Startseite Analysis and prediction of LUCC change in Huang-Huai-Hai river basin
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Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

  • Jianwei Wang , Kun Wang , Tianling Qin EMAIL logo , Hanjiang Nie , Zhenyu Lv , Fang Liu , Xiaoqing Shi und Yong Hu
Veröffentlicht/Copyright: 19. November 2020
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Abstract

Land use/cover change plays an important role in human development and environmental health and stability. Markov chain and a future land use simulation model were used to predict future change and simulate the spatial distribution of land use in the Huang-Huai-Hai river basin. The results show that cultivated land and grassland are the main land-use types in the basin, accounting for about 40% and 30%, respectively. The area of cultivated land decreased and artificial surfaces increased from 1980 to 2010. The degree of dynamic change of land use after the 1990s was greater than that before the 1990s. There is a high probability of exchange among cultivate land, forest and grassland. The area of forest decreased before 2000 and increased after 2000. Under the three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) of IPSL-CM5A-LR climate model, the area of cultivated land will decrease and that of grassland will increase in the upstream area while it will decrease in the downstream area. The above methods and rules will be of great help to future land use planning.

Keywords: LUCC; marcochains; FLUS; climate change; Huang-Huai-Hai river basin

1 Introduction

Land use and land cover change (LUCC) research plays an important part in the global change research [1,2,3]. Human alterations in land cover as a result of the use of land-based natural resources not only have local and regional impacts but can also have important effects at the global level [4]. Under the adjustment of institutional factors, people’s response to economic opportunities promoted changes in land cover [5]. Inundation and resettlement will cause LUCC [6]. Economic, technological, institutional, and location factors are often considered as potential drivers of LUCC [7]. The accelerated industrialization and urbanization following economic reforms and population increases have greatly affected LUCC through the increase of built-up areas and urban sprawl [8]. The fragmentation of cultivated land can be observed due to construction on the land of the countryside [9]. In addition to human activities, climate change also affects LUCC [10]. Climate change will cause changes in temperature and precipitation, affecting vegetation growth and the temporal and spatial distribution of water resources. Grasslands are most responsive to extreme drought events followed by forests and desert vegetation [11]. After experiencing 8 years of extreme drought events in the San Juan National Forest, Colorado, USA, the average dieback rate of Populus tremuloide gradually increased from 15% to 27% in July 2011 and to 38% in July 2012 [12]. In the long-term, repeated drought-induced mortality events, along with unsuccessful recruitment of the dominant tree species in forests may lead to a vegetation succession process where trees would be replaced by understory species (such as drought-tolerant shrub) [13]. Comprehensively consider the impact of human activities and climate change on LUCC and predict future LUCC to provide a basis for our future land planning.

There are many methods for LUCC research. The spatial calculating analysis model is used to analyze LUCC [14]. Remote sensing and Geographic Information Systems (GIS) are also used in LUCC research [15]. Change-vector analysis is a valuable technique for LUCC detection [16]. A system dynamic model can be used to model LUCC [17]. CA (Cellular Automata)-Markov model is commonly used for LUCC prediction [18,19]. Land use simulation model Conversion of Land Use and its Effect at Small regional extent utilizes the spatial dominant dynamic simulation model established by Wageningen University [20,21]. The future land use simulation model (FLUS [The FLUS model could be downloaded from http://www.geosimulation.cn/flus.html]) developed by Liu et al. [22] could be used for multiple LUCC dynamic simulation. The FLUS model considers the impact of climate conditions, geography, and socioeconomic factors on LUCC and simulates the spatial distribution of various land-use type under different land-use conversion targets. The LUCC can provides information for our better decision on the development and utilization of natural resources [23,24]. According to research goals of this article, we choose GIS spatial analysis ang FLUS model as research tools.

