Hydrometric Stations Research Articles

Regional hydrological frequency analysis at ungauged sites with random forest regression

Flood quantile estimation at sites with little or no data is important for the adequate planning and management of water resources. Regional Hydrological Frequency Analysis (RFA) deals with the estimation of hydrological variables at ungauged sites. Random Forest (RF) is an ensemble learning technique which uses multiple Classification and Regression Trees (CART) for classification, regression, and other tasks. The RF technique is gaining popularity in a number of fields because of its powerful non-linear and non-parametric nature. In the present study, we investigate the use of Random Forest Regression (RFR) in the estimation step of RFA based on a case study represented by data collected from 151 hydrometric stations from the province of Quebec, Canada. RFR is applied to the whole data set and to homogeneous regions of stations delineated by canonical correlation analysis (CCA). Using the Out-of-bag error rate feature of RF, the optimal number of trees for the dataset is calculated. The results of the application of the CCA based RFR model (CCA-RFR) are compared to results obtained with a number of other linear and non-linear RFA models. CCA-RFR leads to the best performance in terms of root mean squared error. The use of CCA to delineate neighborhoods improves considerably the performance of RFR. RFR is found to be simple to apply and more efficient than more complex models such as Artificial Neural Network-based models.

Evaluation of Precipitation Products by Using Multiple Hydrological Models over the Upper Yellow River Basin, China

In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models.

Using Budyko-Type Equations for Separating the Impacts of Climate and Vegetation Change on Runoff in the Source Area of the Yellow River

Assessing the contribution rates of climate change and human activities to the runoff change in the source area of the Yellow River can provide support for water management in the Yellow River Basin. This paper firstly uses a multiple linear regression method to evaluate the contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River. Next, the paper uses the Budyko hypothesis method to calculate the contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetation changes caused by human activities to the runoff change of the Tangnaihai Hydrometric Station. The results showed that: (1) the annual runoff and precipitation in the source area of the Yellow River have a downward trend, while the annual potential evaporation and NDVI (Normalized Difference Vegetation Index) show an increasing trend; (2) The contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River is 62.79% and 37.21%, respectively; (3) The runoff change became more and more sensitive to changes in climate and underlying surface characteristic parameters; (4) The contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetation changes caused by human activities to the runoff change at Tangnaihai Hydrological Station are 75.33% and 24.67%, respectively; (5) The impact of precipitation on runoff reduction is more substantial than that of potential evaporation.

Upriver sightings of beluga whales (Delphinapterus leucas) follow storm surges and high water in the Mackenzie Delta, Northwest Territories, Canada

Each summer, Eastern Beaufort Sea beluga whales (Delphinapterus leucas (Pallas, 1776)) form a large congregation in the Tarium Niryutait Marine Protected Area (TNMPA) in the Mackenzie River estuary, a behaviour thought to be linked to warm, freshwater conditions. In 2018, >50 belugas were observed upriver near Aklavik in the Mackenzie River Delta. Community members noted that this upriver occurrence of belugas was unusual and suggested that wind-driven high water levels in the Mackenzie River were a primary driver. We investigated this explanation by searching past communications and reports for documentation of beluga sightings upriver and identifying storm surges and water-level changes at six hydrometric stations in the Mackenzie River Delta. We found three previous occurrences of belugas upriver dating back to 2000, all of which followed prominent surges in river level attributable to coastal storms. Although acknowledging a small sample size, we suggest that upriver occurrences of beluga whales warrant further investigation through extension of the TNMPA beluga monitoring program. As climate-driven changes cause more frequent and intense Arctic storm surges, we expect storm events to increasingly overlap with the annual summer beluga congregation. This may cause upriver movements to become more common, and population-level implications are not known.

