Active metabolites of proinsecticides and activation processes.
\r\n\tTraditionally, medication, psychotherapy and family support have been considered the best approach to the treatment. Recently, other ways are being explored from a neuropsychological perspective. In this regard, neuromodulation includes different techniques (both invasive and non-invasive), aiming to alter neural activity or excitability. Deep brain stimulation, transcranial direct current stimulation, and, mainly, transcranial magnetic stimulation might be a promising treatment for bulimia nervosa, especially when other treatments fail.
",isbn:null,printIsbn:"979-953-307-X-X",doi:null,price:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"d85fcb0f645301fdc50f42e10eb56607",bookSignature:"Prof. Ignacio Jáuregui Lobera",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7884.jpg",keywords:"Food Intake, Food Addiction, Hedonic Regulation, Homeostatic Regulation, Bulimia Binge Episodes, Binge Eating Disorder, Eating Behavior, Body Dissatisfaction, Body Image Distortion, Eating Disorders Pharmacology, Psychotherapy, Eating Disorders Neuromudulation",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 29th 2018",dateEndSecondStepPublish:"November 19th 2018",dateEndThirdStepPublish:"January 18th 2019",dateEndFourthStepPublish:"April 8th 2019",dateEndFifthStepPublish:"June 7th 2019",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,editors:[{id:"55769",title:"Prof.",name:"Ignacio",middleName:null,surname:"Jáuregui Lobera",slug:"ignacio-jauregui-lobera",fullName:"Ignacio Jáuregui Lobera",profilePictureURL:"https://mts.intechopen.com/storage/users/55769/images/system/55769.png",biography:"Ignacio Jáuregui-Lobera, MD, PhD, MSc, is a doctor of Medicine and Psychology. He has a Master of Science and Nutrition from the University of Barcelona, Spain and a Master of Arts in Applied Statistics from the National University of Distance Education, Spain. He is an expert in Biostatistics (UNED), Advanced Methods of Applied Statistics (UNED), and completed his post-graduate studies in Food-Drug Interactions (University of Barcelona, Spain.) He is the Director of the Behavioural Sciences Institute (Sevilla, Spain) leading the lifelong learning programme “Expert in Eating Disorders and Obesity,” and an Associate Professor at the Pablo de Olavide University in Seville, Spain. He has been working in the field of eating disorders since 1993 and is a recipient of the Extraordinary PsyD Award.",institutionString:"Pablo de Olavide",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"3",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"263843",firstName:"Martina",lastName:"Brkljacic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/263843/images/7798_n.png",email:"martina.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Pesticides as such according to FAO may be defined as any substance or mixture of substances intended for preventing, destroying or controlling any pest, including vectors of human or animal disease, unwanted species of plants or animals causing harm during or otherwise interfering with the production, processing, storage, transport or marketing of food, agricultural commodities, wood and wood products or animal feedstuffs, or substances which may be administered to animals for the control of insects, arachnids or other pests in or on their bodies [1]. The term includes substances intended for use as a plant growth regulator, defoliant, desiccant or agent for thinning fruit or preventing the premature fall of fruit, and substances applied to crops either before or after harvest to protect the commodity from deterioration during storage and transport.
\nPresent day pesticides which are common and widely used have number of adverse affects on non-target organisms. The consequences lead to environment toxicity affecting ecosystem [2]. Therefore safer insecticide deserves attention. A propesticide is a biologically inactive compound requiring structural transformation(s) after application to become pesticidally active. Activation process for propesticides can be one of or a combination of the three following types: (a) chemical (nonenzymatic); (b) biochemical (enzymatic); or (c) physical, e.g., photochemical [3]. In practice, however, propesticidal substances are sometimes active without chemical modification when measured in vitro; nevertheless, their metabolites contribute significantly to the overall biological activity of the applied material.
\nSafer pesticides are those chemicals which have no or minimum acute or chronic toxicity to mammals and harmless to non-target organisms as well as non-persistent in the environment. The harmful effects of pesticides to non-target organisms can be overcome to certain extend by increasing the selectivity pf pesticide. Pesticide selectivity as such can be attained by either physical or biochemical means that is often combined in practice. In the former, only the target species is exposed to the control agent, and this can be accomplished by special formulation or precise application techniques [4]. In the later, selectivity is based on differences of the biochemical processes or target receptors of the pest and non-pest species. Furthermore, physiochemical factor such as differential uptake by and translocation within target and non-target organisms can contribute to disparate biological activities [5]. Nevertheless, differences in metabolic pathways, which convert toxic xenobiotics into less harmful and readily excretable product, and in metabolic rates of various organisms are frequently the basis of selectivity.
