Predict Water Flow Research Articles

Using Predictive Model for Strategic Control of Multi-reservoir System Storage Capacity

The paper will describe the algorithm based on adaptive optimization of multi reservoir control, which the medium-term water flow predictions into the reservoirs for several months ahead repeatedly use. Hydrological prediction model was created using ANN method and values of control outflows are searching by optimization based on evolutionary algorithms optimization technique. The objective function was descripted as the sum of squares deviations between required and actual controlled water outflow from reservoirs where objective function is minimized. The algorithm of adaptive control is applied to the operation storage control of selected reservoir system, which open water reservoirs Vir and Brno are created.

Comparison of FAO Aquacrop and SWAP Agro-Hydrological Models to Simulate Water and Salt Transport during Growing Season of Winter Wheat

In this study, the performance of SWAP and FAO Aquacrop agro-hydrological models to predict water flow and salt transport in soil profile was evaluated under water and salt stresses during growing season of winter wheat. Field experiments were conducted with three levels of saline irrigation water including: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dSm -1 and four levels of irrigation depth including: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125 percent of crop water requirement with three replications for 2005-2006 period in Birjand region in Iran. The SWAP and Aquacrop models predicted the soil water content with an appropriate precision. The average normalized root mean square error (NRMSE) of calibration in soil water content prediction by SWAP and Aquacrop models, were calculated 4.81 and 18.91 %, respectively, and for validation, were calculated 7.34 and 19.6 %, respectively. The average normalized root mean square error (NRMSE) of calibration in ECe prediction by SWAP and Aquacrop models, were calculated 8.87 and 37.34 %, respectively, and for validation, were calculated 13.6 and 36.93 %, respectively. Results indicated that the Aquacrop model predicted ECe with more error compared with SWAP model. * Corresponding Author: Vahid Rezaverdinejad  verdinejad@gmail.com

Performance evaluation and engineering considerations for a modular- and culvert-based paddlewheel circulator for split-pond systems

The split-pond system (SPS) has been proposed as an alternative culture method to increase the efficiency of pond aquaculture production. This study evaluated the performance and the engineering models of paddlewheel circulators on commercial catfish SPSs.The power requirement and the water flow rate by the circulators increased as the rotational speed and water depth in the culvert increased. The rotational speed increased the power requirement exponentially and the water flow rate linearly, while the water depth, which determined the wetted surface area of paddles, increased both linearly. Increasing the rotational speed decreased relative pumping efficiency relative to power used. Under the given field operational conditions, the predicted power requirement appropriately corresponded with the measured power requirement and the predicted values were within 95% of measured values. Good agreement was also noted between measured and predicted water flow rates, and the predicted values were 91.4% of the measured values.Based on the models developed, it was projected that increasing the wetted surface area of paddles is more energy efficient than increasing the rotational speed and minimizes mechanical failure due to high torque. However, the power requirement was also projected to increase with increasing wetted surface area at a certain threshold, as attempts to decrease the rotational speed were made to achieve a target water flow rate.

Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control

Abstract Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.

Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India

Abstract Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India. In the ecologically sensitive Himalayan region, land transformations and utilization of natural resources have modified water flow patterns. To ascertain future sustainable water supply it is necessary to predict water flow from the watersheds as affected by rainfall and morphological parameters. Although such predictions may be made using available process- -based models, in mountainous and hilly areas it is extremely difficult to determine the numerous parameters needed to run such models, thus limiting their applicability. Artificial intelligence (AI) based models are a possible alternative in such circumstances. In this study an AI technique, support vector machines (SVM), was used for modeling the rainfall-runoff relationship from three hilly watersheds in the state of Uttaranchal, India. Different SVM models were developed to predict direct runoff, base flow, and total flow based on the daily rainfall, runoff, and morphological parameters collected from each watershed. The results confirm the potential of SVM models in the prediction of runoff, base flow, and total flow in hilly areas.

