Niels Schütze

Towards an integrated arid zone water management using simulation-based optimisation

For ensuring both optimal sustainable water resources management and long-term planning in a changing arid environment, we propose an integrated Assessment-, Prognoses-, Planning- and Management tool (APPM). The new APPM integrates the complex interactions of the strongly nonlinear meteorological, hydrological and agricultural phenomena, considering the socio-economic aspects. It aims at achieving best possible solutions for water allocation, groundwater storage and withdrawals including saline water management together with a substantial increase of the water use efficiency employing novel optimisation strategies for irrigation control and scheduling. To obtain a robust and fast operation of the water management system, it unites process modeling with artificial intelligence tools and evolutionary optimisation techniques for managing both water quality and quantity. We demonstrate some key components of our methodology by an exemplary application to the south Al-Batinah region in the Sultanate of Oman which is affected by saltwater intrusion into a coastal aquifer due to excessive groundwater withdrawal for irrigated agriculture. We show the effectiveness and functionality of a new simulation-based water management system for the optimisation and evaluation of different irrigation practices, crop pattern and resulting abstraction scenarios. The results of several optimisation runs indicate that due to contradicting objectives, such as profit-oriented agriculture versus aquifer sustainability only a multi-objective optimisation can provide sustainable solutions for the management of the water resources in respect of the environment as well as the socio-economic development.

Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability

The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity (WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for optimizing WP is evaluated.

OCCASION: New Planning Tool for Optimal Climate Change Adaption Strategies in Irrigation

To sustain productive irrigated agriculture with limited water resources requires a high water use efficiency. This can be achieved by the precise scheduling of deficit irrigation systems taking into account the crops’ response to water stress at different stages of plant growth. Particularly in the light of climate change with rising population numbers and increasing water scarcity, an optimal solution for this task is of paramount importance. We solve the corresponding complex multidimensional and nonlinear optimization problem, i.e., finding the ideal schedule for maximum crop yield with a given water volume by a well tailored approach which offers straightforward application facilities. A global optimization technique allows, together with physically based modeling, for the risk assessment in yield reduction considering different sources of uncertainty (e.g., climate, soil conditions, and management). A new stochastic framework for decision support is developed which aims at optimal climate change ada...

Optimal planning and operation of irrigation systems under water resource constraints in Oman considering climatic uncertainty

In this contribution, we introduce a stochastic framework for decision support for optimal planning and operation of water supply in irrigation. This consists of (1) a weather generator for simulating regional impacts of climate change on the basis of IPCC scenarios, (2) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply, (3) a mechanistic model for simulating water transport and crop growth in a sound manner, and (4) a kernel density estimator for estimating stochastic productivity, profit, and demand functions by a nonparametric method. As a result of several simulation/optimization runs within the framework, we present stochastic crop-water production functions (SCWPF) for different crops which can be used as a basic tool for assessing the impact of climate variability on the risk for the potential yield for specific crops and specific agricultural areas. A case study for an agricultural area in the Al Batinah region of the Sultanate of Oman is used to illustrate these methodologies. In addition, microeconomic impacts of climate change and the vulnerability of the agro-ecological system are discussed.

Sustainable management of a coupled groundwater-agriculture hydrosystem using multi-criteria simulation based optimisation.

In this paper we present a new simulation-based integrated water management tool for sustainable water resources management in arid coastal environments. This tool delivers optimised groundwater withdrawal scenarios considering saltwater intrusion as a result of agricultural and municipal water abstraction. It also yields a substantially improved water use efficiency of irrigated agriculture. To allow for a robust and fast operation we unified process modelling with artificial intelligence tools and evolutionary optimisation techniques. The aquifer behaviour is represented using an artificial neural network (ANN) which emulates a numerical density-dependent groundwater flow model. The impact of agriculture is represented by stochastic crop water production functions (SCWPF). Simulation-based optimisation techniques together with the SCWPF and ANN deliver optimal groundwater abstraction and cropping patterns. To address contradicting objectives, e.g. profit-oriented agriculture vs. sustainable abstraction scenarios, we performed multi-objective optimisations using a multi-criteria optimisation algorithm.

A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems

The complexity of water resources management increases when decisions about environmental and social issues are considered in addition to economic efficiency. Such complexities are further compounded by multiple uncertainties about the consequences of potential management decisions. In this paper, a new fuzzy-stochastic multiple criteria decision-making approach is proposed for water resources management in which a variety of criteria in terms of economic, environmental and social dimensions are identified and taken into account. The goal is to evaluate multiple conflicting criteria under uncertainties and to rank a set of management alternatives. The methodology uses a simulation-optimization water management model of a strongly interacting groundwater-agriculture system to enumerate criteria based on these bio-physical process interactions. Fuzzy and/or qualitative information regarding the decision-making process for which quantitative data is not available are evaluated in linguistic terms. Afterwards, Monte Carlo simulation is applied to combine these information and to generate a probabilistic decision matrix of management alternatives versus criteria in an uncertain environment. Based on this outcome, total performance values of the management alternatives are calculated using ordered weighted averaging. The proposed approach is applied to a real world example, where excessive groundwater withdrawal from the coastal aquifer for irrigated agriculture has resulted in saltwater intrusion, threatening the economical basis of farmers and associated societal impacts. The analysis has provided potential decision alternatives which can serve as a platform for negotiation and further exploration. Furthermore, sensitivity of different inputs to resulting rankings is investigated. It is found that decision makers’ risk aversion and risk taking attitude may yield different rankings. The presented approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management.