\nA biologically inactive compound requires structural transformation(s) after application to become pesticidally active. Derivatives of known active ingredients that are converted to parent compound for activity. These are various process require for the activation of propesticide to the pesticidally active one [6]. Ideally these activation processes takes place only in the target organism. Even though it can also takes place in the environment, including soil and atmosphere. These can be one of or combination of the following four types:
\nThis activation process is carried out in presence of certain enzyme or enzyme system. Enzymatic conversion of a proinsecticide to the active toxophore at the target tissue results in disruption of activating enzyme [7]. The activating enzymes, present in the tissue of target organism, are carrying out certain biochemical function in the body of target organism. When active toxophore are released from the proinsecticide their normal functions are discontinued resulting in killing of organisms. This is also known as suicide inactivation as their natural process is being inactivated [8]. Likewise, there also takes place the following:
Disruption of secondary enzyme system in the same tissue.
Disruption of enzyme at other target tissue.
By detoxification system we meant the various processes carried out by the target organisms to detoxify any xenobiotics compound [9]. These are usually degradation processes and it includes oxidation, reduction, hydrolysis or conjugation reactions. Here, the xenobiotic compounds, i.e., propesticides are acted upon by these processes resulting in production of a more toxic material than the original one.
\nMost of the insects harbor symbiotic or parasitic microorganisms in their guts or hemolymph which posses enzymes lacking in them [10]. The various endogenous xenobiotic processes can be brought under control to activate proinsecticidal agents in such organism specific fashion.
\nIn this path enzymes are not involved. Here, the activation process is not metabolic but results in toxicity because a change in the propesticide occurs in biological milieu.
\nPropesticides can be classified in two ways like based on the number of activation steps involved and based on the type of pest to control [11]. Based on the number of activation steps involved propesticides are classified as single step activation and multiple step activation.
\n\n
The term juvenogen is used to indicate a new class of the complex chemical compounds which generate products with juvenile hormone activity in response to certain biotic or environmental factors.
Juvenogen esters when topically applied, the wax-like ester enters the insect body where it is enzymatically hydrolyzed by the carboxylesterase enzymes.
About two orders of magnitude faster hydrolysis of the juvenogen substrate has been found in the larvae of Dysdercus. Here juvenogen has much higher juvenile hormone activity than the hydrolysis alcohol product itself.
These are derivatives of toxic methyl carbamates insecticides which can be activated to active toxophore either by enzymatic or nonenzymatic. There are also other two possible mechanism of activation of these groups of compounds. These are:
Acid catalyst hydrolysis of the N-S bond
Thiol induced thiolysis to form a mixed disulfide and toxic methyl carbamate.
\n
Propheromones, xanthene dyes and natural photosensitizers are some types of compounds those functions by photoactivation.
Xanthene dyes like erythrisin, fluorescin act as phototoxic agents against bacteria and insects.
Natural photosensitizers like terthienyl and polyacetylines, DNA-damaging agents like dictamine, harmaline are some compounds which act as insecticides.
Propesticides requiring more than one metabolic process for activation are known as prepropestides. These are:
\n\n
These occur as the toxic principle in legume genera.
One of the first poisons for which the biochemical mode of action was precisely described.
Nissol and Fluemethyl are two commercial product used as acaricides are of relatively lower mammalian toxicity.
This is activated by two stage of activation process. First is hydrolysis of the cycloprate to free acid followed by formation of carnitine ester [12, 13]. Thus it inhibits the activity of carnitine in transport of fatty acids. Here, the carnitine is an amino acids commonly occurring in the liver and in skeletal muscles that function in the transport of fatty acids across mitochondrial membrane.
\nThese are potent inhibitors of juvenile hormone production in insects.
\nAccordingly propesticides are classified as proinsecticides, proherbicides, profungicides and prorodenticides.
