Littoral zooplankton diversity in Himayat Sagar Reservoir,Telangana, India

The present study investigated zooplankton composition and diversity, along with physicochemical parameters from the littoral regions of Himayat Sagar Reservoir, Telangana, India. A total of 84 zooplankton species were found, including 60 rotifer species, 16 cladoceran species and eight copepod species. Keratella tropica, Lecane bulla, Coronatella rectangula and Mesocyclops sp. were observed frequently throught the study period. The Shannon Diversity Index ranged between H′ = 1.8 and 2.1, with a high diversity observed during the summer season. Dominance was greater during the monsoon and winter seasons, being represented by Brachionus calyciflorus, B. angularis, B. falcatus, Keratella tropica, Ceriodaphnia cornuta, Diaphanosoma sarsi and Mesocyclops sp. Correlation coefficients and principal component analysis indicated temperature, pH and nutrients had a major influence on zooplankton community changes in this reservoir. The environmental variables and diversity indices indicated seasonally fluctuating trophic conditions. Himayat Sagar Reservoir requires long‐term monitoring for assessment and better management of its faunal diversity and water quality.     

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.     

Streamflow Response to Climate Change in the Brahmani River Basin,India

Climate change can significantly affect the water resources availability by resulting changes in hydrological cycle. Hydrologic models are usually used to predict the impacts of landuse and climate changes and to evaluate the management strategies. In this study, impacts of climate change on streamflow of the Brahmani River basin were assessed using Precipitation Runoff Modeling System (PRMS) run under the platform of Modular Modeling System (MMS). The plausible hypothetical scenarios of rainfall and temperature changes were used to assess the sensitivity of streamflow to changed climatic condition. The PRMS model was calibrated and validated for the study area. Model performance was evaluated by using joint plots of daily and monthly observed and simulated runoff hydrographs and different statistical indicators. Daily observed and simulated hydrographs showed a reasonable agreement for calibration as well as validation periods. The modeling efficiency (E) varied in the range of 0.69 to 0.93 and 0.85 to 0.95 for the calibration and validation periods, respectively. Simulation studies with temperature rise of 2 and 4°C indicated 6 and 11% decrease in annual streamflow, respectively. However, there is about 62% increase in annual streamflow under the combined effect of 4°C temperature rise and 30% rainfall increase (T4P30). The results of the scenario analysis showed that the basin is more sensitive to changes in rainfall as compared to changes in temperature.     

Assessment of Waterlogging in Sriram Sagar Command Area,India, by Remote Sensing

Waterlogging is becoming a serious problem in irrigated command areas of India. The study area is the Sriram Sagar command, which is about 120 km north of Hyderabad. An attempt has been made to make an assessment of the waterlogged area and those areas sensitive to waterlogging during the pre and post monsoon periods in the Sriram Sagar command using remorely sensed and field data. Indian Remote Sensing Satellite (IRS-1A-LISS-II) digital data (Computer Compatible Tapes) of 12 April and 6 October 1989, were analysed to assess the areas affected by waterlogging and those areas sensitive to waterlogging. The validation of the IRS-derived waterlogged areas, was done using available water table depth data and other field information. The results obtained from this study indicate that in April and October 1989, areas of some 388 and 540 km2 were waterlogged and about 698 and 802 km2, respectively, were sensitive to waterlogging (where the water table lies between 1 to 2 m, respectively, below the ground surface. It is suggested that periodic assessment of waterlogging, using remotely sensed data, should be carried out in the Sriram Sagar command. The IRS data have been proved to be very succesful for the assessment of waterlogging. Density slicing and principal component analysis are useful techniques in making an assessment of waterlogged areas in irrigated command areas.     

Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin,Taiwan

It is well known that sufficiently long and continuous streamflow data are required for accurate estimations and informed decisions in water-resources planning, design, and management. Although streamflow data are measured and available at most river basins, streamflow records often suffer from insufficient length or missing data. In this work, artificial neural networks (ANNs) are applied to extend daily streamflow records at Lilin station located in Gaoping River basin, southern Taiwan. Two ANNs, including feed forward back propagation (FFBP) and radial basis function (RBF) networks, associated with various time-lagged streamflow and rainfall inputs of nearby long-record stations are employed to extend short daily streamflow records. Performances of ANNs are evaluated by root-mean-square error (RMSE), coefficient of efficiency (CE), and histogram-matching dissimilarity (HMD). Inconsistency among these evaluation measures is solved by the technique for order performance by similarity to ideal solution (TOPSIS), a widely used multi-criteria decision-making approach, to find an optimal model. The results indicate that RBF-E1 (entire-year data training with Q _t and Q _t?1 inputs) has the minimum RMSE of 104.4 m³/s, second highest CE of 0.956, and third lowest HMD of 0.0096, which outperforms other ANNs and provide the most accurate reconstruction of daily streamflow records at Lilin station.     

