Effectiveness of Rainwater Harvesting in Runoff Volume Reduction in a Planned Industrial Park,China

It is urgent to effectively mitigate flood disasters in humid mountainous areas in southeastern China for the increasing flood risk under urbanization and industrialization. In this study, a rural district with an area of 13.39 km² that planning to build an industrial park covering an area of 7.98 km² in Changting was selected to estimate the potential of collectable rainwater and the extent to which runoff volume can potentially be mitigated by rainwater harvesting. In addition, the optimum cistern capacity of a rainwater harvesting system in the planned industrial park was evaluated using daily water balance simulation and cost-efficiency analysis. The results showed that rainwater harvesting in the planned industrial park has great potential. The annually collectable rainwater is approximately 9.8?×?10⁶ m³ and the optimum cistern capacity is determined to be 0.9?×?10⁶ m³. With the optimum cistern capacity, the annual rainwater usage rate is 0.99, showing neither financial savings nor deficits. Rainwater harvesting can reduce 100 % of runoff volume in the cases of critical rainfall storm (50 mm) and annual average maximum daily rainfall (111.2 mm), and 58 % of runoff volume in the case of maximum daily rainfall (233.6 mm), respectively. All surface runoff can be collected and stored in the cisterns when rainfall amount is less than 135.5 mm in a rainstorm event.     

Spatiotemporal Change of Blue Water and Green Water Resources in the Headwater of Yellow River Basin,China

The headwater of Yellow River Basin (HYRB) is crucial for the water resources of the whole basin in Northwest China. Based on the semi-distributed hydrological model “Soil and Water Assessment Tool” (SWAT), the spatiotemporal change trends of blue water and green water resources in the HYRB were analyzed quantificationally. By using the Sequential Uncertainty Fitting program (SUFI-2), the model was calibrated at Tangnaihai hydrological station and uncertainty analysis was performed. The results showed that the total water resources decreased by 1.08 billion m³ over the past five decades in the HYRB. Blue water and green water storage (soil water) presented the downtrend, while green water flow (actual evapotranspiration) increased between 1961 and 2010. The decrease in blue water resources were mainly attributed to the decrease in precipitation in the southwest parts of the study area while the increase in actual evapotranspiration and the decrease in soil water were the results of the uptrend of air temperature. In 1990s, an enormous transition occurred between the blue water (24.86 %) and green water flow (63.46 %). At seasonal scale, the largest down trend of blue water and uptrend of actual evapotranspiration all occurred in autumn. The decrease ratios of them were 88.3 and 83.1 % in inter-annual variation, respectively. The study can provided a scientific basis for integrated water resources management under the background of global climate change and human activity.     

Multi-Temporal Analysis of Mean Annual and Seasonal Stream Flow Trends,Including Periodicity and Multiple Non-Linear Regression

Global warming affects the hydrological cycle and the long-term water budget of river basins. Flow variations have been noticed in the Danube River Basin, especially in its south-western parts where a downward trend in mean annual flows has been prevalent in the past several decades. Time series of mean annual and seasonal flows of the Sava River at hydrological stations Sremska Mitrovica and Zagreb are analysed in this paper. The trend is assessed with the Mann-Kendall test including the effect of serial correlation. Additionally, the trends are assessed in the multi-temporal framework. It is concluded that the long-term periodicity of annual flows has a considerable impact on the time series trend. Long-term component with cycles of 40 years in mean annual flows are detected by the time series analysis in frequency domain. Regression analysis showed a significant correlation between mean annual flows of the Sava River and annual precipitation, mean annual atmospheric pressure and air temperatures at meteorological station Ljubljana, as well as with the North Atlantic Oscillation (NAO) Index.     

Impact of El Niño and Climate Change on Rainwater Harvesting in a Caribbean State

The water situation of the Pusey district in St Catherine parish of Jamaica is acute because of the district’s hilly terrains which made connections to centralised public water supply difficult. Residents depend on rainwater harvesting (RWH) systems to meet potable needs, like many other catchments across Jamaica. Rainwater collecting practices and water use habits of the residents were surveyed and the present and future RWH capacity was evaluated using the available 18 years (1996 to 2013) rainfall data and downscaled PRECIS model A2 and B2 climate change scenarios. In addition, the effect of El Niño episodes on rainfall patterns was evaluated. The coefficients of variations for annual rainfall were found to be higher for the El Niño years than in normal years. In two of the El Niño years (1997 and 2009), rainwater harvesting capacity is negatively impacted as rainfall annual total is (42 % and 34 %) lesser than the average annual rainfall. The ability of RWH to meet potable needs in 2030s and 2050s will be reduced based on predicted shorter intense showers and frequent dry spells. A storage tank of 2.5 to 4.0 m³ per household (4 persons) is proposed to meet water demand during the maximum consecutive dry days, and January and February water shortage periods. Design of efficient RWH systems and provision of government subsidy on storage tanks will enable the residents to capture more rainwater to meet their daily domestic needs.     

