An inexact two-stage water management model for planning agricultural irrigation under uncertainty

In this study, an inexact two-stage water management (ITWM) model is developed for planning agricultural irrigation in the Zhangweinan River Basin, China. The ITWM model is derived from the incorporation of interval-parameter programming (IPP) within a two-stage stochastic programming (TSP) framework. It can reflect not only uncertainties expressed as probability distributions but also interval numbers. Moreover, it can provide an effective linkage between conflicting economic benefits and the associated penalties attributed to the violation of the predefined policies. Four decision scenarios associated with different water-resources management policies are examined. Targeted incomes, recourse costs, and net system benefits under different scenarios are analyzed, which indicates that different policies for agricultural irrigation targets correspond to different water shortages and surplus, and thus lead to varied system benefit and system-failure risk. The results are valuable for supporting the adjustment or justification of the existing irrigation patterns and identify a desired water-allocation plan for agricultural irrigation under uncertainty.     

Radial interval chance-constrained programming for agricultural non-point source water pollution control under uncertainty

Inherent uncertainties in agricultural non-point source water pollution control problems cause great difficulties in relevant modeling processes. A radial interval chance-constrained programming (RICCP) approach was developed in this study for supporting source-oriented non-point source pollution control under uncertainty. The proposed RICCP approach could tackle two-layer uncertainty resulting from temporal and spatial variability of many factors and their uncertain interactions. Based on the concept of radial intervals and chance-constrained programming, RICCP could reflect the randomness in the bounds of interval parameters, with or without known probability distributions. RICCP could also allow decision makers to adjust the conservativeness of solutions via protection and significance levels, helping satisfy environmental, economic and resource-conservation requirements in a holistic and interactive manner. The proposed methodology has been applied to an agricultural water pollution control case. The most-profit agricultural development strategies were explored while restricting environmental impacts to an acceptable level. A series of interval solutions for agricultural practices were generated corresponding to varied risk levels of constraint violations, which could help screen optimal alternatives according to decision makers’ profit and risk considerations as well as various system conditions. RICCP model was also compared to its alternatives. Significant differences in the solutions among the compared models further demonstrated the advantages of the proposed approach.     

Management model for decision support when applying low quality water in irrigation

Use of low quality water for irrigation of food crops is an important option to secure crop productivity in dry regions, alleviate water scarcity and recycle nutrients, but it requires assessment of adverse effects on health and environment. In the EU-project “SAFIR¹” a model system was developed that combines irrigation management with risk evaluation, building on research findings from the different research groups in the SAFIR project. The system applies to field scale irrigation management and aims at assisting users in identifying safe modes of irrigation when applying low quality water. The cornerstone in the model system is the deterministic “Plant-Soil-Atmosphere” model DAISY, which simulates crop growth, water and nitrogen dynamics and if required heavy metals and pathogen fate in the soil. The irrigation and fertigation module calculates irrigation and fertigation requirements based on DAISY's water and nitrogen demands. A Water Source Administration module keeps track of water sources available and their water quality, as well as water treatments, storage, and criteria for selection between different sources. At harvest, the soil concentrations of heavy metals and pathogens are evaluated and the risk to consumers and farmers assessed. Crop profits are calculated, considering fixed and variable costs of input and output. The user can run multiple “what-if” scenarios that include access to different water sources (including wastewater), water treatments, irrigation methods and irrigation and fertilization strategies and evaluate model results in terms of crop yield, water use, fertilizer use, heavy metal accumulation, pathogen exposure and expected profit. The management model system can be used for analysis prior to investments or when preparing a strategy for the season.     

Temporal variability in water quality of agricultural tailwaters: Implications for water quality monitoring

Accurate assessments of non-point source pollution and the associated evaluation of mitigation strategies depend on effective water quality monitoring programs. Intensive irrigation season water quality monitoring was conducted on three agricultural drains (6 h to daily sampling) along with analysis of decade long records from two larger agricultural drains (biweekly to monthly sampling) in the San Joaquin Valley, California. Analyses revealed significant temporal variability in concentrations of nutrients, salts, and turbidity over short time-scales (<1 day), as well as significant differences in monthly and annual mean concentrations. Statistical techniques were used to evaluate the sampling intensity required to meet rigorous confidence and accuracy criteria, as well as to evaluate the efficacy of different sampling strategies (e.g. grab samples versus composite samples). The number of samples required to determine mean constituent concentrations within 20% of the mean at a 95% confidence level ranged from 2 to 39 samples per month (SPM) for total phosphorus, 1-16 SPM for total nitrogen, 5-25 SPM for turbidity, and 1-3 SPM for electrical conductivity. Using a daily composite sample (4 subsamples per composite) instead of discrete samples was shown to maintain the same accuracy and confidence standards, while reducing the required sample number by up to 50%. This study emphasizes the value of a statistical approach for evaluating water quality monitoring strategies, and provides a framework through which cost-benefit analysis can be implemented in the development of monitoring plans.     

