IMAGE - User contributions [en]

Land and biodiversity policies/Agricultural production system

2021-11-23T20:25:35Z

Vosdl:

{{PolicyResponsePartTemplate
|PageLabel=Agricultural production system
|Sequence=3
}}
<div class="page_standard">
<h2>Interventions targeting the agricultural production system</h2>
The agricultural production system concerns how animals are raised and crops are cultivated. The characteristics of a particular system, for example what inputs are required to produce one unit of product, define the environmental impacts. Various interventions may increase the efficiency of production systems, and thus lead to reductions in inputs or in environmental impacts.

Results of an efficiency increase in livestock management are presented in the PBL report The Protein Puzzle ([[PBL, 2011]]; [[Stehfest et al., 2013]]). Alternative cropping practices are summarised in Roads from Rio+20 ([[PBL, 2012]]). (see also [[The Protein Puzzle (2011) project]] and [[Roads from Rio+20 (2012) project]].

{{DisplayFigureLeftOptimalTemplate|Flowchart Land and biodiversity policies (B)}}
</div>

'''Carbon tax in agricultural production'''

Agricultural production produces greenhouse-gas emissions: Fertilization of crops and manure from livestock produce N<sub>2</sub>O emissions, and enteric fermentation from ruminants and production of rice in paddy fields results in CH<sub>4</sub> emissions. By placing a carbon tax on these emissions, similar to policy implemented in the [[Climate policy]] model, these emissions can be reduced in a cost-optimal way. This is implemented in the [[Agricultural economy]] model and results in substitution of consumption towards less emission-intensive products, additional intensification of agricultural production, and in reduced consumption leading to effects on food security. This policy is implemented in a model intercomparison study with IMAGE, GLOBIOM, CAPRI and MAGNET ([[Frank et al., 2018]]).
{{#default_form:PolicyResponsePartForm}}
{{PolicyInterventionSetTemplate
|Header=Improve livestock systems
|Description=Interventions to improve livestock systems could include use of breeds that have higher feed conversion rates, require another ratio of feed composites, or produce less manure. Changes in feed conversion or feed composition, for example the ratio of grazing to feed crop feeding, have an impact on demand for grazing and cropland. Thus, changes to these systems will lead to other environmental impacts and other patterns of agricultural land use. For instance, quantity and quality of manure produced affect nitrogen emission levels and thus also nutrient balances and climate change impacts. In addition, biodiversity is affected by nitrogen emissions. Interventions can also be directed to improving animal welfare, but in most cases, higher animal welfare standards require more input per unit of production ([[PBL, 2011]]). Storage and application of manure varies with livestock systems, and affects crop yields and emission levels. A secondary impact of increasing feed efficiencies could be cost reductions, leading to a similar feedback effect as described for changes in demand.

Two interrelated interventions in the cropping system are distinguished:
# improved cropping systems or varieties;
# increasing crop and grass yields or increasing cropping intensity (number of crops per year).
Management in agriculture is an interplay of the cultivar chosen, soil management, fertiliser and other inputs, and the choice and timing of each cultivation step. The first interventions focus on reducing often negative external effects other than land use, and the second intervention targets the use of as small land areas as possible.
|PISet=Improved manure storage;Integrated manure management;Changes in crop and livestock production systems;Intensification/extensification of livestock systems;Changes in feed ration;Improvement of feed conversion;Improved manure storage;Change in grazing intensity;Increased livestock productivity
}}
{{PolicyInterventionSetTemplate
|Header=Improve cropping systems or varieties
|Description=Improved cropping systems or varieties could increase the use efficiency of inputs including water and nutrients. Inputs fine-tuned to crop requirements would lead to less nitrogen emissions or less water use per tonne of crop and, would reduce the impacts on biodiversity and climate. While improved management could also lead to higher yields (see below), improved systems could mean a shift in inputs, such as labour, capital, land, fertiliser and water. This may alter the cost price of agricultural products, market prices and consumption.
|PISet=Changes in crop and livestock production systems
}}
{{PolicyInterventionSetTemplate
|Header=Crops and grass yields
|Description=Yields can be increased with other varieties, for example, to increase the potential yield, or with improved management (thus, close the yield gap). However, other, more suitable crop varieties often also need different types of management in order to produce higher yields.
|PISet=Improved irrigation efficiency;Improved rainwater management;Integrated manure management;Changes in crop and livestock production systems
}}
{{PolicyInterventionSetTemplate
|Header=Cropping intensity
|Description=The cropping intensity can be increased by multiple cropping (more harvests per year) depending on climatic conditions, or by decreasing the area left fallow. Both interventions would decrease the required production area for all crops but could also increase the environmental impacts per hectare of crops. Where lower area requirements decrease biodiversity and climate impacts, the environmental impacts per hectare could increase them again. Thus, to decrease biodiversity loss, yield increases need to go hand in hand with system changes to reduce external impacts. Increased cropping intensity increases the risk of soil degradation without adaptation of cropping rotations or soil management.
|PISet=Changes in crop and livestock production systems
}}
{{PolicyInterventionSetTemplate
|Header=Carbon tax in agricultural production
|Description=Agricultural production produces greenhouse-gas emissions: Fertilization of crops and manure from livestock produce N2O emissions, and enteric fermentation from ruminants and production of rice in paddy fields results in CH4 emissions. By placing a carbon tax on these emissions, similar to policy implemented in the [[Climate policy]] model, these emissions can be reduced in a cost-optimal way. This is implemented in the [[Agricultural economy]] model and results in substitution of consumption towards less emission-intensive products, additional intensification of agricultural production, and in reduced consumption leading to effects on food security. This policy is implemented in a model intercomparison study with IMAGE, GLOBIOM, CAPRI and MAGNET ([[Frank et al., 2018]]).
|PISet=Changes in crop and livestock production systems
}}
{{ContentPartsTemplate}}

Land and biodiversity policies

2021-11-23T20:20:55Z

Vosdl:

{{PolicyResponseComponentTemplate
|Overview=Policy interventions overview
|PITable=No
|IMAGEComponent=Climate policy; Air pollution and energy policies; Forest management; Agricultural economy; Land-use allocation; Livestock systems; Energy demand; Energy supply; Nutrients; Water; Crops and grass;
|ComponentCode=LBP
|FrameworkElementType=response component
|AggregatedComponent=Policy responses
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{{#default_form:PolicyResponseComponentForm}}<div class="page_standard">

The increase in material wealth, population and economic growth have led to a large demand for agricultural products and transformation of large parts of the land surface. The wide range of environmental issues related to agriculture and forestry include distorted nutrient balances, biodiversity loss, greenhouse gas emissions from land use and land-use change, soil degradation, and water stress due to agricultural water demand. These issues can be addressed from a sector perspective focusing on the respective system (e.g., [[Nutrients|nutrients]], [[water]], see the respective components). However, these issues are linked by demand for land-based products, and by land management.

