An ARMA(1,1) model for monthly stream flows

The effects of a discrete wavelet-transformation data-preprocessing method on neural-network-based monthly streamflow prediction models in producing energy from small hydro power plants in the Pungwe River basin in Mozambique were investigated. Data from a Vanduzi gauging station in Pungwe River basin were collected. Eight different single-step-ahead monthly stream flow neural prediction models were developed. Coupled simulation involving use of MATLAB and of a Wavelet-Neural Network was employed. Different models were tested using the same sample in each case, an Artificial Neural Network (ANN) being found to performance best. The major objective of the research project was to analyze the monthly stream flow predictions in the Pungwe River, to be able to make as appropriate decisions as possible during dry or wet spells, and also to resolve as effectively as possible conflicts regarding water resourses.

Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination = 0.801), while the (NAR) model gave (RMSE = 93.4) and = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

The SWAT model was used to estimate the runoff and sediment yield of Lolab watershed. The model was calibrated, validated, and assessed for evaluation to model ambiguity using Nash–Sutcliffe coefficient (NSE) and coefficient of determination (R2). Ten highly sensitive parameters were recognized for stream flow simulation of which CN2 (Initial SCS CN II value) factor was the most sensitive one and four highly sensitive parameters were recognized for sediment yield simulation of which SPCON (Linear parameters for sediment re-entrainment) was most sensitive one. The model was calibrated for a time period between 1993 to 2000 and validated from 2001 to 2004 for flow and sediment yield. The predicted and observed stream flow and sediment yields generally matched well. The results of the model calibration and validation showed reliable estimates of monthly stream flow (R2=0.74 and ENS=0.68) and yearly stream flow (R2=0.90 and ENS=0.68) during the calibration period and monthly stream-flow (R2=0.85 and ENS=0.83) and yearly stream-flow (R2=0.99 and ENS=0.91) during the validation period. For sediment yield, this study shows antremendous model efficiency of monthly sediment yield (R2=0.80 and ENS=0.79) and yearly sediment yield (R2=0.86 and ENS=0.78) during the calibration period and monthly sediment yield (R2=0.88 and ENS=0.86) and yearly sediment yield (R2=0.83 and ENS=0.58) during the validation period. This study showed that the SWAT model is competent of predicting sediment yields and hence can be used as a tool for water resources planning and management in the study watershed.

A study was conducted to predict the stream flow from a catchment area of Hidkal dam situated in Krishna basin of India. The SimHYD model was selected to setup the stream flow model under limited data conditions. Daily rainfall, potential evapotranspiration (PET) and observed discharges were used as input data to setup the model. Sensitivity analysis was carried out to identify the more sensitive parameter and fixed final parameter values. A genetic algorithm was used for calibration and validation of the model with object function of Nash -Sutcliffe Equation (NSE). The performance of model during calibration and validation with monthly stream flow (m3/s) was found to be very good in terms of NSE, R2. The NSE and correlation coefficient were found to be 0.77 and 0.93 during calibration period and 0.90 and 0.95 during validation period respectively. The was a very good agreement between monthly observed and simulated stream flows of catchment area of Hidkal Dam. The NSE value of model during calibration period with daily runoff was found to be satisfactory at 0.51. It was observed that good agreement between observed and simulated daily runoff (in mm) with a correlation coefficient is 0.74. NSE and correlation coefficient during validation period using daily data are found to be 0.80 and 0.9 respectively. This study concluded that the SimHYD model can be used for assessment of stream flow with limited data.

