintain the degraded habitat include controlled one-time remedial peak flows or annual maintenance peak flows designed to flush the spawning gravel and scour the banks, deltas, and pools. The criteria for effective channel restoration or maintenance by streambed flushing and scouring are examined here, as well as the mechanics involved.

Extreme Stream Flow in Hydrological Year 1965 in Bohemia

In contrast to the preceding years 1963-1964 the hydrological year 1965 was characterized by extremely great stream flow. In the basin of the river Elbe it represents the fourth year with the greatest stream flow (from the viewpoint of annual average discharge) in the period 1850-1965 (after the years 1941, 1926, 1940). The annual average discharge on the Elbe in Děčín made 492 m3/s (i. e. 161 % of the long-time average). From the viewpoint of precipitation the hydrological year 1965 was also an above-average one (792 mm, i. e. 116 % of the long-time average). In the vegetation period 70 % of total precipitation in the hydrological year 1965 have fallen and in four months with the greatest stream flow (i. e. March till June) 62 % from the annual average discharge have flowed on the Elbe in Děčín, in June itself more than one fifth. Spring (i. e. March till May) with 277 mm orf precipitation (179 % of the normal) has contributed above all to extreme stream flow of the vegetation period and was the moistest one in the last 90 years. The period of abundant spring precipitation has followed immediately the period of the great melting of snow cover so that the high waterstages on the Bohemian rivers had the longterm character. Abundant precipitation in May (148 mm, 228 % of the normal, the moistest May in the period 1876-1965) and in June has caused several flood waves. In single cases the maximum flood was in July, owing to heavy downpours. October had the least stream flow and precipitation on the greatest territory of Bohemia. In the hydrological year 1965 in general the southern half of Bohemia was more humid than the northern one. From the viewpoint of the index of humidity by M. Konček (1955) the greater part of Bohemia was in the hydrological year 1965 humid, the highest parts of the border mountains perhumid, a little territory moistsubhumid.

Three observations on streamflow depletion by phreatophytes

Phreatophytes cause streamflow losses under natural and regulated flow-conditions, regardless whether the stream is influent or effluent. Indirect losses are said to occur whenever groundwater is intercepted by the vegetation before it reaches the stream. This type of depletion is common under conditions of natural drainage in valleys where the water table lies within 7 feet from the surface and where it has a gradient of more than 0.01. Direct losses from the river occur in a zone of induced infiltration, the width of which depends on the relation between the phreatophytic fluctuations and the transverse water-table gradient in the valley. Direct losses can result also from bankstorage effects brought about by water releases from a reservoir. Extreme streamflow depletion takes place when the river dries up during the summer. This condition is characteristic for places where the consumptive use of the vegetation temporarily exceeds the combined supply of stream discharge and groundwater inflow.