Sensitivity Analysis of the MOHID-Land Hydrological Model: A Case Study of the Ulla River Basin

Hydrological models are increasingly used for studying watershed behavior and its response to past and future events. The main objective of this study was to conduct a sensitivity analysis of the MOHID-Land model and identify the most relevant parameters/processes influencing river flow generation. MOHID-Land is a complex, physically based, three-dimensional model used for catchment-scale applications. A reference simulation was implemented in the Ulla River watershed, northwestern Spain. The sensitivity analysis focused on sixteen parameters/processes influencing water dynamics at that scale. River flow generation was influenced by the resolution of the simulation grid, soil water infiltration, and crop evapotranspiration. Baseflow was affected by soil hydraulic properties, the depth of the soil profile, and the dimensions of the river cross-sections. Peak flows were mostly constrained by Manning’s coefficient in the river network, as well as the dimensions of the river cross-sections. The MOHID-Land model was then used to simulate daily streamflow during a 10-year period (2008−2017). Model simulations were compared against measured data at four hydrometric stations characterizing the natural flow regime of the Ulla River, resulting in coefficients of determination (R2) from 0.56 to 0.85; ratios of the standard deviation of the root mean square error to observation (RSR) between 0.4 and 0.67, and Nash and Sutcliffe model efficiency (NSE) values ranging from 0.55 to 0.84. The MOHID-Land model thus has the capacity to reproduce watershed behavior at a daily scale with reliable accuracy, constituting a powerful tool to improve water governance at the watershed scale.

Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines

AbstractHydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.

Daily streamflow prediction using support vector machine-artificial flora (SVM-AF) hybrid model

Precise estimation of river flow in catchment areas has a significant role in managing water resources and, particularly, making firm decisions during flood and drought crises. In recent years, different procedures have been proposed for estimating river flow, among which hybrid artificial intelligence models have garnered notable attention. This study proposes a hybrid method, so-called support vector machine–artificial flora (SVM-AF), and compares the obtained results with outcomes of wavelet support vector machine models and Bayesian support vector machine. To estimate discharge value of the Dez river basin in the southwest of Iran, the statistical daily watering data recorded by hydrometric stations located at upstream of the dam over the years 2008–2018 were investigated. Four performance criteria of coefficient of determination (R2), root-mean-square error, mean absolute error, and Nash–Sutcliffe efficiency were employed to evaluate and compare performances of the models. Comparison of the models based on the evaluation criteria and Taylor’s diagram showed that the proposed hybrid SVM-AF with the correlation coefficient R2 = 0.933–0.985, root-mean-square error RMSE = 0.008–0.088 m3/s, mean absolute error MAE = 0.004–0.040 m3/s, and Nash-Sutcliffe coefficient NS = 0.951–0.995 had the best performance in estimating daily flow of the river. The estimation results showed that the proposed hybrid SVM-AF model outperformed other models in efficiently predicting flow and daily discharge.

Investigation and evaluation of salinity gradient power in Arvand River estuary using pressure retarded osmosis (PRO) method

The salinity gradient power is the energy of the entropy of mixing the two solutions at different salt concentrations. The estuary of the river as a place of the mixture of seawater and freshwater has great potential of this kind of renewable energy. In this research, this potential in Arvand River estuary and its effective factors is analyzed and evaluated. By examining the calculations on the data of three hydrometric stations located on Arvand River, in Khorramshahr station, the mixture of 1.26 m3 of seawater with salinity of 40 ppt with 0.174 m3of river water with 1.17 ppt of salinity leads to the highest energy at 739.85 MW. Also, the penetration length of saline water and water residence time on the estuaries of the rivers leading to the Persian Gulf has a significant effect on the salinity gradient power. According to the calculations carried out under HWS and Arvand River mean discharge, the maximum saline water penetration length into estuary associated with Arvand River is 41 km. The maximum residence time of seawater in Arvand River estuary is 8.9 days.

Regionalization of mean annual flow for ungauged catchments in case of Abbay River Basin, Ethiopia

Streamflow measurement is one of the features of Ethiopia and other developing countries suffer to estimate. Climate variable (rainfall) and physiographic variables (catchment area, land slope, and elevation) were selected to develop a regression model equation that can be used to estimate the mean annual flow for ungauged catchments in the Abbay river basin. 70% of 27 hydrometric stations were used to train the regression model, and the remaining 30% were used for validation. Furthermore, statistical tests were used for selecting the best trustworthy model. The result shows that both catchment area (A) as the only predictor with both coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) value of 0.93 and catchment area (A), mean annual rainfall (MAR), and the average elevation (AE) as predictors with both coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) value of 0.96 regression equations were the trustworthy models. Moreover, the validation analysis was performed for nine stations distributed in the study area and resulting from a statistical test and graphical visualization the second regression model equation was a trustworthy model. The model parameters (catchment characteristics) are easily accessible for practitioners that want to estimate the mean annual flow in the study area.