Assessing Stability and Dynamics in Flood Risk Governance

European urban agglomerations face increasing flood risks due to urbanization and the effects of climate change. These risks are addressed at European, national and regional policy levels. A diversification and alignment of Flood Risk Management Strategies (FRMSs) can make vulnerable urban agglomerations more resilient to flooding, but this may require new Flood Risk Governance Arrangements (FRGAs) or changes in existing ones. While much technical knowledge on Flood Risk Management is available, scientific insights into the actual and/or necessary FRGAs so far are rather limited and fragmented. This article addresses this knowledge gap by presenting a research approach for assessing FRGAs. This approach allows for the integration of insights from policy scientists and legal scholars into one coherent framework that can be used to identify Flood Risk Management Strategies and analyse Flood Risk Governance Arrangements. In addition, approaches for explaining and evaluating (shifts in) FRGAs are introduced. The research approach is illustrated by referring to the rise of the Dutch risk-based approach called ‘multi-layered safety’ and more specifically its application in the city of Dordrecht. The article is concluded with an overview of potential next steps, including comparative analyses of FRGAs in different regions. Insights in these FRGAS are crucial to enable the identification of action perspectives for flood risk governance for actors at the level of the EU, its member states, regional authorities, and public-private partnerships.     

Assessment of River Water Quality Based on Theory of Variable Fuzzy Sets and Fuzzy Binary Comparison Method

There has been an increasing need for the proper evaluation of river water quality in order to safeguard public health and to protect the valuable fresh water resources. In order to overcome the own limitations of the traditional evaluations which can only use a point value instead of an interval for grading standards, on the basis of the fuzzy binary comparison method (FBCM) and the theory of variable fuzzy sets (VFS), an integrated variable fuzzy evaluation model (VFEM) is proposed for the assessment of river water quality in this paper. This model possesses the preciseness of the algorithm and operability in practice, can well solve the grading standards which are interval form. In order to explore and compare the present method with other traditional methods, two cases studies in the Three Gorges and Tseng-Wen River are made. The results show that the proposed VFEM method can convey water cleanliness to certain degree by using the eigenvector of level H, which is much stricter in the superior level, and that it can improve the veracity for the assessment of water quality.     

Optimal Sites Identification for Rainwater Harvesting in Northeastern Guatemala by Analytical Hierarchy Process

The Guatemala’s rural population have limited resources, high vulnerability to climate change, traditional agriculture practices and adversely affected by water scarcity. These problems engender the need for further economic development and imposed pressure on the existing water resources. In response, Rain Water Harvest (RWH) is the measure as an alternative source towards water shortage problem and a decrease in groundwater extraction. However, the identification of optimal sites for RWH is an important step to maximize the amount of water harvested and minimize the ecological impact. In this study, an Analytical Hierarchy Process (AHP) was used to determinate optimal sites using Geographic Information Systems (GIS) in order to integrate spatial information. Physical and socio-economic features were main decision criteria along with six sub-criteria: potential runoff; land use; soil texture; slope; distance from agricultural land; and distance from roads. In the investigation, several different criteria with different AHP structures were utilized to assess the flexibility of structures. The result maps with respect to different criteria and AHP structurs are overlaid in a systematic scheme to identify the most suitable site for RWH project. The results identified four sites as optimally suitable and eight as highly suitable. The total 424,070.81 m³ volume of water can be potentially harvested from these optimally and highly suitable sites. The study area comprised of 770.61 km² of Guatemala northeastern region and it is suggested that the RWH system for agriculture purpose should be promoted through government and multistakeholder co-operations as an alternative water resource.     

Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

In this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy Inference System (ANFIS), were used to estimate monthly water level change in Lake Beysehir. By using different input combinations consisting of monthly Inflow - Lost flow (I), Precipitation (P), Evaporation (E) and Outflow (O), efforts were made to estimate the change in water level (L). Performance of models established was evaluated using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²). According to the results of models, ε-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir.     