The Huang-Huai-Hai river basin accounts for 15% of China’s total land area. It is an important economic center and food production base in China and has an important strategic position in China’s development [25]. The plains in the eastern part of the basin are China’s agricultural production bases. The area of arable land and grain output account for 20.4% and 23.6% of the country’s total [26]. Since the twenty-first century, the economic development of the Huang-Huai-Hai river basin has driven the process of urbanization and great changes have taken place in land-use types. The Huang-Huai-Hai region is located in China’s three major climatic regions, including monsoon climate, dry climate, and highland climate. The climate change in recent years has an impact on the region, especially for the vegetation. LUCC research is of great significance to the future sustainable development of the Huang-Huai-Hai river basin. Hence, the law of LUCC and the spatial distribution of LUCC under future climate conditions will be studied in this paper.

2 Models and methods

Based on historical land use data, the Raster Calculator in the ArcGIS spatial analysis tool is used to calculate relevant indicators and transfer matrices of LUCC to analyze the laws of historical changes.

The other part is the spatial distribution of LUCC under future climate conditions. Historical land-use data, socioeconomic data, and climate data are input into the FLUS model for verification. The future climate conditions are input into the verified model to simulate the spatial distribution of LUCC under future climate conditions. The article process and structure are shown in Figure 1.

Figure 1 Article structure diagram.
Figure 1

Article structure diagram.

2.1 Metric indexes of LUCC

Land use of a dynamic degree is used to analyze the dynamic change of land use. The single land-use dynamic degree refers to the quantity change of a certain land-use type [27] within a certain period. The formula [28] is as follows:

(1)K=Ub−UaUa×1T×100%

where K is the dynamic degree of one land-use type during the study period; Ua and Ub are the area of a certain land-use type at the beginning and end time of the study period, respectively; and T is the length of the study period. When T is set to the year, K is the annual change rate of a land-use type during the study period.

The comprehensive land-use dynamics degree indicates the overall dynamics of land use in a certain area. The formula [28] is as follows:

(2)LC=∑i=1nLCi=j2∑i=1nLUi×1T×100%

where LC is the rate of change in land use and reflects the degree of change in the amount of land use; LUi is the area of land-use type i at the beginning of the period; LCi–j is the absolute value of the area converted from land use of type i to that of non-type i during the period; and T is the length of the study period. When T is set to the year, LC is the annual change rate of a land-use type during the study period.

2.2 Markov model

The Markov model determines the changing trend of each state of the system through the initial probability of different states of the systems and the transition probability between states, to achieve the purpose of predicting the future. Different land-use types in a certain area are mutually convertible, and the average conversion state change of land-use structure is relatively stable over a period of time. Therefore, the dynamic evolution of land use has the property of the Markov model. The Markov model has been widely used in the LUCC research [29]. With the support of the ArcGIS software, the land use raster of two periods are overlapped and the corresponding property in the overlay map database PAT.DBF is extracted. According to the probability distribution matrix of initial S(0) and the transition probability of the nth stage P(n), the future area of land use could be calculated by computer simulation. The Markov simulation model S(n) [30] is as follows:

(3)S(n)=S(n−1)×P(1)=S(0)×P(n)

2.3 FLUS model and verification

Land-use change models are usually used to detect where the change occurred or will potentially occur [31]. Traditional methods can provide static analysis of the LUCC for the fixed beginning and end date [32]. And the LUCC model also can help people better understand the mechanism of social, economic, and physical variables’ impacts on LUCC [33]. Through knowing the factors contributing to the change, the model provided a probabilistic prediction of where the change may occur [34,35]. LUCC models are also used to assess the cumulative impact of land-use change and develop future scenarios [36]. It helps and supports land-use planning and decision-making [37]. The FLUS model (Figure 2) that interactively integrates top-down system dynamics (SD) with a bottom-up CA model could be used for multiple LUCC dynamic simulation [22]. The SD model is used to project the land-use scenario demands under various socio-economic and environmental driving factors. A self-adaptive inertia and competition mechanism is developed within the CA model to process the complex competitions and interactions among the different land-use types. This model has been successfully applied in the simulation of land use and land cover change in China, as well as on the global scale [38]. In the model simulation process, not all land-use types can be transformed into each other. According to the natural evolution of land resources and the actual situation of human activities, we set up a cost matrix (Table 1). For example, artificial surface cannot be converted into other types of land, cultivated land can be converted into other types of land, and water bodies can be converted into other types of land. In this way, the simulation is more realistic.

