Water Resources Projects Research Articles

Spatio-temporal variability and trends of hydroclimatic variables at Zarima Sub-Basin North Western Ethiopia

Globally, hydroclimatic variables have been changed due to human activities and have an impact on agricultural production and water resource management. This study investigated the spatio-temporal variability of hydroclimatic variables in Zarima subbasin using rainfall and temperature data from 1984 to 2018 and stream flow data from 1990 to 2014. The data were analyzed using the coefficient of variation, standardized anomaly index, Mann–Kendall trend and Sen’s slope estimator test. The results showed that rainfall, maximum and minimum temperatures of the Zarima subbasin varied spatially and temporally. The annual and Kiremit season rainfall and stream flow showed low to moderate variability (CV < 30%) whereas Bega and Belg season rain fall and stream flow showed high degree of variability (CV < 40%). The annual and Kiremit season rainfall showed relatively higher variability along the northern border of the subbasin. However, maximum temperature showed less degree of variability (CV < 10%) over the subbasin, whereas the annual and seasonal minimum temperatures showed less to moderate variability (CV = 2–28%). The minimum temperature showed moderate variability around the south east lower border of the subbasin on the annual time scale and the north east part of the subbasin during the Kiremit season. The Bega and Belg seasons mean minimum temperatures showed less to high degree of variability (CV = 0–40%). The south east lower part of the subbasin showed moderate to high variability during the Bega and Belg seasons. Results of a standardized anomaly index of the rainfall, temperature and stream flow confirmed that the sub basin experienced fluctuations between dry, wet, cool and warm years. The spatial representation of annual rainfall showed a statically significant increasing trend in some parts of the subbasin and an insignificant increasing trend in the majority of the subbasin up to 300 mm per decade, while the Bega and Belg seasons showed statistically insignificant increasing and decreasing trends. Kiremit season rainfall showed heterogenous results in both statistics and trend direction. The mean annual maximum showed an increasing trend from 0.35 to 0.9 °C. The Bega, Belg and Kiremit seasons' maximum temperatures increased by a range of 0.35 to 0.95 °C; 0.4 from 1.2 °C and 0.1 to 0.55 °C pre decade respectively. While the annual minimum temperature increased by 0.05 to 0.5 °C and decreasing trend by 0.1 to 0.3 °C around the south eastern part of the subbasin. The Bega and Belg season minimum temperatures increased up to 0.5 °C and 1 °C and temperatures decreased in the range of 0.1 to 0.5 oC per decade. The Kiremit season minimum temperature increased by a range of 0.05 to 0.5 °C. The annual, Bega, Belg and Kiremit season stream flow increased insignificantly by 0.32, 0.31, 0.02 and 0.68 mm3/s respectively. In general, the subbasin experienced hydroclimatic variability which affects the life of the people, agricultural production and the economy of the area. This needs further investigation for planning and management of water resource management for the success of future water resources projects.

The Role of Innovation Approach in Optimizing Reservoir Operation-Review of Techniques and Approaches

As the world population is growing, its water demand is becoming more and more diversified; the difference between the demand and supply is continuously increasing. The solution to this problem is to systematically utilize the available water resources, or harvest additional water potential from new water resources projects and/or by formulating strategies for proper utilization of the available resources. Because most of the rivers and their tributaries in India are Seasonal, the Key Step taken for the solution to this difficulty is the “Reservoir Projects”. The development of a release policy for a reservoir is a multistage decision-making process. A model is required to be developed for each system of reservoirs. The essential need for the formulation of the optimal policy is to determine the schedule of releases from a reservoir system, which can maximize or minimize the utilities associated with the release of water. Numerous algorithms have been used to advance the results of Reservoir operation and maximize the net benefit, but due to their limitations, the selection of an appropriate model for deriving reservoir operating policies is difficult, and more often there is scope for further improvement as choosing a particular model for reservoir operation depends on the reservoir characteristics considered, availability of data, specified objectives, and constraints. In the present paper, various optimizations Algorithm and techniques are compared along with their Pros and cons, and limitations to find the most appropriate Algorithm for reservoir operation.

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.

Critical soil moisture thresholds of plant water stress in terrestrial ecosystems.

