A Setup for a Scenario-Driven Water Balance at Landscape Scale—Assessment with AKWA-M^®-Embedded in a Model Framework for Land-Use Planning's Decision Support in Mountainous Southwest China

INTRODUCTION

The Yunnan Province in Southwest China possesses a very unique landscape, as well as great biological and cultural diversity. In recent years, the southern part of Yunnan (study area—Naban National Natural Reserve (NNNR), N22°09′; E100°40′ (Xishuangbanna region)) developed rapidly its infrastructure and economic performance. The link between such changes and the maintenance of the natural and cultural heritage is currently the most important challenge for the fast-growing region. It is, therefore, essential that land-use planning for this area takes into account the complexity of the economic, social, and ecological processes. The overall aim of the integrated project 'Living Landscapes China' (LILAC) is to develop such a plan through the use of strategic tools. Economists, ecologists, and sociologists from Germany and China are working together to develop an integrated model based on a geographic information system (GIS). The model should be able to predict the economic, social or ecological effects of different land uses, within a landscape context. The changes in land-use distribution, especially the fast-growing number of rubber plantations, have a distinct link to water fluxes in catchment hydrology. Rubber is not an indigenous plant in the research area. Given that background, a spatially distributed hydrological model (AKWA-M^®: Wahren et al., 2007; Münch, 2004) will be implemented into the existing model framework to assess effects of predicted/planned land-use alterations. Well-founded future land-use scenarios will be developed based on experiences in the research area and analyzed with regard to the consequences.

METHODS AND MATERIAL

Present Model Setup

The LILAC modelling team developed an integrated modelling framework (NabanFrame) to support land-use planning. Within NabanFrame, an agroeconomic, ecological, and social model will be applied which altogether interacts with a land allocation model via defined interfaces. NabanFrame proceeds in three steps: the first step, the pre-processing phase, defines the rules the model system follows to predict land-use conversion. Allocation rules were derived from plant physiological factors, questionnaires on farm structure, interviews about social relations and physical characteristics of the study area. In the second step, the land allocation (CLUENaban) is computed by using the initial land-use distribution and the rules defined before. Land allocation was conducted according to the CLUE-S allocation algorithm (Verburg, 2006; Verburg et al., 2002) using empirically quantified relations (regression analysis) between land use and its driving factors combined with the modelling of competition between land cover types (dependent on location suitability, neighbourhood setting, conversion elasticity and a demand-related iteration variable). In the third step, the post-processing phase, the sociological, economic and ecological impacts of the modelled land-use allocation in the scenarios are evaluated.

To check the capability of the CLUENaban model to simulate the current land cover distribution in the study area, a validation model run has been conducted. It was run for the period 2001 to 2007. The initial land-cover distribution is given by LANDSAT data from the year 2001. To evaluate the model performance, we compared the model results for the year 2007 with the observed land cover distribution using IKONOS data from the same year.

The validation model run is based on 4 location factors: elevation, distance to village, available labour, and area suitable for rubber growing. The calculation was done for a period of 6 years, and the result is the land-use distribution of 2007. Figure 1 shows the patterns of land-use change from 2001 to 2007. The model results reflect the land-use change that has been observed in recent years, in particular, the expansion of rubber plantations.

Figure  1.  Status-quo scenario 2001–2007 for NNNR region (Berkhoff and Herrmann, 2009).

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In the next sections, the impacts of the dramatic land-use change on catchment hydrology will be assessed.

Hydrological Model

Field research in the study area led to the conclusion that water availability and quality are important factors affecting the farm land management decisions. Therefore, the present NabanFrame modelling framework was extended with the spatially distributed hydrological model AKWA-M^®.

The model is based on the water balance model AKWA-M (Münch, 2004; Golf and Luckner, 1991). In recent years, it has been advanced by Dr. Dittrich & Partner Hydro-Consult GmbH (Münch et al., 2007; Wahren et al., 2007). This water balance and rainfallrunoff model simulates the water balance and flood runoff in watersheds and transforms the different processes from individual sites to a larger area (in ideal cases: catchments). It contains physically based components (runoff generation), which represent the site-specific land-use conditions in their spatial distribution, as well as a conceptual background (runoff concentration) concerning the geological and hydromorphological characteristics of a whole river basin or a subcatchment.

