Temporal analysis of the frequency and duration of low and high streamflow: years of record needed to characterize streamflow variability

A temporal analysis of the number and duration of exceedences of high- and low-flow thresholds was conducted to determine the number of years required to detect a level shift using data from Virginia, North Carolina, and South Carolina. Two methods were used—ordinary least squares assuming a known error variance and generalized least squares without a known error variance. Using ordinary least squares, the mean number of years required to detect a one standard deviation level shift in measures of low-flow variability was 57.2 (28.6 on either side of the break), compared to 40.0 years for measures of high-flow variability. These means become 57.6 and 41.6 when generalized least squares is used. No significant relations between years and elevation or drainage area were detected (P>0.05). Cluster analysis did not suggest geographic patterns in years related to physiography or major hydrologic regions. Referring to the number of observations required to detect a one standard deviation shift as ‘characterizing’ the variability, it appears that at least 20 years of record on either side of a shift may be necessary to adequately characterize high-flow variability. A longer streamflow record (about 30 years on either side) may be required to characterize low-flow variability.     

Scaling and physiographic controls on streamflow behaviour on the Precambrian Shield, south-central Ontario

Relationships explaining streamflow behaviour in terms of drainage basin physiography greatly assist efforts to extrapolate streamflow metrics from gauged to ungauged basins in the same landscape. The Dorset Environmental Science Centre (DESC) has monitored streamflow from 22 small basins (3.4–190.5 ha) on the Precambrian Shield in south-central Ontario, in some cases since 1976. The basins exhibit regional coherence in their interannual response to precipitation; however, there is often a poor correlation between streamflow metrics from basins separated by as little as 1 km. This study assesses whether inter-basin variations in such metrics can be explained in terms of basin scale and physiography. Several characteristics (annual maximum, minimum and average flow) exhibited simple scaling with basin area, while magnitude, range and timing of annual maximum daily runoff showed scaling behaviour consistent with the Representative Elementary Area (REA) concept. This REA behaviour is partly attributed to convergence of fractional coverage of the two dominant and hydrologically-contrasting land cover types in the DESC region with increasing basin size. Three Principal Components (PCs) explained 82.4% of the variation among basin physiographic properties, and several runoff metrics (magnitude and timing of annual minimum daily runoff, mean number of days per year with 0 streamflow) exhibited significant relationships with one or more PC. Significant relationships were obtained between basin quickflow (QF) production and the PCs on a seasonal and annual basis, almost all of which were superior to simple area-based relationships. Basin physiography influenced QF generation via its control on slope runoff, water storage and hydrologic connectivity; however, this role was minimized during Spring when QF production in response to large rain-on-snow events was relatively uniform across the DESC basins. The PC-based relationships and inter-seasonal changes in their form were consistent with previous research conducted at point, slope and basin scales in the DESC region, and perceptions of key hydrological processes in these small basins may not have been as readily obtained from scaling studies using streamflow from larger basins. This process understanding provides insights into scaling behaviour beyond those derived from simple scaling and REA analyses. The physiography of the study area is representative of large portions of the Precambrian Shield, such that basin streamflow behaviour could potentially be extended across much of south-central Ontario. This would assist predictions of streamflow conditions at ungauged locations, development and testing of hydrological models for this landscape, and interpretation of inter-basin and intra-annual differences in hydrochemical behaviour on the southern Precambrian Shield.     

Statistical downscaling of GCM simulations to streamflow using relevance vector machine

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.     

Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model

This paper describes an application of the ensemble Kalman filter (EnKF) in which streamflow observations are used to update states in a distributed hydrological model. We demonstrate that the standard implementation of the EnKF is inappropriate because of non-linear relationships between model states and observations. Transforming streamflow into log space before computing error covariances improves filter performance. We also demonstrate that model simulations improve when we use a variant of the EnKF that does not require perturbed observations. Our attempt to propagate information to neighbouring basins was unsuccessful, largely due to inadequacies in modelling the spatial variability of hydrological processes. New methods are needed to produce ensemble simulations that both reflect total model error and adequately simulate the spatial variability of hydrological states and fluxes.     

