Streamflow-gaging Stations Research Articles

Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin

Abstract Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.

Artificial neural network based PERSIANN data sets in evaluation of hydrologic utility of precipitation estimations in a tropical watershed of Sri Lanka

<abstract> <p>The developments of satellite technologies and remote sensing (RS) have provided a way forward with potential for tremendous progress in estimating precipitation in many regions of the world. These products are especially useful in developing countries and regions, where ground-based rain gauge (RG) networks are either sparse or do not exist. In the present study the hydrologic utility of three satellite-based precipitation products (SbPPs) namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Cloud Classification System (PERSIANN-CCS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain Rate near real-time (PDIR-NOW) were examined by using them to drive the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrologic model for the Seethawaka watershed, a sub-basin of the Kelani River Basin of Sri Lanka. The hydrologic utility of SbPPs was examined by comparing the outputs of this modelling exercise against observed discharge records at the Deraniyagala streamflow gauging station during two extreme rainfall events from 2016 and 2017. The observed discharges were simulated considerably better by the model when RG data was used to drive it than when these SbPPs. The results demonstrated that PERSIANN family of precipitation products are not capable of producing peak discharges and timing of peaks essential for near-real time flood-forecasting applications in the Seethawaka watershed. The difference in performance is quantified using the Nash-Sutcliffe Efficiency, which was > 0.80 for the model when driven by RGs, and < 0.08 when driven by the SbPPs. Amongst the SbPPs, PERSIANN performed best. The outcomes of this study will provide useful insights and recommendations for future research expected to be carried out in the Seethawaka watershed using SbPPs. The results of this study calls for the refinement of retrieval algorithms in rainfall estimation techniques of PERSIANN family of rainfall products for the tropical region.</p> </abstract>

From at-site to regional assessment of environmental flows and environmental flows variability in a Mediterranean environment

Study regionData from 28 streamflow gauging stations located in the Campania Region, Southern Italy, were analysed. Study focusThe study was aimed at recommend regional methodologies for environmental flow (EF) and EF variability estimation for a climatological environment particularly affected by strong climate variability. Starting from an at-site statistical analysis of discharge data, a preliminary step where the quantification of EF average value, μ(Q95), and inter-annual variability, CV(Q95), was illustrated. A regional regression approach was then presented for the prediction of μ(Q95) and CV(Q95). New hydrological insights for the regionA step wise procedure highlighted the dominant hydrological variables and catchment attributes for EF prediction. Catchment area and mean annual daily discharge μ(Q) appeared strongly related to μ(Q95) whereas CV(Q95) was found to be dependent on the baseflow index and on precipitation variability. Regional predictions were evaluated on the base of the correlation coefficient and absolute average percentage errors. Prediction errors amounted to about 30 % and 17 % respectively in the case of μ(Q95) and CV(Q95). In the end, an implication for a fully regional approach, simply based on catchment attributes, also embedding the impact of hydrological variables, was presented. It showed clearly different performance capacity compared to the prediction based on the observed hydrological variables but not significantly lower.

An Analysis of Streamflow Trends in the Southern and Southeastern US from 1950–2015

In this article, the mean daily streamflow at 139 streamflow-gaging stations (sites) in the southern and southeastern United States are analyzed for spatial and temporal patterns. One hundred and thirty-nine individual time-series of mean daily streamflow were reduced to five aggregated time series of Z scores for clusters of sites with similar temporal variability. These aggregated time-series correlated significantly with a time-series of several climate indices for the period 1950–2015. The mean daily streamflow data were subset into six time periods—starting in 1950, 1960, 1970, 1980, 1990, and 2000, and each ending in 2015, to determine how streamflow trends at individual sites acted over time. During the period 1950–2015, mean monthly and seasonal streamflow decreased at many sites based on results from traditional Mann–Kendall trend analyses, as well as results from a new analysis (Quantile-Kendall) that summarizes trends across the full range of streamflows. A trend departure index used to compare results from non-reference with reference sites identified that streamflow trends at 88% of the study sites have been influenced by non-climatic factors (such as land- and water-management practices) and that the majority of these sites were located in Texas, Louisiana, and Georgia. Analysis of the results found that for sites throughout the study area that were influenced primarily by climate rather than human activities, the step increase in streamflow in 1970 documented in previous studies was offset by subsequent monotonic decreases in streamflow between 1970 and 2015.