Modeling the interaction between fields and a surrounding hedgerow network and its impact on water and nitrogen flows of a small watershed

The rural landscape in Western Europe, as in many regions of the world, is structured by networks of woody hedgerows that surround agricultural fields. Although many studies at local scale have shown the strong impact of hedgerows on soil water and nitrogen balances, few have quantified the impact of hedgerows at the watershed scale. This study estimated via modeling the impact of hedgerow networks on soil water and nitrogen balances of a small watershed in a temperate climate. The spatially explicit model TNT2 (topography-based nitrogen transfer and transformation), to which a new sub-model of hedgerow functioning was added, was used to perform the study. The unique character of this sub-model is to consider “double-cover” cells (crop plus hedge) to take into account competition for light, water, and nitrogen. The effect of hedge management by branch pruning was also studied. The model was used to simulate a small experimental watershed in western Europe over 17 years with the hedgerow network that existed in 1999 (48mha−1), or without hedgerows. On average, predictions of hedge transpiration and hedge N uptake were consistent with observations from the bibliography. On double-cover cells, the hydro-chemical impact of the hedge resulted from the complex combination of processes, which can have opposite effects. At the watershed scale, the hedgerow network decreased predicted water flow at the outlet by 4.5% and nitrogen flow by 3.3%, respectively, compared to those flows when the watershed had no hedgerows. Finally, hedgerow pruning has a low effect on water and nitrogen flow at the outlet of watershed.

Prediction of water flows in Colorado River, Argentina

Fil: Pierini, Jorge Omar. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca; Argentina. Provincia de Buenos Aires. Gobernacion. Comision de Investigaciones Cientificas; Argentina

Necessary conditions for inverse modeling of flow through variably saturated porous media

Non-unique solutions of inverse problems arise from a lack of information that satisfies necessary conditions for the problem to be well defined. This paper investigates these conditions for inverse modeling of water flow through multi-dimensional variably saturated porous media. It shows that in order to obtain a unique estimate of hydraulic parameters, along each streamline of the flow field (1) spatial and temporal head observations must be given; (2) the number of spatial and temporal head observations required should be greater or equal to the number of unknown parameters; (3) the flux boundary condition or the pumping rate of a well must be specified for the homogeneous case and both boundary flux and pumping rate are a must for the heterogeneous case; (4) head observations must encompass both saturated and unsaturated conditions, and the functional relationships for unsaturated hydraulic conductivity/pressure head and for the moisture retention should be given, and (5) the residual water content value also need to be specified a priori or water content measurements are needed for the estimation of the saturated water content.For field problems, these necessary conditions can be collected or estimated but likely involve uncertainty. While the problems become well defined and have unique solutions, the solutions likely will be uncertain. Because of this uncertainty, stochastic approaches are deemed to be appropriate for inverse problems as they are for forward problems to address uncertainty. Nevertheless, knowledge of these necessary conditions is critical to reduce uncertainty in both characterization of the vadose zone and the aquifer, and prediction of water flow and solute migration in the subsurface.

Functional test of pedotransfer functions to predict water flow and solute transport with the dual-permeability model MACRO

Abstract. Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = −0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the estimation of water flow parameters. Overall, the results are encouraging for the use of this modelling set-up to estimate pesticide leaching risks at the regional-scale, especially where the objective is to identify vulnerable soils and "source" areas of contamination.