The IMAGE framework enables a systems approach to analyse policy interventions targeting the impacts of land use on biodiversity and climate change. To identify interventions that could reduce the impacts of agriculture and forestry on the environment, the system takes account of the chain linking demand for food, feed, wood, and bioenergy, to types of production systems and to landscape impacts.

Policy interventions can target demand for commodities (Figure A), the production system, for instance, with respect to efficiency of natural resource use (Figure B and C), or a more systemic approach to regulating land use for different purposes within a landscape (Figure D). Regulation of land use implies managing the land resource base by designating areas to specific purposes, such as excluding protected natural areas from agricultural use, or preventing deforestation. Alternatively, regulation could be in the form of financial incentives to create value for currently non-market ecosystem services, such as emission reduction from deforestation combined with biodiversity conservation (e.g., {{abbrTemplate|REDD+}} schemes) and other forms of payment for ecosystem services ({{abbrTemplate|PES}}).

While this section focuses on the impacts on biodiversity, climate change, water and nutrient balances, some policy interventions also have implications for other policy domains, such as food security, human health and animal welfare.

==Model description==
The interventions described in this section are implemented in different parts of the IMAGE 3.2 framework, and are also addressed in the components in which the respective processes are described.

Policies that change demand for agricultural products ([[Land and biodiversity policies/Agricultural demand|Agricultural demand part]]) are implemented in the agricultural economic model, thus taking into account the impacts on trade and demand in other regions. In IMAGE 3.2, change in wood demand is addressed in the model via a simple relationship with GDP, or by using external input data on wood demand (see Component [[Agricultural economy]]). Demand for second-generation bioenergy crops is addressed in the [[Energy supply and demand|energy model]].

Changes in production systems ([[Land and biodiversity policies/Agricultural production system|Agricultural production system part]]) are modelled in IMAGE using alternative input parameters. For the relevant inputs in e.g. the [[Land-use allocation|land-use allocation]], [[Livestock systems|livestock]], and [[Nutrients|nutrient]] modules, these changes are consistent with those in the [[Agricultural economy|agro-economic]] model, to ensure appropriate representation of the (cost) structure of production. Production system changes, for example those induced by taxes or scarcity of endowments, are implemented in the agro-economic model and adjusted in other modules, accordingly.

Land-use regulation ([[Land and biodiversity policies/Forestry sector|Forestry sector]] and [[Land and biodiversity policies/Land-use regulation|Land-use regulation]] part), which is the regulation of land supply, is modelled as a consistent resource constraint in the [[Land-use allocation|land-use allocation]] model and the [[Agricultural economy|agro-economic model]]. This last model takes account of the economic effects of restricted land supply. For example, {{abbrTemplate|REDD+}} and {{abbrTemplate|PES}} are implemented not as additional productive functions, but by reducing the land supply in the agro-economic model. The spatial dimension of such land-use regulation, like the expansion of protected area, is taken into account in the [[Land-use allocation|agricultural systems]] module, and affects via the resulting land use pattern all down-stream processes.
</div>

Nutrients

2021-11-23T19:58:23Z

Vosdl:

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project;Shared Socioeconomic Pathways - SSP (2014) project;The Protein Puzzle (2011) project
|IMAGEComponent=Drivers;Agricultural economy;Land-use allocation;Agriculture and land use;Aquatic biodiversity;Emissions;Land cover and land use;Livestock systems
|KeyReference=Beusen, 2014;Beusen et al., 2015;Beusen et al., 2016;Morée et al., 2013
|Reference=Bouwman et al., 2013c;Galloway et al., 2004;Zhang et al., 2010;Diaz and Rosenberg, 2008;UNEP, 2002;Rabalais, 2002;Beusen et al., 2015;Beusen et al., 2016
|InputVar=Population - grid;GDP per capita - grid;Land cover, land use - grid;Animal stocks;Livestock rations;Manure spreading fraction;Nitrogen deposition - grid;Actual crop and grass production - grid;Production system mix;Fertiliser use efficiency
|OutputVar=NH3 emissions - grid;N and P discharge to surface water - grid;Soil N budget - grid;Soil P budget - grid;N and P in wastewater discharge - grid
|ComponentCode=N
|FrameworkElementType=state component
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<div class="page_standard">

Human activity has accelerated the Earth’s biogeochemical nitrogen (N) and phosphorus (P) cycles through increasing fertiliser use in agriculture ([[Bouwman et al., 2013c]]). Increased use of N and P fertilisers has raised food production to support the rapidly growing world population, and increasing per capita consumption particularly of meat and milk ([[Galloway et al., 2004]]).

The side effect is that significant proportions of the mobilised N are lost through ambient emissions of ammonia (NH<sub>3</sub>), nitrous oxide (N<sub>2</sub>O) and nitric oxide (NO). Ammonia contributes to eutrophication and acidification when deposited on land. Nitric oxide plays a role in tropospheric ozone chemistry, and nitrous oxide is a potent greenhouse gas. Moreover, large proportions of mobilised N and P in watersheds enter the groundwater through leaching, and are released to surface waters through groundwater transport and surface runoff. Subsequently, nutrients in streams and rivers are transported to coastal marine systems, reduced by retention but augmented by releases from point sources, such as sewerage systems and industrial facilities.