Multi-site calibration of hydrological model and assessment of water balance in a semi-arid river basin of India

Soil and Water Assessment Tool (SWAT) was calibrated and validated using multi-site streamflow data, and water balance was assessed in the Betwa river basin of central India. Sequential Uncertainty Fitting version-2 (SUFI-2) algorithm available in the SWAT-Calibration and Uncertainty Program (SWAT-CUP) was utilized for sensitivity analysis, calibration, validation and uncertainty analysis on the monthly time step. The model was calibrated for the period 2000–2005 and validated for the period 2006–2011 using data of four streamflow gauging stations. The values of P-factor and R-factor ranged from 0.51 to 0.75 and 0.17 to 0.53, respectively during calibration and validation periods. The values of Coefficient of determination (R2), NSE (Nash-Sutcliffe efficiency), RMSE-observations standard deviation ratio (RSR) and percent bias (PBIAS) varied from 0.83 to 0.92, 0.6 to 0.91, 0.3 to 0.63 and −19.8 to 19.3, respectively, for calibration and validation periods. Analysis of average annual water balance of Betwa river basin for the period 2000–2011 showed that out of the total annual rainfall of 878 mm, about 31% flows as surface runoff, 8% flows as baseflow and 61% is lost as evapotranspiration from the basin. The water yield from the basin constitutes about 37% of annual rainfall. The study revealed that basin is rainfed with most of the runoff (95.3%) occurring during monsoon season and therefore, the runoff occurring during monsoon season should be conserved by adopting suitable water conservation measures like farms ponds, check dams and percolation ponds so that stored water could be utilized during non-monsoon season.

Regional flood frequency analysis and uncertainties: Maximum streamflow estimates in ungauged basins in the region of Lavras, MG, Brazil

Regionalization techniques are an important alternative to overcome the scarcity of streamflow data and to provide adequate estimates for flood management. This work aims to generate a maximum streamflow regional function and evaluate the uncertainties in estimates for a basin in the South of Minas Gerais, Brazil. Six streamflow gauging stations, that presented stationarity and no trends according to the Mann-Kendal test, were used to develop the regional function by the dimensionless curve method. The annual maximum streamflow series were adjusted to the probability density functions (PDFs) 2-parameter Log-Normal, 3-paramenter Log-Normal, Gumbel, and Gamma. Then, the goodness-of-fit of the PDFs was verified by the Anderson-Darling test. For the regional analysis, heterogeneity and discordancy measures verified the homogeneity of the region, and the jack-knifing technique evaluated the predictive performance of the regional function by the coefficient c of Camargo and Sentelhas. The uncertainty analysis for at-site and regional function estimates was assessed by Monte Carlo simulations and bootstrapping. The performance of the regional function was classified as optimum for most of the watersheds, but as good when considering the prediction in certain return periods. The uncertainties were larger in ungauged basins, especially for greater return periods and drainage areas. The regional function developed can be employed in ungauged sites, but also to improve data in poorly monitored watersheds. Thus, this study demonstrates the adequate applicability of regionalization in ungauged watersheds and provides a simple alternative for maximum streamflow estimates in order to promote sustainable flood risk management.

Comparison of single-site, multi-site and multi-variable SWAT calibration strategies

ABSTRACT This study compares single-site, multi-site and multi-variable SWAT calibration. The SWAT model was applied to a large basin (63 884 km2) and calibrated at a monthly time step with the SUFI-2 algorithm, using the Kling-Gupta efficiency (KGE) as the objective function. Multi-variable calibration was performed by combining streamflow and remote sensing-derived actual evapotranspiration data. Parameter transferability was also investigated, by daily time step validation. The KGE for the outlet ranged from 0.73 to 0.86, and the average KGE of all streamflow gauge stations ranged from 0.73 to 0.80, reflecting a good overall simulation performance for the monthly time step. In daily time step validation, KGE ranged from 0.62 to 0.68, and the Nash-Sutcliffe efficiency ranged from 0.40 to 0.60, for the average of all gauge stations. Multi-site and multi-variable calibrations did not significantly improve inner sub-basin simulation performance but improved streamflow uncertainty when compared to single-site calibration.

Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates

AbstractThe low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e., errors) between prior grid‐based streamflow estimates for Australia at 0.05° × 0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally hierarchical correction scheme.