Simulation of water flow and solute transport in intermittent sand filter

Research Article| February 01 2012 Simulation of water flow and solute transport in intermittent sand filter Mahmoud Bali; Mahmoud Bali 1Department of Geology, Faculty of Sciences, Tunis, Tunisia E-mail: mahmoud.bali@yahoo.fr Search for other works by this author on: This Site PubMed Google Scholar Moncef Gueddari Moncef Gueddari 1Department of Geology, Faculty of Sciences, Tunis, Tunisia Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2012) 61 (1): 50–57. https://doi.org/10.2166/aqua.2012.104 Article history Received: December 25 2010 Accepted: November 15 2011 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Mahmoud Bali, Moncef Gueddari; Simulation of water flow and solute transport in intermittent sand filter. Journal of Water Supply: Research and Technology-Aqua 1 February 2012; 61 (1): 50–57. doi: https://doi.org/10.2166/aqua.2012.104 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Water and solute transport in intermittent sand filtration was simulated with a modified HYDRUS 1D program. Field trials were performed through a 10 × 10 m infiltration basin located at Dissa agriculture area in the north of Gabes city (south-east Tunisia). Performance of the calibrated model has been statistically evaluated for its ability to predict water flow and solute transport. Comparisons between measured and simulated water content resulted in R2 and root mean square error (RMSE) average values of 0.88 and 0.74 cm3 cm−3 for the calibration period and of 0.86 and 0.78 cm3 cm−3 for the validation period, respectively. Predicted water quality parameters were found to be in best agreement with the measured results. On average, the model predicted COD and NH4-N concentrations well, with a RMSE of 1.06 and 1.26 mg L−1 respectively. However, it underestimated NO3-N concentration. Similar corresponding values were computed for the validation period indicating that the model is able to predict water flow and solute transport with a reasonable degree of accuracy. intermittent sand filtration, simulation, treatment, wastewater This content is only available as a PDF. © IWA Publishing 2012 You do not currently have access to this content.

Water-Circulating Aerator: Optimizing Structure and Predicting Water Flow Rate and Oxygen Transfer

Thermal stratification is a common phenomenon in deep lakes and reservoirs, which often results in water-quality deterioration, including such problems as hypolimnetic anoxia, the release of pollutants from sediments, and algal blooms. Hypolimnetic oxygenation and destratification are the two commonly used methods for resolving these water-quality problems. A new water-quality improvement device, the water-circulating aerator, was designed to destratify lakes and reservoirs, by circulation and oxygenation of upper and lower layers of water. The design of the structure of the water-circulating aerator is detailed. Three mathematical models were built to optimize this structure, estimate the rate of water flow in the aerator, and calculate the rate of oxygen transfer from air bubbles to water in the aerator. These models were verified by experiments. The water-circulating aerator system has been successfully applied in a stratified reservoir to increase dissolved oxygen to reduce the releasing of ammonia-nitrogen from sediments under anoxic conditions.

Benchmarking of Two Dual‐Permeability Models under Different Land Use and Land Cover

Land use and land cover (LULC) exert strong controls on soil properties and thus water flow in soils. The question arises whether existing models designed for simulating water and solute movement in heterogeneous soil include the effects of LULC on infiltration and percolation. To answer this question, the predictive capacity of two solute transport models was benchmarked to simulate flow processes in soils developed under different LULCs. To benchmark the model performance, dye tracer sprinkling experiments were conducted on five sites displaying similar soil texture and parent material but differences in LULC (two farmland sites, tilled and untilled, two grassland sites, and one deciduous forest site). Water content changes were continuously measured at different depths with time domain reflectometers during 60‐mm irrigation over 4 h. After the tracer application, vertical and horizontal soil sections were excavated and photographed. The experimental data were then compared with the simulations based on a multicriteria benchmarking strategy, including simulated vs. observed water content changes, solute distribution profiles, and maximum infiltration depth. Both models were capable of predicting water flow for one of the grassland sites but revealed severe weaknesses when additional flow processes not explicitly included in the model structure came into play. Poor model outcomes resulted from LULC effects such as a strong surface microtopography altering macropore flow initiation in the agricultural soils and from horizontally oriented roots and surface water repellency in the forest soil. Our results suggest that LULC effects need to be better incorporated into the conceptualization and parameterization of infiltration and percolation in hydrologic models to obtain realistic predictions concerning water quality and quantity.