The study has shown the integration of Arc SWAT with Arc GIS and remote sensing tool are helpful analyze and evaluate both spatial and temporal land use/cover dynamics. It has also shown that Arc SWAT is an effective tool in analyzing the impacts of land use/cover changes on stream flow in areas with limited readily available data. This study is mainly focusing on the investigation of the impacts of land use / land cover changes on the stream flow of Muga watershed which is located in the East Choke Mountains watersheds, Upper Abbay Basin, East Gojjam Zone, Amhara Regional state, Ethiopia. Soil and Water Assessment Tool (SWAT) model were used it investigate the impact of land cover change on the stream flow. For this study SWAT Simulation is used in identifying the most vulnerable sub basins to the stream flow and sediment load changes of Muga watershed. The model was calibrated and validated using historic Stream flow data. The model was calibrated using stream flow data from 1993 to1998, validated from 1999 to2002. The R 2 and NSE values were used to examine model performance and the result indicates 0.81 and 0.87 to R 2 and 0.80 and 0.86 to NSE during calibration and validation respectively. The result of this analysis indicated that the mean monthly stream flow for wet months had increased by 17.75m 3 /s while the dry season decreased by 12.76m 3 /s during the 1995-2013 period due to the land use and land cover change. The highest annual surface runoff was attributed by sub basin 5 whereas sub basin 6 contributes the highest ground water respectively for 1995, 2003 and 2013 land cover maps. In terms of sediment yield, sub basin 1 contributes a maximum load for the study periods. Keywords : Arc SWAT, Arc GIS, Land Use /land cover change, Muga Watershed DOI: 10.7176/CER/13-2-01 Publication date: April 30 th 2021

It is widely accepted that increasing concentration of greenhouse gases in the atmosphere is causing climate change, which may alter the hydrologic cycle and regional water availability. Hydrological modelling to assess the sensitivity of stream flow in the Brahmani basin to different hypothetical climate change scenarios indicated significant changes in mean monthly stream flow. Simulation results indicated 76% increase in annual stream flow with a 30% increase in rainfall and no change in temperature, and a maximum decrease of 33% in annual stream flow with 4°C increase in temperature and 10% decrease in rainfall. Rainfall changes had a greater effect on seasonal as well as annual changes in stream flow than the changes in temperature.

ABSTRACT: This study tests the hypothesis that climatic data can be used to develop a watershed model so that stream flow changes following forest harvest can be determined. Measured independent variables were precipitation, daily maximum and minimum temperature, and concurrent relative humidity. Computed variables were humidity deficit, saturated vapor pressure, and ambient vapor pressure. These climatic variables were combined to compute a monthly evaporation index. Finally, the evaporation index and monthly precipitation were regressed with measured monthly stream flow and the monthly estimates of stream flow were combined for the hydrologic year. A regression of predicted versus measured annual stream flow had a standard error of 1.5 inches (within 6.1 percent of the measured value). When 10, 15, and 20 years of data were used to develop the regression equations, predicted minus measured stream flow for the last 7 years of record (1972–1978) were within 16.8, 11.5, and 9.7 percent of the measured mean, respectively. Although single watershed calibration can be used in special conditions, the paired watershed approach is expected to remain the preferred method for determining the effects of forest management on the water resource.

Comparisons of measured stream flow with drainage and runoff simulated by a soil-vegetation-atmosphere transport model parameterized with GLOBE student data

A simulation study using the Soil and Water Assessment Tool (SWAT) model has been initiated to assess current and alternative nutrient, cropping, and management practices in the Sani River Basin in an effort to help mitigate climate change impact in the basin. The SWAT software system has been constructed to manage the input and output data for the SWAT simulations, and the execution of the model for each scenario. An overview of the modeling structure that has been developed for the basin is presented, including the spatial representation of the region within SWAT. Initial results for the baseline simulation are also presented for monthly stream flows near the outlet of the basin, and for annual stream flows for the basin. Overall, the model demonstrated good performance in capturing the patterns and trend of the observed flow series, which confirmed the appropriateness of the model for future scenario simulation. Therefore, SWAT model can be taken as a potential tool for simulation of the hydrology of watershed which behaves hydro-meteorologically similar with Sani basin. Future studies on Sani watershed modeling should address the issues related to water quality and evaluate best management practices.