Small Catchment Agricultural Management Using Decision Variables Defined at Catchment Scale and a Fuzzy Rule-Based System: A Mediterranean Vineyard Case Study

Physically based hydrological models are increasingly used to simulate the impact of land use changes on water and mass transfers. The problems associated with this type of parameter-rich model from a water management perspective are related to the need for (1) a large number of local parameters instead of only a few catchment-scale decision variables and (2) the technical skills and computational expertise necessary to perform these models. This study aimed to show that it is possible to define a reduced number of decision variables and rules to synthesise numerical simulations carried out through a physically based model. The MHYDAS model was run on a Mediterranean vineyard catchment located in southern France (Roujan, Herault) for an actual, common rainfall event to calculate the runoff coefficient. The simulation results concerned 3,000 samples of contrasted scenarios. The scenarios were characterised by four catchment-scale decision variables related to agricultural practices: the proportion of the area of non agricultural land, the proportion of the area subjected to full chemical weeding practices (with the complement being mechanical weeding), the spatial arrangement of the practices based on the distance to the outlet and the initial soil moisture content. The simulation results were used to generate fuzzy linguistic rules to predict the runoff coefficient, as computed by the physical model from the decision variables. For a common end of spring rainfall event, simulations showed that the runoff coefficient was most heavily influenced by the initial soil moisture and the proportion of the area of full chemical weeding practices and the proportion of the area of other land uses and their spatial arrangement also played a role. The fuzzy rule-based model was able to reproduce the hydrological output with good accuracy (R² = 0.97). Sensitivity analysis to the rainfall magnitude showed that if the amount of rainfall was the key factor explaining the runoff coefficient absolute values, the structure of the rule base remained stable for rainfall events close to the one studied.     

Water and Energy Linkages for Groundwater Exploitation: A Case Study of Gujarat State,India

Water and energy, two important resources for human development, have inextricable inter-linkages between them. Their complementarity, a blessing otherwise, causes a vicious circle in a complex situation like the present case study of Gujarat state, India. This paper analyses the supply-demand situation of both sectors for a state that is primarily agrarian but also with a high industrial growth rate. Due to inequitable distribution of surface water, recurrent droughts and an ever-increasing demand trend, groundwater (a major source in the state) has been over-exploited in many parts, leading to 'water mining' with worsening water quality. With more than 40% energy consumed for extracting groundwater, this has had a serious impact on the energy balance. The paper brings forth the energy requirements to satisfy the water needs and the water requirements for the generation of energy. Finally, the feasible options available to meet the crisis, ranging from the development of mega projects like Sardar Sarovar and Kalpasar to micro water harvesting structures, water pricing and consumer training, etc., are reviewed.     

A Simplified Model for Predicting Drought Magnitudes: a Case of Streamflow Droughts in Canadian Prairies

The model for prediction of drought magnitudes is based on the multiplicative relationship: drought magnitude (M) = drought intensity (I) × drought duration (L), where I, L, and M are presumed to obey respectively the truncated normal probability distribution function (pdf), the geometric pdf, and the normal pdf. The multiplicative relationship is applied in the standardized domain of the streamflows, named as SHI (standardized hydrological index) sequences, which are treated equivalent to standard normal variates. The expected drought magnitude E(M _T ), i.e. the largest value of M over a sampling period of T-time units (T-year, T-month, and T-week) is predicted for hydrological droughts using streamflow data from Canadian prairies. By suitably amalgamating E(L _T ) with mean and variance of I in the extreme number theorem based relationship, the E(M _T ) is evaluated. Using Markov chain (MC), the E(L _T ) is estimated involving the geometric pdf of L. The Markov chains up to order one (MC-1) were found to be adequate in the proposed model for the annual to weekly time scales. For a given level of drought probability (q) and a sampling period T-time units; the evaluation of E(M _T ) requires only 3 parameters viz. lag-1 autocorrelation (ρ ₁ ), first order conditional probability (q _q , present instant being a drought given past instant was a drought) in SHI sequences and a parameter ø (value 0 to 1), which were estimated from historical data of streamflows. A major strength of the proposed model lies in the use of simple and widely familiar normal and geometric pdfs as its basic building blocks for the estimation of drought magnitudes.     