This has resulted in negative impacts on human health and the environment, such as groundwater pollution, loss of habitat and biodiversity, an increases in the frequency and severity of harmful algal blooms, eutrophication, hypoxia and fish kills ([[Diaz and Rosenberg, 2008]]; [[Zhang et al., 2010]]). The harmful effects of eutrophication have spread rapidly around the world, with large-scale implications for biodiversity, water quality, fisheries and recreation, in both industrialised and developing regions ([[UNEP, 2002]]). Input of nutrients in freshwater and coastal marine ecosystems, also disturbs the stoichiometric balance of N, P and Si (silicon) ([[Rabalais, 2002]]) affecting total plant production and the species composition in ecosystems.

To assess eutrophication as a consequence of increasing population, and economic and technological development, IMAGE 3.2 includes a nutrient model ([[Beusen, 2014]]; [[Beusen et al., 2015]]; [[Beusen et al., 2016]]), which comprises three sub-models:
# Wastewater module calculating nutrient flows in wastewater discharges (Figure Flowchart, top);
# Soil nutrient budget module describing all input and output of N and P in soil compartments (Figure Flowchart, middle);
# Nutrient environmental fate describing the fate of soil nutrient surpluses and wastewater nutrients in the aquatic environment (Figure Flowchart, bottom).

{{InputOutputParameterTemplate}}
</div>

Carbon cycle and natural vegetation

2021-11-23T19:43:20Z

Vosdl:

{{ComponentTemplate2
|IMAGEComponent=Carbon, vegetation, agriculture and water;Agriculture and land use;Atmospheric composition and climate;Ecosystem services;Land cover and land use
|Model-Database=HYDE database
|KeyReference=Sitch et al., 2003;Müller et al., 2016a
|Reference=Müller et al., 2007;Ballantyne et al., 2012;Gerten et al., 2004;Bondeau et al., 2007;Klein Goldewijk et al., 1994;Van Minnen et al., 2000;Doelman et al., 2019;Friedlingstein et al., 2019;Braakhekke et al., 2019;Von Bloh et al., 2018
|InputVar=Temperature - grid;Precipitation - grid;Number of wet days - grid;Cloudiness - grid;CO2 concentration;Timber use fraction;Land cover, land use - grid;Irrigation water supply - grid;Forest management type - grid
|Parameter=Soil properties - grid
|OutputVar=Potential natural vegetation - grid;NEP (net ecosystem production) - grid;Land-use CO2 emissions - grid;Carbon pools in vegetation - grid;NPP (net primary production) - grid;Soil respiration - grid;Carbon pools in soil and timber - grid
|ComponentCode=NVCC
|AggregatedComponent=Carbon, vegetation, agriculture and water
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<div class="page_standard">

The terrestrial biosphere plays a key role in global and regional carbon cycles and thus in the climate system. Large amounts of carbon (between 2000 and 3000 PgC) are stored in the vegetation and soil components. Currently, the terrestrial biosphere absorbs about 30% of emitted CO<sub>2</sub> ([[Ballantyne et al., 2012]]), and this carbon sink can be maintained and even enhanced by, for instance, protecting established forests and by establishing new forests ([[Doelman et al., 2019]]). However, deforestation and other land use changes in the last few centuries have contributed considerably to the build-up of atmospheric carbon dioxide ([[Friedlingstein et al., 2019]]) and this trend is projected to continue [[Müller et al., 2007|(Müller et al., 2007]]).

Regardless of land cover and land use, the net carbon sink in the terrestrial biosphere is affected by a range of environmental conditions such as climate, atmospheric CO<sub>2</sub> concentration and moisture. These conditions influence processes that take up and release CO<sub>2</sub> from the terrestrial biosphere such as photosynthesis, plant and soil respiration, transpiration, carbon allocation and turnover, and disturbances such as fires.

In plant photosynthesis, CO<sub>2</sub> is taken from the atmosphere and converted to organic carbon compounds. This CO<sub>2</sub> conversion is referred to as gross primary production ({{abbrTemplate|GPP}}). The sequestered carbon is needed for plant maintenance and growth (autotrophic respiration), and for the development of new plant tissues, forming live biomass carbon pools. All plant parts (including leaf fall and mortality) are ultimately stored as carbon in carbon pools in the soil and atmosphere.

Terrestrial carbon cycle and vegetation models contribute to better understanding of the dynamics of the terrestrial biosphere in relation to these underlying processes and to the terrestrial water cycle (see Component [[Water]]) and land use (see Component [[Agriculture and land use]]).

The IMAGE-2 carbon cycle and biome model ([[Klein Goldewijk et al., 1994]]; [[Van Minnen et al., 2000]]) have been replaced by the Lund-Potsdam-Jena model with Managed Land ([[LPJmL model|LPJmL]]) model ([[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]). An overview of the LPJmL model in the IMAGE context with regard to carbon and biome dynamics is presented here; the model and a sensitivity analysis is described in detail by Müller et al. ([[Müller et al., 2016a|2016]]).

{{InputOutputParameterTemplate}}
</div>

Carbon, vegetation, agriculture and water

2021-11-23T19:40:14Z

Vosdl:

{{AggregatedComponentTemplate
|ComponentCode=VHA
|KeyReference=Sitch et al., 2003;Gerten et al., 2004;Bondeau et al., 2007
|FrameworkElementType=state component
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<div class="page_standard">
==Description of {{ROOTPAGENAME}}==
[[LPJmL model|LPJmL]] is the carbon, vegetation, agricultural and hydrology model in IMAGE 3.2 and consists of the three components: [[Carbon cycle and natural vegetation]], [[Crops and grass]], [[Water]].

Within the Earth system, the terrestrial biosphere is the component that bears the most visible impact of human activity. Large proportions of the land surface and the terrestrial vegetation have been converted for human use, for instance, to cropland and urban areas.

Agriculture, terrestrial carbon, water and nutrient cycles were separate modules in previous versions of IMAGE and thus interactions were not adequately covered. IMAGE 3.2 covers natural and agricultural terrestrial ecosystems, and associated carbon and water dynamics via the link with the dynamic global vegetation, agriculture and water balance model [[LPJmL model|LPJmL]] (Lund-Potsdam-Jena model with managed Land; [[Sitch et al., 2003]]; [[Gerten et al., 2004]]; [[Bondeau et al., 2007]]; [[Schaphoff et al., 2018a]]; [[Schaphoff et al., 2018b]]). This enables more detailed and process-based representation of the interacting dynamics in vegetation, carbon and agricultural production, and extends the model scope to terrestrial freshwater dynamics.