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θcrit). Knowledge of the spatial distribution of θcrit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θcrit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EFmax), θcrit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θcrit, S, and EFmax is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θcrit and show adaptation to water deficits. The negative relationship between θcrit and S indicates that dryland ecosystems minimize θcrit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

Effects of Water Policy Implementation and Management of Common Pool Resources in Turkana County, Kenya

Kenya and the sub-Saharan Africa nations could not accomplish the millennial advancement objectives plan on water and consequently the administration of water is in the core of manageable improvement objectives. This paper explored several reasons explaining why water policies are ineffective in Kenya, including; weak institutional frameworks, poor and unsustainable management schemes, fragmented approach to development of water resources and inadequate development of water infrastructure caused by conflict of interest by the implementing agencies. This study used descriptive research design, and targeted household heads and the county water staff in Loima Sub County. The study sample comprised 392 respondents. Data was collected using semi-structured questionnaire and KII. The study findings revealed that, community participation in water resource project management cycle was important for managing water as a shared resource. Moreover, the study established that non-governmental groups and the private sector were better at managing water as a shared resource. Furthermore, community participation in the management of water as a resource for common use was found not to be a determining factor in such management. The study concludes that water is a development challenge in Loima Sub County; it is thus essential to involve the community in the management of water as a common pool resource. The study therefore recommends that Turkana water government should improve performance of utilities through operationalize Turkana County Water Act 2019 and develop robust performance improvement plans based on diagnostic of the utility’s key challenges. Additionally, development of water and governance policy ought to guide management through co-creation in development of coherent and robust policy framework, operationalization roadmap and redesign the structure of water management committees through adoption of appropriate service delivery options based on context. Keywords: Water assets, Policy implementation, Decision making, Pool resources, Challenges

Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events

Abstract. Climate projection studies of future changes in snow conditions and resulting rain-on-snow (ROS) flood events are subject to large uncertainties. Typically, emission scenario uncertainties and climate model uncertainties are included. This is the first study on this topic to also include quantification of natural climate variability, which is the dominant uncertainty for precipitation at local scales with large implications for runoff projections, for example. To quantify natural climate variability, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12×12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The climate change signal for snow water resources stands out as early as mid-century from the noise originating from the three sources of uncertainty investigated, namely uncertainty in emission scenarios, uncertainty in climate models, and natural climate variability. For ROS events, a climate change signal toward more frequent and intense events was found for an RCP 8.5 scenario at high elevations at the end of the century, consistently with other studies. However, for ROS events with a substantial contribution of snowmelt to runoff (&gt; 20 %), the climate change signal was largely masked by sources of uncertainty. Only those ROS events where snowmelt does not play an important role during the event will occur considerably more frequently in the future, while ROS events with substantial snowmelt contribution will mainly occur earlier in the year but not more frequently. There are two reasons for this: first, although it will rain more frequently in midwinter, the snowpack will typically still be too cold and dry and thus cannot contribute significantly to runoff; second, the very rapid decline in snowpack toward early summer, when conditions typically prevail for substantial contributions from snowmelt, will result in a large decrease in ROS events at that time of the year. Finally, natural climate variability is the primary source of uncertainty in projections of ROS metrics until the end of the century, contributing more than 70 % of the total uncertainty. These results imply that both the inclusion of natural climate variability and the use of a snow model, which includes a physically based process representation of water retention, are important for ROS projections at the local scale.

Assessment of the joint impact of rainfall and river water level on urban flooding in Wuhan City, China

Flood severity in cities along the Yangtze River is vulnerable to multiple influencing factors, such as intense rainfall, higher river water level, and pump operation. This study evaluates the joint impact of rainfall and river water level on urban flooding in Wuhan city. Firstly, the variation characteristics of rainfall in Wuhan and water level at Hankou of the Middle Yangtze River were analyzed by the Mann-Kendall test. High water levels and heavy rainfall mostly occurred in June to July, and the rainstorm and lowest water levels presented increasing trends during this period. Meanwhile, a strong positive dependence was found between rainfall and water level, implying that the probability of flooding cannot be correctly estimated by only considering the influence of a single factor. Secondly, a joint risk probability was estimated using the optimal copula. The joint risk probability was always larger than the exceedance probability of water level or rainfall, which highlighted that ignoring the interaction between different flood drivers can lead to the underestimation of flood hazards. A comparison of the joint risk probabilities during different periods of 2005–2020 and 1990–2004 shows that extreme events of heavy rainstorm and higher water level have occurred frequently in recent years, with the joint risk of urban flood increasing significantly. Finally, flood inundation processes under different combinations of rainfall and water level were simulated using the calibrated model of SWMM, with and without working pumps being considered. The flood volume and flood duration would increase by about 1.7%–3.7% and 0.8%–1.3% respectively with the water level increasing from 21.7 m to 28.0 m, and would reduce by approximately 71.3%–71.9% and 58.2%–58.5% with the rainfall return period decreasing from 100-yr to 5-yr, therefore urban inundation was mainly caused by heavy rainfall. Moreover, the efficient effect of pumping on the urban drainage decreased gradually with the increasing rainfall. Therefore, the results can be used to provide the support for the design of water resource projects and the disaster mitigation measures of urban flooding.

