scholarly journals Trend detection in river flow series: 2. Flood and low-flow index series / Détection de tendance dans des séries de débit fluvial: 2. Séries d'indices de crue et d'étiage

2005 ◽  
Vol 50 (5) ◽  
Author(s):  
Cecilia Svensson ◽  
W. Zbigniew Kundzewicz ◽  
Thomas Maurer
Keyword(s):  
River Flow ◽  
Low Flow ◽  
Trend Detection ◽  
Flow Index ◽  
Low Flow Index

scholarly journals Hydrological characterization of watersheds in the Blue Nile Basin, Ethiopia

2011 ◽  
Vol 15 (1) ◽  
pp. 11-20 ◽  
Author(s):  
S. G. Gebrehiwot ◽  
U. Ilstedt ◽  
A. I. Gärdenas ◽  
K. Bishop
Keyword(s):  
Land Use ◽  
Soil Type ◽  
Low Flow ◽  
Flow Index ◽  
Nile Basin ◽  
Grazing Land ◽  
Blue Nile ◽  
Low Flows ◽  
Blue Nile Basin ◽  
Low Flow Index

Abstract. Thirty-two watersheds (31–4350 km2), in the Blue Nile Basin, Ethiopia, were hydrologically characterized with data from a study of water and land resources by the US Department of Interior, Bureau of Reclamation (USBR) published in 1964. The USBR document contains data on flow, topography, geology, soil type, and land use for the period 1959 to 1963. The aim of the study was to identify watershed variables best explaining the variation in the hydrological regime, with a special focus on low flows. Moreover, this study aimed to identify variables that may be susceptible to management policies for developing and securing water resources in dry periods. Principal Component Analysis (PCA) and Partial Least Square (PLS) were used to analyze the relationship between five hydrologic response variables (total flow, high flow, low flow, runoff coefficient, low flow index) and 30 potential explanatory watershed variables. The explanatory watershed variables were classified into three groups: land use, climate and topography as well as geology and soil type. Each of the three groups had almost equal influence on the variation in hydrologic variables (R2 values ranging from 0.3 to 0.4). Specific variables from within each of the three groups of explanatory variables were better in explaining the variation. Low flow and low flow index were positively correlated to land use types woodland, dense wet forest and savannah grassland, whereas grazing land and bush land were negatively correlated. We concluded that extra care for preserving low flow should be taken on tuffs/basalts which comprise 52% of the Blue Nile Basin. Land use management plans should recognize that woodland, dense wet forest and savannah grassland can promote higher low flows, while grazing land diminishes low flows.

scholarly journals Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging

2011 ◽  
Vol 15 (3) ◽  
pp. 715-727 ◽  
Author(s):  
S. Castiglioni ◽  
A. Castellarin ◽  
A. Montanari ◽  
J. O. Skøien ◽  
G. Laaha ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Central Italy ◽  
Low Flow ◽  
Flow Index ◽  
Ungauged Basins ◽  
Low Flows ◽  
Interpolation Techniques ◽  
Point Data ◽  
Leave One Out ◽  
Low Flow Index

Abstract. Recent studies highlight that spatial interpolation techniques of point data can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative interpolation techniques for the prediction of low-flows in ungauged basins. The first one, named Physiographical-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-kriging are applied for the regionalization of Q355 (i.e., a low-flow index that indicates the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. The two techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90m elevation model. The results of the study show that Top-kriging and PSBI present complementary features. Top-kriging outperforms PSBI at larger river branches while PSBI outperforms Top-kriging for headwater catchments. Overall, they have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

Non-Newtonian oscillatory boundary layer, a numerical approach

1986 ◽  
Vol 51 (6) ◽  
pp. 1240-1258 ◽  
Author(s):  
Ondřej Wein ◽  
Václav Sobolík
Keyword(s):  
Numerical Approach ◽  
Stress Fields ◽  
Low Flow ◽  
Flow Index ◽  
Differential Formulation ◽  
Oscillatory Boundary Layer ◽  
Flow Enhancement ◽  
Pseudoplastic Liquid ◽  
Oscillating Plate ◽  
Low Flow Index

Differential schemes have been constructed by using the integro-differential formulation of the equation of motion in stresses. The problem of periodic flow of the purely-viscous non-Newtonian liquid on a harmonically oscillating plate has been solved numerically for extremally high values of the Reynolds number. The velocity and stress fields as well as estimates of the flow enhancement E are given for the quasi-oscillatory regime, Fr → ∞, for the pseudoplastic liquid with the extremally low flow index, n = 0.15.

scholarly journals Which objective function to calibrate rainfall–runoff models for low-flow index simulations?

