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-Methodology for Integrated Assessment Model of Climate Change on Chinese Agriculture

Methodology for Integrated Assessment Model

of Climate Change on Chinese Agriculture

Yu’e Li1, Ian Holman2, Erda Lin1

1 Agro-meteorology Institute, Chinese Academy of Agricultural Sciences, Beijing 100081

2 Institute of Water and Environment, Cranfield University at Silsoe

 

1. Introduction:

The global average surface temperature has increased 0.6 ± 0.2°C over the 20th century (IPCC, 2001a). Available observational evidence indicates that regional changes in climate, particularly increases in temperature, have already affected a diverse set of physical and biological systems in many parts of the world (IPCC, 2001 b). Because of the increase of greenhouse gas concentrations in the atmosphere, the globally averaged surface temperature is projected to increase by 1.4 to 5.8°C and global mean sea level to rise by 0.09 to 0.88 metres between 1990 and 2100(IPCC, 2001a). The adverse impacts of climate change are expected to fall disproportionately upon developing countries, such as China and the poorest sections of society within these countries (IPCC, 2001d).

     Agriculture is a very important economic sector in China. In 1990, the agricultural sector accounted for 35 percent of total GDP, and produced food for 22 percent of the world’s population despite only having 7 percent of the world’s cultivated land.  It accounted for 20 in 2000.

    However, Chinese agriculture is sensitive to both the direct and indirect impacts of anthropogenically-induced changes.  The future growth of  population in China will increase demand for food, water, and shelter. This fact results in an increased pressure on China’s agriculture land and water resources, which will be amplified by the  rapid urbanization and economic development.

      Changes in  frequency, intensity, and length of duration of extreme events caused by climate change may lead to the increased risks of floods and droughts in many regions, and predominantly adverse impacts on ecological systems, socio-economic sectors, and human health (IPCC, 2001d). The impact of flood and drought disasters on China’s social and economic development is already an outstanding problem (Zhu Erming, 2000). Flood disasters cause 40 percent of the total economic loss from all natural calamities in China. They will be an important factor limiting  sustainable social development of China in the future years to come(Zhang Shougong). Similarly, drought and water shortage have become a major restriction to the stable development of agriculture and the grain production. The average annual agricultural area affected by drought was 20 million ha, of which 8 million ha were seriously damaged. The average reduction in  grain output caused by flood and drought disasters is 15-20 million tones per annum (Zhu Erming, 2000).

      Soil erosion and degradation resulting from climate variability and human activities affects 367 million ha (or 38 percent of the land area of China) and is increasing at a rate of 1 million ha/a. The desertification area reached 262 million ha. (Programme of national Ecological environmental development, 1998).

The importance of China’s agricultural sector to the economic well-being of the nation necessitates that the future effects of climate change are understood.  However, soil degradation and the predicted decline in arable agriculture land may be as important as climate change. Therefore, an integrated assessment of the impacts of climate change on China’s agricultural sector, which recognizes the potential importance of these indirect effects, is increasingly recognized as being important. This paper reviews the previous studies related to integrated assessment of climate change impacts on agriculture and proposes a model framework for an integrated assessment for Chinese agriculture.

2. Climate change impact assessment on agriculture

      The techniques of impact assessment were first applied to the issue of greenhouse gas-induced climate change in the early 1980s (Parry, 1990). The impact assessment studies related to agriculture initially used simulated weather data from General Circulation Models (references) and mostly focused on the impact of climate change on first order biophysical (crop yield) effects (Delecolle et al., 1995; Iglesias & Minguez, 1995;  Kenny et al., 2000, Smith and Lazo, 2001; UNFCCC, 2001; Research Team of China Climate Change Country Study, 1999; Jin et al., 1995) at different agricultural sites (Seino, 1995; Lin et al., 1997) to understand the sensitivity to variations in climate. Most of the studies failed to analyze the implications of biophysical impacts of climate change on socio-economic conditions, cross-sectoral integration of impacts, autonomous adaptation, or proactive adaptation (Antle, 1996; Schneider et al., 2000; Smith and Lazo, 2001; UNFCCC, 2001), or indirect effect such as water availability, competition with pests, change in soil fertility and erosion (Aggarwal and Mall, 2002).

