scholarly journals A method for low-flow estimation at ungauged sites: a case study in Wallonia (Belgium)

2013 ◽  
Vol 17 (4) ◽  
pp. 1319-1330 ◽  
Author(s):  
M. Grandry ◽  
S. Gailliez ◽  
C. Sohier ◽  
A. Verstraete ◽  
A. Degré
Keyword(s):  
Return Period ◽  
Management Practice ◽  
Global Model ◽  
Low Flow ◽  
Quality Analysis ◽  
Flow Index ◽  
Physical Parameters ◽  
Integrated Water Management ◽  
Explanatory Variables ◽  
Ungauged Sites

Abstract. Well-integrated water management can notably require estimating low flows at any point of a river. Depending on the management practice, it can be needed for various return periods. This is seldom addressed in the literature. This paper shows the development of a full analysis chain including quality analysis of gauging stations, low-flow frequency analysis, and building of a global model to assess low-flow indices on the basis of catchment physical parameters. The most common distributions that fit low-flow data in Wallonia were two-parameter lognormal and gamma. The recession coefficient and percolation were the most explanatory variables, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. The regression coefficients were found to be linked to the return period. This was used to design a complete equation that gives the low-flow index based on physical parameters and the desired return period (in a 5 to 50 yr range). The interest of regionalisation and the development of regional models are also discussed. Four homogeneous regions are identified, but to date the global model remains more robust due to the limited number of 20-yr-long gauging stations. This should be reconsidered in the future when enough data will be available.

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 method for low flow estimation at ungauged sites, case study in Wallonia (Belgium)

2012 ◽  
Vol 9 (10) ◽  
pp. 11583-11614 ◽  
Author(s):  
M. Grandry ◽  
S. Gailliez ◽  
C. Sohier ◽  
A. Verstraete ◽  
A. Degré
Keyword(s):  
Return Period ◽  
Integrated Management ◽  
Low Flow ◽  
Ungauged Catchments ◽  
Flow Estimation ◽  
Low Flows ◽  
Ungauged Sites ◽  
Flow Frequency ◽  
Two Parameter

Abstract. Being able to estimate low flows at any point of a river is really important nowadays for a good integrated management of rivers. Knowing the magnitude as well as the frequency of such extreme events becomes essential. In order to build a model of low flow calculation, usable in ungauged catchments and which takes also into account low flow frequency, we started with a low flow frequency analysis including a comparison of different distributions. Two-parameter Lognormal and Gamma were the most common distributions that fit low flow data in Wallonia. This was followed by a regionalisation of low flows using 25 different climatic and physical catchment variables, and the development of regression models that can be used to estimate the minimum 7-day average flow for different return periods, using catchment characteristics. The variables the most correlated to specific minimum 7-day average flows were the recession coefficient and percolation, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. Finally, regression coefficients were logarithmically linked to the return period. This enabled us to develop a single model per region and for the whole study area, in function of the return period. In conclusion, the method developed in this study allows us to estimate low flows in gauged and ungauged catchments of a given region for a given return period. The interest of regionalisation and development of regional models is also discussed.

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

2014 ◽  
Vol 41 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Mike Hulley ◽  
Colin Clarke ◽  
Ed Watt
Keyword(s):  
Return Period ◽  
Regional Analysis ◽  
Low Flow ◽  
Ecological Framework ◽  
Insufficient Data ◽  
Regression Equations ◽  
Low Flows ◽  
Ungauged Sites ◽  
Regional Regression ◽  
Natural Flow

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.

scholarly journals Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

2017 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Flow Index ◽  
Time Varying ◽  
Explanatory Variables ◽  
Flow Generation ◽  
Flow Frequency

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced a variety of non-stationary 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, a nonstationary framework of low-flow frequency analysis has been developed on basis of the Generalized Linear Model (GLM) to consider time-varying distribution parameters. In GLMs, the candidate explanatory variables to explain the time-varying parameters are comprised of the eight measuring indices of the climate and catchment conditions in low flow generation, i.e., total precipitation (P), mean frequency of precipitation events (λ), temperature (T), potential evapotranspiration (ET), climate aridity index (AIET), base-flow index (BFI), recession constant (K) and the recession-related aridity index (AIK). This framework was applied to the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China. Stepwise regression analysis was performed to obtain the best subset of those candidate explanatory variables for the final optimum model. The results show that the inter-annual variability in the variables of those selected best subsets plays an important role in modeling annual low flow series. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that AIK is of the highest relative importance among the best subset of eight candidates, followed by BFI and AIET. 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 predict future occurrences of low-flow extremes in similar areas.