LPJmL is one of the most extensively evaluated dynamic global vegetation models ({{abbrTemplate|DGVM}}) and is widely applied either stand alone or linked to other models. To show the complex dynamics in the terrestrial biosphere and to reflect the historical IMAGE modules, LPJmL is described in three components: [[Carbon cycle and natural vegetation|carbon cycle and vegetation]]; [[Crops and grass|agricultural land use]]; and [[Water|terrestrial freshwater flows]].

IMAGE 3.2 and LPJmL are linked through an interface that enables close and consistent interaction between the two models in annual time steps ([[Müller et al., 2016a]]). An even more direct link to simulate detailed land-atmosphere interaction would require higher temporal resolutions also in other IMAGE components (e.g., the climate model), which is not necessarily congruent with the philosophy of an integrated assessment model. Incorporating nutrient cycles and improving representations of grassland management in LPJmL will require further adjustments to other IMAGE 3.2 components, and will increase consistency.

The dynamic coupling between IMAGE and LPJmL makes it the standard approach to always take impacts of a changing climate into account: most importantly the effects on crop yields, natural vegetation and water dynamics from changes in temperature, precipitation and CO<sub>2</sub> concentrations. The CO<sub>2</sub> fertilisation effect on crop yields in LPJml is found to be relatively optimistic compared to other crop models, partly because other processes negatively affect yields such as nutrient limitations, and the effects of drought and extreme weather events are not accounted for ([[Toreti et al., 2020]]). Therefore, IMAGE assumes a 50% efficacy of the default CO<sub>2</sub> fertilisation in LPJmL.
</div>

Water/Description

2021-11-02T15:10:53Z

Vosdl:

{{ComponentDescriptionTemplate
|Reference=Nilsson et al., 2005;Alcamo et al., 2003;Davies et al., 2013;Pastor et al., 2014;Bondeau et al., 2007;Sitch et al., 2003;Gerten et al., 2004;Rost et al., 2008;Biemans et al., 2013
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In IMAGE, the hydrological cycle is represented by LPJmL ([[Bondeau et al., 2007]]; [[Gerten et al., 2013]];[[Schaphoff et al., 2018]]), which simulates the global hydrological cycle as part of the dynamics of natural vegetation and agricultural production systems. Because LPJmL is linked to IMAGE, there is consistency in the way the [[Carbon cycle and natural vegetation|carbon cycle, natural vegetation]] dynamics, [[Crops and grass|crop growth and production]], [[land-use allocation]] and the water balance are modelled.

Data on annual [[land cover and land use]] are used as input to LPJmL, including information on the location of irrigated areas and crop types (Figure Flowchart and Input/Output Table at [[Water|Introduction part]]). This affects the amount of water that evaporates and runs off, as well as the amount of water needed for irrigated crops during the (simulated) growing season. Simultaneously, information on water availability calculated by LPJmL is taken into account in the [[Land-use allocation]] model to identify suitable locations to expand irrigated areas.

Climate is used as input in LPJmL to determine potential evapotranspiration, and the precipitation input to the water balance ([[Gerten et al., 2004]]). The [[Crops and grass]] module, which is also part of LPJmL, calculates irrigation water demand based on crop characteristics, soil moisture and climate. If the amount of water available for irrigation is limited, water stress will occur which leads to reduction of crop yields calculated by the [[Crops and grass|crop and grassland]] model.

===The natural hydrological cycle===
The Hydrology module in LPJmL consists of a vertical water balance ([[Gerten et al., 2004]];[[Schaphoff et al., 2013]]) and a lateral flow component ([[Rost et al., 2008]]) which are run at 0.5 degree resolution in daily time steps (Figure Flowchart). The soil in each grid cell is represented by a five-layer soil column of 0.2, 0.3, 0.5, 1.0 and 1.0 m depth, partly covered with natural vegetation or crops.

The potential evapotranspiration rate in each grid cell depends primarily on net radiation and temperature, and is calculated using the Priestley-Taylor approach ([[Gerten et al., 2004]]). The actual evapotranspiration is calculated as the sum of three components: evaporation of water stored in the canopy (interception), bare soil evaporation and plant transpiration ([[Gerten et al., 2004]]). Water storage in the canopy is a function of vegetation type, leaf area index ({{abbrTemplate|LAI}}) and precipitation amount. Plant transpiration is modelled as the minimum of atmospheric demand and plant water supply. Plant water supply depends on the plant-dependent maximum transpiration rate and relative soil moisture. Soil evaporation occurs in the proportion of land in the grid cell that is not covered by vegetation. It equals potential evaporation when the soil moisture of the upper 20 cm is at field capacity, and declines linearly with relative soil moisture.

Precipitation reaching the soil (throughfall, precipitation minus interception) either accumulates as snow or infiltrates into the soil. Snowmelt is calculated using a simple degree-day method ([[Gerten et al., 2004]]). The soil is parameterised as a bucket model. The status of soil moisture of the soil layers is updated daily, accounting for throughfall, snowmelt, evapotranspiration, percolation and runoff. Percolation rates for the soil layers depend on soil type and decline exponentially with soil moisture. Total runoff is calculated as water in excess of field capacity from the soil layers and water percolating through the second soil layer. The current version of LPJmL has no explicit representation of groundwater recharge, but a groundwater scheme is under development. The daily (subsurface) runoff includes the renewable fraction of groundwater, but without any time delay.

All runoff is routed daily through a gridded river network, representing a system of rivers, natural lakes and reservoirs, using a simple routing algorithm ([[Rost et al., 2008]]). Local runoff is added to surface water storage in the cell, and subsequently flows downstream at a constant flow velocity of 1 m s-1 until reaching a lake or reservoir. Water accumulates in lakes and reservoirs, and outflow depends on actual storage relative to the maximum storage capacity (for lakes) and the operational purpose of the reservoir ([[Biemans et al., 2011]]). For man-made reservoirs, see further below ([[Biemans et al., 2011]])

===Supply and demand for irrigation water===
Water availability and demand in agriculture is simulated with LPJmL’s irrigation algorithm and an algorithm to simulate the operation of large reservoirs to supply water to irrigated areas ([[Biemans et al., 2013]]).