\nN-Methylcarbamates are another major group of insecticides inhibiting AChE. Although the proinsecticidal features of OP compounds were discovered after their development, proinsecticidal carbamates were designed in Fukuto’s laboratory by systematic derivatization to N-phosphoryl, N-sulfenyl, and related carbamates [14, 15, 16]. The biological and toxicological properties of these carbamates could be tailored according to particular use requirements by changing the derivatizing moiety, and thus the physicochemical properties, such as lipophilicity (log P), of the resulting product. The propesticide is activated in the insect by chemical hydrolysis by thiols or other nucleophiles. Nereistoxin is a cyclic disulfide isolated from a marine annelid [17, 18]. It served as the lead compound for the development of the proinsecticides cartap and thiocyclam, both converted into dithiolane acting at the nicotinic acetylcholine receptor of insects. The precocenes, such as precocene 2, on the other hand, were isolated from Ageratum sp. plants and found to inhibit the terminal (oxidative) step of JH biosynthesis in the corpora allata, causing precocious development of the insect larva. These anti-juvenile hormones, also called proallatotoxins, are “suicide inhibitors” because the cytochrome P450 catalyzed oxidation of the chromene generates epoxide that reacts with neighboring nucleophiles of the enzyme protein, causing massive cellular damage. Diafenthiuron is a thiourea insecticide inhibiting mitochondrial ATPase and acts via its carbodiimide metabolite [19]. The phenylpyrazole fipronil contains a sulfoxide group that can undergo cytochrome P450-catalyzed oxidation in insects to yield a more potent sulfone metabolite. These are meant for controlling insect pest [20]. Some of the proinsecticides along with their active metabolite and activation processes are given in the Table 1.
\nPropesticides | \nActive metabolite | \nActivation process | \n
---|---|---|
Parathion | \nParaoxon | \nOxidative disulfuration | \n
Malathion | \nMalaoxon | \nOxidative disulfuration | \n
Disulfoton | \nOxydisulfoton | \nOxidation | \n
Trichlorfon | \nDichlorvos | \nRearrangement/dehydrochlorination | \n
Acephate | \nMethamidophos | \nHydrolysis | \n
Carbosulfan | \nCarbofuran | \nHydrolysis | \n
Furathiocarb | \nCarbofuran | \nHydrolysis | \n
Benfuracarb | \nCarbofuran | \nHydrolysis | \n
Thiodicarb | \nMethomyl | \nHydrolysis | \n
Cartap | \nNereistoxin | \nHydrolysis | \n
Bensultap | \nNereistoxin | \nHydrolysis/ | \n
Thiocyclam | \nNereistoxin | \nSulfur extrusion/cyclization | \n
Diafenthiuron | \n— | \nOxidative desulfuration | \n
Cycloporate | \nCyclopropanecarboxylic acid | \nHydrolysis | \n
Chlorfenapyr | \n— | \nOxidation | \n
Sulfluramide | \n— | \nHydrolysis | \n
Fipronil | \nFipronil sulfon | \nOxidation | \n
Tralomethrin | \nDeltamethrin | \nDebromination | \n
Active metabolites of proinsecticides and activation processes.
Fluoroacetic acid and fluoroacetamide are “lethal precursors” to 2-fluorocitrate. Bitter scilliroside, from the red squill, can be hydrolyzed by glycosidases in vivo to scillirosidin, its aglycone, which was suggested to be the ultimate rat toxicant. There are a few rodenticides that have either been designed to act as prorodenticides or were found to act as such.
\nPropesticides | \nActive metabolite | \nActivation process | \n
---|---|---|
Zn3P2 | \nPH3 | \nHydrolysis/acidolysis | \n
Flouroacetic acid or flouroacetamide | \nFlourocitric acid | \nCondensation with oxaloacetate/hydrolysis and condensation with oxaloacetate | \n
Scilliroside | \nScillirosidin | \nHydrolysis | \n
Bromethalin | \n— | \nN-Dimethylation | \n
Profungicides is thiram, or tetramethylthiuram disulfide that is reduced to the corresponding dithiocarbamate, the actual bioactive principle. Dithiocarbamate derivatives of glycerol and other polyols releasing or other related fungicides have also been prepared. The carbonyl group was shown to be reduced stereoselectively into the more potent fungicide triadimenol in fungi and plants, as well as in bacteria. Spirolactone derivatives of the benzoquinone fungicide chloranil provided photostable derivatives that release the parent compound by slow hydrolysis. These are meant for controlling pathogens causing plant diseases. Some of the profungicides along with their active metabolites and activation processes are given in Table 2.