Subwatershed-based lake and river routing products for hydrologic and land surface models applied over Canada

Lakes and reservoirs have critical impacts on hydrological, biogeochemical, and ecological processes, and they should be an essential component of regional-scale hydrological and eco-hydrological models. This is particularly important in Canada with its tens of thousands of lakes. Past large-scale hydrologic modelling efforts tend to either ignore the impacts of all lakes or explicitly simulate the behaviour of only the largest lakes in a watershed. This research derives a suite of Pan-Canadian subwatershed-based lake and river routing GIS products at multiple spatial resolutions (average catchment size ranges from 60 to 306 km2 while the number of lakes explicitly represented ranges from 11,000 to 87,000). These publicly available data products supply all the necessary hydrologic routing model inputs, including network topology, subwatershed geometry, channel characteristics (slope, length, roughness, and geometry), and lake characteristics (area, volume, and outlet description), and were derived primarily from the HydroLAKES, HydroBASINS and HydroSHEDS databases. All Water Survey of Canada streamflow gauging stations are used to define subwatershed outlets in the products. The routing product is used to inform a hydrologic routing model in the Raven hydrologic modelling framework. This is the first demonstration of Raven in routing-only mode. As a case study, the Hudson Bay drainage basin (∼40% of Canada) is simulated using GEM-Surf land surface model gridded runoff and recharge as inputs and includes more than 20,000 river reaches and more than 10,000 lakes explicitly represented at an hourly timestep. Uncalibrated streamflows compare reasonably well to measured streamflows at select locations. The sensitivity of the routing model prediction quality to the discretization level for represented lakes is evaluated and shows, for example, that ignoring multiple smaller lakes can have a significant impact on predictions.

A methodology to estimate flow duration curves at partially ungauged basins

Abstract. The flow duration curve (FDC) of streamflow at a specific site has a key role in the knowledge on the distribution and characteristics of streamflow at that site. The FDC gives information on the water regime, providing information to optimally manage the water resources of the river. In spite of its importance, because of the lack of streamflow gauging stations, the FDC construction can be a not straightforward task. In partially gauged basins, FDCs are usually built using regionalization among the other methods. In this paper we show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Different weather conditions lead to different FDCs for the same catchment. The differences can often be significant. Similarly, the FDC built at a site for a specific period cannot be used to retrieve the FDC at a different site for the same time window. In this paper, we propose a new methodology to estimate FDCs at partially gauged basins (i.e., target sites) using precipitation data gauged at another basin (i.e., donor site). The main idea is that it is possible to retrieve the FDC of a target period of time using the data gauged during a given donor time period for which data are available at both target and donor sites. To test the methodology, several donor and target time periods are analyzed and results are shown for different sites in the USA. The comparison between estimated and actually observed FDCs shows the reasonability of the approach, especially for intermediate percentiles.

The Use of the Normalized Difference Vegetation Index to Analyze the Influence of Vegetation Cover Changes on the Streamflow in the Manhuaçu River Basin, Brazil

The intensification of anthropogenic activities in river basins has significantly altered river streamflow. Thus, the aim of this work is to evaluate the influence of temporal and spatial changes in vegetation cover on the streamflow of the Manhuacu River, Brazil using the normalized difference vegetation index (NDVI). The NDVI values were obtained from Landsat images for 1986 to 2014 and processed on the Google Earth Engine (GEE). To characterize the hydrologic behavior of the basin, we used data from eight streamflow gauge stations and 18 rain gauge stations, and we conducted Mann-Kendall and Pettitt tests to evaluate the stationarity hypothesis. To verify the relationship between the vegetation cover changes and the streamflow, we used multiple regression models. In the analyzed period, we observed a slight increase in the vegetation cover and a reduction in pasture areas. In most cases, the annual low flow exhibited a decreasing tendency because of an increase in vegetation area. We obtained satisfactory adjustments of the multiple regression models by examining the correlations among the change in vegetation cover, rainfall and evapotranspiration data, which resulted in adjusted $$ {\mathrm{R}}_{\mathrm{a}}^2 $$ values ranging from 0.59 to 0.96. The NDVI values were a good indicator of vegetation cover dynamics in the Manhuacu River basin, which influenced the nonstationary streamflow behavior.

Assessment of recession flow variability and the surficial lithology impact: a case study of Buffalo River catchment, Eastern Cape, South Africa