    Sectoral impact studies, such as those described above, tend to analyse first order biophysical effects only.  Many important factors have been ignored.  Direct effects of CO2 fertilization, pests and diseases, and soil properties variability are often not incorporated.  In common with many countries, climate change impact research in China (Research Team of China Climate Change Country Study, 1999) has included parallel assessments of key economic sectors, which interact with the agricultural sector. The results of impact assessments on water resources and sea level (which will have direct impacts on the agricultural area due to flooding and drought, and on crop yield because of the availability of irrigation water) have not informed agricultural impact analyses. Also scenarios of socio-economic development, technology change (such as irrigation and fertilization technological changes, variety improvement), policy strategies for adapting the climate change and market effects are not incorporated into these studies.

      All the studied failed to simulate the climate change impact on agricultural economy and socio-economy. Therefore, the studies conducted thus far have not been able to fully understand the impact of climate change on agriculture and therefore have limited usefulness for policy makers.

      Policy concerns have now changed. Current policy questions require more specific information on the interactions between sectors and an evaluation of adaptation options for individual stakeholders at the local and regional scale. The science of climate change impacts’ assessment is therefore in transition. The paradigm shift is from impacts to adaptation, and from sectoral concerns to integrated assessments of landscapes and economies. 

3 Integrated Assessment

3.1 What is integrated assessment and integrated assessment model

     There are probably as many definitions of integrated assessment as there are integrated assessments. In the IPCC Third Assessment Report (IPCC, 2001c) integrated assessment was defined as “an interdisciplinary process that combines, interprets, and communicates knowledge from diverse scientific disciplines from the natural and social sciences to investigate and understand causal relationships within and between complicated systems”. It is generally agreed that there are two main principles to integrated assessment, i.e. integration over a range of relevant disciplines; and the provision of information suitable for decision making (Harremoes and Turner, 2001).

    The objectives of integrated assessment of climate change are, therefore, to put available knowledge together in order to evaluate what has been learned, the policy implications and research needs (Morgan and Dowlatabadi, 1996), and to promote a better understanding of, and more informed decisions on, how locales and regions contribute to, and are affected by, climate change (Yarnal, 1998). The main research activity in integrated assessment involves the development of methods for linking knowledge across domains or disciplines, particularly emphasizng the role of important feedback mechanism, nonlinearities and uncertainties (Easterlin, 1997).

      Integrated assessment models (IAM) are considered to be the main research tool for conducting integrated assessment of climate change. IAMs (Figure 1)s eek to combine knowledge from multiple disciplines (global atmospheric chemistry, climate and oceans, human activities and ecosystem) in formal integrated representations; inform policy-making, structure knowledge, and prioritize key uncertainties; and advance knowledge of broad system linkages and feedbacks, particularly between socioeconomic and biophysical processes (Parson EA and FisherVanden K, 1997).

The Driving force, Pressure, State, Impact, Response (DPSIR) framework, as proposed by the Organization of Economic Co-operation and Development (1993) and used by the European Environment Agency (1998) is one approach to linking knowledge in integrated assessment (Peirce, 1998; Holman, et al., 2002). Scientific, policy, and societal stakeholders should be involved in this interdisciplinary, iterative process (Yarnal, 1998) which explicitly recognizes that sectors will not be affected by climate change in isolation (Rosenzweig and Hillel, 1998).


Figure 1. Key components of full-scale integrated assessment models of global change processes (IPCC, 1996)

3.2 The strengths and weaknesses for the agricultural related integrated assessment models

      With the better understanding of climate change impacts, several integrated assessments at different level of complexity have been conducted in recent years (for example, Parry, 1990; Rosenberg, et al., 1993; Alcamo et al. 1994a; Matsuoka, et al., 2001; Kenny et al., 2001; Holman et al., 2002).  Table 1 summarises the key points of those integrated assessment models which included the impact of climate change on agriculture.

      Each of the integrated models listed in table 1 has its strengths and weaknesses. Some of them not only consider the first order effects, but also considered the second bio-physical effects of climate change (Parry, 1990; Matsuoka, et al., 2001; Holman et al., 2002). In these assessment studies, impacts on agriculture, including climate change models, models for simulate the effects crop yield, plant growth (first-order), production, employment, regional and national output (higher-order). Some assessments considers the effects of climate change on agriculture and their interactions with other physical system, such as effects on pest and disease, changes in rates of soil erosion, groundwater depletion (Parry, 1990), water resources (Matsuoka, et al., 2001), coast and river flooding and socioeconomic condition (Holman, et al., 2002). The influence of fertilizer and technology inputs (tractors, management know how) were taken into account in the scenarios (Alcamo, et al., 1994b). In Regis study, the irrigation efficiency changes were considered under the climate change condition. The outstanding characteristics of RegIS study is that the stakeholders view was incorporated in the assessment (Holman, et al., 2002). Some of the models are geographically detailed (Alcamo, et al., 1994a; Holman et al., 2002 Kenny, et al., 2001).