To parameterise the model, spatial information (geology, soil, land-use/vegetation type, elevation/morphology, river network-Fig. 2) and climate data (precipitation, temperature, sunshine duration, humidity, and wind speed) are needed. The spatial information is combined by overlaying GIS maps with the relevant information. The result of the overlay is a map defining hydrological response units (HRUs). For the calibration and validation process, measured river runoff data or additional information like soil moisture, evaporation measurements, etc. can be used.

Figure  2.  River network of the study area (NNNR, 2006).

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The application of AKWA-M^® is manifold tasks in practice, research, and education. With the help of this model, the following processes can be simulated and quantified: water balance for agricultural/silvicultural water supply and natural ecosystems; plant water fluxes (transpiration, interception); soil water fluxes (evaporation, plant available soil water, water stress); surface and subsurface runoff, groundwater recharge; runoff concentration, river discharge. Assessment of land-use changes (river revitalization, afforestation, cash crops, road construction, urbanization etc.) and climate with regard to water fluxes; water balances for dimensioning, management, and controlling inland water resources (dams, irrigation, hydromelioration, groundwater use, water reservoir, groundwater recovery, artificial infiltration of precipitation etc.); flood runoff events and hydrographs from historical and predicted precipitation and statistical design storms.

For the model framework, the hydrological model delivers information on water availability for each individual HRU in the study area. Water availability can be used as a location factor in the CLUENaban land allocation model. The main task for the hydrological model is to evaluate the impacts of land-use changes in the post-processing module of NabanFrame.

EXPECTED RESULTS AND DISCUSSION

Rubber plantations can change the water balance and affect the microclimate in the study area. An indication for these changes is the fact that the number of cloudy days decreased in Jinghong City of Xishuangbanna from 166 d (average) in 1950 to 91 d (average) in 1980 due to the expansion of rubber plantations (Wu et al., 2001). Hence, it is plausible to anticipate that the mentioned changes in land-use may have modified the water balance and cycling (soil water supply, evapotranspiration, groundwater recharge). As a consequence, plant production and runoff and, thus, also on soil erosion in the rainy season may have been subject to distinct changes.

Replacing natural or secondary forest by rubber plantations means changing the multi-layer evergreen mixed broadleaf stands into monocultural deciduous forest in some cases without any ground vegetation. This procedure involves several risks. It is commonly accepted that monocultures are more vulnerable to calamities (insects, fungal diseases etc.). Additionally, the rubber tree is not a native species in the study area. During the dry season, this tropical plant sheds its leaves, which reduces fog interception and canopy drip (Guardiola-Claramonte et al., 2008). One other effect which may result from the not site-adapted plant is the so-called 'leaf flushing paradox'. As a tropical plant, the rubber tree starts to flush the leaves around the equinox≈6 to 8 weeks before the rainy season starts. Hence, the root water demand is the highest when the climatic water supply is the lowest (Guardiola-Claramonte et al., 2008). The lacking ground vegetation can lead to erosion and decrease the stand stability.

This gathered information in the study area suggests that a distinct change in water balance will go along with changes in land use (increasing number of rubber plantation). As a result, the socio-economy and the ecology in the NNNR region will be affected by the hydrological changes. The assessment of the feedback between the disciplines is part of the post-processing step and could lead to an additional model run of the whole framework procedure.

CONCLUSIONS

The authors are in the initial phase of implementing the hydrological model into the framework approach (social, economic, and ecological based land-use prognosis). The benefits of integrated approach are: combination of heterogeneous data of different disciplines; linkage of different scientific approaches; evaluation of land-use changes from different perspectives; spatially explicit results of future development; land-use change maps, spatial analysis of scenarios.

The addition of hydrological information into the framework contributes with spatially distributed soil water availability for the initial state (2001) and for the present state. The provision of information on hydrological consequences resulting from the recent changes in land use can be assessed, and present observations can be explained. The next step is the assessment of the future prognosis from the NabanFrame model in post-processing and the adaptation of the future scenarios with regard to site and catchment hydrology. The state-of-the-art research (decrease of fog days, 'leaf flushing paradox', etc.) will be implemented into the parameterisation of the hydrological model.

The result is an integrated modelling approach for decision support in land-use planning. The evaluation in the post-processing phase of NabanFrame covers the ecological, agro-economic, social, and hydrological impacts of land cover changes, which now is also able to consider climate change information additionally.