Field methods of a near-bed suspended sediment experiment in the Yangtze River, China

Using experimental data of near-bed suspended sediment concentrations at five typical hydrometric stations of the Three Gorges Reservoir at the early reserving stage, the differences were investigated between the common method and improved method during flood seasons and non-flood seasons. The impact of taking measurements below 0.2 times the water depth on the results was discussed. The results show that the average discharges and velocities at each station calculated by the common method were slightly larger than those calculated by the improved method. Regarding the suspended sediment concentration at each station, the errors in the reservoir and downstream channels in dynamic equilibrium state were small, and the largest errors occurred where the river bed was strongly scoured in the downstream reach below the large dam. There was no significant relationship between water discharge and flow velocity, and the missed measurement phenomenon also occurred. The sediment discharge error was affected by the suspended sediment concentration, implying that errors usually occurred in channels with serious erosion during flood seasons. The correction coefficients (R2) of sediment discharge at each station were given during the experiment, which showed that the sediment discharges at the hydrometric stations where a large amount of sediment transport occurred near the river bottom, needed to be modified. Furthermore, the test methods proposed in this study were applied to calculate the sediment discharges of three rivers, and the results indicate that this method can narrow the gap between bathymetric comparisons and sediment load measurements.

Forecasting the Yellow River runoff based on functional data analysis methods

This study examines the runoff prediction of each hydrometric station and each month in the mainstream of the Yellow River in China. From the perspective of functional data, the monthly runoff of each hydrometric station can be regarded as a function of both time and space. A sequence of such functions is formed by collecting the data over the years. We propose a new approach by combining the two-dimensional functional principal component analysis (FPCA) and time series analysis methods to predict the runoff. In the simulation, we compared the proposed method with two others: one based on one-dimensional FPCA and the seasonal auto-regressive integrated moving average (SARIMA) method. The method combining standard two-dimensional FPCA and time series analysis outperforms others in most cases, and is used to predict the runoff of each hydrometric station and each month in the Yellow River in 2018.

Rapid and long-distance channel incision in the Lower Yellow River owing to upstream damming

The operation of the Xiaolangdi Reservoir has significantly altered the flow and sediment regime of the Yellow River, which has led to rapid and long-distance channel incision in the Lower Yellow River. Variations in the channel incision depth and stage-discharge relationships were identified in the braided, transitional and meandering reaches based on the cross-sectional profiles at 91 sedimentation sections and hydrological data at hydrometric stations. The results show that the cumulative reach-scale channel incision depths in the braided, transitional and meandering reaches in response to upstream damming were 3.0 m, 2.5 m and 2.3 m, respectively. In addition, water levels at given discharges dramatically decreased at three hydrometric stations in the different reaches. The previous 5-year average incoming sediment coefficients are the key fluvial factor influencing the cumulative channel incision depth in the Lower Yellow River. The cumulative bank retreat width increased with the cumulative channel incision depth in the braided reach, and a linear function between these parameters was established. However, bank retreat occurred when the channel incision depths reached critical values in the transitional and meandering reaches. Empirical equations were established between the cumulative channel incision depth and the relative bed roughness in the braided and transitional reaches, and they indicate that the riverbed coarsening process can mitigate channel incision to some extent.

Trend Analysis of the Streamflow in the Lighvan River hydrometric Stations (Upstream and Downstream)

Trend Analysis of the Streamflow in the Lighvan River hydrometric Stations (Upstream and Downstream)

Impact of the City on the Rapid Increase in the Runoff and Transport of Suspended and Dissolved Solids During Rainfall—The Example of the Silnica River (Kielce, Poland)