The irrigation demand submodel (Figure Flowchart) is described in detail by Rost et al. ([[Rost et al., 2008|2008]]). Crop net irrigation demand is defined as the minimum atmospheric evaporative demand and the amount of water needed to fill the soil to field capacity. The irrigation withdrawal demand – the gross demand – is subsequently calculated as the product of the crop irrigation demand and a country-specific irrigation efficiency factor that reflects the type and efficiency of prevailing irrigation systems ([[Rost et al., 2008]]). The efficiency, i.e. the losses of withdrawn water during transport between withdrawal point and irrigated field depends on the type of conveyance system (e.g., open channel or pipeline). Thus, the quantity of water demanded by crops (water consumption) is always less than the quantity withdrawn (water use).

Irrigation water is extracted from the rivers and lakes in the grid cell or a neighbouring grid cell. If these local surface water sources cannot meet the total demand, water is extracted from nearby reservoirs, if available. Finally, water can be supplied from an unlimited source that can be interpreted as non-sustainable groundwater or water imported from another basin. By excluding these water sources in a series of model runs, irrigation water supply and crop production can be attributed to different water sources.

===Large reservoirs===
Some 50% of global river systems are regulated by dams, most of which are in basins where there is irrigation and economic activity ([[Nilsson et al., 2005]]). The main purpose of approximately one-third of all large reservoirs is irrigation. Thus, in estimating agricultural water use, man-made reservoirs have to be taken into account.
The reservoir operation module in LPJmL ([[Biemans et al., 2011]]) distinguishes three types of reservoirs: reservoirs used primarily for irrigation; reservoirs used primarily for other purposes (e.g., hydropower and flood control) but also for irrigation; and reservoirs not used for irrigation. Each type of reservoir is managed differently. The outflow of irrigation reservoirs follows the temporal pattern of irrigation demand, whereas the other reservoirs are intended to release equal quantities of water throughout the year. Water from irrigation reservoirs is supplied to downstream irrigated areas.

===Water demand in other sectors===
IMAGE-LPJmL only calculates agricultural water demand internally, and water demand in other sectors is calculated separately. For household and manufacturing sectors, data and algorithms are adopted from the WaterGAP model ([[Alcamo et al., 2003]]). For the electricity sector, a process-based estimation is used based on the study by Davies et al. ([[Davies et al., 2013|2013]]), and Livestock water demand follows from the number of animals estimated in the [[Livestock systems]] model, with the water demand per head adjusted for climate conditions. Domestic demand is a function of population size and per capita income, corrected for the proportion of the population without access to a piped water supply (see Component [[Human development]]). Manufacturing demand is a function of industrial value added, corrected for changes in sector composition, such as the structural change factor used for [[Energy demand]].

For the electricity sector, a technology-based approach was adopted from the study by Davies et al. ([[Davies et al., 2013|2013]]).The type of power plant (e.g., standard steam cycle, combined steam cycle) determines the demand for cooling capacity. As plants cogenerating heat and power require less cooling capacity, demand is also corrected for these plants. In addition, the type of cooling facility determines the quantity of water required. Once-through cooling systems use large volumes of surface water that are returned almost entirely to the water body from which they were extracted, albeit at an elevated temperature. Wet cooling towers exploit the evaporation heat capacity of water and, thus require much lower water volumes. However, a significant part of the cooling water evaporates during the process and does not return to the original water body. In some regions, cooling ponds are used, where cooling water is pumped and recycled in a closed loop, with water demand somewhere between the once-through and wet tower cooling systems. Finally, dry cooling systems are deployed that use air as a coolant and thus do not require cooling water. Based on data from Davies et al. ([[Davies et al., 2013|2013]]), market share for types of cooling systems – for each power plant type distinguished in [[TIMER model|TIMER]] in each world region – are combined with energy input requirements to obtain the total water demand for the electricity sector.

===Water extractions===
Water requirements in other sectors are extracted from local surface water, if available (rather than from reservoirs). Meeting the demand from these sectors receives priority over water withdrawal for irrigation.

The current version of IMAGE-LPJmL does not take into account the water needs of ecosystems, or other uses, such as shipping and recreation. However, a new module to calculate environmental flow requirements is under development ([[Pastor et al., 2014]]). This module, which constrains water withdrawals so that a minimum environmental flow is guaranteed, will be used to identify possible areas of conflict between water users.
</div>{{DisplayFigureTemplate|Baseline figure Water}}
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===Impact indicators===

Water stress is often presented as a spatial and temporal average water withdrawal-to-availability ratio at basin or country level. The population living with water stress is estimated by overlaying such a water-stress (or water availability) map with a population density map. These indicators are used to present IMAGE-LPJmL results (for instance, in the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook]], see Figure) but they mask the potential occurrence of water shortages in the short-term or on sub-basin scale. Thus, water stress should also be calculated at higher spatial and temporal resolutions, as can principally be done with LPJmL (see [[Biemans, 2012]]).

The impacts of water stress differ per sector, but the indicators described above do not provide deeper insight into these impacts. In addition to the general water stress indicators, the model also considers production reduction in irrigated agriculture due to limited water availability as an indicator of agricultural water stress ([[Biemans, 2012]]).
</div>

Water/Description

2021-11-02T11:20:15Z

Vosdl:

{{ComponentDescriptionTemplate
|Reference=Nilsson et al., 2005;Alcamo et al., 2003;Davies et al., 2013;Pastor et al., 2014;Bondeau et al., 2007;Sitch et al., 2003;Gerten et al., 2004;Rost et al., 2008;Biemans et al., 2013
}}
<div class="page_standard">

In IMAGE, the hydrological cycle is represented by LPJmL ([[Bondeau et al., 2007]]; [[Gerten et al., 2013]];[[Schaphoff et al., 2018]]), which simulates the global hydrological cycle as part of the dynamics of natural vegetation and agricultural production systems. Because LPJmL is linked to IMAGE, there is consistency in the way the [[Carbon cycle and natural vegetation|carbon cycle, natural vegetation]] dynamics, [[Crops and grass|crop growth and production]], [[land-use allocation]] and the water balance are modelled.

