Design and operational parameters of a rooftop rainwater harvesting system: definition, sensitivity and verification

The appropriate design and evaluation of a rainwater harvesting (RWH) system is necessary to improve system performance and the stability of the water supply. The main design parameters (DPs) of an RWH system are rainfall, catchment area, collection efficiency, tank volume and water demand. Its operational parameters (OPs) include rainwater use efficiency (RUE), water saving efficiency (WSE) and cycle number (CN). The sensitivity analysis of a rooftop RWH system's DPs to its OPs reveals that the ratio of tank volume to catchment area (V/A) for an RWH system in Seoul, South Korea is recommended between 0.03 and 0.08 in terms of rate of change in RUE. The appropriate design value of V/A is varied with D/A. The extra tank volume up to V/A of 0.15～0.2 is also available, if necessary to secure more water. Accordingly, we should figure out suitable value or range of DPs based on the sensitivity analysis to optimize design of an RWH system or improve operation efficiency. The operational data employed in this study, which was carried out to validate the design and evaluation method of an RWH system, were obtained from the system in use at a dormitory complex at Seoul National University (SNU) in Korea. The results of these operational data are in good agreement with those used in the initial simulation. The proposed method and the results of this research will be useful in evaluating and comparing the performance of RWH systems. It is found that RUE can be increased by expanding the variety of rainwater uses, particularly in the high rainfall season.     

TEMPORARY WATER TRANSFERS FOR DRY-YEAR WATER SUPPLY1

ABSTRACT: Temporary transfers of water for dry year water supply are analyzed for cost and operational feasibility. The temporary transfer is implemented as part of a water rights option agreement (WROA) between a lesson and a lessee. First, engineering analysis determines the technical feasibility and operations plan under the Colorado doctrine of prior appropriation. The cost of the WROA to a water utility is estimated. Other considerations in the agreement are discussed. The WROA is compared to other dry-year supply alternatives using a water system simulation model to obtain expected cost and operational performance characteristics.     

Comparison of Evapotranspiration Simulation Performance by APEX Model in Dryland and Irrigated Cropping Systems

Accurate estimation of evapotranspiration (ET) is essential to improve water use efficiency of crop production systems managed under different water regimes. The Agricultural Policy/Environmental eXtender (APEX) model was used to simulate ET using four potential ET (ET_p) methods. The objectives were to determine sensitive ET parameters in dryland and irrigated cropping systems and compare ET simulation in the two systems using multiple performance criteria. Measured ET and crop yield data from lysimeter fields located in the United States Department of Agriculture‐Agricultural Research Service Bushland, Texas were used for evaluation. The number of sensitive parameters was higher for dryland (11–14) than irrigated cropping systems (6–8). Only four input parameters: soil evaporation plant cover factor, root growth soil strength, maximum rain intercept, and rain intercept coefficient were sensitive in both cropping systems. Overall, it is possible to find a set of robust parameter values to simulate ET accurately in APEX in both cropping systems using any ET_p method. However, more computation time is required for dryland than irrigated cropping system due to a relatively larger number of sensitive input parameters. When all inputs are available, the Penman–Monteith method takes the shortest computation time to obtain one model run with robust parameter values in both cropping systems. However, in areas with limited datasets, one can still obtain reasonable ET simulations using either Priestley–Taylor or Hargreaves. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed1

Herr, Joel W., Krish Vijayaraghavan, and Eladio Knipping, 2010. Comparison of Measured and MM5 Modeled Meteorology Data for Simulating Flow in a Mountain Watershed. Journal of the American Water Resources Association (JAWRA) 46(6):1255–1263. DOI: 10.1111/j.1752-1688.2010.00489.x Abstract: Accurate simulation of time-varying flow in a river system depends on the quality of meteorology inputs. The density of meteorology measurement stations can be insufficient to capture spatial heterogeneity of precipitation, especially in mountainous areas. The Watershed Analysis Risk Management Framework (WARMF) model was applied to the Catawba River watershed of North and South Carolina to simulate flow and water quality in rivers and a series of 11 reservoirs. WARMF was linked with the AMSTERDAM air model to analyze the water quality benefit from reduced atmospheric emissions. The linkage requires accurate simulation of meteorology for all seasons and for all types of precipitation events. WARMF was driven by the mesoscale meteorology model MM5 processed by the Meteorology Chemistry Interface Processor, which provides greater spatial density but less accuracy than meteorology stations. WARMF was also run with measured data from the National Climatic Data Center (NCDC) to compare the performance of the watershed model using measured data vs. modeled meteorology as input. A one year simulation using MM5 modeled meteorology performed better overall than the simulation using NCDC data for the volumetric water balance measure used for calibration, but MM5 represented precipitation from a dissipated hurricane poorly, which propagated into errors of simulated flow.     

FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION1

ABSTRACT: This research investigates the benefits of forecasting in water supply systems. Questions relating operational losses to forecast period and accuracy are addressed. Some simple available forecasting techniques are assessed for their accuracy and applicability. These issues are addressed through the use of a simulation model of the Cedar and South Fork Tolt Rivers, where the system is modeled as a single purpose reservoir supplying municipal and industrial water to the Seattle metropolitan area. The following conclusions were made for this system: (1) reservoir operation deteriorates markedly with the loss of forecast accuracy; (2) the optimal length of forecasting period is five months; (3) reservoir operation may be improved by as much as 88 percent if perfect predictive abilities are available; (4) the mean of the historic data is not recommended to predict future flows because Markov methods are always superior; and (5) lag-one autoregressive Markov schemes exhibit about a 9 percent improvement in operation over no forecasting.     

A Decision‐Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes1

Brown, Casey, William Werick, Wendy Leger, and David Fay, 2011. A Decision‐Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. Journal of the American Water Resources Association (JAWRA) 47(3):524‐534. DOI: 10.1111/j.1752‐1688.2011.00552.x Abstract: In this paper, we present a risk analysis and management process designed for use in water resources planning and management under climate change. The process incorporates climate information through a method called decision‐scaling, whereby information related to climate projections is tailored for use in a decision‐analytic framework. The climate risk management process begins with the identification of vulnerabilities by asking stakeholders and resource experts what water conditions they could cope with and which would require substantial policy or investment shifts. The identified vulnerabilities and thresholds are formalized with a water resources systems model that relates changes in the physical climate conditions to the performance metrics corresponding to vulnerabilities. The irreducible uncertainty of climate change projections is addressed through a dynamic management plan embedded within an adaptive management process. Implementation of the process is described as applied in the ongoing International Upper Great Lakes Study.     

ARTIFICIAL NEURAL NETWORKS FOR SUBSURFACE DRAINAGE AND SUBIRRIGATION SYSTEMS IN ONTARIO,CANADA1

ABSTRACT: Artificial neural network (ANN) models were developed to simulate fluctuations in midspan water table depths (WTD) given rainfall, potential evapotranspiration, and irrigation inputs on a Brookston clay loam in Woodslee, Ontario, having a dual‐purpose subsurface drainage/subirrigation setup. Water table depths and meteorologic data collected at this site from 1992 to 1994 and from 1996 to 1997 were used to train the ANNs. The ANNs were then used for real‐time control and time series simulations. The lowest root mean squared errors (RMSE) for the various ANNs were 60.6 mm for real‐time control simulation, and 88.4 mm for time‐series simulation of water table depths. It was possible to simulate WTD for the different modes of water table management in one network by incorporating an indicator for switching from one to the other. The ANN simulations were quite good even though the training data sets had irregular measurement intervals. With fewer input parameters and small network structures, ANNs still provided accurate results and required little time for training and execution. ANNs are therefore easier and faster to develop and run than conventional models and can contribute to the proper management of subsurface drainage and subirrigation systems.     

Simulating the Impacts of Irrigation Levels on Soybean Production in Texas High Plains to Manage Diminishing Groundwater Levels

There is an increasing need to strategize and plan irrigation systems under varied climatic conditions to support efficient irrigation practices while maintaining and improving the sustainability of groundwater systems. This study was undertaken to simulate the growth and production of soybean [Glycine max (L.)] under different irrigation scenarios. The objectives of this study were to calibrate and validate the CROPGRO‐Soybean model under Texas High Plains’ (THP) climatic conditions and to apply the calibrated model to simulate the impacts of different irrigation levels and triggers on soybean production. The methodology involved combining short‐term experimental data with long‐term historical weather data (1951–2012), and use of mechanistic crop growth simulation algorithms to determine optimum irrigation management strategies. Irrigation was scheduled based on five different plant extractable water levels (irrigation threshold [ITHR]) set at 20%, 35%, 50%, 65%, and 80%. The calibrated model was able to satisfactorily reproduce measured leaf area index, biomass, and evapotranspiration for soybean, indicating it can be used for investigating different strategies for irrigating soybean in the THP. Calculations of crop water productivity for biomass and yield along with irrigation water use efficiency indicated soybean can be irrigated at ITHR set at 50% or 65% with minimal yield loss as compared to 80% ITHR, thus conserving water and contributing toward lower groundwater withdrawals. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