River Basin Management: A Case Study of Narmada Valley Development with Special Reference to the Sardar Sarovar Project in Gujarat,India

Intra-basin and inter-basin transfer of water have become a necessity in view of severe regional imbalances in availability of water and drought conditions in India. This paper addresses the fundamental issues of river basin management in a multi-objective framework with a unique case study of Narmada River Valley Development, and demonstrates that the socioeconomic needs of the people override the rest of the working objectives. In inter-state water allocation, even a non-riparian state is considered when looking at the needs of the people. This paper focuses on the Sardar Sarovar Project on the river Narmada and highlights its rehabilitation, environmental and social aspects, and concludes that the project is a lifeline for people in western India.     

Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

Water Resources Management - Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the...     

TOPMODEL for Streamflow Simulation of a Tropical Catchment Using Different Resolutions of ASTER DEM: Optimization Through Response Surface Methodology

The unavailability of proper hydrological data quality combined with the complexity of most physical based hydrologic models limits research on rainfall-runoff relationships, particularly in the tropics. In this paper, an attempt has been made to use different resolutions of DEM generated from freely available 30 m-based ASTER imagery as primary input to the topographically-based hydrological (TOPMODEL) model to simulate the runoff of a medium catchment located in the tropics. Response surface methodology (RSM) was applied to optimize the most sensitive parameters for streamflow simulation. DEM resolutions from 30 to 300 m have been used to assess their effects on the topographic index distribution (TI) and TOPMODEL simulation. It is found that changing DEM resolutions reduces the TOPMODEL simulation performance as the resolutions are varied from 30 to 300 m. The study concluded that the ASTER 30 m DEM can be used for reasonable streamflow simulation of a data scarce tropical catchment compared with the resampled DEMs.     

Calibration, Validation and Sensitivity Analysis of the MIKE-SHE Model Using the Neuenkirchen Catchment as Case Study

Calibration and validation of the MIKE-SHE model was performed using the Neuenkirchen research catchment hydrologic characteristics and a two-year time series of stream flows at the outlet of the catchment. A reasonable match was obtained between the observed and simulated hydrograph at the catchment outlet with minor calibration effort. For the validation runs, the base flows were overestimated in the period of high rainfall intensity while the peak flows were reasonably matched. Sensitivity of the model to structural parameters such as grid size and time step, and to the functional parameters, including hydraulic resistance coefficient, surface and subsurface hydraulic properties, was investigated. The results indicated that the peak overland flow and the total overland flow were very sensitive to the flow resistance parameters and to the vertical hydraulic conductivity of the surface soil, while the peak aquifer discharge and the total aquifer discharge were sensitive to the horizontal hydraulic conductivity in the saturated zone. The model output variables considered were neither affected, to a significant extent, by the vegetation parameters nor by the specific storage coefficient.     

Rational Pricing of Water as an Instrument of Improving Water Use Efficiency in the Agricultural Sector: A Case Study in Gujarat,India

The agricultural sector in India accounts for over 85% of the total water used for various purposes in the country. However, the efficiency of water use in agriculture is very low, approximately 40% for surface irrigation and 60% for groundwater irrigation. Part of the reason for the low efficiency is the highly subsidized price of irrigation water that encourages the excessive application of water to crops. This paper is based on a case study conducted in the command area of a public irrigation canal in the state of Gujarat, India. It attempts to explore the role of the rational pricing of canal irrigation water in motivating farmers to use water judiciously and thereby enhance the water use efficiency in irrigated agriculture. The paper contends that farmers are sensitive to an increase in irrigation water charges, but unless the administered price is increased to the level that would prevail in a free market, it will not have its intended effect on irrigators.     

Rainwater harvesting to alleviate water scarcity in dry conditions: A case study in Faria Catchment, Palestine

In arid and semi-arid regions, the availability of adequate water of appropriate quality has become a limiting factor for development.This paper aims to evaluate the potential for rainwater harvesting in the arid to semi-arid Faria Catchment, in the West Bank, Palestine.Under current conditions, the supply-demand gap is increasing due to the increasing water demands of a growing population with hydrologically limited and uncertain supplies.By 2015, the gap is estimated to reach 4.5×106 m3.This study used the process-oriented and physically-based TRAIN-ZIN model to evaluate two different rainwater harvesting techniques during two rainfall events.The analysis shows that there is a theoretical potential for harvesting an additional 4 × 106 m3 of surface water over the entire catchment.Thus, it is essential to manage the potential available surface water supplies in the catchment to save water for dry periods when the supply-demand gap is comparatively high.Then a valuable contribution to bridging the supply-demand gap can be made.     