Figure 2 FLUS model frame diagram.
Figure 2

FLUS model frame diagram.

Table 1

Cost matrix in the FLUS model

Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized land
Cultivated land111111
Forest110010
Grassland111010
Water bodies000100
Artificial surfaces000010
Unutilized land111111

Note: 1 means conversion is allowed; 0 means conversion is not allowed.

Receiver operation characteristic (ROC) curve analysis can obtain multiple pairs of true (false) positive rate values [39]. With the false positive rate on the abscissa and the true positive rate on the ordinate, the drawn curve is called the ROC curve, and the area under the ROC curve is the AUC value (area under the curve), which reflects the value of the diagnostic test. It is generally believed that when the AUC value is 0.5–0.7, the diagnostic value is low; when the AUC value is 0.7–0.9, the diagnostic value is moderate; when it is greater than 0.9, the diagnostic value is good. Cohen’s kappa coefficient [40,41] is a measure of agreement between two variables. The ROC curve and kappa are used to test the simulation effect of the FLUS model.

3 Study area and datasets

3.1 Study area

The geographical range of the Huang-Huai-Hai area is 95°53′∼122°60′E, 32°10′∼43°N, with a total area of about 1.43 million km2. The Huang-Huai-Hai region is located in China’s three major climatic regions, including monsoon climate, dry climate, and highland climate. And it includes three first-level water resource regions, namely, the Yellow river basin, the Huai river basin, and the Hai river basin. The geographical location of the Huang-Huai-Hai area is shown in Figure 3.

Figure 3 Location of the Huang-Huai-Hai river basin.
Figure 3

Location of the Huang-Huai-Hai river basin.

3.2 Data datasets

The data used in this article are land use data, socioeconomic data, terrain data, and historical climate data. The time resolution of the data is years, and the spatial resolution is 1 km × 1 km. Future climate data (liner interpolated and bias-corrected) obtained from Global Climate Model (IPSL-CM5A-LR) provided by ISI-MIP (The Inter-Sectoral Impact Model Intercomparison Project) [42]. The details are listed in Table 2.

Table 2

List of data used in this study

CategoryDataYearData resourceDescription
Land useLand use1980\1990\2000\2010http://www.resdc.cnDownload from the data resource and the size is 1 km × 1 km
Socioeconomic dataPopulation2005
GDP
TerrainDEM2000http://www.gscloud.cnSame as above
Aspect—Calculated from DEM
Slope—Calculated from DEM
RoadsRoadhttp://www.openstreetmap.orgConvert shp file to 1 km × 1 km raster file
ClimateTemperature2001–2010http://www.resdc.cnDownload from the data resource and the size is 1 km × 1 km
Precipitation
Future climateTemperature2011–2030http://www.isi-mip.orgResampled 0.5° × 0.5° raster into 1 km × 1 km

4 Results

4.1 Law of land use conversion

4.1.1 Dynamic change of land use

Land cover proportions in the Huang-Huai-Hai river basin during 1980–2010 were calculated as listed in Table 3. The land use types in the Huang-Huai-Hai river basin were mainly composed of cultivated land, forest, and grassland in the past 20 years. And the area of the three land types accounted for more than 85% of the total area. From the perspective of the dynamics of land use change, the cultivated land has been decreasing, the artificial surfaces have been increasing, and the forest area has been reduced before increasing in the three periods (year 1980–1990, year 1990–2000, year 2000–2010). Overall, the degree land-use change after the 1990s was significantly higher than that before the 1990s.