MULTIRESERVOIR ANALYSIS TECHNIQUES IN WATER QUANTITY STUDIES1

ABSTRACT. This paper deals with the subject of applying different types of systems analysis tools to water quantity studies of multireservoir networks of increasing degrees of complexity. The object is to show how each tool can be used, modified and combined with other tools to solve specific problems and to indicate the degrees of complexity at which more sophisticated tools should be applied. Firstly, several applications and limitations of linear programming and dynamic programming are discussed. Secondly, it is shown that mass curve analysis is useful, can be extended to serve in computing reservoir rules for conventional multireservoir simulation models, and can be applied in conjunction with either historic or generated sequences of hydrologic input data. Thirdly, extended and limiting features of conventional time-interval-by-time-interval multireservoir simulation models are analyzed. And fourthly, a two-model series for problems which defy analysis by more basic tools is described in detail, the first model using network analysis (Out-of-Kilter Algorithm) for all space and time arcs simultaneously and providing data for the second general-purpose model using network analysis each time interval. The importance of efficient computer procedures is stressed. The background for the paper includes systems analysis of water availability and hydro-thermal power studies carried out during the past six years in that part of Canada lying between the Great Lakes and the Rocky Mountain Divide.     

COMPARATIVE STUDY OF DROUGHT PREDICTION TECHNIQUES FOR RESERVOIR OPERATION1

ABSTRACT: Predicting the likelihood of a drought markedly enhances the efficiency of reservoir operations. This study applies the kriging method and time series analysis to predict inflows to Shihmen Reservoir in northern Taiwan. A subsequent reservoir operation simulation is employed to determine the drought lead time (DLT), the time before the onset of a drought. A more efficient reservoir operational strategy can be established with the aid of DLT and the probability of successful drought prediction (P s). Simulation results of reservoir operation over a period of three decades demonstrate that, at one month DLT, the kriging approach achieves 0.86 of P s for moderate droughts and 0.94 of P s for severe droughts. The kriging approach generally outperformed the time series approach in terms of DLT, P s of drought prediction, and the number of correctly predicted drought events.     

Tamarix and Diorhabda Leaf Beetle Interactions: Implications for Tamarix Water Use and Riparian Habitat

Tamarix leaf beetles (Diorhabda carinulata) have been widely released on western United States rivers to control introduced shrubs in the genus Tamarix, with the goals of saving water through removal of an assumed high water‐use plant, and of improving habitat value by removing a competitor of native riparian trees. We review recent studies addressing three questions: (1) to what extent are Tamarix weakened or killed by recurrent cycles of defoliation; (2) can significant water salvage be expected from defoliation; and (3) what are the effects of defoliation on riparian ecology, particularly on avian habit? Defoliation has been patchy at many sites, and shrubs at some sites recover each year even after multiple years of defoliation. Tamarix evapotranspiration (ET) is much lower than originally assumed in estimates of potential water savings, and are the same or lower than possible replacement plants. There is concern that the endangered southwestern willow flycatcher (Empidonax trailli extimus) will be negatively affected by defoliation because the birds build nests early in the season when Tamarix is still green, but are still on their nests during the period of summer defoliation. Affected river systems will require continued monitoring and development of adaptive management practices to maintain or enhance riparian habitat values. Multiplatform remote sensing methods are playing an essential role in monitoring defoliation and rates of ET on affected river systems.     

Water Dispatch Model for Middle Route of a South‐to‐North Water Transfer Project in China1

Chang, Jian‐xia, Yi‐min Wang, and Qiang Huang, 2011. Water Dispatch Model for Middle Route of a South‐to‐North Water Transfer Project in China. Journal of the American Water Resources Association (JAWRA) 47(1):70‐80. DOI: 10.1111/j.1752‐1688.2010.00478.x Abstract: The objective of this paper is to present a simulation model to address the water dispatch problem of the south‐to‐north water transfer project for the Middle Route system in China. Reasonable rules and a system network structure are established. This model consists of five modules: (1) a data‐processing module, (2) an initial control module, (3) a multisource simulation dispatch module, (4) a system identification module, and (5) a revision module. Water allocated to each province and city along the route is obtained by simulation, and the long‐term operation results show that water supply reliabilities are significantly improved if the transferred water is jointly dispatched with the local water resources.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

How to Constrain Multi‐Objective Calibrations of the SWAT Model Using Water Balance Components

Accurate discharge simulation is one of the most common objectives of hydrological modeling studies. However, a good simulation of discharge is not necessarily the result of a realistic simulation of hydrological processes within the catchment. We propose an evaluation framework that considers both discharge and water balance components as evaluation criteria for calibration of the Soil and Water Assessment Tool (SWAT). In this study, we integrated average annual values of surface runoff, groundwater flow, and evapotranspiration in the model evaluation procedure to constrain the selection of good model runs for the Little River Experimental Watershed in Georgia, United States. For evaluating water balance and discharge dynamics, the Nash‐Sutcliffe efficiency (NSE) and percent bias (PBIAS) were used. In addition, the ratio of root mean square error and standard deviation of measured data (RSR) was calculated for individual segments of the flow duration curve to identify the best model runs in terms of discharge magnitude. Our results indicate that good statistics for discharge do not guarantee realistic simulations of individual water balance components. Therefore, we recommend constraining the ranges of water balance components to achieve a more realistic simulation of the entire hydrological system, even if tradeoffs between good statistics for discharge simulations and reasonable amounts of the water balance components are unavoidable. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Water treatment by aquatic ecosystem: Nutrient removal by reservoirs and flooded fields