Data on annual [[land cover and land use]] are used as input to LPJmL, including information on the location of irrigated areas and crop types (Figure Flowchart and Input/Output Table at [[Water|Introduction part]]). This affects the amount of water that evaporates and runs off, as well as the amount of water needed for irrigated crops during the (simulated) growing season. Simultaneously, information on water availability calculated by LPJmL is taken into account in the [[Land-use allocation]] model to identify suitable locations to expand irrigated areas.

Climate is used as input in LPJmL to determine potential evapotranspiration, and the precipitation input to the water balance ([[Gerten et al., 2004]]). The [[Crops and grass]] module, which is also part of LPJmL, calculates irrigation water demand based on crop characteristics, soil moisture and climate. If the amount of water available for irrigation is limited, water stress will occur which leads to reduction of crop yields calculated by the [[Crops and grass|crop and grassland]] model.

===The natural hydrological cycle===
The Hydrology module in LPJmL consists of a vertical water balance ([[Gerten et al., 2004]];[[Schaphoff et al., 2013]]) and a lateral flow component ([[Rost et al., 2008]]) which are run at 0.5 degree resolution in daily time steps (Figure Flowchart). The soil in each grid cell is represented by a five-layer soil column of 0.2, 0.3, 0.5, 1.0 and 1.0 m depth, partly covered with natural vegetation or crops.

The potential evapotranspiration rate in each grid cell depends primarily on net radiation and temperature, and is calculated using the Priestley-Taylor approach ([[Gerten et al., 2004]]). The actual evapotranspiration is calculated as the sum of three components: evaporation of water stored in the canopy (interception), bare soil evaporation and plant transpiration ([[Gerten et al., 2004]]). Water storage in the canopy is a function of vegetation type, leaf area index ({{abbrTemplate|LAI}}) and precipitation amount. Plant transpiration is modelled as the minimum of atmospheric demand and plant water supply. Plant water supply depends on the plant-dependent maximum transpiration rate and relative soil moisture. Soil evaporation occurs in the proportion of land in the grid cell that is not covered by vegetation. It equals potential evaporation when the soil moisture of the upper 20 cm is at field capacity, and declines linearly with relative soil moisture.

Precipitation reaching the soil (throughfall, precipitation minus interception) either accumulates as snow or infiltrates into the soil. Snowmelt is calculated using a simple degree-day method ([[Gerten et al., 2004]]). The soil is parameterised as a bucket model. The status of soil moisture of the two soil layers is updated daily, accounting for throughfall, snowmelt, evapotranspiration, percolation and runoff. Percolation rates for the two soil layers depend on soil type and decline exponentially with soil moisture. Total runoff is calculated as water in excess of field capacity from the two soil layers and water percolating through the second soil layer. The current version of LPJmL has no explicit representation of groundwater recharge, but a groundwater scheme is under development. The daily (subsurface) runoff includes the renewable fraction of groundwater, but without any time delay.

All runoff is routed daily through a gridded river network, representing a system of rivers, natural lakes and reservoirs, using a simple routing algorithm ([[Rost et al., 2008]]). Local runoff is added to surface water storage in the cell, and subsequently flows downstream at a constant flow velocity of 1 m s-1 until reaching a lake or reservoir. Water accumulates in lakes and reservoirs, and outflow depends on actual storage relative to the maximum storage capacity (for lakes) and the operational purpose of the reservoir ([[Biemans et al., 2011]]). For man-made reservoirs, see further below ([[Biemans et al., 2011]])

===Supply and demand for irrigation water===
Water availability and demand in agriculture is simulated with LPJmL’s irrigation algorithm and an algorithm to simulate the operation of large reservoirs to supply water to irrigated areas ([[Biemans et al., 2013]]).

The irrigation demand submodel (Figure Flowchart) is described in detail by Rost et al. ([[Rost et al., 2008|2008]]). Crop net irrigation demand is defined as the minimum atmospheric evaporative demand and the amount of water needed to fill the soil to field capacity. The irrigation withdrawal demand – the gross demand – is subsequently calculated as the product of the crop irrigation demand and a country-specific irrigation efficiency factor that reflects the type and efficiency of prevailing irrigation systems ([[Rost et al., 2008]]). The efficiency, i.e. the losses of withdrawn water during transport between withdrawal point and irrigated field depends on the type of conveyance system (e.g., open channel or pipeline). Thus, the quantity of water demanded by crops (water consumption) is always less than the quantity withdrawn (water use).

Irrigation water is extracted from the rivers and lakes in the grid cell or a neighbouring grid cell. If these local surface water sources cannot meet the total demand, water is extracted from nearby reservoirs, if available. Finally, water can be supplied from an unlimited source that can be interpreted as non-sustainable groundwater or water imported from another basin. By excluding these water sources in a series of model runs, irrigation water supply and crop production can be attributed to different water sources.

===Large reservoirs===
Some 50% of global river systems are regulated by dams, most of which are in basins where there is irrigation and economic activity ([[Nilsson et al., 2005]]). The main purpose of approximately one-third of all large reservoirs is irrigation. Thus, in estimating agricultural water use, man-made reservoirs have to be taken into account.
The reservoir operation module in LPJmL ([[Biemans et al., 2011]]) distinguishes three types of reservoirs: reservoirs used primarily for irrigation; reservoirs used primarily for other purposes (e.g., hydropower and flood control) but also for irrigation; and reservoirs not used for irrigation. Each type of reservoir is managed differently. The outflow of irrigation reservoirs follows the temporal pattern of irrigation demand, whereas the other reservoirs are intended to release equal quantities of water throughout the year. Water from irrigation reservoirs is supplied to downstream irrigated areas.

===Water demand in other sectors===
IMAGE-LPJmL only calculates agricultural water demand internally, and water demand in other sectors is calculated separately. For household and manufacturing sectors, data and algorithms are adopted from the WaterGAP model ([[Alcamo et al., 2003]]). For the electricity sector, a process-based estimation is used based on the study by Davies et al. ([[Davies et al., 2013|2013]]), and Livestock water demand follows from the number of animals estimated in the [[Livestock systems]] model, with the water demand per head adjusted for climate conditions. Domestic demand is a function of population size and per capita income, corrected for the proportion of the population without access to a piped water supply (see Component [[Human development]]). Manufacturing demand is a function of industrial value added, corrected for changes in sector composition, such as the structural change factor used for [[Energy demand]].