Table 3

Land cover proportion and dynamics in Huang-Huai-Hai river basin during 1980–2010 unit: %

Land-use typeCover proportionDynamics
19801990200020101980–19901990–20002000–2010
Cultivated land41.9741.9441.7941.10−0.06−0.36−1.67
Forest13.1013.0813.0613.25−0.15−0.141.46
Grassland31.9831.9931.7731.620.04−0.67−0.49
Water bodies2.572.402.442.49−6.451.611.96
Artificial surfaces5.175.395.836.404.118.319.66
Unutilized land5.225.205.105.15−0.29−1.991.01
Total1001001001000.35*0.83*0.87*

Note: * indicates a comprehensive land-use dynamics degree.

4.1.2 Net change, exchange change, and the total change in land-use types

Based on the analysis of statistical results in Table 4, during the three periods, artificial land was dominated by expansion and cultivated land was dominated by the conversion of spatial location. Water bodies and grassland were degraded and were accompanied by some spatial conversion. In particular, the forest area increased during 2000–2010 and its net change accounted for 76% of the total change.

Table 4

Summary of land-use change in the Huang-Huai-Hai river basin during 1980–2010 unit: %

Statistical variablesYear rangeLand-use typesTotal
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized land
Increase1980–19900.190.020.110.090.210.070.70
1990–20000.470.120.320.150.450.151.66
2000–20100.220.250.320.160.580.211.73
Loss1980–19900.210.040.100.260.000.090.70
1990–20000.620.130.530.110.010.261.66
2000–20100.920.060.470.110.010.161.73
Net change1980–19900.020.020.010.170.210.020.45
1990–20000.150.010.210.040.440.110.96
2000–20100.70.190.150.050.570.051.71
Exchange change1980–19900.380.040.20.1800.140.94
1990–20000.940.240.640.220.020.32.36
2000–20100.440.120.640.220.020.321.76
Total change1980–19900.40.060.210.350.210.161.39
1990–20001.090.250.850.260.460.413.32
2000–20101.140.310.790.270.590.373.47

4.1.3 Land-use conversion

The land-use changes during 1980–2010 were analyzed from the perspective of quantitative. In order to analyze the mutual transformation of land use, the land use conversion matrix was calculated based on the ArcGIS platform. The results from 1980 to 1990 are listed in Table 5. The loss of cultivated land was mainly converted into artificial surfaces, and the degradation of water bodies was mainly converted into cultivated land. The land-use conversion results from 1990 to 2000 are listed in Table 6. The loss of cultivated land was mainly converted into artificial surfaces and also converted into water bodies and grassland. At the same time, the water bodies and grassland were mainly converted into cultivated land. The land-use conversion results from 2000 to 2010 are listed in Table 7. The law of cultivated land conversion was still the same, but the change of forest was more obvious than the previous two periods.

Table 5

The conversion matrix of land use from 1980 to 1990

Year 1980 (km2)Year 1990 (km2)
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized landTotal
Cultivated land6,00,639108888511,966496,03,701
Forest1151,87,77844792211,88,372
Grassland6351204,58,5382241672744,59,958
Water bodies1,5374874633,24665673436,967
Artificial surfaces1803274,382074,405
Unutilized land4073730925027373,81075,086
Total6,03,3511,88,0914,60,13134,58277,46674,868
Table 6

The conversion matrix of land use from 1990 to 2000

Year 1990 (km2)Year 2000 (km2)
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized landTotal
Cultivated land5,94,4204511,2021,0225,8823746,03,351
Forest6351,86,1591,1222892551,88,091
Grassland4,2501,0324,52,4663933301,6604,60,131
Water bodies1,1455218233,0325511634,582
Artificial surfaces59221377,390077,466
Unutilized land7001232,06965015371,17374,868
Total6,01,2091,87,8194,57,04335,13883,90273,378
Table 7

The conversion matrix of land use from 2000 to 2010

Year 2000 (km2)Year 2010 (km2)
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized landTotal
Cultivated land5,87,9771,8082,7471,4656,8593536,01,209
Forest1221,86,935312101298511,87,819
Grassland1,7541,6334,50,2724546312,2994,57,043
Water bodies7616022533,52028828435,138
Artificial surfaces6818455583,708883,902
Unutilized land4791071,21323122771,12173,378
Total5,91,1611,90,5614,54,81435,82692,01174,116

4.2 Prediction and spatial distribution simulation of land-use change

4.2.1 Prediction of land-use change

The prediction steps are as follows: the first step is the generation of the initial state matrix. Select the land use types in 2000 as the initial matrix as follows (unit: km2).

