Modeling runoff with AnnAGNPS model in a small agricultural catchment, in Mediterranean environment

Agricultural activities, as part of the natural resource management practice, impact soil and water quality at the watershed or catchment level. Field monitoring is often used to evaluate and acquire knowledge of the impacts of management practices on productivity and environment. Computer simulation models, after calibrated and validated, provide an efficient and effective alternative for evaluating the effects of agricultural practices on soil and water quality at the watershed level. The main objective is calibrate and validate the AnnAGNPS model relatively to runoff and peak flow using five hydrologic years data, for the rain and irrigation season. The study watershed is located in Portugal, and covers an area of 189 ha, divided into 18 fields belonging to four farmers. The climate is typically Mediterranean with continental influence, and the main crops are oat, tobacco, sorghum and maize. The calibration was done manually, but in a systematic away, in order to select values for the statistical parameters so that the model closely simulates runoff and peak flow. The results obtained in calibration and validation of the AnnAGNPS model, confirm a good or very good performance to simulate the peak flow and runoff volume at daily or event scale, in rainfall season. Also, the obtained results are a good indication of the validity of AnnAGNPS model to simulate runoff in irrigation to larger periods of time, for example irrigation season

Assessment of Runoff and Sediment Yields Using the AnnAGNPS Model in a Three-Gorge Watershed of China

Soil erosion has been recognized as one of the major threats to our environment and water quality worldwide, especially in China. To mitigate nonpoint source water quality problems caused by soil erosion, best management practices (BMPs) and/or conservation programs have been adopted. Watershed models, such as the Annualized Agricultural Non-Point Source Pollutant Loading model (AnnAGNPS), have been developed to aid in the evaluation of watershed response to watershed management practices. The model has been applied worldwide and proven to be a very effective tool in identifying the critical areas which had serious erosion, and in aiding in decision-making processes for adopting BMPs and/or conservation programs so that cost/benefit can be maximized and non-point source pollution control can be achieved in the most efficient way. The main goal of this study was to assess the characteristics of soil erosion, sediment and sediment delivery of a watershed so that effective conservation measures can be implemented. To achieve the overall objective of this study, all necessary data for the 4,184 km2 Daning River watershed in the Three-Gorge region of the Yangtze River of China were assembled. The model was calibrated using observed monthly runoff from 1998 to 1999 (Nash-Sutcliffe coefficient of efficiency of 0.94 and R2 of 0.94) and validated using the observed monthly runoff from 2003 to 2005 (Nash-Sutcliffe coefficient of efficiency of 0.93 and R2 of 0.93). Additionally, the model was validated using annual average sediment of 2000–2002 (relative error of −0.34) and 2003–2004 (relative error of 0.18) at Wuxi station. Post validation simulation showed that approximately 48% of the watershed was under the soil loss tolerance released by the Ministry of Water Resources of China (500 t·km−2·y−1). However, 8% of the watershed had soil erosion of exceeding 5,000 t·km−2·y−1. Sloping areas and low coverage areas are the main source of soil loss in the watershed

Prediction of maximum annual flood discharges using artificial neural network approaches

U radu se istražuje primjenjivost pristupa umjetnih neuronskih mreža (ANN) za određivanje maksimalnih godišnjih protoka. Uspoređuje se učinkovitost triju modela neuronskih mreža: višeslojne perceptronske neuronske mreže (MLP_NN), generalizirane neuronske mreže usmjerene prema naprijed (GFF_NN) i analiza osnovnih komponenata pomoću neuronskih mreža (PCA_NN). Predloženi pristupi primijenjeni su na 33 vodomjerne. Utvrđeno je da je optimalna metoda PCA_NN s tri skrivena sloja prikladnija za određivanje maksimalnih godišnjih protoka od optimalnih modela MLP_NN i GFF_NN.The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows

Prediction of maximum annual flood discharges using artificial neural network approaches

The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_ NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows

Runoff and sediment yield modeling in a medium-size mediterranean watershed

The AnnAGNPS model was used to estimate runoff, peak discharge and sediment yield at the event scale in the Carapelle watershed, a Mediterranean medium-size watershed (506 km2) located in Apulia, Southern Italy. The model was calibrated and validated using five years of runoff and sediment yield data measured at a monitoring station located at Ordona – Ponte dei Sauri Bridge. A total of 36 events was used to estimate the output of the model during the period 2007-2011, in comparison to the corresponding observations at the watershed outlet. The model performed well in predicting runoff, as was testified by the high values of the coefficients of efficiency and determination during the validation process. The peak flows predictions were satisfactory especially for the high flow events; the prediction capability of sediment yield was good, even if a slight over-estimation was observed. Finally, the model was used to evaluate the effectiveness of different Management practices (MPs) on the watershed (converting wheat to forest, using vegetated streams, crop rotation corn/soybean, no tillage). While the maximum reduction in sediment yield was achieved converting wheat to forest, the best compromises between soil conservation and agriculture resulted to be crop rotations

Use of the AnnAGNPS Pollutant Loading Model for Prediction of Sediment Yields in a Mountainous Cumberland Plateau Region: Correlations with the Stream Bed Sediment Characteristics

This study attempts to develop a relationship with the hillslope sediment yield (estimated from a computer model) and the deposited sediment particle size characteristics within stream channels. By using specific hydrological parameters within a watershed, a calibrated Annualized Agricultural Non-Point Source (AnnAGNPS) pollutant loading model was created for four different sub-watersheds in the mountainous New River Basin of eastern Tennessee. The AnnAGNPS pollutant loading model predicted daily runoff and sediment yield reasonably well, but it poorly predicted daily peak flow rate for most sub-watersheds analyzed in the New River Basin. Overall, the AnnAGNPS pollutant loading model provided satisfactory results in a mountainous, nonagricultural landscape with a limited amount of climatic data available. The average annual hillslope sediment yield, in terms of clays, silts, and sands, was calculated with the AnnAGNPS model for years 2006 and 2007, to compare with sediment deposition characteristics in the streams. The fine particle size characteristics collected at specific bed deposition points were suspected to have a strong correlation with predicted sediment yield output from a calibrated AnnAGNPS pollutant loading model. The sites of the captured sediment were at locations just downstream of specific land use disturbances such as dirt roads, surface mining, and forest logging, all of which can be detrimental to the health of a stream environment and habitat if disturbances are not properly managed. In this study, the sediment collected at the channel bed deposition points represented the distribution of different material sizes that have recently moved within the stream during large discharge events. This investigation concluded that the certain measurements of the clays, silts, sands, and gravel material found in downstream sediment depositional points had a variety of significant relationships (p-value \u3c 0.05) with the clays, silts, sands, and total sediment yield occurring on the watershed hillslopes. Overall, there are a limited amount of studies that analyze these collections of fine sediment deposited in areas of the stream that have interrupted velocity forces due to channel shape, objects, or formations. This study showed that the use of the AnnAGNPS pollutant loading model and the analyzation of specific fine sediment at depositional points in the stream, proper watershed management of a rural mountainous region can be better established