Role In Water Resources Management Research Articles

Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting

Abstract Monthly streamflow forecasting plays an important role in water resources management, especially for dam operation. In this paper, an approach of model fusion technique named selected model fusion (SMF) is applied and assessed under two strategies of model selection in order to improve the accuracy of streamflow forecasting. The two strategies of SMF are: fusion of the outputs of best individual forecasting models (IFMs) selected by dendrogram analysis (S1), and fusion of the best outputs of all IFMs resulting from an ordered selection algorithm (S2). In both strategies, five data-driven models including: artificial neural network, generalized regression neural network, least square-support vector regression, K-nearest neighbor regression, and multiple linear regression with optimized structure are performed as IFMs. The SMF strategies are applied for forecasting the monthly inflow to Karkheh reservoir, Iran, owning various patterns between predictor and predicted variables in different months. Results show that applying SMF approach based on both strategies results in more accurate forecasts in comparison with fusion of all IFMs outputs (S3), as the benchmark. However, comparison of the two SMF strategies reveals that the implementation of strategy (S2) considerably improves the accuracy of forecasts than strategy (S1) as well as the best IFM results (S4) in all months.

A uniform spatial allocation strategy in solving water resources optimization dispatch problem

Along with social economy development, the total water consumption increased year by year. The conflicts between water supply and water consumption is growing. Water resources optimization dispatch, which is used to allocate water resources to meet the demand of water user on both time scale and spatial scale, plays an important role in water resources management. Aiming at improving the spatial allocation ability of traditional optimization algorithms, a uniform spatial allocation strategy is proposed accordingly. The proposed method is used to improve the performance of optimization algorithm to obtain solutions which can uniform the water supply in spatial scale. Simulation results show that the maximum ratio of water deficiency gained by the proposed method is smaller than that obtained by the original algorithm. The proposed method is effective to balance the demand of water users in spatial scale. Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).

Numerical Investigation of Inverse Curvature Ogee Spillway

Design of water structures and their segments including spillways play an important role in water resources management and agricultural activities. In the the linear body part of an ogee spillway, for speeding up the flow rate, the flow should be transferred to the stilling basin by inverse curve so that the water energy can be reduced. This study aims to evaluate the effect of the inverse profile curvature on the pressure of spillway surface using Fluent software. For this purpose, five different curvatures of inverse profile were considered to be equal to no-curvature (zero), 1, 1.5, 2 and 2.5 of the spillway design head. The results indicated that by increasing the curvature radius, the maximum pressure dramatically reduced. And for this purpose, some relationships were given to predict the pressure reduction. Pressure increment in zero curvature is caused by sudden collision of flow lines and turbulence caused by it. By increase in inverse profile curvature, the turbulence is created in flow lines and the maximum pressure shows a lower value than before. In general, there was little change in the average absolute pressure.

Regional Climate Change Impacts on Irrigation Vulnerable Season Shifts in Agricultural Water Availability for South Korea

Reservoirs are principal water resources that supply irrigation water to paddy fields and play an important role in water resources management in South Korea. For optimal irrigation reservoir operation and management, it is necessary to determine the duration of irrigation water shortages. Management of reservoir operation and irrigation scheduling should take into consideration essential variables that include the water supply in a reservoir and the water demand in the associated irrigation district. The agricultural water supply and demand show different patterns based on the variability and uncertainty of meteorological and hydrological phenomena. The duration of excessive water supply can be quantitatively determined through analysis of deviations and changes in the timing of agricultural water supply and demand. In this study, we introduce an approach to assess the vulnerable seasons of paddy irrigation to enable more effective operation and management of reservoirs. The vulnerable seasons were evaluated through comparison of the potential water supply capacity and irrigation water requirements based on water budget analysis via a time series change analysis. We have assessed the changing in the total duration and duration shifts of the vulnerable irrigation seasons for four agricultural reservoirs using past observed data (1981–2010) from meteorological stations maintained by the Korea Meteorological Administration (KMA) and projected climate change scenarios (2011–2100) as depicted by the Representative Concentration Pathways (RCPs) emission scenarios. For irrigation vulnerable seasons under both the RCP 4.5 and RCP 8.5 scenarios, the results showed periods of significant increases in which total vulnerable seasons compared to the historical period; the longest duration of vulnerability occurred during the 2071–2100 period under the RCP 8.5. Identification of the vulnerable seasons for paddy irrigation can be applied in agricultural water management to more effectively manage reservoir operation during irrigation periods with climate changes.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh–Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985–Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R2), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models.