(4)S(0)=CultivatedlandforestGrasslandwaterbodiesartificialsurfacesuntilizedland=6,01,2091,87,8194,57,04335,138 83,09273,378

The land-use type area transition matrix of the initial state (Table 6) was obtained from 2000 to 2010. Thus, the initial state transition probability matrix (unit: %) from 2000 to 2010 (Table 8) and the annual average transition probability matrix were calculated to predict the land-use areas in 2030.

Table 8

The initial state transition probability matrix from 2000 to 2010 (n = 0)

20002010
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized land
Cultivated land97.800.300.460.241.140.06
Forest0.0699.530.170.050.160.03
Grassland0.380.3698.520.100.140.50
Water bodies2.170.170.6495.400.820.81
Artificial surfaces0.080.020.050.0799.770.01
Unutilized land0.650.151.650.310.3196.92

The future land-use area of change trend was calculated with the formula (3), formula (4), and Table 7. The results of 2,030 are listed in Table 9.

Table 9

Land use prediction results in 2030

YearArea (km2)
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized land
20305,71,7801,95,9054,50,39437,0571,07,85975,495

4.2.2 Spatial distribution simulation of land-use change

4.2.2.1 Model establishment and verification

Based on the land use of 2000, the geographical and socio-economic factors are considered to simulate the land use distribution in 2010 with the FLUS model. And the result was verified with actual data of 2010.

The simulation result of spatial land use and the actual one of 2010 are shown in Figure 4, with three small regions to display more details of the simulated results. The figure shows that the simulated pattern is well correlated with the actual. To quantitatively validate the simulated result, the confusion matrix of simulated results and the actual land use was calculated (Table 10). Then, the overall accuracy and Cohen’s Kappa coefficient for all land-use types were calculated. In addition, ROC curves of different land-use types in the simulation results were drawn, as shown in Figure 5. The results showed that, except for the poor simulation of unutilized land, the simulation results of the other five types were very good. Therefore, the model can be used for later predation.

Figure 4 The simulated land-use pattern and the actual land-use pattern in 2010.
Figure 4

The simulated land-use pattern and the actual land-use pattern in 2010.

Table 10

Confusion matrix of the predicted land-use pattern versus the actual pattern in 2010

Land use typesActual land use in 2010Total
Cultivated landForestGrasslandWater bodiesArtificial surfacesUnutilized land
Cultivated land55,87580488260764913258,949
Forest76217,39871046332218,971
Grassland90662343,736896049245,906
Water bodies71347432,71424453,586
Artificial surfaces6562644328,26969,033
Unutilized land489436122176,7717,403
Total58,96018,90745,8513,6109,0527,4681,43,848

Note: Kappa coefficient = 0.91, overall accuracy = 0.93.

Figure 5 ROC curves of different land-use types based on simulation results in 2010.
Figure 5

ROC curves of different land-use types based on simulation results in 2010.

4.2.2.2 Prediction of land use in future climate scenarios

Based on the previous step, the result of Markov’s prediction was used as the target for future land use and was as the input into the model. IPSL-CM5A-LR is comparatively better among all five climate models in this study area [41]. The spatial distribution of land use in 2030 was simulated under different climate scenarios (RCP2.6, RCP4.5, and RCP8.5). RCP2.6, RCP4.5, and RCP8.5 represent three scenarios of low, medium, and high greenhouse gas emissions, respectively, and represent possible future climate changes. The analysis under the conditions of the three climatic models makes our research on land-use changes under the future climatic conditions more comprehensive. The results are shown in Figure 6.

