Water Resources Management Project Research Articles

Enhancing flood risk assessment in the Johor River Basin through trivariate copula

ABSTRACT Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.

A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today's more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.

Intercomparison of Rainfall Estimates of Three Distributions for Computation of PFD using Rational Formula and SUH Approach

Peak flood discharge (PFD) for a given return period is considered as one of the most important parameters for planning and design of hydraulic structures, river protection works, and development of integrated water resources management projects. This can be achieved by adopting various methods such as flood frequency analysis, rational formula, envelope curves and Synthetic Unit Hydrograph (SUH) approach. Out of which, rational formula is applied for estimation of PFD for ungauged catchments with catchment area less than 25 km2 while SUH approach is adopted for ungauged catchments with catchment area more than 25 km2. This paper aims to investigate the study on intercomparison of probability distributions for estimation of extreme (i.e., 1-day maximum) rainfall for computation of PFD by adopting rational formula and SUH approach for the Vadhavan Port Project (VPP). The annual 1-day maximum rainfall series is extracted from the daily rainfall data observed at Dahanu site during the period 1969 to 2019 and also used for estimation of rainfall. The probability distributions viz., Extreme Value Type-1, 2-parameter Log Normal and Log Pearson Type-3 (LP3) adopted in extreme value analysis of rainfall is evaluated through diagnostic test using root mean square error and accordingly LP3 is adjudged as the best fit for estimation of rainfall. The 1-day maximum rainfall obtained from LP3 is considered to compute the rainfall intensity by applying one-third rule of IMD (India Meteorological Department) and also used for computation of PFD through rational formula. In SUH approach, the areal rainfall, physiographic and SUH parameters of the ungauged catchments are considered for computation of PFD. The study suggests the 25-year, 50-year and 100-year return period PFDs at six locations within the study area using rational formula and SUH approach could be considered for design purposes while designing any civil and hydraulic structures in the VPP.

Understanding the structure of stakeholders − projects network in endangered lakes restoration programs using social network analysis

Lakes are among the most vulnerable wetlands. Many lakes are disappearing, which can result in adverse social and environmental consequences in the world. The success of water resources management projects for restoring these lakes depends on understanding relevant active stakeholders and their networks in these systems. This study identifies the network structure of stakeholder-operational restoration projects in the Urmia Lake, located in northwest Iran, an example of aquatic ecosystems vulnerable to anthropogenic activities and climate change. The study uses the Social Network Analysis (SNA) technique to learn stakeholders’ roles and positions for enhancing a coherent and adaptive management system for restoring this lake. Document analyses and interviews with key stakeholders of the Urmia Lake Restoration Program (ULRP) indicated the restoration projects and active stakeholders at the national, provincial, and local levels. SNA identifies that although the stakeholders have moderate interactions for implementing operational projects, both stakeholders and projects have varying influence and power in the network. The stakeholders are also classified into different groups in terms of varied restoration projects and their roles. The poor collaboration of different categories of stakeholders for implementing projects can reduce their effectiveness, lead to conflicts in their actions, and prohibit the sustainable restoration of endangered lakes. The restoration programs of inland wetlands, particularly endangered lakes in arid and semi-arid areas can use the capacity of influential stakeholders and key projects to develop the integrity of stakeholders and water policies, enhance their involvement in relevant restoration projects and improve water resources management.

The effect of natural and engineered hydraulic conditions on river-floodplain connectivity using hydrodynamic modeling and particle tracking analysis

Hydraulic conditions and water resources management projects can significantly alter river-floodplain connectivity, which in turn can alter hydrologic and biogeochemical processes in river corridors. In this study, the hydrodynamics of river-floodplain connectivity under different flood conditions and the effect of the Nanchang Water Resources Project Group (NWRPG) in the middle branch of the Ganjang River were investigated using a combination of two-dimensional hydrodynamic simulations and particle tracking. The hydrodynamic model was calibrated and validated using data from several gauging stations and field measurements. Floods in the Ganjang River can be limited to the river itself (“River Flood”, flood with normal lake level) or further extended to the Poyang Lake (“Lake Flood”, flood with high lake level). The results show that compared with “River Flood” scenarios, “Lake Flood” scenarios increased the water level flooding a larger area. The flow velocity decreased and the residence time (RT) of particles increased. The particle travel distance (PTD) of “River Flood” was larger than that of “Lake Flood”. The larger the flood, the greater the transboundary flux between the river and the floodplain, and the shorter the RT and PTD. The effect of NWRPG was the permanent flooding of part of the river floodplain, causing some habitat loss. Due to the increase in discharge, the implementation of the NWRPG results in a shorter RT with a smaller standard deviation, which has little effect on the PTD distribution. These findings can facilitate river connectivity restoration efforts in the Ganjang River and also provide a reference for assessing the impact of barrage projects.