Spatiotemporal estimation of snow depth using point data from snow stakes, digital terrain models, and satellite data

AbstractSnow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.

Water in the city: Green open spaces, land use planning and flood management – An Australian case study

With increasing worldwide recognition of the influence of urban development on the hydrological functions of water, there is growing pressure for urban planning to play a greater role in water resources management. Planning for green open spaces in particular can play an important role, as they support important ecosystem services, including those that assist in flood management. It has been argued that interconnected and strategically planned networks of green open spaces should be planned for early in land use planning and design processes, with consideration of water-related ecosystem values and landscape functions in concert with land development, growth management and physical infrastructure planning. Although there is growing recognition of the importance of green open space planning for water sensitive cities and supportive planning measures, there are few analyses of the actual inclusion of this recognition in plans and strategies, or the presence of related actions and planning mechanisms. This paper addresses this gap by comparatively analysing the approaches taken to regional green open space planning in three Australian capital city-regions. Findings indicate the acknowledgement of relationships between flood regulation and green open space planning and various associated planning mechanisms. However, there is limited explicit integration of flood management and green open spaces planning, and significant on-ground barriers to enabling this integration to occur given the legacy of past planning decisions and the lack of information to support implementation. The paper concludes with recommendations for further research to assist planning for green open spaces as an ally to ecosystem services relating to flood management.

Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges-Brahmaputra River Basin Using Multi-Satellite Observations

Surface water storage is a key component of the terrestrial hydrological and biogeochemical cycles that also plays a major role in water resources management. In this study, surface water storage (SWS) variations are estimated at monthly time-scale over 15 years (1993–2007) using a hypsographic approach based on the combination of topographic information from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hydrological Modeling and Analysis Platform (HyMAP)-based Global Digital Elevation Models (GDEM) and the Global Inundation Extent Multi-Satellite (GIEMS) product in the Ganges-Brahmaputra basin. The monthly variations of the surface water storage are in good accordance with precipitation from Global Precipitation Climatology Project (GPCP), river discharges at the outlet of the Ganges and the Brahmaputra, and terrestrial water storage (TWS) from the Gravity Recovery And Climate Experiment (GRACE), with correlations higher than 0.85. Surface water storage presents a strong seasonal signal (~496 km3 estimated by GIEMS/ASTER and ~378 km3 by GIEMS/HyMAPs), representing ~51% and ~41% respectively of the total water storage signal and it exhibits a large inter-annual variability with strong negative anomalies during the drought-like conditions of 1994 or strong positive anomalies such as in 1998. This new dataset of SWS is a new, highly valuable source of information for hydrological and climate modeling studies of the Ganges-Brahmaputra river basin.

The application of multiple linear regression method in reference evapotranspiration trend calculation

Trend analysis of reference evapotranspiration (ET0), as a key factor in irrigation programming, has an important role in water resources management. Many parameters affect ET0 and their variations can change its values. In this paper, the effect of temporal variation of meteorological variables including wind speed, temperature, solar radiation and saturation vapor pressure deficit on temporal variations of ET0 was analyzed. Trend analysis of ET0 and its more effective meteorological parameters was accomplished in 30 synoptic stations which are located in Iran using Spearman’s Rho test. The multiple linear regressions were also used to determine the relationship between ET0 trend and the trend of its more effective parameters. Increasing and decreasing trends in ET0 were obtained at annual and seasonal scales. Many studied stations which had decreasing trend in the annual and seasonal periods have been located in the arid climates while all stations which have been located in humid and very-humid climates, had an increasing trend in annual and seasonal periods. The trend results in studied variables showed that annual and seasonal values of wind speed, temperature and saturation vapor pressure deficit decrease however the values of solar radiation increases in most studied stations. Multiple linear regressions results demonstrated that ET0 trend can be calculated by the trend of two more effective variables including wind speed and saturation vapor pressure deficit.

Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method

Evapotranspiration (ET) is an essential part of the hydrological cycle and accurately estimating it plays a crucial role in water resource management. Surface energy balance (SEB) models are widely used to estimate regional ET with remote sensing. The presence of horizontal advection, however, perturbs the surface energy balance system and contributes to the uncertainty of energy influxes. Thus, it is vital to consider horizontal advection when applying SEB models to estimate ET. This study proposes an innovative and simplified approach, the surface energy balance-advection (SEB-A) method, which is based on the energy balance theory and also takes into account the horizontal advection to determine ET by remote sensing. The SEB-A method considers that the actual ET consists of two parts: the local ET that is regulated by the energy balance system and the exotic ET that arises from horizontal advection. To evaluate the SEB-A method, it was applied to the middle region of the Heihe River in China. Instantaneous ET for three days were acquired and assessed with ET measurements from eddy covariance (EC) systems. The results demonstrated that the ET estimates had a high accuracy, with a correlation coefficient (R2) of 0.713, a mean average error (MAE) of 39.3 W/m2 and a root mean square error (RMSE) of 54.6 W/m2 between the estimates and corresponding measurements. Percent error was calculated to more rigorously assess the accuracy of these estimates, and it ranged from 0% to 35%, with over 80% of the locations within a 20% error. To better understand the SEB-A method, the relationship between the ET estimates and land use types was analyzed, and the results indicated that the ET estimates had spatial distributions that correlated with vegetation patterns and could well demonstrate the ET differences caused by different land use types. The sensitivity analysis suggested that the SEB-A method requested accurate estimation of the available energy, R n − G , but was less constrained with the difference between ground and air temperature, T 0 − T a – l o c .

A compound cloud model for harmoniousness assessment of water allocation

Water allocation plays an important role in water resources management. Faced with continuously rapid economic growth, as well as an accompanying, ever-increasing demand for water, harmonious water allocation is a new strategy to address existing water problems. In order to assess water allocation, this study proposes an index system that adopts 15 basic indicators from four dimensions (flexibility, efficiency, health and development) and a harmony index (HI) to measure the harmony level of water allocation. For the calculation of HI, a compound cloud model (CCM) that takes advantage of the features of the cloud model is presented and combined with a weight cloud module, a qualification cloud module and a comprehensive assessment module. The weight cloud module is employed to calculate the weights of the indicators and criteria. The qualification cloud module is used to transform the attribute values of the indicators to evaluation values. The comprehensive assessment module is used to integrate the overall performance to obtain the HI value. The CCM was applied to Nanjing city in China. The results showed that water allocation in Nanjing was in the improving stage between 2006 and 2013, increasing from grade III (generally harmonious) to grade IV (moderately harmonious); and that Nanjing performed noticeably well in the flexibility dimension, less well in the development dimension and worst in the health dimension. The CCM accounts for both randomness and fuzziness in the calculation of weights and the unification of attribute values of the indicators, and this model has been verified as feasible and effective by the results of the case study. A harmoniousness assessment of water allocation provides a support tool to give decision-makers a better sense of past water allocation strategies such they can adjust accordingly in the future.