       A weakness in some of the IAMs is the lack of explicit accounting for human effects to adapt to climate change damages and opportunities (Alcamo, et al., 1994a; Kenny, et al., 2001). Regional variability in ability to adapt to climate change is absent or only partially represented (Brown and Rosenberg, 1999; MINK, http://sedac.ciesin.columbia.edu/ mva/iamcc.tg/TGsec4-1-1.html). Some of these models mentioned above considered the consequences of technological change such as the improvement of variety, the development of fertilization and irrigation technology on agricultural productivity (Kenny, et al., 2001; Matsuoka, et al., 2001). Most of the integrated assessment did not consider the extreme climate event and soil degradation caused by climate change (Alcamo et al., 1994b; Matsuoka, et al., 2001; Parry, 1990; Holman et al., 2002; Brown and Rosenberg, 1999), with the principle exception of the site scale assessment model in CLIMPACTS (Ye, et al., 1999). The very well-known fully integrated assessment model IMAGE2.0, omits many factors that could alter the outcome and conclusions (Alcamo et al., 1994b).

      Agriculture will be directly effected not only by climate change such as temperature increase, changes in precipitation, CO2 fertilization and sea level rise, but also by non climate factors, such as policies, population increase, changes in technologies, and adaptation measures. Agriculture will be indirectly effected by the changes in water resources (water supply for irrigation), soil degradation, and loss of biodiversity under the climate change condition. None of the assessments listed in table 1 has incorporate all of these factors in one model framework

4. Integrated assessment model for agriculture in China

4.1 Framework of the integrated assessment model

     A framework for an integrated assessment model for Chinese agricultural sector has been developed (Figure 2) with two guiding principles. Firstly, it aims to be comprehensive, which means that all relevant aspects which might affect agriculture should ideally be incorporated. And secondly that it should be practical, which means it can be used as a tool to conduct the integrated assessment quantitatively.

The main elements in the integrated assessment model include the following components:

(1) Scenarios of climate change, socio-economic change and adaptation options;

(2) impact models, including crop models, crop choice model, water resource model, pest and disease model, biodiversity model, and soil carbon model;

(3) GIS output.


Table 1 The comparison of the integrated assessment models

Model

Spatial

Temporal

Models included

Second order impact on agriculture

Integration the agricultural sector with other sectors

Feedback

Extreme climate event

Policy Adaptation

Automatic adaptation

Stakeholders involved

IMAGEa, b

Calculation on crop productivity is performed on a spatial grid of 0.5° lat/long.

Economic calculations are performed for 13 world regions. a

-2100

Time steps of different submodels vary depending on their mathematical & computational requirements, typically from 1 day to 5 yrs. a

Three fully linked sub-systems: Energy-Industry, Terrestrial Environment, and Atmosphere-Ocean. Thirteen submodels are included in these subsystems.

The FAO Crop Suitability model was used to compute the potential productivity of eight crop classes on a global grid and then adjusted for grid-specific soil conditions by a "soil factor" which takes into account three soil quality indicators. a

No

No. Water resources, soil degradation, sea level rise sectors is not linked to agriculture impact assessment.

 

Yes. Emissions from the terrestrial environment are input to the Atmosphere-Ocean models to compute the GHG concentrations and climate change. The computed changes then fed back to the Terrestrial Environment System

No

No

The effect of nitrogen fertilizer and technological inputs were considered in the crop potential yield calculations. b

No

AIMc

National level, focus on the Asian-Pacific region

17 regions for bottom-up and land-use models

1990-2100

5 yr step

Including three main models---emission, climate and impact models.

Crop productivity model was used to calculate the impact of climate change on agricultural production.

Yes, Link the crop productivity model to agricultural trade model and macro economy impacts model of country level.

Yes, the crop productivity model coupled with regional climate, surface water runoff model and soil data..

Yes. In the framework of AIM, adaptation scenarios are input to the reduction scenarios and emission model. The computed changes then feed back to the impact models.