Soil and Crop Hydraulic Properties in Surface Irrigation

Proper design and management are essential to the efficient application of water with surface irrigation systems. Simulation models of the surface-irrigation process developed over the last five decades have become the main tool for predicting water flow and ultimate distribution over the length of a single furrow, basin, or border strip. With the advent of modern computer technologies, computations can be carried out in a matter of seconds. With the computational problems associated with mathematical modeling largely overcome, at least with simple geometries, in more recent times, the primary emphasis has shifted from simulation and research to practical design and management. Transferring this technology to practical users rests on the ability to supply the essential field data to enter into the design, management, and simulation of surface irrigation systems. In recognition of the need to bring these issues to the profession, in 1999, the ASCE/ EWRI Task Committee on Soil and Crop Hydraulic Properties was formed by its parent On-Farm Irrigation Committee of the EWRI Irrigation and Drainage Council. The initial focus of the Committee was on the estimation of parameters of empirical formulations commonly used in irrigation practice. It was thought at the time, that the Committee members would compile a set of techniques from the literature, evaluate them in terms of their reliability, cost, and accuracy, and make recommendations for selection. It was found, instead, that irrigation researchers were nowhere near ready to confine their review to existing methods. The study opened up a whole area of research, with a need to probe more deeply into the ramifications of one or another previously neglected approach. Over the ten years of its existence, the Task Committee has sponsored 16 published conference presentations and two internal documents, and triggered new research and comments on old research presented in this special issue. The first paper, “Field Properties in Surface Irrigation Management and Design,” presents an overview of those hydraulic characteristics of a field that influence simulation, design, and management. Some of the issues discussed have not been studied in the past, but are included to give future researchers a head start, in the event it proves necessary to explain puzzling disagreement between simulation and measurement.

The impact of new developments on river water quality from an integrated system modelling perspective

New housing areas are a ubiquitous feature of modern life in the developing and developed world alike built in response to rising social, demographic and economic pressures. Inevitably, these new developments will have an impact on the environment around them. Empirical evidence confirms the close relationship between urbanisation and ambient water quality. However, what is lacking so far is a detailed and more generalised analysis of environmental impact at a relatively small scale. The aim of this paper is to quantify the impact of new developments on river water quality within an integrated system modelling perspective. To conduct the impact analyses, an existing integrated urban wastewater model was used to predict water flow and quality in the sewer system, treatment plant and receiving water body. The impact on combined sewer overflow (CSO) discharges, treatment plant effluent, and within the river at various reaches is analysed by ‘locating’ a new development on a semi-hypothetical urban catchment. River water quality is used as feedback to constrain the scale of the new development within different thresholds in compliance with water quality standards. Further, the regional sensitivity analysis (RSA) method is applied to reveal the parameters with the greatest impact on water quality. These analyses will help to inform town planners and water specialists who advise them, how to minimise the impact of such developments given the specific context.

Numerical simulation of water flow in three dimensional heterogeneous porous media observed in a magnetic resonance imaging experiment

Magnetic resonance imaging (MRI) was used to obtain sequential images of water (i.e., 1H) doped with a paramagnetic tracer as it flowed through a three‐dimensional (3D) flowcell packed with a spatially correlated heterogeneous distribution of 1 cm3 blocks, each containing one of five different sand fractions. Tracer concentration breakthrough curves (BTCs) were obtained from MRI signal intensity profiles at each voxel (0.1875 × 0.1875 × 0.225 cm3). Voxel scale BTCs were averaged over 0.25 × 0.25 cm2, 1 × 1 cm2, and entire flowcell cross‐sections, all at 0.25 cm increments along the main flow direction, and compared with numerical simulations. Hydraulic conductivity (K) and dispersivity values for each of the five sand types were varied, and root‐mean squared error (RMSE) values for mean arrival times and second central moments, and RMSE values between simulated and measured BTCs, at each of the three averaging scales were calculated. Measured values of K with a porosity 5% higher than measured values, and longitudinal dispersivity values on the order of the grain size, yielded simulated BTCs that adequately captured experimental BTCs averaged over the entire flowcell cross‐section, and BTCs at smaller scales (i.e., 1 × 1 cm2 and 0.25 × 0.25 cm2) in regions of the flow cell characterized by more uniform K fields. However, model simulations deviated more from BTCs at smaller scales in regions of the flowcell characterized by greater K contrast, where flow‐bypassing occurred. This may be due to small discrepancies between the experimental and numerical conductivity fields. Comparison of mechanical dispersion and diffusion in low K zones indicates that molecular diffusion is equally important to mechanical dispersion in these zones and transverse dispersion did not have much effect on improving prediction. These results illustrate that predicting water flow at fine scales (relative to permeability variations) is very challenging, even under the most controlled conditions. This may have large implications for modeling reactive transport, where reactant residence time and mixing can be greatly impacted by water flowpaths.