2017 ◽  
Vol 62 (7) ◽  
pp. 1149-1166 ◽  
Author(s):  
Florine Garcia ◽  
Nathalie Folton ◽  
Ludovic Oudin
Keyword(s):  
Objective Function ◽  
Low Flow ◽  
Flow Index ◽  
Rainfall Runoff ◽  
Low Flow Index

A Principal Component Regression Method for Estimating Low Flow Index

2010 ◽  
Vol 24 (11) ◽  
pp. 2553-2566 ◽  
Author(s):  
Saeid Eslamian ◽  
Mehdi Ghasemizadeh ◽  
Monireh Biabanaki ◽  
Mansoor Talebizadeh
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Regression Method ◽  
Low Flow ◽  
Flow Index ◽  
Low Flow Index

Sources of Chlorophenolic Compounds to the Fraser River Estuary

1988 ◽  
Vol 23 (1) ◽  
pp. 55-68 ◽  
Author(s):  
J. H. Carey ◽  
J. H. Hart
Keyword(s):  
River Flow ◽  
River Estuary ◽  
Low Flow ◽  
Fraser River ◽  
Pulp Mills ◽  
Flow Conditions ◽  
Downstream Site ◽  
Upstream Site ◽  
The North ◽  
Main River

Abstract The identity and concentrations of chlorophenolic compounds in the Fraser River estuary were determined under conditions of high and low river flow at three sites: a site upstream from the trifurcation and at downstream sites for each main river arm. Major chlorophenolics present under both flow regimes were 2,4,6-trichlorophenol (2,4,6-TCP), 2,3,4,6-tetrachlorophenol (2,3,4,6-TeCP), pentachlorophenol (PCP), tetrachloroguaiacol (TeCG) and a compound tentatively identified as 3,4,5-trichloroguaiacol (3,4,5-TCG). Under high flow conditions, concentrations of the guaiacols were higher than any of the Chlorophenols and concentrations of all five chlorophenolics appeared to correlate. Under low flow conditions, concentrations of chloroguaiacols were higher than Chlorophenols at the upstream site and at the downstream site on the Main Arm, whereas at the downstream site on the North Arm, concentrations of 2,3,4,6-TeCP and PCP were higher than the chloroguaiacols in some samples. Overall, the results indicate that pulp mills upstream from the estuary are important sources of chlorophenolics to the estuary under all flow conditions. Additional episodic inputs of 2,3,4,6-TeCP and PCP from lumber mills occur along the North Arm. When these inputs occur, they can cause the concentrations of Chlorophenols in the North Arm to exceed provisional objectives. If chloroguaiacols are included as part of the objective, concentrations of total chlorophenolics in water entering the estuary can approach and exceed these objectives, especially under low flow conditions.

scholarly journals Multiple causes of nonstationarity in the Weihe annual low-flow series

2018 ◽  
Vol 22 (2) ◽  
pp. 1525-1542 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Distribution Model ◽  
Flow Index ◽  
Explanatory Variables ◽  
Distribution Parameters

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

scholarly journals A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

2019 ◽  
Vol 23 (1) ◽  
pp. 73-91 ◽  
Author(s):  
Theano Iliopoulou ◽  
Cristina Aguilar ◽  
Berit Arheimer ◽  
María Bermúdez ◽  
Nejc Bezak ◽  
...  
Keyword(s):  
River Flow ◽  
Frequency Estimation ◽  
Low Flow ◽  
Time Lags ◽  
Low Flows ◽  
Flow Formation ◽  
Gaussian Probability Distribution ◽  
High Flows ◽  
Seasonal Correlation ◽  
Seasonal Streamflow

Abstract. The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.

scholarly journals Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

2020 ◽  
Vol 24 (3) ◽  
pp. 1031-1054 ◽  
Author(s):  
Thibault Hallouin ◽  
Michael Bruen ◽  
Fiachra E. O'Loughlin
Keyword(s):  
Flow Regime ◽  
River Flow ◽  
Freshwater Ecosystems ◽  
Living Environment ◽  
Rate Of Change ◽  
Low Flow ◽  
Hydrological Models ◽  
Flow Conditions ◽  
Local Flow ◽  
Trade Off

Abstract. The ecological integrity of freshwater ecosystems is intimately linked to natural fluctuations in the river flow regime. In catchments with little human-induced alterations of the flow regime (e.g. abstractions and regulations), existing hydrological models can be used to predict changes in the local flow regime to assess any changes in its rivers' living environment for endemic species. However, hydrological models are traditionally calibrated to give a good general fit to observed hydrographs, e.g. using criteria such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE). Much ecological research has shown that aquatic species respond to a range of specific characteristics of the hydrograph, including magnitude, frequency, duration, timing, and the rate of change of flow events. This study investigates the performance of specially developed and tailored criteria formed from combinations of those specific streamflow characteristics (SFCs) found to be ecologically relevant in previous ecohydrological studies. These are compared with the more traditional Kling–Gupta criterion for 33 Irish catchments. A split-sample test with a rolling window is applied to reduce the influence on the conclusions of differences between the calibration and evaluation periods. These tailored criteria are shown to be marginally better suited to predicting the targeted streamflow characteristics; however, traditional criteria are more robust and produce more consistent behavioural parameter sets, suggesting a trade-off between model performance and model parameter consistency when predicting specific streamflow characteristics. Analysis of the fitting to each of 165 streamflow characteristics revealed a general lack of versatility for criteria with a strong focus on low-flow conditions, especially in predicting high-flow conditions. On the other hand, the Kling–Gupta efficiency applied to the square root of flow values performs as well as two sets of tailored criteria across the 165 streamflow characteristics. These findings suggest that traditional composite criteria such as the Kling–Gupta efficiency may still be preferable over tailored criteria for the prediction of streamflow characteristics, when robustness and consistency are important.

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