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of 4 well-known algorithms as benchmarks – multilayer perceptron (MLP-BAT), adaptive neuro-fuzzy inference system (ANFIS-BAT), support vector regression (SVR-BAT) and random forest (RF-BAT), the CNN-BAT model is tested for daily streamflow (Q_t) prediction in the Korkorsar catchment in northern Iran. Fifteen years of daily rainfall (R_t) and streamflow data from 1997 to 2012 were collected and used for model development and evaluation. The dataset was divided into two groups for building and testing models. The correlation coefficient (r) between rainfall and streamflow with and without antecedent events (i.e., R_t-1, R_t-2, etc.) (as the input variables) and Q_t (as the output variable) served as the basis for constructing different input scenarios. Several quantitative and visually-based evaluation metrics were used to validate and compare the model’s performance. The results indicate that R_t was the most effective input variable on Q_t prediction and the integration of R_t, R_t-1, and Q_t-1 was the optimal input combination. The evaluation metrics show that the CNN-BAT algorithm outperforms the other algorithms. The Friedman and Wilcoxon signed-rank test indicates that the prediction power of CNN-BAT algorithm is significantly/statistically different from the other developed algorithms.

     

Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

In the present study, soil erosion assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out. The river basin was divided into 200 × 200 m grid cells. The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data and the USLE was used to predict the spatial distribution of the average annual soil loss on grid basis. The average rainfall erositivity factor (R) for Dikrong river basin was found to be 1,894.6 MJ mm ha⁻¹ h⁻¹ year⁻¹. The soil erodibility factor (K) with a magnitude of 0.055 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ is the highest, with 0.039 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ is the least for the watershed. The highest and lowest value of slope length factor (LS) is 53.5 and 5.39 respectively for the watershed. The highest and lowest values of crop management factor (C) were found out to be 0.004 and 1.0 respectively for the watershed. The highest and lowest value of conservation factor (P) were found to be 1 and 0.28 respectively for the watershed. The average annual soil loss of the Dikrong river basin is 51 t ha⁻¹ year⁻¹. About 25.61% of the watershed area is found out to be under slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones are 26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas need immediate attention from soil conservation point of view.     

Effects of Anthropogenic Influences on the Trophic State,Land Uses and Aquatic Vegetation in a Shallow Mediterranean Lake: Implications for Restoration

Lake Pamvotis, NW Greece is a shallow Mediterranean eutrophic lake that has changed drastically over the past 50 years. Strong effects, resulted mainly from anthropogenic causes, in the hydrological regime are shown for this area using long term hydrological data and a GIS system for extracting land cover/use changes. A set of aerial imagery acquired in 1945 through 2002 were used to monitor and assess the spatial and temporal changes in land cover/use, focused mainly on the lake’s surface area and its surrounding ecosystem (Natura 2000 area). The significance of the changes in land cover/use distribution within Pamvotis wetland is further discussed depicting the role of the anthropogenic influence on the fragile ecosystem that resulted in the shrinkage of lake’s habitats extent. The purpose of this analysis was to examine the long-term changes on macrophyte community composition, species occurrence and relative abundance with water quality and water level changes over the past century, using historical data, aerial photos and GIS techniques. The results showed that for the last 25 years annual water level fluctuation ranged from 70 to 159 cm. Water level starts decreasing in mid June and increasing again gradually from November until March–April. Intra annual water level fluctuation seems to be affected by land use for agricultural purpose through intensive irrigation and the summer drought as well. A dramatic decline of the submerged vegetation is apparent mainly attributed to anthropogenic pressures. Regarding the land cover/use changes, the most notable and significant alterations are concerning the urban development around the lake, the disappearance of wet meadows and the extension of reed beds. Finally it seems that water budget data as well as the response of the key eutrophication parameters are affected from both hydrological alterations and point/non-point pollution sources.     

A Comparative Study of Autoregressive,Autoregressive Moving Average,Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow

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.     

澜沧江扎泥沟泥石流特征研究

通过对澜沧江流域扎泥沟典型泥石流冲沟的地质环境、沟谷特征以及泥石流活动特征的论述,较全面地反映了扎泥沟泥石流物质组成和动力学特征,为地质环境相似的泥石流灾害研究和防治提供了一个典型实例和依据。     

Vulnerability Analysis of Future Public Water Supply Under Changing Climate Conditions: A Study of the Moy Catchment,Western Ireland

An application of the Water Evaluation and Planning tool Version 21 (WEAP21) is developed to analyse the vulnerability of the future public water supply in the River Moy catchment, western Ireland. The River Moy’s future hydrology is modelled using the WEAP21 integrated rainfall runoff module and an ensemble of statistically downscaled future climate series. This approach facilitates the identification of the most vulnerable future public water supplies without being constrained by the availability of historically observed streamflow records. The model is calibrated by linking the model-independent parameter estimation tool (PEST) with the hydrological model and verified by reproducing observed streamflow records. This research suggests an emerging vulnerability to water stress of the public water supply sector under the four modelled scenarios, for areas which currently have plenty of water available. These results present a basis for future planning and management of the Moy catchment and its water resources.