Evolution of unsaturated hydraulic conductivity of aggregated soils due to compressive forces

Prediction of water flow and transport processes in soils susceptible to structural alteration such as compaction of tilled agricultural lands or newly constructed landfills rely on accurate description of changes in soil unsaturated hydraulic conductivity. Recent studies have documented the critical impact of aggregate contact characteristics on water flow rates and pathways in unsaturated aggregated soils. We developed an analytical model for aggregate contact size evolution as a basis for quantifying effects of compression on saturated and unsaturated hydraulic conductivity of aggregated soil. Relating confined one‐dimensional sample strain with aggregate deformation facilitates prediction of the increase in interaggregate contact area and concurrent decrease in macropore size with degree of sample compression. The hydrologic component of the model predicts unsaturated hydraulic conductivity of a pack of idealized aggregates (spheres) on the basis of contact size and saturation conditions under prescribed sample deformation. Calculated contact areas and hydraulic conductivity for pairs of aggregates agreed surprisingly well with measured values, determined from compaction experiments employing neutron and X‐ray‐radiography and image analysis. Model calculations for a unit cell of uniform spherical aggregates in cubic packing were able to mimic some of the differences in saturated and unsaturated hydraulic conductivity observed for aggregates and bulk soil.

Estimating Effective Hydraulic Parameters of Unsaturated Layered Sediments Using a Cantor Bar Composite Medium Model

The estimation of effective hydraulic parameters of variably saturated layered sediments has been extensively studied using deterministic, stochastic, and combined modeling approaches. Heterogeneity and scale dependence remain as major obstacles, however, for the prediction of water flow and contaminant transport at many U.S. Department of Energy sites. We used a physically based Cantor bar model to describe scale dependence of the fractions of coarse and fine materials in layered sediments. The Cantor bar is determined by three fractal parameters: the subdivision factor, b, the fractal dimension, D, and the iteration level, i, which can be estimated from observation (e.g., borehole logs). Because b and D are scale invariant, the model can be used to predict layering at scales other than the observation scale. Together with a composite medium approximation (COMA), the Cantor bar model can be used to predict effective hydraulic properties as a function of scale. Numerical simulation results showed that COMA works well for steady‐state unsaturated flow through a stratigraphic sequence comprised of thin layers of fine material interbedded within a coarse material. Further work is necessary to validate the model predictions by performing measurements at different scales, and to assess the applicability of this approach for transient flow.

Sorption and predicted mobility of herbicides in Baltic soils

This study was undertaken to determine sorption coefficients of eight herbicides (alachlor, amitrole, atrazine, simazine, dicamba, imazamox, imazethapyr, and pendimethalin) to seven agricultural soils from sites throughout Lithuania. The measured sorption coefficients were used to predict the susceptibility of these herbicides to leach to groundwater. Soil-water partitioning coefficients were measured in batch equilibrium studies using radiolabeled herbicides. In most soils, sorption followed the general trend pendimethalin > alachlor > atrazine∼ amitrole∼ simazine > imazethapyr > imazamox > dicamba, consistent with the trends in hydrophobicity (log Kow) except in the case of amitrole. For several herbicides, sorption coefficients and calculated retardation factors were lowest (predicted to be most susceptible to leaching) in a soil of intermediate organic carbon content and sand content. Calculated herbicide retardation factors were high for soils with high organic carbon contents. Estimated leaching times under saturated conditions, assuming no herbicide degradation and no preferential water flow, were more strongly affected by soil textural effects on predicted water flow than by herbicide sorption effects. All herbicides were predicted to be slowest to leach in soils with high clay and low sand contents, and fastest to leach in soils with high sand content and low organic matter content. Herbicide management is important to the continued increase in agricultural production and profitability in the Baltic region, and these results will be useful in identifying critical areas requiring improved management practices to reduce water contamination by pesticides.