The examination of reproducibility in hydro-ecological characteristics by daily synthetic flow models

In the recent years, artificial intelligence techniques have attracted much attention in hydrological studies, while time series models are rarely used in this field. The present study evaluates the performance of artificial intelligence techniques including gene expression programming (GEP), Bayesian networks (BN), as well as time series models, namely autoregressive (AR) and autoregressive moving average (ARMA) for estimation of monthly streamflow. In addition, simple multiple linear regression (MLR) was also used. To fulfill this objective, the monthly streamflow data of Ponel and Toolelat stations located on Shafarood and Polrood Rivers, respectively in Northern Iran were used for the period of October 1964 to September 2014. In order to investigate the models’ accuracy, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were employed as the error statistics. The obtained results demonstrated that the single AR and ARMA time series models had better performance in comparison with the single GEP, BN and MLR methods. Furthermore, in this study, six hybrid models known as GEP-AR, GEP-ARMA, BN-AR, BN-ARMA, MLR-AR and MLR-ARMA were developed to enhance the estimation accuracy of the monthly streamflow. It was concluded that the developed hybrid models were more accurate than the corresponding single artificial intelligence and time series models. The obtained results confirmed that the integration of time series models and artificial intelligence techniques could be of use to improve the accuracy of single models in modeling purposes related to the hydrological studies.

Improving litter nutrients import/export modeling in a forested watershed through enhanced forest growth representation

Sediment yield estimation along with identification of soil erosion mechanisms is essential for developing sophisticated management approaches, assessing, and balancing different management scenarios and prioritizing better soil and water conservation planning and management. At the watershed scale, land management practices are commonly utilized to minimize sediment loads. The goal of this research was to estimate sediment yield and prioritize the spatial dispersion of sediment-producing hotspot areas in the Nashe catchment using the Soil and Water Assessment Tool (SWAT). Moreover, to reduce catchment sediment output, this study also aims to assess the effectiveness of certain management practices. For calibration and validation of the model, monthly stream flow and sediment data were utilized. The model performance indicators show good agreement between measured and simulated stream flow and sediment yields. The study examined four best management practice (BMP) scenarios for the catchment’s designated sub-watersheds: S0 (baseline scenario), S1 (filter strip), S2 (stone/soil bunds), S3 (contouring), and S4 (terracing). According to the SWAT model result, the watershed’s mean yearly sediment output was 25.96 t/ha. yr. under baseline circumstances. The model also revealed areas producing the maximum sediment quantities indicating the model’s effectiveness for implementing and evaluating the sensitivity of sediment yield to various management strategies. At the watershed scale, treating the watershed with various management scenarios S1, S2, S3, and S4 decreased average annual sediment yield by 34.88%, 57.98%, 39.55%, and 54.77%, respectively. The implementations of the soil/stone bund and terracing scenarios resulted in the maximum sediment yield reduction. The findings of this study will help policymakers to make better and well-informed decisions regarding suitable land use activities and best management strategies.

The climatic processes such as change in precipitation, temperature and atmospheric CO2 concentration can intensify the effects on water resources. The long-term climate change affects assessment on water resources is essential to develop water quality improvement programs. This study was conducted in Upper Pearl River Watershed (UPRW) in east-central Mississippi to assess the effects of long-term potential future climate change on average mean monthly stream flow from the five spatially distributed USGS gage stations using Soil and Water Assessment Tool (SWAT).

Simulation of runoff in ungauged catchments has always been a challenging issue, receiving significant attention more importantly in practical applications. This study aims at calibration of an Artificial Neural Network (ANN) model which is capable to apply in an ungauged basin. The methodology is applied to two sub-catchments located in the Northern East of Iran. To examine the effect of physical characteristics of the catchment on the capability of the model generalization, it is attempted to synthesize effective parameters using empirical methods of runoff estimation. Firstly, the model was designed for a pilot sub-catchment and the statistical comparison between simulated runoff, and target depicted the capability of ANN to accurately estimate runoff over a catchment. Then, the calibrated model was generalized to another sub-catchment assumed as an ungauged basin while there are runoff data to compare the result. The result showed that the designed model is relatively capable to estimate monthly runoff for a homogenous ungauged catchment. The method presented in this study in addition to adding effective spatial parameters in simulation runoff and calibration of model by using empirical methods and the integration of any useful accessible data, examines the adaptability of model to an ungauged catchment.