Establishing Stage–Discharge Rating Curves in Developing Countries: Lake Tana Basin, Ethiopia

A significant constraint in water resource development in developing countries is the lack of accurate river discharge data. Stage–discharge measurements are infrequent, and rating curves are not updated after major storms. Therefore, the objective is to develop accurate stage–discharge rating curves with limited measurements. The Lake Tana basin in the upper reaches of the Blue Nile in the Ethiopian Highlands is typical for the lack of reliable streamflow data in Africa. On average, one stage–discharge measurement per year is available for the 21 gaging stations over 60 years or less. To obtain accurate and unique stage–discharge curves, the discharge was expressed as a function of the water level and a time-dependent offset from zero. The offset was expressed as polynomial functions of time (up to order 4). The rating curve constants and the coefficients for the polynomial were found by minimizing the errors between observed and predicted fluxes for the available stage–discharge data. It resulted in unique rating curves with R2 > 0.85 for the four main rivers. One of the river bottoms of the alluvial channels increased in height by up to 3 m in 60 years. In the upland channels, most offsets changed by less than 50 cm. The unique rating curves that account for temporal riverbed changes can aid civil engineers in the design of reservoirs, water managers in improving reservoir management, programmers in calibration and validation of hydrology models and scientists in ecological research.

Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.

Agricultural intensification vs climate change: What drives long-term changes of sediment load?

Climate change and agricultural intensification are expected to increase soil erosion and sediment production from arable land in many regions. However, so far, most studies have been based on short-term monitoring and/or modeling, making it difficult to assess their reliability in terms of long-term changes. We present the results from a unique data set consisting of measurements of sediment loads from a 60ha catchment (the HOAL Petzenkirchen in Austria) over a time window spanning 72 years. Specifically, we compare Period I (1946–1954) and Period II (2002–2017) by fitting sediment rating curves for the growth and dormant seasons for each of the periods. The results suggest a significant increase in sediment yield from Period I to Period II with an average of 11.6 ± 10.8 ton·yr−1 to 63.6 ± 84.0 ton·yr−1. The sediment flux changed mainly due to a shift of the sediment rating curves (SRC), given that the annual streamflow varied little between the periods (5.6 l·s−1 and 7.6 l·s−1, respectively, on average). The slopes of the log regression lines of the SRC for the growing season and the dormant season of Period I were 16.72 and 4.9, respectively, whilst they were 5.38 and 1.17 for Period II, respectively. Climate change, considered in terms of rainfall erosivity, was not responsible for this shift, given that erosivity decreased by 30.4 % from the dormant season of Period I to that of Period II, and no significant difference was found between the growing seasons of Periods I and II. However, the sediment flux changes can be explained by changes in crop type and parcel structure. During low and median streamflow conditions (i.e. Q < Q20 %), land consolidation in Period II (i.e. theparcel effect) did not exert an apparent influence on sediment production. Whilst with increasing stream flow (Q > Q20 %), parcel structure played an increasingly role in sediment yield contribution, and leading to a dominant role due to enhanced sediment connectivity in the landscape at extremely high flow conditions (i.e. Q > Q2 %). The increase in cropland in Period II at the expense of grassland had an unfavourable effect on sediment flux, independent of streamflow, with declining relevance as flow increased. We conclude that both land cover change and land consolidation should be accounted for simultaneously when assessing sediment flux changes. Especially during extremely high flow conditions, land consolidation substantially alters sediment fluxes, which is most relevant for long-term sediment loads and land degradation. Increased attention to improving parcel structure is therefore needed in climate adaptation and agricultural catchment management.

Application of Stage-Fall-Discharge Rating Curves to a Reservoir Based on Acoustic Doppler Velocity Meter Measurement Data

The applicability of the stage-fall-discharge (SFD) method in combination with acoustic Doppler velocity meter (ADVM) data, upstream of a hydraulic structure, specifically, the Sejong-weir located in the Geum River, Korea, was examined. We developed three rating curves: a conventional simple rating curve with the data measured using an acoustic Doppler current profiler (ADCP) and floating objects, an SFD rating curve with the data measured using the ADCP and floating objects, and an SFD rating curve with the data measured using an ADVM. Because of the gate operation effect, every rating curve involved many uncertainties under 1000 m3/s (3.13 m2/s, specific discharge). In terms of the hydrograph reconstruction, compared with the conventional simple rating curve, the SFD developed using ADVM data exhibited a higher agreement with the measured data in terms of the pattern. Furthermore, the measured discharge over 1000 m3/s primarily ranged between 97.5% and 2.5% in the graph comparing the ratio of the median and observed discharge. Based on this experiment, it is confirmed that the SFD rating curve with data to represent the backwater effect, such as ADVM data, can reduce the uncertainties induced by the typical rating curve