Urbanisation changes the water cycle and affects the parameters of transported, suspended and dissolved matter, especially in small river catchments. This paper presents the reasons why river runoff and fluvial transport rapidly increase during rainfall-induced summer floods in the stretch of the Silnica River that flows through the centre of Kielce, a city with a population of 200,000. Examples of implemented hydrotechnical solutions that aim to reduce the height of flood waves and eliminate water accumulation are also presented. The 18.05 km long Silnica River drains a catchment area of 49.4 km2. It flows through areas of varied land use, which have determined the location of five hydrometric stations (outlets) at different sub-catchments: Dabrowa(forest), Piaski (suburbia) and Jesionowa (includes a reservoir), as well as Pakosz and Bialogon (largely impervious areas in the city centre). Specific runoff, suspended and dissolved solids concentration and the specific load of these two types of fluvial transport were determined. It was found that the maximum specific runoff in the outlets of urban sub-catchments was significantly higher during floods than those of the sub-catchments upstream of the city centre; the suspended solids concentration was several times higher, and the suspended solids load was approximately 200 times higher. Recognition of the basic parameters of rainfall-induced flood waves, as well as the dynamics and size of fluvial transport at the hydrometric stations, especially at the outlets of sub-catchments with a large proportion of impervious area (approximately 30%), has become the basis for the development and implementation of modernisation projects and the construction of hydrotechnical facilities and devices in the river channel in the centre of Kielce.

Extreme flow variability analysis at the Bianouan hydrometric station on the Bia River watershed in Côte d'Ivoire

Abstract. Extreme hydrological phenomena recurrence in West Africa in recent decades is a problem in the anthropized watershed management. Link analysis between extreme hydrological phenomena and climate variability is necessary to guard against their consequences. However, qualitative data accessing difficulties and explanatory variable definition of extremes hydrological phenomena limit extreme flows rate studies. This study proposes the analysis the variability of the floods and the low flows at the Bianouan hydrometric station. Thus, five (5) floods characteristics variables (QCX5, QXJA, F90p, F95p and F99p) and three (3) for, the low flows (VCN10, QJNA and F10p) were extracted daily flows from the Bianouan hydrometric station from 1 January 1962 to 31 December 2005. The variability of these extremes has been studied by the trend analysis (linear regression) and of the stationarity (Pettitt and Hubert tests). The results show that for flood variables the negative trend is very significant and ruptures are observed in 1980. For low flow variables the negative trend is significant and the ruptures are observed in 1981. From this study, we can deduce that the ruptures observed in the rains and flows average in the end 1960s and early 1970s have affected later the extreme flows around the 1980s.

Calibration of the hydrological model GR4J from potential evapotranspiration estimates by the Penman-Monteith and Oudin methods in the Ouémé watershed (West Africa)

Abstract. The Ouémé watersheds at Bétérou and Bonou has been recently facing increased sensitivity to extreme hydroclimatic phenomena that occurred by flooding or drought events. In the same time, the population growth and the related socio-economic activities increased the pressure state on water resources. In this context, hydrological modeling is an important issue and this study aims at analyzing the calibration of the hydrological model GR4J based on PET Penman-Monteith and Oudin methods. Daily rainfall, Penman-Monteith and Oudin evapotranspiration and daily data flow from the Bétérou and Bonou hydrometric stations on the Ouémé Basin have been implemented in the GR4J model over the period 1971 to 2010. Oudin PET values are slightly higher than the Penman-Monteith PET ones. However, the difference between the two PET methods have only few impacts on the optimization and performance criteria of the GR4J model. The Nash values ranges from 0.83 to 0.91 in Bonou, and 0.52 to 0.70 in Bétérou for the calibration in dry period, while in validation, they are 0.59 to 0.78 in Bétérou, and 0.56 to 0.88 in Bonou in wet season. In view of these results, with the two PET methods used which do not result from the same climatic variables, it should be said that the formulation of PET has only few impacts on the results of GR4J for these tropical basins.

Modified Mann-Kendall trend test for hydrological time series under the scaling hypothesis and its application

ABSTRACT The existence of persistence contaminates the results of the Mann-Kendall (MK) trend test. This paper proposes an improved MK test for hydrological data with both positive and negative persistence. To this end, the variance and distribution patterns of S were modified under the scaling hypothesis. Different estimators of the scaling coefficient were compared. According to the results, the generalized normal distribution is the best approximation to fit the exact distribution of S with both positive and negative persistence. The centred detrending moving average was demonstrated to be the best estimator of the scaling coefficient for series with a trend. The improved MK test was proven to be more advantageous than other trend detection methods for the synthetic time series and observed precipitation data obtained from nine hydrometric stations located in China. With this modification, the application of the MK test can be extended to long-term persistent data.

