Springer Science and Business Media LLC

A tank model consisting of three tanks was developed for prediction of runoff and sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. The sediment concentration of the first tank was computed from its storage and the sediment concentration distribution (SCD); the sediment concentration of the next lower tank was obtained by its storage and the sediment infiltration of the upper tank; and so on. The SCD, caused by the incremental source runoff (or the effective rainfall), was obtained by the theory of the instantaneous unit sediment graph (IUSG) and a sediment routing function. Using the SCD, the sediment yield was computed from the tank model as well as by the IUSG model. The sediment yield obtained from the tank model was then compared with that from the IUSG model. Finally, the tank model was verified on an upland watershed in northwestern Mississippi.

Soil erosion on arable land and on steep vineyards is a major problem in the state of Hesse (21,115 km²) in central Germany. The aim of a joint study between the Research Centre Jülich, the Hessian Agency for the Environment and Geology and the Hessian Ministry for the Environment, Energy, Agriculture and Consumer Protection was to delineate parcels which are severely affected by erosion and to identify sediment source areas. For this purpose, the ABAG, an adaptation of the USLE approach to German conditions, has been employed with the best available data sets on K-, C-, R- and LS-factor. Model results at the field scale show that soil losses in Hesse vary between <0.5 and >15 tonnes/hectare/year. The mean loss amounts to ca. 4.3 tonnes/hectare/year. The sediment delivery ratios for 450 sub-catchments range between 0.5 and 78% with a mean of 18%. Further analysis showed that LS- and C-factor are of highest sensitivity for the model output. Therefore, the effects of alternative algorithms or sources for LS- and C-factor on the results were assessed. An uncertainty analysis based on Gaussian error propagation and Monte Carlo simulation showed that the uncertainty of model results induced by input parameters is 1.7 tonnes/hectare/year or 34% of the mean annual soil loss. The model results are a good basis for further works concerning a soil erosion atlas and internet-based soil data viewer.

This study develops a multiobjective stochastic programming model for informing hedging decisions for hydropower operations under an electricity market environment considering the benefit from selling energy production and the cost of penalizing energy shortfall. Aiming to determine the optimal strategy that hedges the risk of energy shortfall while keeping a high level of direct revenue from energy production under uncertain streamflows and inexact penalizing price conditions, competing objectives of minimizing energy shortfall percentage and maximizing direct revenue from energy production are analyzed. The conflict is resolved by determining the optimal level of energy shortfall percentage such that the net benefit of the hydropower system is maximized. The first-order optimality condition of maximized system net revenue is derived, which states that the marginal benefit of hedging equals the marginal cost of hedging at optimality. The tradeoff ratio between the competing objectives serves as the marginal cost of hedging and the penalizing price of energy shortfall represents the marginal benefit of hedging. Using the optimality condition, sensitivity tests are conducted for investigating the influence of different ranges of penalizing prices and reservoir initial storages on hedging decisions. The proposed method is evaluated on the operations of the Three Gorges cascade hydropower system during the drawdown season. Results show that: (1) minimizing the energy shortfall percentage adversely affects the maximization in system direct revenue from energy production, and the conflicting results are related to the depletion strategies of reservoir storage; (2) to reduce the energy shortfall percentage to the lowest level could result in significant reduction in total energy production and the direct revenue, especially when reservoir initial storages are low; and (3) the optimal level of energy shortfall percentage would decrease as penalizing price increases, when the influence of penalizing cost from energy shortfall gradually dominates the influence of energy production on the net revenue. The model framework and the implications could be applied to rationalize hedging decisions for hydropower operations under inexact information upon streamflow and penalizing prices.

In this study, the soft computing technique of Gene expression programming (GEP) has been employed to generate a predictive equation of infiltration rate (fp). Infiltration experiments were conducted at 124 different sites and soil samples were collected to assess various soil properties throughout the Himalayan lake catchment. Parameters determined from observed data using nonlinear-Levenberg Marquardt algorithm were substituted in Horton, Kostiakov and Philip infiltration models and fp were predicted. Using soil data generated by laboratory investigation of soil samples, the GEP model was developed. Training and testing of the GEP model was performed using 70% and 30% of data respectively. Performance of GEP developed functional relationship was evaluated by comparing predictions from it and aforementioned infiltration models with field observed fp, and by applying overall performance index (OPI) computed using Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (ENS), Willmott’s Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Expression developed using GEP indicated feasibility of developed equation with ENS, R2, W, RMSE and MAE of 0.84, 0.84, 0.96, 1.9, and 0.8, respectively for training data-set and 0.84, 0.85, 0.95, 1.2, and 0.95, respectively for testing data-set. Comparative analysis revealed that though with a slightly higher OPI value (0.7–0.8), the performance of conventional models is better compared to the GEP model (0.66) but the GEP model having satisfactory performance may be used for fp prediction particularly in absence of observed data.

The wholeness of ecological resources in watersheds and the divisiveness of administrative regions make the protection of watersheds exceptional, and their environmental development has always been a hot issue of social concern. To realize the high-quality development of the watershed, this paper studies the ecological protection mechanism at the inter-provincial and intra-provincial levels. At the inter-provincial level, we constructed a game model for the evolution of ecological compensation between upstream and downstream governments in the watershed. We explored the changes in the strategy choices of both sides of the game under the condition of an unconstrained mechanism and the constraints of the reward and punishment mechanism of the central government. At the intra-provincial level, the local government, enterprise, and public ecological protection evolution game models are constructed to analyze the strategic choices of the three-party game subjects. The study results show that firstly, the combination of strategies (governance, compensation) cannot be realized by upstream and downstream governments alone, and the central government can intervene by introducing specific incentives and penalties. Secondly, constructing upstream and downstream government ecological compensation mechanisms under the central government constraint has prompted the local government, enterprises, and the public to achieve the ideal stable state of the tripartite game subjects (strict regulation, treatment, supervision). Thirdly, factors such as local government strategy choices and regulatory efforts can impact the rate of evolution of enterprises and the public.

Estimation of suspended sediment yield is subject to uncertainty and bias. Many methods have been developed for estimating sediment yield but they still lack accuracy and robustness. This paper investigates the use of a machine-coded linear genetic programming (LGP) in daily suspended sediment estimation. The accuracy of LGP is compared with those of the Gene-expression programming (GEP), which is another branch of GP, and artificial neural network (ANN) technique. Daily streamflow and suspended sediment data from two stations on the Tongue River in Montana, USA, are used as case studies. Root mean square error (RMSE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the LGP performs better than the GEP and ANN techniques. The GEP was also found to be better than the ANN. For the upstream and downstream stations, it is found that the LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE = 254 ton/day, R2 = 0.959 in test period is superior in estimating daily suspended sediments than the best accurate GEP model with RMSE = 231 ton/day, R2 = 0.941 and RMSE = 331 ton/day, R2 = 0.934, respectively.