Modelling the impact of drought and heat stress on common bean with two different photosynthesis model approaches

Extreme temperature and drought stress are major environmental factors limiting agriculture worldwide. A comprehensive understanding of plant behavior under different environmental conditions can be gained through experiments and through the application of biophysical crop models. This study presents a field experiment conducted with bean exposed to heat and drought stress. Based on an experimental data collection a crop model was set up, calibrated and validated. Hereby, the two different photosynthesis model approaches already implemented in the model, a simple empirical (the Goudriaan and van Laar or GvL model) and a biochemical photosynthesis model approach (the Farquhar-Ball-Collatz or FBC model), were tested. Both photosynthesis model approaches performed adequately under no stress conditions. Under heat stress conditions, yield was underestimated by both models. However, the FBC model performed better than the simpler photosynthesis model approach of the GvL model. The FBC crop model was able to predict the soil water dynamics, the plant growth and the stomatal conductance. We tested two different photosynthesis model approaches implemented in a biophysical crop model.A comprehensive experimental data collection was used to set up, calibrate and validate the crop model.The effect of the photosynthesis model choice was evaluated with multiple model runs based on observed data testing several feasible growth and climatic conditions.The application of the Farquhar-Ball-Collatz model implemented in a field-scale crop model is recommended for drought and heat stress simulation studies.

An integrated approach to conceptualise hydrological and socio-economic interaction for supporting management decisions of coupled groundwater–agricultural systems

The management of complex interacting hydrosystems is challenging if in addition to the physical processes also socio-economic and environmental aspects have to be considered. This causes conflicts of interests among various water actors with mostly contradicting objectives and uncertainties about the consequences of potential management interventions. The objective of this paper is to present a methodological framework to support decision making under uncertainties for the management of complex hydrosystems. The proposed framework conceptualises hydrological and socio-economic interactions by constructing a Bayesian network (BN)-based decision support tool for a typical management problem of agricultural coastal regions. Thereby, the paper demonstrates the value of combining two different commonly used integrated modelling approaches. Coupled domain models are applied to simulate the nonlinearities and feedbacks of a strongly interacting groundwater–agriculture hydrosystem. Afterwards, a BN is used to integrate their results together with empirical knowledge and expert opinions regarding potential management interventions. A prototype application is performed for a coastal arid region, which is affected by saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture. It addresses the issues of contradicting management objectives such as sustainable aquifer management vs. profitable agricultural production and the problem of finding appropriate management interventions or policies. Several policy combinations have been analysed regarding their efficiency within different management scenarios in a probabilistic way, which enables decision makers to assess the risks associated with implementing alternative management strategies. In addition, efficient metrics for evaluating performance and uncertainty of the developed task-specific BN are used which underline the reliability of the results.

Determining crop-production functions using multi-objective evolutionary algorithms

The determination of crop production functions which describe the relationship between irrigation water and crop yield under the assumption of optimal irrigation scheduling is a major building block for a more efficient and sustainable water management. In this paper we introduce a methodology to determine the entire crop production function for a given scenario within a single run of a multi-objective evolutionary algorithm. Further we compare the performance of four major algorithms (NSGA-II, NSDE, DEMO, and MO-CMA-ES), and a single-objective approach based on differential evolution on three different scenarios and two different population initialization methods on this problem. We show that the combination of a problem specific initialization with MO-CMA-ES is able to determine crop production functions which are extremely close to actual ones.

Flash Flood Forecasting Combining Meteorological Ensemble Forecasts and Uncertainty of Initial Hydrological Conditions

Abstract Flood forecasting for fast responding catchments encounters problems, especially in terms of short warning periods and a very limited reliability. Within a new stochastic framework based on rigorous rainfall-runoff modelling and Monte Carlo simulations, we consider uncertainties of two sources: (i) uncertainty from the estimation of initial hydrological conditions, and (ii) the uncertainty of the meteorological rainfall forecast. We avoided the high computational demand of extensive Monte Carlo simulations by using a symbiosis between physically-based hydrological modelling and computationally highly efficient artificial intelligence techniques. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting; see Schmitz et al, 2005, and Cullmann, 2006) employs a physically-based hydrological/hydraulic model of the considered catchment for generating, in a first step, the complete range of realistic possible flood scenarios on the basis of a catchment specific meteorological analysis. The resulting database of corresponding input/output vectors – supplemented by generally available hydrological and meteorological data for characterising the catchment situation prior to a storm event – serves, in a second step, for setting up a set of task-specific artificial neural networks (ANN), which finally portray both the rainfall-runoff process and the hydrodynamic flood wave propagation in the river. We subsequently use this tool for investigating the global uncertainty of flash flood forecasting in a small-to medium-sized catchment on the basis of a comprehensive Monte Carlo analysis. Along these lines, the computationally highly efficient PAI-OFF technique allowed performing an extensive number of runs for obtaining ensembles of predicted stream flow that can be used to evaluate probabilities of exceedance of critical river stages/flows via an integration of both the hydrological uncertainty and the meteorological uncertainty. This approach was then implemented and applied to the Freiberger Mulde catchment in the Ore Mountains in Eastern Germany (with an area of about 3000 km2). The results of the overall ensemble predictions in the form of the ensemble mean values unveiled an astonishing underestimation of the recorded flood peak – most due to the bias of the considered initial hydrological conditions.