Potential use of reservoirs and flooded fields stocked with aquatic plants for reduction of the nutrient levels of organic soil drainage water was evaluated. The treatment systems include 1) a large single reservoir (R1) stocked with waterhyacinth (Eichhornia crassipes), elodea (Egeria densa), and cattails (Typha sp.) in series; 2) three small reservoirs in series with waterhyacinth (R2), elodea (R3), and cattails (R4), grown in independent reservoirs; 3) a control reservoir (R5) with no cultivated plants; 4) a large single flooded field planted to cattails; 5) three small flooded fields in a series planted to cattails; and 6) a flooded field with no cultivated plants. Drainage water was pumped daily (6 hours a day, and 6 days a week) into these systems for a period of 27 months at predetermined constant flow rates. Water samples were collected at the inlet and outlet of each treatment system and analyzed for N and P forms.The series of reservoirs stocked with aquatic plants functioned effectively in the removal of N and P from agricultural drainage water, compared to a single large reservoir. Allowing the water to flow through the reservoir stocked with waterhyacinth plants with a residence time of 3.6 days was adequate to remove about 50% of the incoming inorganic N. Allowing the water to flow through a series of two small reservoirs, R2 and R3, with a residence time of 7.3 days was necessary to remove about 60% of the incoming ortho-P. Flooded fields were effective in the removal of inorganic N, but showed poor efficiency in the removal of ortho-P.Florida Agricultural Experiment Stations Journal Series No. 2320.     

WATER QUALITY MODEL CALIBRATION: A COMPARISON OF INPUT AND OUTPUT ERROR CRITERIA1

ABSTRACT: A common framework for the analysis of water resources systems is the input-parameter-output representation. The system, described by its parameters, is driven by inputs and responds with outputs. To calibrate (estimate the parameters) models of these systems requires data on both inputs and outputs, both of which are subject to random errors. When one is uncertain as to whether the predominant source of error is associated with inputs or outputs, uncertainty also exists as to the correct specification of a calibration criterion. This paper develops and analyzes two alternative least squares criteria for calibrating a numerical water quality model. The first criterion assumes that errors are associated with inputs while the second assumes output errors. Statistical properties of the resulting estimators are examined under conditions of pure input or output error and mixed error conditions from a theoretical perspective and then using simulated results from a series of Monte Carlo experiments.     

Model‐Based Nitrogen and Phosphorus (Nutrient) Criteria for Large Temperate Rivers: 1. Model Development and Application

An initial inquiry into model‐based numeric nitrogen and phosphorus (nutrient) criteria for large rivers is presented. Field data collection and associated modeling were conducted on a segment of the lower Yellowstone River in the northwestern United States to assess the feasibility of deriving numeric nutrient criteria using mechanistic water‐quality models. The steady‐state one‐dimensional model QUAL2K and a transect‐based companion model AT2K were calibrated and confirmed against low‐flow conditions at a time when river loadings, water column chemistry, and diurnal indicators were approximately steady state. Predictive simulation was then implemented via nutrient perturbation to evaluate the steady‐state and diurnal response of the river to incremental nutrient additions. In this first part of a two‐part series, we detail our modeling approach, model selection, calibration and confirmation, sensitivity analysis, model outcomes, and associated uncertainty. In the second part (Suplee et al., 2015) we describe the criteria development process using the tools described herein. Both articles provide a fundamental understanding of the process required to develop site‐specific numeric nutrient criteria using models in applied regulatory settings.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES1

ABSTRACT: Growing interest in water quality has resulted in the development of monitoring networks and intensive sampling for various constituents. Common purposes are regulatory, source and sink understanding, and trend observations. Water quality monitoring involves monitoring system design; sampling site instrumentation; and sampling, analysis, quality control, and assurance. Sampling is a process to gather information with the least cost and least error. Various water quality sampling schemes have been applied for different sampling objectives and time frames. In this study, a flow proportional composite sampling scheme is applied to variable flow remote canals where the flow rate is not known a priori. In this scheme, historical weekly flow data are analyzed to develop high flow and low flow sampling trigger volumes for auto‐samplers. The median flow is used to estimate low flow sampling trigger volume and the five percent exceedence probability flow is used for high flow sampling trigger volume. A computer simulation of high resolution sampling is used to demonstrate the comparative bias in load estimation and operational cost among four sampling schemes. Weekly flow proportional composite auto‐sampling resulted in the least bias in load estimation with competitive operational cost compared to daily grab, weekly grab sampling and time proportional auto‐sampling.