\nPropesticide | \nActive metabolite | \nActivation process | \n
---|---|---|
Thiram | \nN,N-dimethylthiocarbamate | \nReduction | \n
Dinobuton | \nDinoseb | \nHydrolysis | \n
Benomyl | \nCarbendazim and butyl isocyanate | \nElimination/hydrolysis | \n
Thiophanate-methyl | \nCarbendazim | \nHydrolysis/cyclization | \n
Triadimefon | \nTriadimenol | \nReduction | \n
Bupirimate | \nEthirimol | \nHydrolysis | \n
Pyrazaphos | \n\n | Hydrolysis | \n
Probenazole | \nSaccharin | \nHydrolysis | \n
Acibenzolar-S-methyl | \nCGA 210007 | \nHydrolysis | \n
Active metabolites of profungicides and activation processes.
MCPB and related homologous aryloxyalkanoic acids with an odd number of CH2 groups provide aryloxyacetic acids, such as (2-methyl-4-chlorophenoxy) acetic acid, whereas those with an even number are degraded to herbicidally inactive phenols [21]. The occurrence of resistance in weeds to triallate has been attributed to reduced sulfoxidation, i.e., bioactivation, rates. The photosynthesis inhibitor N,N-dimethyl phenylurea diuron is converted into the corresponding N-methyl phenylurea DCPMU upon oxidative phosphorylation [22]. Dealkylation of the N,N,N_,N_-tetraethyl triazine derivative chlorazine to trietazine then to simazine increases the photosynthesis inhibitory activity by several orders of magnitude. For the rice herbicide thiobencarb (S-4-chlorobenzyl diethylthiocarbamate), reductive dehalogenation occurring in soil yields the S-benzyl derivative, believed to be responsible for phytotoxicity in vivo. These are meant for controlling weeds. Some of the proherbicides along with their active metabolites and activation processes are given in the Table 3.
\nPropesticides | \nActive metabolite | \nActivation process | \n
---|---|---|
MCPB | \nMCPA | \nβ-oxidation | \n
Naproanilide | \n2-Naphthoxyacetic acid | \nHydrolysis | \n
Chlorazine | \nTrietazine | \nDealkalization | \n
Trietazine | \nSimazine | \nDealkalization | \n
EPTC | \nEPTC sulfoxide | \nOxidation | \n
Triallate | \nTriallate sulfoxide | \nOxidation | \n
Diuron | \nDCPMU | \nOxidative dealkylation | \n
Linuron | \nDCPMU | \nDemethoxylation | \n
Methazole | \nDCPMU | \nHydrolysis/reduction | \n
Chlorthiamid | \nDichlobenil | \nDehydrosulfuration | \n
Metflurazone | \nNorflurazon | \nOxidative dealkylation | \n
Flamprop-methyl | \nFlamprop | \nHydrolysis | \n
Bilanafos | \nPhosphinothricin | \nHydrolysis | \n
2,4-DEP | \n2,4-D | \nHydrolysis/oxidation | \n
Cinmethylin | \n2-Hydroxy-1,4-cineole | \nOxidative dealkylation | \n
Pyrazolynate | \nDestosyl pyrazolinate | \nHydrolysis | \n
Pyridate | \nCL9673 | \nHydrolysis | \n
Ethephon | \nEthylene | \nElimination | \n
Active metabolites of proherbicides and activation processes.
Although commonly used structural modifications, carried out during routine structure-activity relationship studies and lead structure optimizations, affect both pharmacokinetics and pharmacodynamics, chemical alterations used in propesticide design are aimed to improve the biological profile by optimizing exclusively the pharmacokinetics of the toxicant. Potential advantages of a propesticide can be summarized as follows:
\nAlteration of physicochemical properties.
Altered physicochemical properties leading to improved stability, solubility, or lipophilicity influencing distribution in organism (systemicity).
Sustaining activity
Delayed or sustained action due to the slow release of the active agent from its derivatives.
Increases selectivity
Increased selectivity, that is, decrease toxicity toward non-target species, due to different metabolism of the parent compound and its derivatives.
The structural and metabolic diversity of various pest control chemicals shown above demonstrates the usefulness of the “Trojan horse” principle of chemical formulation. Future research efforts, either capitalizing on known pesticide design practices or discovering new ones based on differences between the xenobiotic metabolisms of various organisms, will lead to new and selective agrochemicals that find and hit the target enzyme or receptor of the pest as “magic bullets.”