The recession identity is an essential indicator of river performance, health and diminution status. This study, therefore, presents an assessment of streamflow recession characteristics in relation to the hydrostratigraphy property of watershed. The streamflow recession assessment was carried out by computing the flow duration curve (FDC) and baseflow index (BFI) analyses of 28 year streamflow records at six distinct streamflow gauging stations. Digital processed aeromagnetic map was hybridized with geological survey and map review for the construction and characterization of concise watershed surficial lithology. The FDC plot of low-flow slopes and Q95 reported the flow per station as thus; Buffalo (− 0.0113; 0.0026), Tshoxa (− 0.0029, 0.0016), Yellowwoods (− 0.0022, 0.0008), Mgqakwebe (− 0.0017, 0.0007), Quencwe (− 0.0009, 0.0002), and Ngqokweni (− 0.0005, 0.0001). Similarly, BFI results show the following rank: Buffalo (0.541), Yellowwoods (0.488), Tshoxa (0.450), Mgqakwebe (0.443), Quencwe (0.415), and Ngqokweni (0.332). The recession analysis revealed that the Q95 slope of the stochastic FDC approach is a more reliable recession estimate for environmental flow. The assessment of the recession–lithology relationship suggests that the porosity network of the contact zone and fracture system of dolerite may produce a weightier impact of baseflow discharge in support of environmental flow over the hydraulic conductivity of sandstone. The physiographic trends of streamflow response suggest the influence of the high relief on streamflow flux. The overall results suggest that the hybrid approach of FDC and BFI analyses are highly effective for replicating the streamflow recession at the catchment stage and could be adopted for investigation of river sustainability.

Monthly Flow Duration Curve Model for Ungauged River Basins

Flow duration curve (FDC) is widely used in hydrology to assess streamflow in a river basin. In this study, a simple FDC model is developed for monthly streamflow data. The model consists of several steps including the nondimensionalization and then normalization in case the monthly streamflow data do not fit the normal probability distribution function. The normalized quantiles are calculated after which a back transformation is applied to the normalized quantiles to return back to the original dimensional streamflow data. In order to calculate annual streamflow of the river basin, an empirical regression equation is proposed using the drainage area and the annual total precipitation only as the input. As the final step of the model, dimensional quantiles of FDC are calculated. Ceyhan River basin in southern Turkey is chosen for the case study. Forty-two streamflow gauging stations are considered; two thirds of the gauging stations are used for the model calibration, and one third for validation. The modeled FDCs are compared to the observation and assessed with a number of performance metrics. They are found similar to the observed ones with a relatively good performance; they are good in the mid and high flow parts particularly while the low flow part of FDCs might require further detailed analysis.

Multisite Daily Streamflow Simulation With Time Irreversibility

AbstractSynthetic daily streamflow generation requires a critical understanding of the underlying dynamics resulting from the inherent temporal asymmetry (time irreversibility) in the rising and falling limbs of the hydrograph. Most models considering the time irreversibility deal with single‐site streamflow simulation. Addressing intersite dependencies is, however, crucial for interconnected stream networks. This study presents a time irreversibility dynamics‐based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. This framework explicitly takes into account the spatial correlation and time irreversible dynamics of streamflow. To demonstrate the utility and applicability, a few selected streamflow gauging stations in the Godavari River Basin, located in southern India, are considered. The proposed framework shows the ability to adequately generate multisite simulations capturing at‐site statistics as well as intersite correlations for the case study. Furthermore, the approach ensures that the simulated flow values are not merely resampled from the historical data, uses the physical features of the hydrograph, and shows variability beyond that observed in the historical sequence. Such a rich variety of streamflow sequences can help water managers to investigate how existing water resources systems on interconnected stream networks will operate in scenarios which have not been observed in the historical record.

Calibração conjunta de um modelo hidrológico com parâmetros de curva-chave para simulações de inundações bruscas em áreas urbanas

ABSTRACT Calibration process is usually time demanding and as much streamflow information as possible in rainfall-runoff models. Nevertheless, from a practical point of view, the available information is measurement of water level, which is essential to design rating curves. This manuscript proposes a set of joint calibration of hydrological model parameters with a range of rating curves, developed for the main channel of the catchment in a crowded urban area. As an alternative of free calibration, the simulations were carried out based on a list of proficient parameters. Four streamflow gauging stations were analysed and used to subdivide the basin. The hourly lumped rainfall-runoff model GR4H was applied to four critical flash flood events to create a rank of Nash-Sutcliffe efficiency criteria (NSE) applying the best set of parameters. The results indicated that the hydrological model errors were compensated by hydraulics errors and they presented an equifinality in the process.

CFD modeling as part of a hybrid modeling case study for a gauging station with challenging hydraulics

ABSTRACTEstimates of peak flood discharges are most often based on stage measurements, and the discharge is determined indirectly from the extrapolation of a rating-curve. It is possible to improve the extrapolation of the rating-curve by hydraulic modeling, such as scale or numerical models. In this paper, we discuss a case study for a streamflow gauging station in a mountainous river in Norway. A computational fluid dynamics model is used as part of a hybrid modeling study to study the flow at this gauging station with challenging geometry and flow and derive a reliable rating-curve. The numerical model produces stage-discharge data with comparable accuracy to the scale model. Analysis of the results of the hybrid modeling shows that the original location of stage measurements is not suitable for field measurements or for modeling the rating curve. Based on the modeling, an alternative point of measurement with better flow conditions at high flows is identified. The results and findings highlight the importance of 3-dimensional flow at the site during floods and shows how computational fluid dynamics can be used to provide stage-discharge data for rating curves and provide additional insight in hybrid modeling.