Studying spatial and temporal changes of aridity in Iran

Optimum use of water in agriculture, drinking, and industry is necessary in arid and semi-arid regions like Iran, which face serious water tensions. Since studying aridity and its trend of changes has a significant role in water resources management and urban planning, this research has investigated spatial and temporal changes and trend of aridity changes in Iran as an arid country. To study the issue, 51 weather stations of Iran which covered a 30-year statistical period (1981–2010) were selected. Climatic variables, namely precipitation, vapor pressure, local pressure, temperature, maximum temperature, and relative humidity and required data to calculate the potential evapotranspiration were extracted from Iran meteorology organization data. In next step by applying Ai, Im, and Iq aridity indices, zoning of the aridity was done after studying the aridity traits and its change. Then the most compatible aridity index was introduced according to the values of correlation between climatic factors of aridity indices and the magnitude of each main climatic area of Iran. Furthermore, in order to recognize aridity changes in Iran during the selected 30-year period, Mann-Kendal nonparametric test was used. Correlation analysis results showed that 35.4 and 64.6 % of Iran arid areas show more compatibility with Im and Ai, respectively, which means that these indices are more appropriate to study aridity in arid regions of Iran. Also, it was revealed that 5.6 and 94.3 % of Caspian south plain region has more compatibility with Ai and Iq indices, respectively. As a matter of fact, 77.8, 0.75, and 21.4 % of the country area can be studied better by Ai, Iq, and Im, respectively. Also, the results of Mann-Kendal nonparametric test confirmed that aridity is developing in Iran. In other words, 80.4 % of Iran area had increasing trend of aridity during the selected period and in just 19.6 % of the area, aridity trend was not significant or decreasing.

Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree

Accurate prediction of daily pan evaporations plays a crucial role in water resources management since it has direct effect on water reservoirs, therefore drinking water supply systems. In this study, three machine learning (ML) methods such as classification and regression tree (C&RT), chi-squared automatic interaction detector (CHAID) and artificial neural networks (ANN) are applied to predict the daily pan evaporations in Ankara and Polatli stations in Turkey, both of which have dry climatic conditions. First part of the study focuses investigating the ability of C&RT, CHAID and ANN methods on predicting the daily pan evaporations in Ankara and Polatli stations have been investigated, separately. The estimations are made by using the inputs from the associated stations itselves. However, in the second part of the study, daily pan evaporations in Polatli station have been estimated by using the inputs obtained from Ankara station. The inputs for both parts consist of daily climatic data of maximum and minimum temperature, solar radiation, relative humidity and wind speed. Therefore, this study not only aims to compare the ability of ML models on two different stations, but it also investigates the ability of these models on prediction of daily pan evaporations in a station by using the inputs that were obtained from a nearby station. By comparing these models, it has been revealed that, ANN model has performed slightly better than the other models in both applications. Therefore, it can be concluded that daily pan evaporations could be successfully predicted by employing ANN model in both type of applications.

Water Resource Pricing Study Based on Water Quality Fuzzy Evaluation: A Case Study of Hefei City

Price plays an important role in water resources management. The price of water resources can also be considered as a “water resource tax” which reflects the value and opportunity cost of water, and people will pay for the right to use water. Currently, the water resource fees’ effect of regulating resource differential revenues is not manifest and it’s not enough to reflect the principle of paid use of resources as well as regulating resources differential revenues. Due to the ambiguity and complexity of water resources price, this paper uses methods relating to fuzzy mathematics for modeling and processing. The study had a comprehensive consideration of five factors including water quality, water resources per capita, household consumption level, per capita GNP, population or population density to evaluate the water resource price.

Optimal Operation of a Network of Multi-purpose Reservoir: A Review

Due to the effects of climate change and population growth, reservoirs play a more and more important role in water resources management. The management of a multi-reservoir system is complex due to the curse of dimensionalities, nonlinearities and conflicts between different objectives. The optimal operation of a multi-reservoir system operation typically involves optimization and simulation models, which can provide the quantitative information to improve operational water management. The objectives of this paper are to extend previous state-of-the-art reviews in the operational management of a network of multi-purpose reservoirs with recent developments and to focus on the application of Model Predictive Control for real time control of a reservoir system.

Trend and Variability in Observed Hydrological Extremes in the United States

AbstractIdentification of changes in hydrological extremes plays an important role in water resources management. This study investigates various properties in hydrological extremes including annual maximum daily flow (high flow) and annual minimum 7-day flow (low flow) in 252 unimpaired streamflow gauges in the continental United States. Three statistical methodologies are performed: the nonparametric Mann-Kendall test is used to evaluate temporal trends; the non-parametric change point Pettitt test, to identify abrupt shifts; and the quantile perturbation method, to assess temporal variability. In the results of trend analysis, low flow exhibited some spatial patterns compared to high flow. In addition, low flow has increased over 64 years (1950–2013) in many stations. The Pettitt test indicates that low flows have experienced more significant abrupt changes compared to high flow. The results of the quantile perturbation method confirm that many significant increasing trends obtained during the 64 years...