Water quality modeling in the watershed-based approach for waste load allocations

1998 ◽  
Vol 38 (10) ◽  
pp. 165-172 ◽  
Author(s):  
Ruochuan Gu ◽  
Mei Dong
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Water Quality Modeling ◽  
Point Sources ◽  
Low Flow ◽  
Quality Analysis ◽  
Management Approach ◽  
Water Quality Control ◽  
Quality Modeling ◽  
Waste Load

The conventional method for waste load allocations (WLA) employs spatial-differentiation, considering individual point sources, and temporal-integration, using a constant flow, typically 7Q10 low flow. This paper presents a watershed-based seasonal management approach, in which non-point source as well as point sources are incorporated, seasonal design flows are used for water quality analysis, and WLA are performend in a watershed scale. The strategy for surface water quality modeling in the watershed-based approach is described. The concept of seasonal discharge management is discussed and suggested for the watershed-based approach. A case study using the method for the Des Moines River, Iowa, USA is conducted. Modeling considerations and procedure are presented. The significance of non-point source pollutant load and its impact on water quality of the river is evaluated by analyzing field data. A water quality model is selected and validated against field measurements. The model is applied to projections of future water quality situations under different watershed management and water quality control scenarios with respect to river flow and pollutant loading rate.

scholarly journals Assessment of medium and small river health based on macroinvertebrates habitat suitability curves: a case study in a tributary of Yangtze River, China

Water Policy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 602-621
Author(s):  
Yifan Su ◽  
Weiming Li ◽  
Liu Liu ◽  
Jinjing Li ◽  
Xuyang Sun ◽  
...  
Keyword(s):  
Health Status ◽  
Habitat Suitability ◽  
Yangtze River ◽  
Evaluation Method ◽  
Pearson Correlation ◽  
Small River ◽  
Low Flow ◽  
Small Rivers ◽  
Physical Parameters ◽  
River Health

Abstract The health of medium and small river ecosystems is threatened by increasing hydropower development and human activities. How to properly diagnose rivers has become a global concern. As a well-accepted theory, the aquatic organism density can be an indicator of river health. A new river health assessment method based on macroinvertebrates habitat suitability curves (M-HSC) was proposed. In this study, the health of Qiaobian River (QBR), a tributary of Yangtze River, China was evaluated by investigating the distribution of macroinvertebrates, chemical and physical parameters during winter 2018 (low flow season) and summer 2019 (high flow season). Based on habitat suitability of dominant macroinvertebrates, the key habitat factors were screened by canonical correspondence analysis (CCA) and Pearson correlation analysis. Suitability curves were determined by Generalized Additive Model (GAM). Ecosystem health comprehensive index method was used to evaluate the health status. The results show most suitable conditions for Corbicula fluminea containing chemical oxygen demand (CODMn) of 1.48 mg L−1, total nitrogen (TN) of 0.27 mg L−1, dissolved oxygen (DO) of 11.17 mg L−1, pH of 8.42, turbidity of 1.76 NTU, and water depth (Dep) of 0.35 m. The health status of QBR is spatially heterogeneous with the apparently better upstream than the downstream. In general, 25, 12.5, 12.5% of the samples were classified as nature, health and sub-health status, respectively and the rest 50% were lower than sub-health. The results are consistent with the environmental quality standards for surface water in China (GB3838-2002), suggesting the applicability of macroinvertebrates habitat suitability for evaluating river health. By minimizing the temporal and spatial limitations of comprehensive evaluation method and indicator species method, this study, for the first time, used macroinvertebrates habitat suitability curves to assess the health of medium and small rivers. The study will provide new insights for future river health assessments.

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 Extremeness of recent drought events in Switzerland: dependence on variable and return period choice

2019 ◽  
Vol 19 (10) ◽  
pp. 2311-2323 ◽  
Author(s):  
Manuela I. Brunner ◽  
Katharina Liechti ◽  
Massimiliano Zappa
Keyword(s):  
Soil Moisture ◽  
Frequency Analysis ◽  
Return Period ◽  
Threshold Level ◽  
Low Flow ◽  
Drought Event ◽  
Return Periods ◽  
Event Identification ◽  
Groundwater Drought ◽  
Drought Frequency Analysis

Abstract. The 2018 drought event had severe ecological, economic, and social impacts. How extreme was it in Switzerland? We addressed this question by looking at different types of drought, including meteorological, hydrological, agricultural, and groundwater drought, and at the two characteristics deficit and deficit duration. The analysis consisted of three main steps: (1) event identification using a threshold-level approach, (2) drought frequency analysis, and (3) comparison of the 2018 event to the severe 2003 and 2015 events. In Step 2 the variables precipitation, discharge, soil moisture, and low-flow storage were first considered separately in a univariate frequency analysis; pairs of variables were then investigated jointly in a bivariate frequency analysis using a copula model for expressing the dependence between the two variables under consideration. Our results show that the 2018 event was especially severe in north-eastern Switzerland in terms of soil moisture, with return periods locally exceeding 100 years. Slightly longer return periods were estimated when discharge and soil moisture deficits were considered together. The return period estimates depended on the region, variable, and return period considered. A single answer to the question of how extreme the 2018 drought event was in Switzerland is therefore not possible – rather, it depends on the processes one is interested in.

Modelling uncertainty of flood quantile estimations at ungauged sites by Bayesian networks

2013 ◽  
Vol 16 (4) ◽  
pp. 822-838 ◽  
Author(s):  
D. Santillán ◽  
L. Mediero ◽  
L. Garrote
Keyword(s):  
Bayesian Networks ◽  
Explanatory Variables ◽  
Modelling Uncertainty ◽  
Ungauged Sites ◽  
Estimation Uncertainty ◽  
Linear Relationships ◽  
Flood Quantiles ◽  
Quantile Estimations ◽  
Probabilistic Nature

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.

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.

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