For the electricity sector, a technology-based approach was adopted from the study by Davies et al. ([[Davies et al., 2013|2013]]).The type of power plant (e.g., standard steam cycle, combined steam cycle) determines the demand for cooling capacity. As plants cogenerating heat and power require less cooling capacity, demand is also corrected for these plants. In addition, the type of cooling facility determines the quantity of water required. Once-through cooling systems use large volumes of surface water that are returned almost entirely to the water body from which they were extracted, albeit at an elevated temperature. Wet cooling towers exploit the evaporation heat capacity of water and, thus require much lower water volumes. However, a significant part of the cooling water evaporates during the process and does not return to the original water body. In some regions, cooling ponds are used, where cooling water is pumped and recycled in a closed loop, with water demand somewhere between the once-through and wet tower cooling systems. Finally, dry cooling systems are deployed that use air as a coolant and thus do not require cooling water. Based on data from Davies et al. ([[Davies et al., 2013|2013]]), market share for types of cooling systems – for each power plant type distinguished in [[TIMER model|TIMER]] in each world region – are combined with energy input requirements to obtain the total water demand for the electricity sector.

===Water extractions===
Water requirements in other sectors are extracted from local surface water, if available (rather than from reservoirs). Meeting the demand from these sectors receives priority over water withdrawal for irrigation.

The current version of IMAGE-LPJmL does not take into account the water needs of ecosystems, or other uses, such as shipping and recreation. However, a new module to calculate environmental flow requirements is under development ([[Pastor et al., 2014]]). This module, which constrains water withdrawals so that a minimum environmental flow is guaranteed, will be used to identify possible areas of conflict between water users.
</div>{{DisplayFigureTemplate|Baseline figure Water}}
<div class="page_standard">
===Impact indicators===

Water stress is often presented as a spatial and temporal average water withdrawal-to-availability ratio at basin or country level. The population living with water stress is estimated by overlaying such a water-stress (or water availability) map with a population density map. These indicators are used to present IMAGE-LPJmL results (for instance, in the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook]], see Figure) but they mask the potential occurrence of water shortages in the short-term or on sub-basin scale. Thus, water stress should also be calculated at higher spatial and temporal resolutions, as can principally be done with LPJmL (see [[Biemans, 2012]]).

The impacts of water stress differ per sector, but the indicators described above do not provide deeper insight into these impacts. In addition to the general water stress indicators, the model also considers production reduction in irrigated agriculture due to limited water availability as an indicator of agricultural water stress ([[Biemans, 2012]]).
</div>

Water/Description

2021-11-02T11:10:39Z

Vosdl:

{{ComponentDescriptionTemplate
|Reference=Nilsson et al., 2005;Alcamo et al., 2003;Davies et al., 2013;Pastor et al., 2014;Bondeau et al., 2007;Sitch et al., 2003;Gerten et al., 2004;Rost et al., 2008;Biemans et al., 2013
}}
<div class="page_standard">

In IMAGE, the hydrological cycle is represented by LPJmL ([[Bondeau et al., 2007]]; [[Gerten et al., 2013]];[[Schaphoff et al., 2018]]), which simulates the global hydrological cycle as part of the dynamics of natural vegetation and agricultural production systems. Because LPJmL is linked to IMAGE, there is consistency in the way the [[Carbon cycle and natural vegetation|carbon cycle, natural vegetation]] dynamics, [[Crops and grass|crop growth and production]], [[land-use allocation]] and the water balance are modelled.

Data on annual [[land cover and land use]] are used as input to LPJmL, including information on the location of irrigated areas and crop types (Figure Flowchart and Input/Output Table at [[Water|Introduction part]]). This affects the amount of water that evaporates and runs off, as well as the amount of water needed for irrigated crops during the (simulated) growing season. Simultaneously, information on water availability calculated by LPJmL is taken into account in the [[Land-use allocation]] model to identify suitable locations to expand irrigated areas.

Climate is used as input in LPJmL to determine potential evapotranspiration, and the precipitation input to the water balance ([[Gerten et al., 2004]]). The [[Crops and grass]] module, which is also part of LPJmL, calculates irrigation water demand based on crop characteristics, soil moisture and climate. If the amount of water available for irrigation is limited, water stress will occur which leads to reduction of crop yields calculated by the [[Crops and grass|crop and grassland]] model.

===The natural hydrological cycle===
The Hydrology module in LPJmL consists of a vertical water balance ([[Gerten et al., 2004]]) and a lateral flow component ([[Rost et al., 2008]]) which are run at 0.5 degree resolution in daily time steps (Figure Flowchart). The soil in each grid cell is represented by a two-layer soil column of 0.5 and 1.0 m depth, partly covered with natural vegetation or crops.

The potential evapotranspiration rate in each grid cell depends primarily on net radiation and temperature, and is calculated using the Priestley-Taylor approach ([[Gerten et al., 2004]]). The actual evapotranspiration is calculated as the sum of three components: evaporation of water stored in the canopy (interception), bare soil evaporation and plant transpiration ([[Gerten et al., 2004]]). Water storage in the canopy is a function of vegetation type, leaf area index ({{abbrTemplate|LAI}}) and precipitation amount. Plant transpiration is modelled as the minimum of atmospheric demand and plant water supply. Plant water supply depends on the plant-dependent maximum transpiration rate and relative soil moisture. Soil evaporation occurs in the proportion of land in the grid cell that is not covered by vegetation. It equals potential evaporation when the soil moisture of the upper 20 cm is at field capacity, and declines linearly with relative soil moisture.

Precipitation reaching the soil (throughfall, precipitation minus interception) either accumulates as snow or infiltrates into the soil. Snowmelt is calculated using a simple degree-day method ([[Gerten et al., 2004]]). The soil is parameterised as a bucket model. The status of soil moisture of the two soil layers is updated daily, accounting for throughfall, snowmelt, evapotranspiration, percolation and runoff. Percolation rates for the two soil layers depend on soil type and decline exponentially with soil moisture. Total runoff is calculated as water in excess of field capacity from the two soil layers and water percolating through the second soil layer. The current version of LPJmL has no explicit representation of groundwater recharge, but a groundwater scheme is under development. The daily (subsurface) runoff includes the renewable fraction of groundwater, but without any time delay.