No

Yes, in the framework, the adaptation scenarios are included.

No

No

RegISd, e

5 km ´ 5 km

two regions in UK

1990-2050

 

Coastal and river flooding modelling, soil/crop model, Farm management model, Surface water nitrate catchment model.

Using climate scenarios rather than climate model.

Yes. The variability of a farmer’s perception has been included

integrate climate change, socio-economic scenarios and stakeholder views, sea level rise, land use change with crop models

No

No

No

Yes, the irrigation efficiency was considered.

Closely involves stakeholders

Partially integrated assessment f

National, regional and international

 

 

Yes, crop yield change and production were estimated.

Yes, consider the effects of climate change on pest and disease, changes in rates of soil erosion and so on.

No

No

Yes

Yes.  technical adjustment

No

CLIMPACTSg, h

For national scale; 0.05 ° lat/long

For regional scale: 0.01° lat/long

1990-2100

Global balance climate model, NZ climate scenario generator, impact modes.

No

Integrate together relevant scenarios, climate data, and impact models

No

Yes, at site scale impact assessment studies.

No

No

No

ICAMi

7 regions

1990-2100, 1yr step

?

Yes

Integrates environmental conditions

No

No

Yes

Yes, different crops, agricultural practices, and their interactions

No

MINKj

Four States in USA

2030

Agricultural model (EPIC)

Yes,

No

No

Yes, small-scale climate variability and extreme events by using past weather data.

No

Yes, in farm level

No

a Alcamo et al., 1994a; b: Alcamo et al., 1994b; c; Matsuoka, et al., 2001; d Holman er al; 2002; e Audsley et al., 2002; f Parry, 1990; g Kenny et al., 2001; h Ye et al., 1999; i:    j http://sedac.ciesin.colimbia.edu/mva/iamcc.tg/Tgsec4-1-1.htm


Figure 2: The framework of integrated assessment model for Chinese agriculture

4.2 The methodologies for the integrated assessment model

4.2.1 Scenarios

(1) Climate change scenarios

In this integrated model framework, climate scenarios from a highly complex linked

 

atmosphere-land-ocean RCM (Reference) are used in preference to integrating a GCM within the framework

     Climate change scenarios for China for the 2020s and 2050s will be developed using Providing Regional Climates Changes for Impacts Studies (PRECIS) (Hadley Centre). There are three emissions scenarios, baseline scenario (i.e. current climate), Medium-Low Emissions (SRES Emission Scenario B2), Medium-High Emission (A2) for which simulated daily temperature and precipitation will be derived in each 60km ´ 60km grid square.  The Baseline climate scenario will be used for the validation of the regional climate change model and to calculate the crop yield without climate change.

 (2) The socio-economic Scenarios

      There are 3 socio-economic scenarios under 2020s and 2050s respectively. The main parameters in Socio-economic scenarios include population scenarios and urbanisation scenarios. The population increase and urbanization have a major effect upon the availability of agricultural land. The population and urbanization scenarios will be spatialized into each grid to determine the agricultural land area in each grid for which the land use model can predict agricultural land use changes.

(3) Adaptation scenarios

      Three levels of adaptation measures will be considered in the model framework. (1) No adaptation; (2) Autonomous adaptation, which imply little additional cost to farmers and not necessary policy changes, such as shifts in planting dates, variety improvement, improvements in fertilisation and irrigation techniques, food price; (3) Policy adaptation, which implies significant additional costs to the farmers or the government. In policy context, the are three parts will have influence on agriculture area and agriculture productivity:1) Increase water resources, including South-North water transfer, inter basin water transfers; 2)Ecological programme; 3)Farmland regulation; and institutional reform; and 4) Increased fertilisation application, installation of irrigation system

4.2.2 Models

(1)  Flooding impacts

Estimates of sea-level rise, extracted from the coupled models of regional climate change and regional ocean model, will be used to explore the changes in flood frequency using ‘flood exceedance curves’ and models, which combine sea-level change, tidal surge, wave height and erosion (reference).  Areas prone to frequent inundation will be assumed to go out of agriculture due to soil salinization.

(1) Crop choice model

        Land use model is used to simulate the crop composition changes in each grid based on the climate, socio-economy, price and culture.