A Water Model for Water and Environmental Management at Mae Moh Mine Area in Thailand

It is revealed that the water quality in Mae Moh Reservoir, Thailand, has been deteriorated by lignite mine drainage and power station effluent. This study aims to manipulate water quantity and quality to reduce environmental impacts in Mae Moh area through a model for water management. The model was constructed on the basis of materials balance to predict water flow, which includes concentrations of TDS and SO2–4. Data collected during 1996–2000 were used. Model validation showed that the mean of predicted and actual values of TDS and SO2–4 load were significantly similar at 95% confidence limit. The test result is acceptable and the water model can be used as a tool for water system management in the area. In 2006, Mae Moh mine excess water will be discharged at 10.76 Mm3, with a pH of 7.3, TDS and SO2–4 concentrations of 2,547 and 1,803 mg/l, respectively. Mae Moh power station effluent will be 14.59 Mm3, with pH of 7.1, TDS and SO2–4 concentrations of 610 and 358 mg/l, respectively. Predicted results showed that the outflow of Mae Moh Reservoir will be 83.67 Mm3 and the concentrations of TDS and SO2–4 will be as high as 1,501 and 822 mg/l, respectively. Mine excess water management measures are recommended according to the following strategy. All mine excess water should be stored during dry season. During wet season, 50% of the excess water should be stored and the remaining treated at 90% of TDS removal before being discharged. The end result would be a significant improvement in water quality in the Mae Moh Reservoir over the 4-year period to 2010. Pollutants in terms of TDS would be reduced by 35% from 1,501 mg/l in the beginning of 2006 to 975 mg/l at the end of 2009.

Impact of climate change and development scenarios on flow patterns in the Okavango River

This paper lays the foundation for the use of scenario modelling as a tool for integrated water resource management in the Okavango River basin. The Pitman hydrological model is used to assess the impact of various development and climate change scenarios on downstream river flow. The simulated impact on modelled river discharge of increased water use for domestic use, livestock, and informal irrigation (proportional to expected population increase) is very limited. Implementation of all likely potential formal irrigation schemes mentioned in available reports is expected to decrease the annual flow by 2% and the minimum monthly flow by 5%. The maximum possible impact of irrigation on annual average flow is estimated as 8%, with a reduction of minimum monthly flow by 17%. Deforestation of all areas within a 1 km buffer around the rivers is estimated to increase the flow by 6%. However, construction of all potential hydropower reservoirs in the basin may change the monthly mean flow distribution dramatically, although under the assumed operational rules, the impact of the dams is only substantial during wet years. The simulated impacts of climate change are considerable larger that those of the development scenarios (with exception of the high development scenario of hydropower schemes) although the results are sensitive to the choice of GCM and the IPCC SRES greenhouse gas (GHG) emission scenarios. The annual mean water flow predictions for the period 2020-2050 averaged over scenarios from all the four GCMs used in this study are close to the present situation for both the A2 and B2 GHG scenarios. For the 2050-2080 and 2070-2099 periods the all-GCM mean shows a flow decrease of 20% (14%) and 26% (17%) respectively for the A2 (B2) GHG scenarios. However, the uncertainty in the magnitude of simulated future changes remains high. The simulated effect of climate change on minimum monthly flow is proportionally higher.