Analysis of agriculture water pricing reform in a water-deficit area of Northwest China

Abstract Northwest China frequently experiences significant droughts and water shortages. To better balance the supply and demand, and provide sufficient funds for water resource projects, this study applied a computable general equilibrium model to construct an agricultural water pricing policy model that depends on the crops' economic value and regional water use characteristics. Wuwei City in Gansu Province was used as a case study to test the model. Three agricultural water price increase policies and three supporting subsidy policies were developed. The study quantitatively investigated the impact of these policies on the agricultural economy, water use, and efficiency of water use from different water sources. The results indicated that water pricing reform promotes water conservation and improves water use efficiency. Subsidies can reduce the negative impact of water pricing policies on the agricultural economy. Economic crops (e.g. vegetables) are more sensitive to water prices compared to food crops (e.g. wheat). Finally, to the best of our knowledge, this study provided the best water pricing reform scenario expected to be the most effective for local development.

AN INTEGRATED STUDY USING GIS AND SWAT METHOD FOR RAINFALL RUNOFF MODELLING IN KULSI WATERSHED, ASSAM

The present study is based on the estimation of runoff using GIS and SWAT in the Kulsi Watershed, Assam. Soil and Water assessment tool (SWAT) is a physically based distributed parameter model which was developed to predict runoff, sediment, erosion from specific watersheds under different input conditions. For understanding the rainfall-runoff behavior of the basin, SWAT hydrological model was used and divided in 19 subwatersheds and 188 Hydrogical Response Units. The watershed comprises of four Land use classifications (River/water bodies, Open forest, dense forests and barren land) with three soil classes. The delineated area of the study is found to be 1754.39 km². The study indicates that the annual rainfall runoff relationship correlates with magnitude of total runoff. From the calibration output, the simulated stream flow hydrograph deviates slightly with the observed stream flow hydrograph. The model was auto calibrated and validated using SWAT-CUP SUFI2 for 10 year i.e. from 1988-1997 .The average Nash Sutcliffe efficiency (NSE) for monthly calibration and validation was found to be 0.18 and 0.70 respectively. The coefficient of determination (R²) value for monthly calibration and validation was found to be 0.31 and 0.73 and RSR value for calibration and validation was found to be 0.91 and 0.55 respectively. The study was also carried out to perform the sensitivity analysis of different parameters responsible for stream flow generation. Based on the analysis, SCS runoff curve number (CN2.mgt), Groundwater "revap" coefficient (GW_REVAP.gw), Groundwater delay days (GW_DELAY.gw), Saturated hydraulic conductivity (SOL_AWC.sol), Base flow alpha factor (ALPHA_BF.gw), Threshold depth of water in the shallow aquifer required for return flow to occur (mm) (GWQMN.gw) were found to be the most sensitive parameters. Overall the SWAT model performance was found to be satisfactory for stream flow simulation and the calibrated model can be used for runoff generation and sustainable water resource projects and development.

Sustainability of Boro rice cultivation in the canal irrigated command area of India