Temperature Vegetation Dryness Index Estimation of Soil Moisture under Different Tree Species

The Laoshan forest is the largest forest in Nanjing, and it plays an important role in water resource management in Nanjing. The objectives of this study are to determine if the temperature vegetation dryness index (TVDI) is suitable to estimate the soil moisture and if soil moisture is significantly affected by tree species in the Laoshan forest. This paper calculated the spatial distribution of TVDI using LANDSAT-5 TM data. Sixty-two observation points of in situ soil moisture measurements were selected to validate the effectiveness of the TVDI as an index for assessing soil moisture in the Laoshan forest. With the aid of the three different temporal patterns, which are 10 January 2011, 18 May 2011 and 23 September 2011, this paper used the TVDI to investigate the differences of soil moisture under four kinds of mono-species forests and two kinds of mixed forests. The results showed that there is a strong and significant negative correlation between the TVDI and the in situ measured soil moisture (R2 = 0.15–0.8, SE = 0.015–0.041 cm3/cm3). This means that the TVDI can reflect the soil moisture status under different tree species in the Laoshan forest. The soil moisture under these six types of land cover from low to high is listed in the following order: Eucommia ulmoides, Quercus acutissima, broadleaf mixed forest, Cunninghamia lanceolata, coniferous and broadleaf mixed forest and Pinus massoniana.

A Quantitative Inspection on Spatio-Temporal Variation of Remote Sensing-Based Estimates of Land Surface Evapotranspiration in South Asia

Evapotranspiration (ET) plays a key role in water resource management. It is important to understand the ET spatio-temporal pattern of South Asia for understanding and anticipating serious water resource shortages. In this study, daily ET in 2008 was estimated over South Asia by using MODerate Resolution Imaging Spectroradiometer (MODIS) products combined with field observations and Global Land Data Assimilation System (GLDAS) product through Surface Energy Balance System (SEBS) model. Monthly ET data were calculated based on daily ET and evaluated by the GLDAS ET data. Good agreements were found between two datasets for winter months (October to February) with R2 from 0.5 to 0.7. Spatio-temporal analysis of ET was conducted. Ten specific sites with different land cover types at typical climate regions were selected to analyze the ET temporal change pattern, and the result indicated that the semi-arid or arid areas in the northwest had the lowest average daily ET (around 0.3 mm) with a big fluctuation in the monsoon season, while the sites in the Indo-Gangetic Plain and in southern India has bigger daily ET (more than 3 mm) due to a large water supplement. It is suggested that the monsoon climate has a large impact on ET spatio-temporal variation in the whole region.

Intercomparison of three indices for addressing drought variability in North China Plain during 1962−2012

Drought is one of the most complex climatic phenomena causing detrimental damage to the society and environment. Although a universal definition of drought is still lacking, it is usually related to a long and sustained period during which water becomes scarce. Compared with other hydrological hazards, such as floods, the development of droughts is slow and the inception and termination of droughts are very difficult to identify. Moreover, droughts are only perceived when human activities and the environment are affected. Thus, droughts are the best example of a “creeping and penetrating” type natural hazard (Gillette 1950, Wilhite et al. 1985, 2005). The North China Plain (NCP), one of the huge granaries and the centre of politics, economics and culture in China, has frequently been subjected to droughts during recent decades. With population growth and agriculture expansion, the sustainable development of NCP has been challenged by constrained water resources and associated disasters. In other words, NCP is turning into one of the most vulnerable “hotspots” in China in terms of water shortage and water security. Accurate assessments of drought for an early warning would play a critical role in water resources management and planning. The present study compares the spatio-temporal variations of droughts in NCP by using three commonly-used indices and aims to elucidate the spatiotemporal patterns of drought and explore a relative, reasonable index for describing regional water security in NCP.