State-of-the-Art Statistical Approaches for Estimating Flood Events.

Reliable quantile estimates of annual peak flow discharges (APFDs) are needed for the design and operation of major hydraulic infrastructures and for more general flood risk management and planning. In the present study, linear higher order-moments (LH-moments) and nonparametric kernel functions were applied to APFDs at 18 stream gauge stations in Punjab, Pakistan. The main purpose of this study was to evaluate the impacts of different quantile estimation methods towards water resources management and engineering applications by means of comparing the state-of-the-art approaches and their quantile estimates calculated from LH-moments and nonparametric kernel functions. The LH-moments (η = 0, 1, 2) were calculated for the three best-fitted distributions, namely, generalized logistic (GLO), generalized extreme value (GEV), and generalized Pareto (GPA), and the performances of these distributions for each level of LH-moments (η = 0, 1, 2) were compared in terms of Anderson–Darling, Kolmogorov–Smirnov, and Cramér–Von Mises tests and LH-moment ratio diagrams. The findings indicated that GPA and GEV distributions were best fitted for most stations, followed by GLO distribution. The quantile estimates derived from LH-moments (η = 0, 1, 2) had a lower relative absolute error, particularly for higher return periods. However, the Gaussian kernel function provided a close estimate among nonparametric kernel functions for small return periods when compared to LH-moments (η = 0, 1, 2), thus highlighting the importance of using LH-moments (η = 0, 1, 2) and nonparametric kernel functions in water resources management and engineering projects.

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions.

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

Impact of Gridded Weather Data Sources and its Temporal Resolution on Crop Evapotranspiration and Effective Rainfall of Major Crops in Eastern Region of India

Synthetic climate data are being increasingly used as key input in number of climate change impact assessment studies, hydrological assessments, and water resource management projects. The present study quantified the impact of two-gridded climate data sources [India Meteorological Department (IMD) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC)] on estimation of evapotranspiration (ETc ) and effective rainfall (ER) of paddy (Kharif) and wheat (Rabi) crops across four agroecological sub-regions, and compared the results with observed station data. The effect of daily, decadal, and monthly temporal resolutions of climatic data were evaluated on ETc and ER using climatological model CROPWAT 8.0. Results showed that compared to daily resolution, the monthly temporal resolution estimated significantly higher (1.7-4.0%) ETc for wheat crop. Effect of temporal resolution of climate data on ER of both crops was insignificant, implying that the uncertainties in estimation of ER emanating from use of daily, decadal, or monthly temporal resolutions would be within acceptable limits. Across the locations, the CPC-based estimates of paddy ETc deviated from (-)1.3% to 21.6% of the station ETc , while the deviations for wheat ETc were in the range of (-)7.1% to 9.3 per cent. Significant variations from station etc were also observed for CPC (1.7 - 19.4%) and IMD [(-)34.3 - 31.1%] based ETc estimates of wheat ETc . The study recommends that the technological and policy research outputs based on gridded climate data products should be carefully analysed keeping in view the uncertainty accruing on account of use of gridded data products.

Farmers’ Contributions to Achieving Water Sustainability: A Meta-Analytic Path Analysis of Predicting Water Conservation Behavior