Abstract A comprehensive assessment and monitoring of rice intensification in canal irrigated command areas and its consequences on groundwater dynamics have enormous importance for the water–food–energy nexus. This study analyzed the spatiotemporal transition of Boro rice cultivation, canal irrigation practices during the cultivation season, and its effect on groundwater of a pioneering multipurpose water resource project in India. The rice maps from 1989 to 2018 were generated from Landsat and Sentinel-2A images using the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA), the normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI) to understand the historical distribution of rice cultivation. The ISODATA clustering algorithm performed well compared to the NDVI and the EVI with medium-resolution satellite images. The impact of Boro rice cultivation on groundwater resources was investigated by considering GLDAS NOAH model-based crop evapotranspiration, rainfall, and canal water supply. The area under rice cultivation increased from 33 to 66% during 1989–2018 due to its higher stress tolerance ability and productivity. However, during the cultivation period, the canal water supply was limited to the upstream and middle section of the command area, irrigating only 19% of the rice-cultivated area on average. The unavailability of the canal water supply during the cultivation season has led to 31 m of groundwater level depletion, which, if unchecked, may lead to overexploitation of groundwater in the middle and lower sections of the command area. The results of this study can be used to identify vulnerable areas that require urgent attention and accurate implementation of any agricultural or hydrological policy.

Climate-Streamflow Relationship and Consequences of Its Instability in Large Rivers of Pakistan: An Elasticity Perspective

Precipitation elasticity provides a basic estimate of the sensitivity of long-term streamflow to changes in long-term precipitation, and it is especially useful as the first assessment of climate change impact in land and water resource projects. This study estimated and compared the precipitation elasticity (εp) of streamflow in 86 catchments within Pakistan over 50 major rivers using three widely used analytical models: bivariate nonparametric (NP) estimator, multivariate NP analysis, and multivariate double logarithm (DL) model. All the three models gave similar values of elasticity in the range of 0.1–3.5 for over 70–75% of the catchments. This signifies that a 1% change in the annual mean precipitation compared to the long-term historic mean annual precipitation will amplify the streamflow by 0.1–3.5%. In addition, the results suggested that elasticity estimation of streamflow sensitivity using the multivariate DL model is more reliable and realistic. Precipitation elasticity of streamflow is observed high at altitudes ranging between 250 m and 1000 m while the longitudinal and latitudinal pattern of εp shows higher values in the range of 70–75 and 32–36 decimal degrees, respectively. The εp values were found to have a direct relationship with the mean annual precipitation and an inverse relationship with the catchment areas. Likewise, high εp values were noticed in areas where the mean annual temperature ranges between 15 and 24 °C.

A study of Environmental and Ecological impacts due to Construction and Operation of Tehri-Polavaram Dam

Any water resources project has both a positive and a negative impact on the environment. Sometimes these environmental losses are unavoidable, so it is most important to minimize the environmental losses and their social-ecological and geopolitical impact. Environmental impact assessment plays an important role in identifying, predicting, and mitigating the various impacts on the environment. This paper presents the details of the construction of Tehri and Polavaram dam projects and their various impacts on the environment and ecological aspects. Any water resource expansion that fulfills minimum needs without endangering future generations’ ability to fulfils their own will be considered sustainable. There are a number of factors to consider when it comes to the long-term effects of dams on the surrounding environment, including redevelopment and relocation of people and businesses, socioeconomic influence on the surrounding area, and environmental concerns such as sedimentation, among others. Sustainable development also means that, in addition to minimizing the dam’s negative effects on the environment, making sure that the benefits of the dam outweigh the negative ones. As a result, it is recognized that water resource projects must be intended, implemented, and managed to meet future population demands while reducing disturbance to the existing eco-system and incorporating effective control measures at various phases to ameliorate any adverse effects.

Optimization of Resource Allocations to Maximize Benefit Cost Ratio of the Irrigation System

Abstract: Water resources projects are very complex in nature, requires huge financial investment and requires to consider socioeconomic, political, environmental aspects apart from technical aspects. There are many techniques evolved over the years to solve complex water resource problems. Pimpalgaon Dhale medium irrigation project system is considered in this project is located in Barshi taluka of Solapur district. It is planned to irrigate an ICA of 2400 ha of 6 villages namely Pimpalgaon, Pangaon, Yawali, Sakat, Undegaon and Irle. Dam is completed in 2008 however distribution system is still incomplete. The system is optimized to calculate maximize net benefit from the crops subjected to various constraints (viz. water availability, land availability, male and female labour availability, capital availability etc,). Single objective linear programming model is formulated and constraints are written and solver program of MS excel is used to derive maximum net benefit from the irrigation system under consideration. Benefit Cost ratio is calculated and compared it with that calculated adopting conventional methodology. The data required for model formulation is adopted from various sources such as Government reports/ documents, reports available on websites, research papers etc. The constraints such as capital availability within irrigation system during kharif and rabi season, female labour hours availability during kharif and rabi season limits the area under crops as well as net benefits however there is substantial increase in area under irrigation and net benefits from the irrigation system. The Solver program of MS Excel is very useful and convenient to use for solving linear programming. Keywords: Water Resource Projects, Maximize net benefit, Benefit Cost Ratio, Linear programming.,