\nAs is well-known, the current situation in the Jordan River Basin (JRB) is characterized by water scarcity and a history of water-related conflicts. The World Resources Institute (WRI) [1] classifies the JRB riparian countries among the most water-stressed countries in the world with a ratio of withdrawals to supply of more than 80%. Jordan is considered the poorest country in terms of water resources and most of its land is considered to be dry land. Except for the north-western highlands, 90% of the country receives less than 200 mm rainfall per year with an uneven distribution over regions and high fluctuation from year to year [2]. The population in the JRB region suffers from repeated water shortages which are more severe during the hot summers. Households devote considerable efforts to ensure their daily supply of water. In rural areas, the scarcity of water is among the main difficulties encountered by farmers and in urban areas, tap water is sometimes of bad quality, with frequent shortages. Households are forced to buy bottled or tank water at higher prices for their essential needs.
\nThe severe water-related inequalities [3] are important drivers behind the regional conflicts and have created long-term political instability in the Middle East [4]. Rivalry has persisted over time as an imminent problem that has often been settled through force rather than peaceful cooperation [5]. Individual, uncoordinated actions by all riparian states have resulted in a dramatic change in water flows in the Jordan River Basin. According to United Nations Economic and Social Commission for Western Asia (UN-ESCWA) and Bundesanstalt für Geowissenschaften und Rohstoffe (BGR) [6], annual discharge of water into the Dead Sea under near-natural conditions would be approximately 1300 million m3, but man-made interventions along the Jordan River and its tributaries have reduced this to 20–200 million m3 at present. The massive reduction in water availability, particularly in the lower Jordan area, has fueled disputes between the riparian countries. Such disputes continue to obfuscate the relationships between Israel and Lebanon and between Israel and Syria [7]. Looking at the future, prospects for the region seem to be bleak. In the short run, the Syrian crisis with its large regional impact poses a serious threat to livelihoods and development. In the long run, climate change and a rapidly expanding population will continue to put more pressure on water resources.
\nClimate change is projected to have large impacts on weather patterns across the globe in the future. The Intergovernmental Panel on Climate Change (IPCC) in its 5th Assessment Report (AR5) noted that over the period 1800–2012, the average global temperature increased by 0.85°C. Moreover, Flato et al. [8] indicated that precipitation patterns deviate more frequently from long-term average trends in both volume and erraticism with adverse impacts on the society [9]. Realities of climate change are underpinned by Easterling et al. [10] who related climate change to increased drought incidence, flooding hazards, and reduced biodiversity. This clearly justifies calls for action to mitigate deteriorative effects of climate change. In this study, the focus is on the impact of climate change and water availability for households, effects on recharge to the aquifers, and on economic revenue in the Jordan River Basin, which as was argued above, is an area in the world where water scarcity is a major threat to economic growth and political stability.
\nBefore we come up with practical and well-informed policy solutions, two challenges need to be addressed. First, the low resolution of global climate change models (100–500 km) ignores in-grid variability like complex topographical features and is, therefore, of limited use for impact studies [11]. Second, climate change effects cannot be restricted to changes in rainfall and temperature patterns alone but require a systemic response that accounts for spatial and temporal diversity of the natural resource base, interconnectedness of surface, and subsurface flows and influence on availability of water in volume and quality.
\nThe first issue is addressed by using downscaled precipitation and climate parameters of Abdulla et al. [12] for meteorological stations in the Yarmouk Basin, covering parts of Syria and Jordan. The second challenge is met by using a water economy model of the JRB, which describes the natural and controlled flows in volume and quantity for Jordan River Basin as hydrological entity [13].
\nThe remainder of this chapter is organized as follows. Section 2 gives a brief description of the structure, empirical basis, and calibration of the JRB water economy model. Section 3 presents the scenario formulation, including downscaling. Section 4 reports on the impact of climate change and Section 5 concludes and indicates pathways for further research.
\nTheoretically, the JRB model is a special case of the general class of welfare optimization models [13], where the innovative part is the inclusion of hydrology as central component of the production technology. Hence, control of flows (extraction of groundwater; use of water by humans; animals; and agriculture; transfer of water through canals; wastewater treatment and desalinization) conforms to basic principles of microeconomics with constraints that respect conservation. For economics, this implies that commodity balances hold; for hydrology, mass balances for pure water as well as for pollutants dissolved in water are maintained.