(3) Pest and diseases model

        The model used for estimate pest and diseases is…. The inputs are …. The outputs are …. Incorporating the outputs of the pest and disease model into the crop models to predict the impact of pest and diseases on the crop productivity under the climate change scenarios.

 (2) Soil carbon model

        Using DNDC model to predict the soil carbon and nutrient content in 2020s, 2050s. The outputs of the regional climate model for temperature and precipitation as inputs for DNDC model. Both the soil data and land use and management data will be downscaled to 60km ´ 60km using ARC/GIS. The outputs of the DNDC model include total carbon, total nitrogen, ammonium, total mineral nitrogen, nitrate leaching, soil water dynamics and so on. Some of the outputs will be linked with crop models such as soil water dynamics, total nitrogen, ammonium, total mineral nitrogen and soil carbon.

(6) Crop models

      The yield for the main crops in each grid square is simulated using Gird-CERES, which allow for the effects of CO2 fertilization. The crop models will be linked with the soil carbon, pest and disease, biodiversity, and water resource models, linked with climate change, socio-economic and adaptation scenarios.

(4) Biodiversity

     BIOME3 will be used to assess the impacts of climate change on the distribution and biogeochemistry of natural ecosystems. BIOME3 will be adapted for China situation, and used to predict the ‘functional types’ expected under different scenarios. Individual species can then be related to those functional types.

(5) Water resource model

   Water resource model is used to predict the how much water can be used for irrigation in each grid under the conditions of with policy adaptation and without policy adaptation measures. The projected total water needs are compared with projections of available irrigation water to estimate any water surplus and /or deficit and to determine the expected frequency of water deficits.

4.2.3 The Geographic Information System

    Common grid size of 60km ´ 60km grid will be used by all the models, both data input and model output. All the necessary model input data should be imported into a common geographically referenced database within an ARC/Info Geographic information system (GIS). The project GIS can facilitate the integration of sectoral analysis. The incorporation of model outputs within the same system has also assisted the visualisation and presentation of research results. The database holds:

·         Climate change scenarios, including temperature and precipitation, extreme climate events, sea level rise; CO2 concentrations;

·         Current climate data, soil property data, land use data;

·         Three population and urbanisation scenarios;

Output from the relevant model runs, including crop yield changes, agricultural area, soil properties, soil water moisture, water availability for irrigation, pest and disease outbreak change, biodiversity changes.

4.3 The challenges ahead

      In addition to the challenges identified in previous studies such as basic scientific challenges, uncertainties (Dowlatabadi, 1995), difficulties in incorporating changes in extreme weather events and difficulties in modeling the effects of adaptation (IPCC, 2001b), there are some particular challenges ahead:

1)      Spatial and temporal resolution: The models in the integrated assessment framework have different spatial and temporal scales. Procedures used to match and adjust resolutions involve substantial technical difficulties.

2)      GIS visualization. Integrated assessment of climate change on agriculture may be used to improve scientific understanding, but the ultimate aim must be to assist decision-makers by providing them with information on the impact of climate change, what measure should be taken and the consequences of each measure. Presenting the results of such an assessment, which are both spatial and temporal data and which include significant inherent uncertainties, in a form which is meaningful to decision-makers, is a great challenge.

3)      Stakeholder involvement. An important factor in the production of a successful assessment is the development of a good relationship between decision-makers and scientists. Public involvement can help to improve the quality of assumptions. The stakeholders have considerable knowledge that may be extremely valuable in the assessment of impact, impact cost and adaptation strategies. But the involvement of stakeholders such as policy-makers and the general public have not yet given adequate attention. How to incorporate the stakeholders’ knowledge into the integrated assessment framework is another challenge. The biggest difficulty in IAMs may not be integrating the assessment, but integrating the audience.

5. Conclusion:

There are some integrated assessment models exist, in which the agriculture impacts assessment included. Each of them has some weakness and can not be directly applied to China. Five important methodological innovations are identified in the development of the framework: (1) The integrated assessment model is highly integrated with other sectors such as policy, technological improvement. The impacts of these factors on agriculture, sometimes, have relatively higher than climate change; (2) The calculations are based on GIS reference database; (3) The integrated model framework includes the impacts of climate variability and extreme events by using climate scenarios generated by China Regional Climate Scenarios rather than GCMs runs; (4) The changes of land use and crop distributions are taken into account in the integrated assessment model; (5) Different levels of adaptation measures are considered in the course of the impact assessment.

 

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Date:Sep 11,2002