Multi-Criteria Evaluation of Small-Scale Sprinkler Irrigation Systems Using Grey Relational Analysis

The technical and socioeconomic evaluation of small-scale sprinkler irrigation systems is a multi-criteria problem characterized by complexity and uncertainty. In order to solve that, the application of Grey Relational Analysis (GRA) was presented. An evaluation model with ten sub-criteria under four groups, namely, technical, economic, environmental and social, was established. Among the criteria, calculation method of labor use in the small-scale sprinkler systems was originally addressed, and Life Cycle Cost (LCC) was used as an economic indicator. In the design of GRA, a combination weighting method based on Analytical Hierarchy Process (AHP) and entropy measurement was employed to take into account the experts’ knowledge and the inherent information in the experimental data. Six irrigation systems for three field sizes 0.5 ha, 2 ha and 5 ha respectively were considered to verify the model. The systems were optimized with Genetic Algorithms (GAs) first to figure out the optimal combinations of sprinklers and pipes and further, field tests were performed. The discussions show that: the developed approach has successfully provided the ranking of systems for three field sizes. When different types of sprinklers are used, the criteria including atomize index, application efficiency and specific energy consumption change greatly. And the ownership cost, particularly the energy consumption fee, accounts for the largest part of LCC in most of the systems. In comparison, System 5 and System 1 are generally the best. The evaluation model solved by GRA integrated with GAs is effective and can be extended to the comprehensive evaluation and optimization of other irrigation systems.     

Water Demand Forecasting Model for the Metropolitan Area of São Paulo,Brazil

This work is concerned with forecasting water demand in the metropolitan area of São Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting water consumption at Cantareira water treatment station (WTS) in the MASP, Brazil were analyzed for the year 2005. Eight model configurations were developed and used for the Cantareira WTS. The best performance was obtained for 12-h average of the input variables. The ANN model performed best with three times steps in advance. The hourly forecasting was obtained with acceptable error levels. Model results indicate an overall tendency for small errors. The proposed method is useful tool for water demand forecasting and water systems management. The paper is an important contribution since it takes into account weather variables and introduces some diagnostic studies on water consumption in one of the largest urban environments of the planet with its unique peculiarities such as anthropic affects on weather and climate that feeds back into the water consumption. The averaging is a low pass filter indeed and we used it to improve Signal to Noise Ratio (SNR).     

Assessing the Accuracy of Soil and Water Quality Characterization Using Remote Sensing

Assessing the risks of agricultural management practices on agro-ecosystem sustainability has special relevance in Ohio, USA due to the states prominence in agricultural production. However, identifying detrimental management practices remains controversial, a situation that may explain the inability to halt the recurring harmful algal blooms in inland waters, or the build-up of nutrients in the agricultural soils. Thus, detailed and accurate information is required to identify soils and water susceptible to degradation, and to support counteractive remedial measures. In this study soil and water spectral reflectance data were acquired with an Analytical Spectral Device, and modeled with laboratory measured physical and chemical properties using the Analysis of Variance (ANOVA) and decision trees. Results reveal no site differences in pH for the water, but the differences in electrical conductivity (EC) were significant. Similarly, the pH for soils did not vary significantly with depth increments. However, the no till (NT) managed soils had significantly higher pH. EC varied with depth of the water, whereas the soil carbon: nitrogen (C/N) ratio varied with management in 4 out of 5 sites. Finally, this study shows that remotely sensed data can be utilized to effectively characterize agricultural management practices based on inherent soil and water properties, thus providing information critical for assessing the efficacy of Water Quality Trading initiatives.     

Simulation of Soil Water Dynamics Under Surface Drip Irrigation from Equidistant Line Sources

One of the most important aspects of planning and management of a drip irrigation system is the determination of the soil moisture patterns formed under the emitter. In the present study soil water dynamics under surface drip irrigation from equidistant line sources are investigated using a simulation model, which combines hysteresis in the soil water characteristic curve, evaporation from the soil surface, and water extraction by roots. In this model a two-dimensional distribution of roots as well as a more rational way for the temporal distribution of the daily potential evapotranspiration are also incorporated. Soil water distribution patterns for two soil types (loamy sand, silt loam), two discharge rates (2 and 4 l m^?1 h^?1), two irrigation depths (30 and 40 mm), and two drip line sources spacing patterns (60 and 80 cm) are investigated. The numerical results showed that the soil water dynamics mainly depend on the soil hydraulic properties, the irrigation depth, and the drip line sources spacing. The results also showed that the irrigation efficiency and the actual evaporation decrease when the irrigation dose or the distance between the line sources increases. By contrast, the deep percolation increases when the irrigation dose or the distance between the line sources increases.     