Modeling stage\u2013discharge\u2013sediment using support vector machine and artificial neural network coupled with wavelet transform

Many real water issues involve rivers’ sediment load or the load that rivers can bring without degrading the fluvial ecosystem. Therefore, the assessment of sediments carried by a river is also crucial in the planning and designing of various water resource projects. In the current study, five different data-driven techniques, namely artificial neural network (ANN), wavelet-based artificial neural network (WANN), support vector machine (SVM), wavelet-based support vector machine (WSVM), and multiple-linear regression (MLR) techniques, were employed for time-series modeling of daily suspended sediment concentration (SSC). Hydrological datasets containing the daily stage (h), discharge (Q), and SSC for 10 years (2004–2013) from June to October at Adityapur and Ghatshila station of Subernrekha river basin, Jharkhand, India, were considered for analysis. The Gamma test was used to determine the input variables in the first step. Various combinations were made by lagging the maximum three-day time step for predicting current-day SSC. The outcomes of ANN, SVM, WAAN, WSVM, and MLR models were evaluated with the actual values of SSC based on statistical metrics. Pearson correlation coefficient (PCC), root-mean-square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Wilmot index (WI) as well as visual inspection of time variation, scatter plots, and Taylor diagrams. Our results stated that the WSVM model discovered the best trustworthy models among all existing models. PCC, RMSE, NSE, and WI values were 0.844 and 0.781, 0.096 g/l and 0.057 g/l, 0.711 and 0.591, 0.907 and 0.878, respectively, throughout the training and testing processes at the Adityapur site. Also, at the Ghatshila location, it was the most accurate model. During the training and testing stages, PCC, RMSE, NSE, and WI values were 0.928 and 0.751, 0.117 g/l and 0.095 g/l, 0.861 and 0.541, 0.962 and 0.859, respectively. Our findings showed that the WANN model was the second-best model during the testing phase for both sites. Hence, the WSVM technique can model SSC at this location and other similar (i.e., geomorphology and flow regime type) rivers.

Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity

Making estimation of river flow with hydrological and methodological data of the past period and using in water resources project studies have been used for a long time in many studies in our country and in the world. In addition to this, along with frequent drought problems in recent years, it is also important to store and use the water in the reservoirs correctly. This study aims to research how two separate Artificial-Neural-Network-Functions, which are generated based on the study of neural networks in the human brain, can be used in areas with a certain reservoir capacity such as dams or ponds and to provide an example of the most appropriate ANN model for predicting the level changes that will occur depending on the next years. In this context, two separate functions have been evaluated. These are the Levenberg-Marquardt-(LM) training function and Gradient-Descent-with-Momentum(GDM) training functions. Here, instead of the best results obtained from the models, the best three model outputs were evaluated and the models were considered from a wide frame. Gökçe dam basin in Marmara region was selected as the Model Study Area. For the period of December 31, 2019 - January 1, 2000, the monthly average data prepared with daily data from DSI and different architectural ANN training models were used to forecast the monthly basin capacities for 2019. It can be stated that LM Training models created in terms of Basin reservoir level estimates give results closer to the measured value.

A Novel Approach for Estimation of Sediment Load in Dam Reservoir With Hybrid Intelligent Algorithms