Merge L-Moment Method, Regional Frequency Analysis and SDI for Monitoring and Zoning Map of Short-Term and Long-Term Hydrologic Droughts in the Khuzestan Province of Iran

In recent years, the climatic change and global warming have affected on meteorological and hydrologic conditions in dry and warm regions. The severity of occurred droughts has increased in these regions. This research monitors 3- , 6- , 9- and 12-month hydrologic drought severity in the Khuzestan Province, with 51 hydrometric stations, based on drought return periods and streamflow drought index (SDI). The length of the statistical period is 62 years (1954–2016). Application of homogeneity statistic shows that the Khuzestan Province has 3 homogeneous regions for three-month time scale and 2 homogeneous regions for other time scales. The applied method for regional frequency analysis is linear moment method. By increasing of linear moment parameters, intensity of droughts will increase and drought return periods will reduce. Results of research show that severe and extreme hydrologic droughts can observe at the whole of this province in near future. Return period of these droughts is larger than 5 years. Also, intensity of hydrologic drought is milder in north and east of this province.

Temporal Variability of Sediments, Dissolved Solids and Dissolved Organic Matter Fluxes in the Congo River at Brazzaville/Kinshasa

For three decades, the solid and dissolved fluxes of the Congo River have been regularly monitored on a monthly basis, despite 12 years of deficiencies (1994–2005). Two programs successively carried out these follow-ups: PEGI/GBF (1987–1993) and SO HYBAM (2006–2017), upstream and downstream, respectively, of the Malebo Pool near Brazzaville, the main hydrometric station of the Congo River. The objective of this study is to examine the temporal dynamic of TSS, TDS and DOC, to explore how these descriptors change over time. Comparison with the two time programs will shed more light on how these descriptors are related to discharge. Afterward, we then find a relationship between total TSS in the water column and that measured in surface for eventual estimation of TSS by remote sensing. In the last decade, compared to the PEGI/GBF period, the discharge of the Congo River was mainly marked by a 4% increase, leading to a significant change on TDS and DOC behaviors. The TSS was quite stable (from 8.2 and 9.3 t km−2 yr−1) due to the low physical erosion well known in this region. The TDS concentrations decreased slightly, by a simple dilution effect. However, the mineral dissolved fluxes (from 11.6 and 10.1 t km-2 yr-1) due to the chemical weathering and atmospheric inputs still predominate over the solid fluxes. Therefore, there was no radical change in the monthly geochemical regime of Congo River Basin (CRB) during these last 30 years. Contrariwise, the DOC concentration marking the biogeochemical processes significantly increased, from 9.0+/−3.0 mg L−1 to 12.7+/−5.0 mg L−1, due to more flooding events in the central part of the CRB. The change for the DOC fluxes is more relevant, with an increase of 45% between the two studied periods, from 11.1 × 106 to 16.2 × 106 t yr−1. This highlights the continuous and actual importance of the “Cuvette Centrale” in the heart of the CRB for dissolved organic matter transport by the Congo River.

Response of streamflow to climate variability in the source region of Jhelum River Basin in Kashmir valley, India

Successful management of the water resources directly depends on our understanding of the heterogeneity of changing climate and consequent response of annual and seasonal streamflows in different climatic regions. This study was undertaken to quantify the spatial and temporal variability of different climatic variables and their subsequent impacts on streamflows of river Jhelum and its tributaries during last more than three decades. Mann–Kendall trend statistic and Sen’s slope estimator test were used for assessing the trends and variability in climatic and streamflow variables. Data obtained from 11 hydrometric and 6 meteorological stations for different time intervals were used for analysis. The results pointed toward various significant trends in both hydrological and meteorological stations. Kashmir Himalayas witnessed rise in mean maximum temperature (+ 0.05 °C/year) and mean minimum temperature (+ 0.01 °C/year) with substantial reduction in precipitation (− 4.2 mm/year) from 1980 to 2015. Analysis of the streamflow trends revealed significant decreasing trends in all the hydrometric stations with the highest decrease in spring and summer seasons. Furthermore, the correlation analysis revealed a significant negative correlation between increase in temperature and streamflow, while strong positive correlation was realized with precipitation at both annual and seasonal scales.