A Solution for Flood Control in Urban Area: Using Street Block and Raft Foundation Space Operation Model

The overdevelopment of urban area has decreased the city’s ground permeability and increased its surface runoff. Moreover, the urban area existing flood control system meets the high risk and new challenge for water resource management due to global warming and climate change. A huge idle raft foundation space of existing buildings in urban area is found to be practicable solution for urban flood control systems. Through the operation model using street block and raft foundation space in urban area can effectively reduce peak runoff during typhoon seasons and extreme rainfall periods. This study selected a certain street block in Taipei City and 47 typical typhoons as validation, and constructed a Street Block-Raft Foundation flood control model (SB-RF model). Firstly, the optimal solution for reducing peak runoff for the 47 typhoons was obtained using Linear Programming. Data from the optimal solution and Back Propagation Neural Network were then used to simulate the SB-RF model flood control. This paper not only demonstrates the effectiveness of proposed model in controlling urban floods, but establishes an expected average peak runoff reduction rate and proposes a methodology for optimizing flow controls. Results are proved to be useful as reference for urban flood control for urban area such as Taipei City during typhoon season.     

Estimation of Groundwater Recharge from the Rainfall and Irrigation in an Arid Environment Using Inverse Modeling Approach and RS

Quantifying recharge from agricultural areas is important to sustain long-term groundwater use, make intelligent groundwater allocation decisions, and develop on-farm water management strategies. The scarcity of data in many arid regions, especially in the Middle East, has necessitated the use of combined mathematical models and field observations to estimate groundwater recharge. This study was designed to assess the recharge contribution to groundwater from rainfall and irrigation return flow in the Mosian plain, west of Iran. The Inverse modeling approach and remote sensing technology (RS) were used to quantify the groundwater recharge. The recharge for steady–state conditions was estimated using the Recharge Package of MODFLOW. The land-use map for the research area was produced using remote sensing and satellite images technology. According to results, groundwater recharge from the rainfall and irrigation return flow was at the rate of 0.15 mm/day. The recharge to the groundwater from rainfall was about 0.08 mm/day (10.8 % of total rainfall). The average of groundwater recharge contribution in the study area was about 0.39 mm/day that include 15.2 % of the total water used in the irrigated fields. We can conclude that irrigation water is the most important resource of groundwater recharge in this area, consequently, it should be integrated into relevant hydrological models as the main source of groundwater recharge.     

GIS Based Estimation of Sediment Discharge and Areas of Soil Erosion and Deposition for the Torrential Lukovska River Catchment in Serbia

In the present study the SHETRAN river basin modelling system was used in conjunction with Geographic Information System (GIS) to estimate potential erosion and deposition rates within the catchment and the concentrations of sediment in a flow at the catchment outlet on the example of the 114.31 km² mountainous torrential Lukovska River catchment in Serbia. The streams in the Lukovska River catchment are short, steep and often produce hazardous torrential floods as a consequence of strong rainfall of short duration. The soil erosion and sediment discharge were analysed in view of the catchment response to physical characteristics of the catchment. Considering that the most of total annual sediment discharge in watersheds of torrential character is achieved during storm events, the SHETRAN modelling system was calibrated on the example of a storm event in 1986 and validated for three other storm events in 1974, 1976 and 1979. The simulated results of discharges and sediment concentrations at the catchment outlet for both calibration and validation events were compared with the observed data and found to be reasonable. The changes of erosion and deposition rates within the catchment and in the course of time were estimated for the calibration event in 1986. The simulated erosion rates were within the range of 1 to 10.5 t/ha and corresponded to the observed rates of erosion in Europe during extreme rain events. The presented methodology is useful in identifying the erosion vulnerable regions in a catchment where erosion control measures should be implemented.     