This article intends to summarize the findings of studies on the relationship between farmers’ behavioral intentions (BI) and water-conservation behavior (WCB) using the theory of planned behavior (TPB). A systematic review of transcripts obtained from Internet-based searching on reliable scientific databases (e.g., SID, ProQuest, Springer, Science Direct, John Wiley, Sage, Taylor & Francis, Emerald Insight, and Google Scholar) was followed by outfitting data for the Comprehensive Meta-Analysis (CMA) software. Data from a total of 28 studies on WCB were synthesized and analyzed through the CMA procedure. The resulting evidence demonstrates that the total and summarized estimate point (i.e., correlation) for the associations of attitude (ATT), subjective norms (SNs), and perceived behavioral control (PBC) with BI was 0.46, 0.36, and 0.26, respectively (r(t)PBC < r(t) SNs < r(t)ATT → BI). Furthermore, the effect size of the relationship between PBC and WCB was 0.27. The largest effect size pertained to the relationship between BI and WCB (BI → WCB) (r(t)BI = 0.52). The take-home message of the article is that hypothetical statements of the TPB are confirmed, as would be hypothesized. These findings should still be regarded in the field of WCB research; thus, policymakers need to provide solutions and adapt their policy initiatives for water resource management based on these findings. For example, one of the solutions to improve water resource management based on the research results is to examine the views and realities constructed by farmers of water resources and related management styles before carrying out water resource management projects. Identifying the potential capabilities of farmers and their economic and social background to expand water-conservation behavior and the tendency and acceptance of water resource management project to be implemented is also an important requirement in making resource management projects effective. The use of various methods of the individual, group, and collective communication to interact with farmers combined with provision of extension training, as well as mobilizing and organizing farmers to facilitate effective management of water resources are recommended.

A methodological framework for the hydrological model selection process in water resource management projects

AbstractThis study aims to present a process for hydrological model exploration for selecting an appropriate model compatible with the modeling objectives. The process consists of three stages: (1) initial choice based on the modeling objectives; (2) model selection based on intercomparison among underlying conceptualizations of the models; and (3) final model selection based on influencing criteria such as availability of the model software and documentation, and availability of appropriate data. As an applied example, the process was used to find an appropriate model for a project to evaluate water supply and demand under climate and land use change scenarios in the Gorgan‐rud River Basin, Iran. The criteria affecting the final choice of a hydrological model were classified into three categories: (1) criteria related to the model, (2) criteria related to the model user, and (3) criteria related to the study area.

Role of IRS-1C in Developing Remote Sensing Applications for Water Management in India

Efficient water management is essential for water and food security leading to socio-economic development. In order to build resilience and achieve water security, planning and management of water resources projects needs to be based on realistic assessment of related aspects. This requires comprehensive, reliable and easily accessible information on demand, availability and utilisation of water resources. Observational capabilities from ground monitoring systems and supplementary/complementary data from satellite remote sensing provide unique opportunities for data integration and value addition supporting water management decisions. While LANDSAT-1 initiated various remote sensing based activities with respect to management of hydrology and water resources across the globe, the launch of Indian remote sensing satellite, IRS-1A in 1988, along with its follow-up satellites, provided fillip to these activities mainly in India. The launch of IRS-1C in December 1995, with unique combination of sensors, has greatly improved extensive utilisation of satellite remote sensing for various facets of water management. This article presents a review of various applications carried out using IRS-1C data while highlighting the capabilities and advantage of IRS-1C sensors achieving significant improvement in mapping water bodies, inventory and monitoring of irrigated crops, irrigation system performance evaluation, assessment of reservoir sedimentation, mapping and monitoring of snow cover, snowmelt runoff forecasting, watershed developmental planning and management, etc.

Integrated Water Resources Management Approaches to Improve Water Resources Governance

The water crisis can alternatively be called a governance crisis. Thus, the demand for good water governance to ensure effective water resources management and to attain specific water goals is growing. Many countries subscribe to the Integrated Water Resources Management (IWRM) approach to achieve this goal. The Integrated Water Resources Management approach aims to ensure a process that promotes the coordinated development and management of water, land, and related resources in a drainage basin to maximise economic and social welfare equitably without compromising the sustainability of vital ecosystems. The design of the Integrated Water Resources Management approach, including its pillars and principles, aspires to good water governance and effective resource management. However, empirical studies examining this hypothesis and analysing the impact of the Integrated Water Resources Management approach on water resources governance are limited, especially in developing countries. Therefore, we characterised and compared the water resources governance aspects of two catchments in Uganda’s Lake Albert basin. One of the catchments was exposed to integrated water resources management projects, while the other had no exposure to integrated water resources management projects. Some of the factors that supported the comparability of the two sites included spatial proximity linking into a related hydrological and social-economic setup, common water needs and belonging to the same water administration zone. Comparing both areas led us to analyse whether there was a difference in water resources governance actions, as well as in the quality of water resources governance, under the same overall water management and administrative zone. The data were based on field surveys using questionnaires and information guides in both catchments. The results show that the performance of water resources governance is markedly better in the catchment with Integrated Water Resources Management practices than the base catchment unaffected by these practices. Key themes examined include water resources governance styles, water resources governance systems presence, functionality, the performance of good governance principles, and water resources management effectiveness. The findings contribute to the aspirations for the promotion of integrated water management approaches for improved water resources governance, and the concept that the effectiveness of water resources management measures depends on governance effectiveness. Water governance is significant, as it spells out the power, rights, decisions, and priorities relating to given water resources and communities.