\nIn its representation of the water economy of the JRB, the model distinguishes 48 districts, and 26 (two-weekly) time steps. Water can flow within and between five different layers. These comprise a surface layer on land for natural flows, a surface layer on land representing anthropogenic influences on water, a river layer, a root zone, and a layer representing the aquifer zones in the basin. Finally, next to clean water, the model can accommodate three types of pollutants: salinity, biological oxygen demand (BOD), and nitrate. The combination of place, time, layer, and quality defines a “cell” within the model, which acts as a source and destination of flows, representing the high level of interconnectedness of flows within the JRB.
\nFigure 1 provides a schematic overview of flows within the JRB between the main layers, as well as flows entering the basin as whole (rainfall and lateral flows from outside the basin) or leaving the basin (evaporation, lateral flows leaving the basin). In the model, each of the layers and connections with other layers is modeled in detail, following hydrological laws as well as reflecting anthropogenic activity (pumping, irrigation, use by humans, livestock, industry and municipalities, sewage, waste water treatment and reuse, and water harvesting).
\nSchematic overview of flows within the JRB basin.
As mentioned before, the JRB water economy model represents pollution of three specific types—salinity, nitrate, and BOD. Instead of using a set of attributes to represent water quality in each of these three dimensions at each point in time and space, the model represents these pollutants as flows, for which balances must hold, as for pure water. Hence, conceptually, pollutants are represented as flows with standard concentrations: 25 g/l for Cl, 2 g/l for NO3, and 10 g/l for BOD. Observed concentrations are obtained by mixing the volumes of these flows with the volume of pure water. However, such a separation of flows would not do justice to the fact that pollutants flow with the water, and are not separable in reality. Hence, in the model application, flows are always combined. Where water quality changes, this is explicitly represented as a production process (water treatment) or a natural process (decreased quality through the uptake of pollutants from the soil). Consumers, livestock, and plants (irrigation) have to take the quality of water as it is at the location where it is offered, and this represents the channel through which quality changes enter into the system and affect yields, quality of produce and revenue.
\nGiven the overriding importance of agriculture as economic activity in the JRB, the focus in assessing economic impact is on agriculture. In the JRB, crop cultivation is characterized by a dual system, where irrigation-based agriculture and rain-fed agriculture both exist. Hence, in principle, each farmer may have both types of land under cultivation. If profit maximization is taken as point of departure, a farmer in principle controls: (1) the amount of land under cultivation, (2) the share of the land that is irrigated, and (3) the crop(s) cultivated. In its simplest interpretation, profit maximization would be driven by the amount of water available for irrigation; the costs of inputs as well as irrigation water; and the output prices of different crops, while constraints would be defined by the response of different crops to the supply of water and other inputs.
\nThe water response module of the model is richer than this simple representation, but also imposes some simplifying assumptions. To start with the latter, it is assumed that the crop composition observed in the base year 2010 is maintained under changing water availability. Furthermore, prices for inputs, water, and crops are assumed to remain constant, as are costs for irrigation. Richness of the module is achieved by acknowledging the following facts of agriculture in the JRB:
Whenever land is irrigated, irrigation applied is optimal for the crop under cultivation
Quality of land is not uniform over the JRB or even within districts
The highest-quality land is taken into production first; then, lesser qualities are used for cultivation
Water available for irrigation may contain (high levels of) salt, BOD, or nitrate
There is a large gap between yields on irrigated lands and yields on rain-fed areas
Yields on rain-fed areas mainly respond to changes in water availability
The area of rain-fed land does not respond to changes in water availability
This results in a decision tree for the farmer as depicted in Figure 2. For rain-fed lands, the farmer has no control over the response, as water availability in the root zone as well as water quality determine the yield on the given area. For irrigated lands, the farmer controls the area under irrigation (this block is marked purple, to indicate this is a decision variable). Yield response for the total area under irrigation is the result of expansion of irrigated areas to include land of lesser quality, where the optimal yield under irrigation is lower, leading to lower average yields for the total irrigated area. As for rain-fed agriculture, salinity has a negative impact on crop yields, and hence, also impacts on the yield response. Below, the chapter expands on the formal description of the water response module.
\nFarmer’s response to changes in water quality and quantity.