All runoff is routed daily through a gridded river network, representing a system of rivers, natural lakes and reservoirs, using a simple routing algorithm ([[Rost et al., 2008]]). Local runoff is added to surface water storage in the cell, and subsequently flows downstream at a constant flow velocity of 1 m s-1 until reaching a lake or reservoir. Water accumulates in lakes and reservoirs, and outflow depends on actual storage relative to the maximum storage capacity (for lakes) and the operational purpose of the reservoir ([[Biemans et al., 2011]]). For man-made reservoirs, see further below ([[Biemans et al., 2011]])

===Supply and demand for irrigation water===
Water availability and demand in agriculture is simulated with LPJmL’s irrigation algorithm and an algorithm to simulate the operation of large reservoirs to supply water to irrigated areas ([[Biemans et al., 2013]]).

The irrigation demand submodel (Figure Flowchart) is described in detail by Rost et al. ([[Rost et al., 2008|2008]]). Crop net irrigation demand is defined as the minimum atmospheric evaporative demand and the amount of water needed to fill the soil to field capacity. The irrigation withdrawal demand – the gross demand – is subsequently calculated as the product of the crop irrigation demand and a country-specific irrigation efficiency factor that reflects the type and efficiency of prevailing irrigation systems ([[Rost et al., 2008]]). The efficiency, i.e. the losses of withdrawn water during transport between withdrawal point and irrigated field depends on the type of conveyance system (e.g., open channel or pipeline). Thus, the quantity of water demanded by crops (water consumption) is always less than the quantity withdrawn (water use).

Irrigation water is extracted from the rivers and lakes in the grid cell or a neighbouring grid cell. If these local surface water sources cannot meet the total demand, water is extracted from nearby reservoirs, if available. Finally, water can be supplied from an unlimited source that can be interpreted as non-sustainable groundwater or water imported from another basin. By excluding these water sources in a series of model runs, irrigation water supply and crop production can be attributed to different water sources.

===Large reservoirs===
Some 50% of global river systems are regulated by dams, most of which are in basins where there is irrigation and economic activity ([[Nilsson et al., 2005]]). The main purpose of approximately one-third of all large reservoirs is irrigation. Thus, in estimating agricultural water use, man-made reservoirs have to be taken into account.
The reservoir operation module in LPJmL ([[Biemans et al., 2011]]) distinguishes three types of reservoirs: reservoirs used primarily for irrigation; reservoirs used primarily for other purposes (e.g., hydropower and flood control) but also for irrigation; and reservoirs not used for irrigation. Each type of reservoir is managed differently. The outflow of irrigation reservoirs follows the temporal pattern of irrigation demand, whereas the other reservoirs are intended to release equal quantities of water throughout the year. Water from irrigation reservoirs is supplied to downstream irrigated areas.

===Water demand in other sectors===
IMAGE-LPJmL only calculates agricultural water demand internally, and water demand in other sectors is calculated separately. For household and manufacturing sectors, data and algorithms are adopted from the WaterGAP model ([[Alcamo et al., 2003]]). For the electricity sector, a process-based estimation is used based on the study by Davies et al. ([[Davies et al., 2013|2013]]), and Livestock water demand follows from the number of animals estimated in the [[Livestock systems]] model, with the water demand per head adjusted for climate conditions. Domestic demand is a function of population size and per capita income, corrected for the proportion of the population without access to a piped water supply (see Component [[Human development]]). Manufacturing demand is a function of industrial value added, corrected for changes in sector composition, such as the structural change factor used for [[Energy demand]].

For the electricity sector, a technology-based approach was adopted from the study by Davies et al. ([[Davies et al., 2013|2013]]).The type of power plant (e.g., standard steam cycle, combined steam cycle) determines the demand for cooling capacity. As plants cogenerating heat and power require less cooling capacity, demand is also corrected for these plants. In addition, the type of cooling facility determines the quantity of water required. Once-through cooling systems use large volumes of surface water that are returned almost entirely to the water body from which they were extracted, albeit at an elevated temperature. Wet cooling towers exploit the evaporation heat capacity of water and, thus require much lower water volumes. However, a significant part of the cooling water evaporates during the process and does not return to the original water body. In some regions, cooling ponds are used, where cooling water is pumped and recycled in a closed loop, with water demand somewhere between the once-through and wet tower cooling systems. Finally, dry cooling systems are deployed that use air as a coolant and thus do not require cooling water. Based on data from Davies et al. ([[Davies et al., 2013|2013]]), market share for types of cooling systems – for each power plant type distinguished in [[TIMER model|TIMER]] in each world region – are combined with energy input requirements to obtain the total water demand for the electricity sector.

===Water extractions===
Water requirements in other sectors are extracted from local surface water, if available (rather than from reservoirs). Meeting the demand from these sectors receives priority over water withdrawal for irrigation.

The current version of IMAGE-LPJmL does not take into account the water needs of ecosystems, or other uses, such as shipping and recreation. However, a new module to calculate environmental flow requirements is under development ([[Pastor et al., 2014]]). This module, which constrains water withdrawals so that a minimum environmental flow is guaranteed, will be used to identify possible areas of conflict between water users.
</div>{{DisplayFigureTemplate|Baseline figure Water}}
<div class="page_standard">
===Impact indicators===

Water stress is often presented as a spatial and temporal average water withdrawal-to-availability ratio at basin or country level. The population living with water stress is estimated by overlaying such a water-stress (or water availability) map with a population density map. These indicators are used to present IMAGE-LPJmL results (for instance, in the [[OECD Environmental Outlook to 2050 (2012) project|OECD Environmental Outlook]], see Figure) but they mask the potential occurrence of water shortages in the short-term or on sub-basin scale. Thus, water stress should also be calculated at higher spatial and temporal resolutions, as can principally be done with LPJmL (see [[Biemans, 2012]]).

The impacts of water stress differ per sector, but the indicators described above do not provide deeper insight into these impacts. In addition to the general water stress indicators, the model also considers production reduction in irrigated agriculture due to limited water availability as an indicator of agricultural water stress ([[Biemans, 2012]]).
</div>