Wetlands Management in Northern Greece: An Empirical Survey

Water management projects have an important role in regional environmental protection and socio-economic development. Environmental policies, strategies, and special measures are designed in order to balance the use and non-use values arising for the local communities. The region of Serres in Northern Greece hosts two wetland management projects—the artificial Lake Kerkini and the re-arrangement of Strymonas River. The case study aims to investigate the residents’ views and attitudes regarding these two water resources management projects, which significantly affect their socio-economic performance and produce several environmental impacts for the broader area. Simple random sampling was used and, by the application of reality and factor analyses along with the logit model support, significant insights were retrieved. The findings revealed that gender, age, education level, and marital status affect the residents’ perceived values for both projects and their contribution to local growth and could be utilized in policy making for the better organization of wetland management.

Using Extreme Value Theory to Estimate Available Water in the Upper Awach-Kibuon Catchment in Nyamira County, Kenya

In the management of water resources projects, water managers are often interested in quantifying the frequency and magnitude of floods that will occur at the management areas or units. The frequency analysis of these events is one of the most important aspects that define the relationship between the magnitude and the frequency of an event, for which that event is exceeded. Frequency analysis therefore find its usefulness in the design and implementation of water projects such as in irrigation water requirement where the interest is on how much water can be available from a water resource to support irrigation throughout the year. The objective of this paper therefore was to assess the available water, using the extreme value analyses methods, that can be put to other uses such as irrigation water demand, domestic water requirement after due consideration to the environmental flow. The available water was estimated by deducting the following from the 80% probable flows orQ80: i) Deduction of estimated existing/future abstractions- determined from available information on irrigation activities along the three rivers (upstream and downstream) and ii) Deduction of the environmental flow - taken as 30% of the Q95 probable flow based on monthly mean flows. In particular this research was biased towards supporting the water requirement for a proposed irrigation project in the study area. The area of study was Awach-Kibuon catchment of the Lake Victoria South Catchment Area. This river catchment drains parts of Nyamira County through the Awach-Kasipul sub-catchent (Nyabomite, Charachani and Eaka tributaries). Data used included daily rainfall and temperature data obtained from the Kenya Meteorological Department Headquarters in Nairobi while daily discharge or flow levels data from the three tributaries i.e. Nyabomite, Charachani and Eaka were obtained from the Water Resources Authority (Kisii sub-regional office). The flow levels from Nyabomite, Charachani and Eaka were converted to river discharge using appropriate rating curves. Rainfall data was used with the Curve Number method to estimate river discharge at Eaka where flow levels were hardly available. Flood frequency distributions (GEV, the Gumbel) with different methods of parameter estimations (moments, maximum likelihood, probability weighted moments) were then used to estimate flow magnitudes corresponding to specific return periods (Q50, Q80 and Q95) and generate flow duration curves. Results from the flood frequency analysis from the General Extreme Value and Extreme Value type 1 distribution using the methods of moments (mom), maximum likelihood (ML) and Probability Weighted Moments (PWM) indicated the best distribution to be EV1-PWM since it exibited the lowest standard error estimates. Based on the most suitable distribution (EV1-PWM), the probabilities of exceedance were computed and used to estimate the water available for irrigation purposes at the three target gauging stations in the sub-catchment.From the results, a larger volume of water is available for irrigation at Charachani, for example, lowest being 0.388 cumecs in the month of July compared to 0.147 cumecs for Nyabomite and 0.249 cumecs for Eaka.. The largest amount of water for irrigation is available during the months of May and November, with peaks corresponding to those of the rainy seasons. The months with the least available water for irrigation are December, January and February, which also corresponds with the dry seasons. These results were used to inform planning in setting up of flow control structures for irrigation project in the Charachani-Eaka-Nyabomite cluster in Nyamira County.

Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression (GPR), Support vector machine (SVM) and kernel extreme learning machine (KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions as inputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number (Fr), ratio of average velocity (V) to shear velocity (U*) and shields number (θ) yields a correlation coefficient (R) of 0.951, a Nash-Sutcliffe efficiency (NSE) of 0.903 and root mean squared error (RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.

Derivation of digital terrain models and morphological parameters from very high resolution satellite images

Jordan is considered among the poorest countries in water resources in the world; therefore, many projects were introduced to the area to deal with this issue. The presented work focuses on two specific aspects - that could help in water resources management projects - the accuracy potential of these data in such areas and the benefit for hydrological models and applications using these data. We present methodology, ground truth validation and assessment for digital terrain model (DTM) generation by Pleiades data as well as for the derivation of drainage networks and morphometric parameters. The results were compared with DTM generated from laser scanning from airborne platforms (LiDar) data, which showed the ability of Pleiades data to generate DTMs in mountainous regions, and steep areas. The drainage density and morphometric parameters were also tested and compared. The absolute mean difference in elevation for the generated DTM from Pleiades data was 0.444 m (less than 1 pixel) with 0.509 standard deviation and root mean square error (RMSE) of 0.503, where the computed drainage density from both LiDar Pleiades data showed no significant difference.

Derivation of digital terrain models and morphological parameters from very high resolution satellite images

Jordan is considered among the poorest countries in water resources in the world; therefore, many projects were introduced to the area to deal with this issue. The presented work focuses on two specific aspects - that could help in water resources management projects - the accuracy potential of these data in such areas and the benefit for hydrological models and applications using these data. We present methodology, ground truth validation and assessment for digital terrain model (DTM) generation by Pleiades data as well as for the derivation of drainage networks and morphometric parameters. The results were compared with DTM generated from laser scanning from airborne platforms (LiDar) data, which showed the ability of Pleiades data to generate DTMs in mountainous regions, and steep areas. The drainage density and morphometric parameters were also tested and compared. The absolute mean difference in elevation for the generated DTM from Pleiades data was 0.444 m (less than 1 pixel) with 0.509 standard deviation and root mean square error (RMSE) of 0.503, where the computed drainage density from both LiDar Pleiades data showed no significant difference.

Design of Expert System for Managing the System of AthTharthar Lake

The operation and management of water resources projects have direct and significant effects on the optimum use of water. Artificial intelligence techniques are a new tool used to help in making optimized decisions, based on knowledge bases in the planning, implementation, operation and management of projects as well as controlling flowing water quantities to prevent flooding and storage of excess water and use it during drought.
 In this research, an Expert System was designed for operating and managing the system of AthTharthar Lake (ESSTAR). It was applied for all expected conditions of flow, including the cases of drought, normal flow, and during floods. Moreover, the cases of hypothetical operating of flow under extreme conditions were considered. The results showed a good capability of the designed expert system to operate the potential cases for those discharges and with high efficiency. Also the results showed the inability of AthTharthar Lake System to pass the discharge up to 12600 m3/s during floods. It was recommended to accomplish Tigris River training and increasing the level of levees, reshaping the cross-sections and longitudinal section of the river. It was suggested to expanded AthTharthar Head Regulator and the intake canal to accommodate more flood discharges.

Development of Stage – Distance – Discharge Relationship and Rating Curve using Least Square Method

For any river, besides the importance of stage – discharge relationship (rating curve), a stage-discharge- distance relationship is of more significance. The accurate estimation of both relationships along a river reach is considered a key point for various applications of water resources engineering such as operation and management of water resources projects, designing of hydraulic structures, and sediment analysis. In this paper, both relationships were established for the Shatt Al – Hillah river reach by applying multiple linear regression and simple linear regression using least square method for determining regression equations. Twelve gauging stations including three primary and nine secondary stations were considered for this method. Moreover, for evaluating the performance of both regressions, statistical measures such as coefficient of determination, root mean square error, mean square error, and Thiel's factor were used. The study results generally indicate a superior performance of both modeling techniques. MLR model was able to predict and mimic the stage-discharge-distance relationship with a coefficient correlation of about 0.932, while SLR model was able to predict three rating curves for the three primary stations with coefficient correlation of about 0.960, 0.943, and 0.924 respectively.