Formally, the model can be represented by the following four equations:
\nwhere b is the exogenous water availability (net rainfall, springs), \n
Eq. (4) shows for each \n
Eqs. 1–3 specify each of the destinations as fixed fractions of the total available model stock x. Since retention is part of the natural outflows, the fractions logically have to add to one. Furthermore, the fractions have to reflect one important restriction, viz. that the user has to accept the quality of the available stock x. Therefore, the fractions do not depend on index h. More precisely, (1)–(3) can be written as:
\nAlthough the equations look relatively simple, they cover a wealth of different flows especially due to the dimensionality of the system. We distinguish a hydrological component, anthropogenic activities, and actual human water use.
\nThe hydrological component is represented by matrix A. Element \n
In addition to these natural flows, the model has exogenous natural flows denoted as vector b. This parameter covers both net rainfall and water from springs. Net rainfall is measured after subtraction of immediate evaporation. It is important to be precise in this respect since immediate evaporation amounts to more than 10% of gross rainfall for the JRB as a whole. Spring water is essentially transferred from the groundwater zone to the surface zone of the district where it is used or to the river segment that it feeds. Hence, it has negative and positive values, summing to zero. For the first period \n
Anthropogenic activities are represented by several projects j: groundwater pumping, rainwater harvesting, river outlets (dams), surface water transfer (canals, carriers), and irrigation. The input volumes of these projects are given by vectors \n
Five types of water use are distinguished: household use, municipal use, industrial use, crop water use, and livestock use. Household water is further subdivided into tap water, truck water, roof water, and bottled water. Together, these destinations make up the set of water use types i. The public water distribution system delivers household tap water, municipal water, and industrial water. Eq. (1) specifies each type of water use as fraction of the available water stock. Therefore, \n
When strictly following its definition above, matrix \n
Here, we summarize the main properties of the model:
No water creation:
for each \n
for each \n
for each \n
Stock-driven flows: all endogenous natural flows, all project inputs, and all volumes of water use are specified relative to the total available water stock
Blending property: all users have to accept the quality of the available water stock; hence the fixed fractions in Eqs. (5)–(7) are independent of water quality class \n
Exhaustive allocation of outflows: the sum of the fixed fractions of all destinations, including those outside the JRB, is equal to one.
In model symbols the latter condition reads:
\nDue to this condition, we can write Eq. (4) also fully in terms of total available stock \n
This formulation is used in the iterative calculation of the equilibrium stock levels that solve the model, starting from an initial value, say \n
Once the equilibrium stock \n
To this end, the simulation model includes a crop module that distinguishes K different crops, indexed k, and two land types, indexed m, viz. irrigated land (with or without protection) and rain-fed land. Water use and crop yields per hectare are different across these two land types. We introduce the following notation.
\n\n\n
Net revenue is calculated as follows:
\nThe relation between actual harvested area \n
In Eq. (12), \n
In Eq. (13), \n
Factor \n
In these equations, prices \n
The following steps were taken to evaluate climate change impacts. First, we organized the daily precipitation data of the five climate change scenarios over the period 1980–2100 for 23 stations in the Yarmouk Basin in a excel spread sheet. Second, since variety of the modeled data are difficult to compare at daily level, we aggregated precipitation at yearly level and averaged the data of individual stations over cohorts of 20 consecutive years (2000–2020, 2021–2040, etc.) for each district. Third, we calculated relative changes over 40 (average of 2000–2020 minus 2040–2060) and 80 (average of 2000–2020 minus 2080–2100) years (Table 1). Fourth, using a linear regression that estimated a small positive gradient for the northern latitude [16], the predicted rainfall patterns for the Yarmouk river basin have been extrapolated to the entire JRB, constituting a database for the scenario analysis of daily rainfall at district level for the year 2050 (regression results are found in the Annex). Finally, we selected the CanESM2 model since, here, grid of the model overlaid best with the JRB area (in particular, better than CGCM3, see Figure 3); within CanESM2, RCP8.5 is selected as the most pessimistic scenario on future rainfall.
\nRelative changes in annual precipitation by climate change scenario.
Grid overlay of CanESM2 and CGCM3 with JRB.
Following this procedure, we predict that for the whole of the JRB, rainfall in 2050 will be around 10% lower than present precipitation, but with substantial spatial spreading (see Figure 4), where rainfall is even predicted to increase in the eastern part of the JRB.
\nRainfall in 2050: Predicted change relative to present conditions.