FSWM: A hybrid fuzzy-stochastic water-management model for agricultural sustainability under uncertainty

A hybrid fuzzy-stochastic water-management (FSWM) model is developed for agricultural sustainability under uncertainty, based on advancement of a multistage fuzzy-stochastic quadratic programming (MFSQP) approach. In MFSQP, uncertainties presented in terms of fuzziness and randomness can be incorporated within a multilayer scenario tree, such that revised decisions are permitted in each time period based on the realized values of the uncertain events. Moreover, fuzzy quadratic terms are used in the objective function to minimize the variation of satisfaction degrees among the constraints; it allows an increased flexibility in controlling the system risk in the optimization process. Results of the case study indicate that useful solutions for the planning of agricultural water management have been obtained. In the FSWM model, a number of policies for agricultural water supply are conducted. The results obtained can help decision makers to identify desired water-allocation schemes for agricultural sustainability under uncertainty, particularly when limited water resources are available for multiple competing users.     

A robust modeling approach for regional water management under multiple uncertainties

In regional water management systems, various uncertainties may be derived from random feature of resource conditions and natural processes, errors in estimated modeling parameters, as well as imprecision or fuzziness human-induced. This leads to difficulties in formulating and solving the resulting regional water management problems. In this study, a robust multistage interval-stochastic programming (RISP) method is developed for dealing with vague and random information in regional water management systems. The decision variables are useful for justifying and/or adjusting the decision schemes for agricultural activities through the incorporation of their implicit knowledge on regional water management. Different policies for agricultural water supply have been analyzed. The results can help to identify desired water-allocation schemes for agricultural sustainable development that the prerequisite water demand for supporting crops’ survival can be guaranteed when the water resource is scarce.     

Environmental decision support system development for seasonal wetland salt management in a river basin subjected to water quality regulation

Seasonally managed wetlands in the Grasslands Basin on the west-side of California’s San Joaquin Valley provide food and shelter for migratory wildfowl during winter months and sport for waterfowl hunters during the annual duck season. Surface water supply to these wetlands contain salt which, when drained to the San Joaquin River (SJR) during the annual drawdown period, can negatively impact water quality and cause concern to downstream agricultural riparian water diverters. Recent environmental regulation, limiting discharges salinity to the SJR and primarily targeting agricultural non-point sources, now also targets return flows from seasonally managed wetlands. Real-time water quality management has been advocated as a means of continuously matching salt loads discharged from agricultural, wetland and municipal operations to the assimilative capacity of the SJR. Past attempts to build environmental monitoring and decision support systems (EDSS’s) to implement this concept have enjoyed limited success for reasons that are discussed in this paper. These reasons are discussed in the context of more general challenges facing the successful implementation of a comprehensive environmental monitoring, modelling and decision support system for the SJR Basin.     

Controlling nitrogen release from farm ponds with a subsurface outflow device: Implications for improved water quality in receiving streams

The retention of nutrients in farm ponds has many potential benefits, including reduction of nitrogen and phosphorus (promoters of eutrophication) in receiving streams. The aim of this study was to evaluate the efficacy of a commercial subsurface pond outflow control device (Pond Management System™) on nutrient retention in farm ponds. Four ponds of similar size and water chemistry in the upper Tar River basin of North Carolina, USA were studied; three were equipped with the pond outflow control device and one was retained without a device (normal surface outflow) that served as a reference site. Water samples were collected monthly from each pond at 0.3 m intervals from the surface to 2.1 m at a fixed station adjacent to the pond standpipe and from the pond outflow pipe from March to October 2005. The water samples were analyzed for total Kjeldahl nitrogen (N), total phosphorus (P), chlorophyll a, and a suite of other physicochemical variables. In ponds with the subsurface outflow device, the mean N concentrations in the outflow were substantially less (6.2–20.7%) than concentrations at the pond surface. Concentrations of N in the outflow were similar to N concentrations at intermediate pond depths (0.9–1.5 m), the depth of the outflow devices, indicating water was drawn from these depths and that N was being retained in the surface layers of the pond. Also, mean water temperatures were 1.1–1.9 °C cooler at intermediate depths compared to the surface, suggesting potential application of the outflow device for minimizing warm water outflows to receiving streams. These results provide evidence that under these conditions a subsurface pond outflow device can reduce nutrient release to receiving streams, thereby increasing overall stream water quality.