Predicting the amount of sediment in water resource projects is one of the most important measures to be taken, while sediments have an unknown nature in their behavior. In this research, using the data recorded at the Mazrae station between 2002 and 2013, the amount of sediment in the catchment area of Maku Dam has been predicted using different models of intelligent algorithms. Recorded data including river flow (m3/s), sediment concentration (mg/L), and temperature (°C) were considered input data, and sediment load (ton/day) was considered output data. Initially, using the correlation test, the relationship between each input data with output data was considered. The results show high correlation of sediment concentration data and river flow with sediment load and low correlation of temperature data with these data. In order to find the best combination of data for prediction, the combination of single, binary, and triple data was considered in sensitivity analysis. In order to achieve the purpose of this study, first with the classical adaptive neuro-fuzzy inference system (ANFIS), the amount of sediment load was predicted, and then using evolutionary algorithms in ANFIS training, their performance was examined. The intelligent algorithms used in this study were ant colony optimization extended to continuous domain, particle swarm optimization, differential evolution, and genetic algorithm. The results showed that adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain, adaptive neuro-fuzzy inference system–particle swarm optimization, adaptive neuro-fuzzy inference system–genetic algorithm, adaptive neuro-fuzzy inference system–differential evolution, and classical ANFIS had the best performance in predicting the amount of sediment load. In the meantime, it was observed that the coefficient of determination, root mean square error, and scatter index in the test mode for the adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain algorithm with the best prediction dataset (sediment concentration + river flow) are equal to 0.991, 13.001, and (ton/day), 0.112, and those for the ANFIS with the weakest prediction (temperature + river flow) are equal to 0.490, 107.383 (ton/day), and 0.929, respectively. The present study showed that the use of intelligent algorithms in ANFIS training has been able to improve its performance in predicting the amount of sediment load in the catchment area of Maku Dam.

Sedimentation in East Coast Hilly Terrain Reservoirs; Balimela, Odisha

Globally reservoirs behind dams are changing their hydrologic cycle under a shifting climate. Sediment, the flagship for water resources projects provide negative impact on environment and dam benefits. Sedimentation behind dams is continuous, regular through sequential paths that comprises of erosion, entrainment, transference from upper basin and deposition within the reservoirs. Present study envisages the morphometric study of the Balimela Reservoir active from 1972. The downloading of Geographic Information System (GIS), Remote sensing (RS) data from LANDSATS and making Stream order, Contour, slope and aspect maps by constructing Digital Elevation Model using the various Arc-GIS tools. The average bifurcation ratio is safe.. Considering the water-spread areas at various elevations of the years 2000, 2002, 2013, 2016, 2019 and 2022, the sediment deposits and reduction of storage volume by 29.945% by Jan 2022 since first impounding. The dam housed in hilly terrains, so there is less increase of catchment erosion except deforestation. The water from the dam optimally used by both the states Odisha and Andhra Pradesh. Preparation and strict adherence of concrete reservoir operation rule, upstream proper upstream maintenance and best catchment treatment plans shall increase the dam’s longevity and utilities.

SUPPLEMENTARY TOOLS IN BASELINE INFORMATION BUILDING FOR WATER RESOURCES PROJECTS UNDER THE NEW NORMAL

Research and industry-related engineering projects are affected in many ways by the COVID-19 pandemic. For example, data gathering becomes more challenging in recent times. The main objective of this paper is to demonstrate alternative ways to gather baseline data related to water resources projects. There are supplemental open-source tools available for baseline information building that are used in the past years that are expected to play more significant roles now. The data gathering phase of some projects were used as examples. Firstly, the importance and basic know-hows of Google Earth Pro ™ in the context of rapid reconnaissance and appraisal (RRA) are portrayed. Google Earth Pro’s Street View and available historical satellite images enable spatially and temporally varied virtual tour of study areas, which can help in site characterization and identification of important study and design parameters. Secondly, the use of hydrologic modeling to obtain estimates of flow data is illustrated. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) is shown to be capable of generating approximate flood hydrographs for ungaged rivers and flow duration curves for water supply studies. Lastly, the capability of hydraulic models to estimate water levels is presented. Instead of getting anecdotal water levels in the field, hydraulic modeling packages such as Hydrologic Engineering Center – River Analysis System (HEC-RAS) and SOBEK-River are technically feasible to get water levels for water resources projects. Although the results are subjected to some limitations, as in the case of uncalibrated models, these provide opportunities to obtain valuable information amidst the pandemic.

Regional Flood Frequency Analysis for Sustainable Water Resources Management of Genale–Dawa River Basin, Ethiopia

Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.

Preparing for the future: the impact of climate change on the civil engineering profession

The Intergovernmental Panel on Climate Change uses an average global temperature in its analysis of a warming Earth – yet in reality the world has many widely different regional environments. This paper proposes that the Köppen–Geiger regional zoning system would be a practical basis for civil engineers to progress towards net zero greenhouse gas emissions. The effect of climate change on the profession is discussed along with reducing the emissions of construction materials, such as by using more wood and other organic materials. The impact on civil engineering design is considered with reference to water resources projects, plus how this needs to be included in teaching.