Application of the JRB model using these rainfall figures leads to the conclusion that the most important impact is an overall reduction of the net revenue from crop cultivation in the JRB as a whole of 150 million USD, with major losses in Israel, Jordan, and the West Bank. Syrian revenues would increase, reflecting the increase in rainfall in large parts of the country located in the JRB (see Figure 5).
\nImpact of climate change on net revenue.
However, the rainfall shock is not fully transmitted to agricultural activity: although the reduction in rainfall is a major shock, it is partly compensated by reduced evaporation (about half the shock), and lower river flows and hence lower extraction volumes. In addition, recharge of groundwater is affected very negatively, and the outflow to the Dead Sea is also substantially lower (20 million km3). Despite the drop in recharge, salinity of groundwater resources increases only marginally (0.01% for the JRB as a whole, with a “peak” of 0.02% for Israel). Decreasing availability of water implies a decrease in the amount of untreated waste water, as household demand falls and less waste water is produced. This in turn implies that irrigation water contains relatively less untreated waste water and that leakage of contaminated water to the root zone also decreases, leading to a reduction in BOD in water used for irrigation of 5.5% on average for the JRB. Figures 6–8 illustrate the impact on run-off to the rivers, extraction from the rivers and Lake Tiberias and groundwater stock changes relative to the present baseline. Particularly, the latter result is cause for concern about the future as it is clear that unchanged extraction policies would lead to unsustainable extraction from groundwater resources in the coming decade.
\nImpact of climate change on run-off.
Impact of climate change on extraction.
Impact of climate change on groundwater stock changes.
Climate change is a major concern for policy makers in the Middle East who aim to protect their constituency from adverse effects on water availability for food security and the environment. Yet, to provide a proper foundation for informed decisions on adoption or mitigation of climate change effects, two issues have to be resolved. First, results of global circulation models should be downscaled from their coarse (50–100 km) grids to a meaningful spatial resolution. Second, effects of changes in rainfall should reflect the hydrological complexity of natural and controlled surface and subsurface flows that jointly are responsible for water supply.
\nIn this study, we addressed these two issues for the JRB by a statistical downscaling of the climate change scenarios for precipitation from global circulation models in conjunction with the application of a water economy model that describes the water flows in detail and, additionally, reports on the impact of climate change on water availability (run-off and groundwater recharge) and agricultural productivity in JRB.
\nThe results reveal that there is a significant reduction in the surface run-off of an amount greater than 160 MCM (28%) in the JRB that affects the extraction volumes out of the rivers. Most reduction in run-off to rivers is found in Syria (42%) and Jordan (40%). There is also a significant reduction in groundwater recharge of an amount of about 180 MCM (11%), which seriously threatens the already overexploited groundwater stock. Accordingly, the agricultural productivity reduces by 160 million USD, with largest effects on Jordan (23%), the West Bank (16%), and Israel (12%). The impact of climate change on water quality is minor; quality even improves due to lower waste water volumes that blend with fresh water resources.
\nThe results obtained in this study could be used as a reference for regional water resources management in the JRB. The results also provide detailed information on spatially explicit effects that allow local policy makers to take matters in their own hand. As such, the model outcomes can also underpin the stakeholder discussions on distribution of water resources to support negotiations on water transfers, within and between basins.
\nFurther validation exercises should strengthen the reliability of results obtained by the global climatic models (GCMs). Incorporation of local knowledge on water management that transcends academic disciplines with practical solutions and on the ground reality further strengthens the water economy model’s representation and its utility as decision support tool.
\nSee Figures a1–a10.
\nRelative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP2.6, time lapse 40 years.
Relative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP2.6, time lapse 80 years.
Relative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP4.5, time lapse 40 years.
Relative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP4.5, time lapse 80 years.
Relative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP8.5, time lapse 40 years.
Relative future changes in precipiation against South-North gradient. Scenario: CanESM2 GCM, RCP8.5, time lapse 80 years.
Relative future changes in precipiation against South-North gradient. Scenario: CGCM3 GCM, A1B, time lapse 40 years.
Relative future changes in precipiation against South-North gradient. Scenario: CGCM3 GCM, A1B, time lapse 80 years.
Relative future changes in precipiation against South-North gradient. Scenario: CGCM3 GCM, A2, time lapse 40 years.
Relative future changes in precipiation against South-North gradient. Scenario: CGCM3 GCM, A2, time lapse 80 years.
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