Trend analysis of mean monthly, seasonally and annual streamflow of Daday Stream in Kastamonu

Water is one of the most important natural resource and assessment of the trends in water has a great importance to estimate the future of water resources. The main purpose of this study is to present a trend analysis of the streamflow of Daday Stream in Kastamonu, Turkey by monthly, seasonal and annual analyses. Monthly streamflow data were obtained from streamflow gauging station on the stream between 1988 and 2007. Trends of monthly, seasonal and annual runoff of Daday Stream were analysed by Trend analysis, non-parametric Mann-Kendall and Spearman tests. The results showed that mean annual streamflow of Daday Stream has a significant tendency to decrease for this period (p<0.01). Seasonal trend analysis results demonstrated statistically significant decreasing trends were found for all seasons. Trend analyses for monthly displayed that there were also statistically significant decreasing trends for all month excluding February, March, April, and June. In conclusion, decreasing trends in the streamflow of Daday Stream have been predicted for this period and for the future. The fluctuation in water resources could be affected by some reasons such as decreasing rainfall, rising temperature depending upon climate change.

Climate change effects on annual streamflow of Filyos River (Turkey)

AbstractThe main purpose of this study was to estimate possible climate change effects on the annual streamflow of Filyos River (Turkey). Data for annual streamflow and climatic parameters were obtained from streamflow gauging stations on the river and Bartın, Karabük, Zonguldak meteorological observation stations. Time series analysis was performed on 46 years of annual streamflow data and 57 years of annual mean climatic data from three monitoring stations to understand the trends. Pettitt change-point analysis was applied to determine the change time and trend analysis was performed to forecast trends. To reveal the relationship between climatic parameters and streamflow, correlation tests, namely, Spearman's rho and Kendall's tau were applied. The results of Pettitt change-point analysis pointed to 2000 as the change year for streamflow. Change years for temperature and precipitation were detected as 1997 and 2000, respectively. Trend analysis results indicated decreasing trends in the streamflow and precipitation, and increasing trend in temperature. These changes were found statistically significant for streamflow (p < 0.05) and temperature (p < 0.01). Also, a statistically significant (p < 0.05) correlation was found between streamflow and precipitation. In conclusion, decreasing precipitation and increasing temperature as a result of climate change initiated a decrease in the river streamflow.

Changes in flood events inferred from centennial length streamflow data records

Abstract This research examines ways that climate change may alter the risk of flooding in cold regions focusing on changes in the flood regimes and changes and shifts in the dominant flood generating processes at 27 natural watersheds across Canada and the northern United States. Changes in flood regimes are examined using data from long term hydrometric reference streamflow gauging stations whose data record spans the past 100 years; stations included are considered to have good quality data and were screened to avoid the influences of regulation, diversions, or land use change. Changes in flood regimes are complex and require different approaches to properly characterize the variety of changes that have occurred and are likely to occur in the future. Peaks over threshold data are used to explore changes to the magnitude, timing, volume and duration of threshold exceedences. Circular statistics are used to explore changes in the nature of the flood regime based on changes in the timing and regularity of flood threshold exceedences. All flood regimes show an increased number of threshold exceeding events. An increased prevalence of rainfall flood responses is observed as flood events occur more often during the rainfall dominated portion of the seasonal cycle resulting in a shift for nival regime stations to a more mixed regime and for mixed regime stations towards a more pluvial regime. The results support viewing hydrologic regime as a continuum from nival to pluvial with several stations shifting towards the pluvial end.

Use of Non-Parametric Approaches on Normality of Hydrologic Variables

Parametric approaches in statistical analysis assume that any given data are normally distributed. Therefore, the test of whether this conventional assumption is valid should be made in this context of the available data‘s normality before being passed to the application of statistical tests. The paper is focused on the normality methodologies commonly used in literature, named Kolmogorov-Smirnov, Jarque-Bera, D‘agostino, Anderson Darling, Shapiro-Wilk and Ryan Joiner. In the study, the seasonal maximum data from eight streamflow gauging stations in Yesilirmak Basin was used as material. The normality in the 59% of the whole data sets were obtained as the highest result by the Kolmogorov –Smirnov approach, when compared to the other normality tests considered in the study.