Decision Support for Mainstreaming Climate Change Adaptation in Water Resources Management

Climate change adaptation (CCA) has recently emerged as a new fundamental dimension to be considered in the planning and management of water resources. Because of the need to consider the already perceived changes in climate trends, variability and extremes, and their interactions with evolving social and ecological systems, water management is now facing new challenges. The research community is expected to contribute with innovative methods and tools to support to decision- and policy-makers. Decision Support Systems (DSSs), have a relatively long history in the water management sector. They are usually developed upon pre-existing hydrologic simulation models, providing interfaces for facilitated use beyond the limited group of model developers, and specific routines for decision making (e.g. optimization methods). In recent years, the traditional focus of DSS research has shifted away from the software component, towards the process of structuring problems and aiding decisions, thus including in particular robust methods for stakeholders’ participation. The paper analyses the scientific literature, identifies the main open issues, and proposes an innovative operational approach for the implementation of participatory planning and decision-making processes for CCA in the water domain.     

Effect of Various On-Farm Water Management Scenarios on Equity and Productivity in Irrigation Networks

This research investigated the optimum on-farm water management methods for a summer crop (Maize). Water equity and productivity were optimized simultaneously by using genetic algorithms in Doroodzan Irrigation Network. Increase in water reduction fraction (WRF) (0.0 to 0.8) has the incremental effect on water equity (on average 19.4 %), however by increasing WRF, water productivity initially increased (on average 25.3 % at WRF?=?0.4) and then decreased. With increasing irrigation application efficiency (E_a) (40 to 90 %), the values of water equity and productivity increased by on average 52.8 and 91.5 %, respectively. Increment of conveyance efficiency of channels (E_c) (70 to 90 %) resulted in minimum incremental effect on water equity and productivity (on average 18.5 and 11.9 %, respectively). Furthermore, the values of performance measure decreased from wet water year to drought water year. Tape irrigation system was considered as the best choice at low quantities of WRF (<=0.4), however for higher values of WRF (>?=?0.6), sprinkler irrigation system was considered as the best choice for achieving higher values of water equity and productivity. Meanwhile, when equity and productivity were considered together for a specific method of deficit irrigation scheduling, under specified quantity of irrigation water, with increasing equity the water productivity reduction was negligible.     

区域雨水资源化潜力计算及时空分布规律研究

为充分利用雨水资源应对区域水资源紧缺问题,以黑龙江省松嫩平原为研究区,考虑地下水补给量和基流量对雨水资源化潜力的影响,引入分布式水文模型SWAT,提出了适用于研究区的雨水资源化潜力计算模型,计算了研究区的雨水资源化潜力,分析了研究区雨水资源化潜力时空分布规律。结果表明:研究区雨水资源化潜力在时间和空间上均存在一定差异,各分区之间以及同一分区不同年份雨水资源化潜力及其构成也存在较大的差异;生育期多年平均可利用雨水资源化潜力为343.78亿m~3,占生育期多年平均雨水总量的50.28%;各分区产流量在雨水资源化潜力中占比均较大,建议要注重提高降水径流汇集、径流蓄存和集雨补灌等相关技术;在土壤水增量占比大的分区要注重节水灌溉技术的应用。总体来看,黑龙江省松嫩平原地区雨水资源化潜力巨大,应该科学合理提高雨水资源利用率,保障区域农业可持续发展。     

Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

Accurate simulation of rainfall-runoff process is of great importance in hydrology and water resources management. Rainfall–runoff modeling is a non-linear process and highly affected by the inputs to the simulation model. In this study, three kinds of soft computing methods, namely artificial neural networks (ANNs), model tree (MT) and multivariate adaptive regression splines (MARS), have been employed and compared for rainfall-runoff process simulation. Moreover, this study investigates the effect of input size, including number of input variables and number of data time series on runoff simulation by the developed models. Inputs to the simulation models for calibration and validation purposes consist two parts: I1: five variables, including daily rainfall and runoff time series (30 years) with lag times, and I2: twelve variables, including daily rainfall and runoff time series (10 years). To increase the model performances, optimal number and type for input variables are identified. The efficiency of the training and testing performances using the ANNs, MT and MARS models is then evaluated using several evaluation criteria. To implement the methodology, Tajan catchment in the northern part of Iran is selected. Based on the results, it was found that using I1 as input to the developed models results in higher simulation performance. The results also provided evidence that MT (R = 0.897, RMSE = 6.70, RSE = 0.33) with set I2 is capable of reliable model for rainfall-runoff process compared with MARS (R = 0.892, RMSE = 7.47, RSE = 0.83) and ANNs (R = 0.884, RMSE = 7.40, RSE = 0.43) models. Therefore, size (length of data time series) and type of input variables have significant effects on the modeling results.