Ensemble flood forecasting: A review

Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.     

Effect of streamflow forecast uncertainty on real-time reservoir operation

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (pseudo-PSF, pPSF), and ensemble or probabilistic streamflow forecast (denoted as real-PSF, rPSF). DSF represents forecast uncertainty in the form of deterministic forecast errors, pPSF a conditional distribution of forecast uncertainty for a given DSF, and rPSF a probabilistic uncertainty distribution. Compared to previous studies that treat the forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the dynamic evolution of uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. Through a hypothetical example of a single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases but the magnitude depends on the forecast products used. In general, the utility of the reservoir operation with rPSF is nearly as high as the utility obtained with a perfect forecast. Meanwhile, the utilities of DSF and pPSF are similar to each other but not as high as rPSF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, pPSF, and rPSF.     

加拿大Hudson海湾地区的水文研究

Hudson海湾的河川径流量在整个加拿大河川径流量中占有很大的比例，所有河流排泄量约30％流人了Hudson和James海湾。研究了该地区河川径流与当地气候变化之间的关系；把河川向James海湾的排泄与曼尼托巴省Churchill地区海水位的变化联系起来，讨论了其对Hudson海湾再循环的影响；根据曼尼托巴省Churchill地区海水位的观测资料重点讨论了该地区海水位在历史时期的变化及未来的预测变化等三个重要水文问题。最后指出了今后在Hudson海湾地区进行水文研究的方向。     

Streamflow routing in the indian arid zone

A lumped model for streamflow routing in arid ephemeral channels has been developed. The governing equations for movement of flood waves subjected to transmission losses are simplified through a time averaging process to develop an ordinary differential equation describing transmission losses as a function of distance, inflow, channel width, time parameters of flow and effective hydraulic conductivity. The resulting equation has an analytical solution and simulates runoff volume and peak discharge rates for individual storm events. The outflow hydrograph is fairly well approximated with a triangular approximation. The model is simplified and constructed to require a minimum of observed data for calibration. It can also be used for ungauged basins in arid regions through parameterization.     

Field application of a numerical method for the derivation of baseflow recession constant

In an earlier paper (Bako and Hunt, 1988), a method for the derivation of the baseflow recession constant (K) using one-way analysis of variance was presented. This paper presents the results of the field application of this method. The K values obtained by using the numerical equation of Bako and Hunt (1988) were inserted in the exponential recession equation (Barnes, 1939) to generate a series of baseflows. The fit between the model and the historical flows was found to be greater than 99 per cent thus confirming the applicability of the numerical method under field situation. The main advantage of this technique is its amenability for computerized application thus making it relatively faster than any of the existing techniques of fitting the recession equation. For this reason, the subjectivity inherent in most of the existing techniques is eliminated and a measure of procedural consistency can be guaranteed. Consistency is necessary if intercatchment comparison or interpolation of K values is to be meaningful.     

Assessing the impacts of land use changes on watershed hydrology using MIKE SHE

A fully distributed, physically-based hydrologic modeling system, MIKE SHE, was used in this study to investigate whole-watershed hydrologic response to land use changes within the Gyeongancheon watershed in Korea. A grid of 200 × 200 m was established to represent spatial variations in geology, soil, and land use. Initial model performance was evaluated by comparing observed and simulated streamflow from 1988 to 1991. Results indicated that the calibrated MIKE SHE model was able to predict streamflow well during the calibration and validation periods. Proportional changes in five classes of land use within the watershed were derived from multi-temporal Landsat TM imageries taken in 1980, 1990 and 2000. These imageries revealed that the watershed experienced conversion of approximately 10% non-urban area to urban area between 1980 and 2000. The calibrated MIKE SHE model was then programmed to repeatedly analyze an artificial dataset under the various land use proportions identified in the Landsat TM imageries. The analysis was made to quantitatively assess the impact of land use changes (predominantly urbanization) on watershed hydrology. There were increases in total runoff (5.5%) and overland flow (24.8%) as a response to the land use change.