Figure 6 (a) Simulation result under RCP2.6, (b) simulation result under RCP4.5, and (c) simulation result under RCP8.5.
Figure 6

(a) Simulation result under RCP2.6, (b) simulation result under RCP4.5, and (c) simulation result under RCP8.5.

According to the prediction results shown in Table 9, the area of cultivated land will decrease largely in 2030 and that of the artificial surface will increase correspondingly. Based on the prediction results of 2030, the spatial distribution of different land-use types in three different scenarios is shown in Figure 4. In the three scenarios, the spatial distribution of artificial surfaces and water bodies are the same. However, there are some differences in the spatial distribution of cultivated land, grassland, and forest. Huang-Huai-Hai river basin includes three first-level sub-watershed (Figure 7) and seventeen second-level sub-watershed (Figure 8). The areas with a great difference in the spatial distribution of land use are shown in Figure 9. The specific results are shown in Figure 10 and the meaning of basin number is listed in Table 11.

Figure 7 First-level sub-watershed.
Figure 7

First-level sub-watershed.

Figure 8 Second-level sub-watershed.
Figure 8

Second-level sub-watershed.

Figure 9 Areas with different land use space simulation results.
Figure 9

Areas with different land use space simulation results.

Figure 10 Areas with different land use spatial distribution in different climate condition.
Figure 10

Areas with different land use spatial distribution in different climate condition.

Table 11

The meaning of the basin number

Basin numberMeaning
1_3Catchment area from Longyangxia to Lanzhou
1_5Catchment area above Longyangxia
1_6Catchment area from Sanmenxia to Huayuankou
2_1Catchment area of coastal rivers of Shangdong peninsula
2_5Yishusi river basin
3_3South of Haihe river basin

5 Discussion

5.1 Impact of historical climate and human activities on land use/cover

The comprehensive land-use dynamics degree of the 1980s, 1990s, and 2000s were 0.35, 0.83, and 0.87, respectively, as shown in Table 2. After the 1990s, under the influence of Economic Reform and open up, China’s economy developed rapidly. The transformation of the land by humans was huge. In particular, the acceleration of urbanization and rapid population growth have turned cultivated land, woodland, and grassland into resident land, and the water area had degraded. From a climate perspective, the temperature has increased by 1°C (1980–2010), and the increase in temperature is helpful for vegetation growth (photosynthesis) [42]. But the total amount of vegetation was still decreased. There may be two reasons for this. In the 1990s, people only paid attention to economic development and neglected the protection of vegetation. During this period, forest land and grassland decreased. In the twenty-first century, our country not only pays attention to social development but also pays attention to the protection of vegetation. Therefore, during the period from 2000 to 2010, vegetation was partially restored. The other is precipitation. Since 1980, the total amount of precipitation has not changed dramatically, but the temporal and spatial distribution of precipitation has been quite different. Especially after the 1990s, the occurrence of extreme hydrological events (droughts, floods) had an impact on the change of land cover [43,44]. The survival rate of trees is affected by extreme floods and the closer to the river, the lower the survival rate [45]. Drought reduces soil moisture content, and prolonged drought causes trees to die due to long-term lack of moisture [46]. The impact of repeated extreme precipitation events, such as repeated extreme drought events, even several years apart, may also have a greater negative impact on vegetation mortality [12]. As the North China Plain is strongly affected by the temperate monsoon, floods caused by heavy rains have had a huge negative impact on the Loess Plateau in the Yellow River Basin. More and more extreme precipitation has caused many environmental problems and ecological crises, such as soil erosion and land desertification [47]; for the Huai River Basin, since 1950, small floods have occurred every 2 years, and major floods have occurred every 5 years [48]; for the Haihe River Basin, although the annual precipitation is not large, most of the rainfall in the past 50 years has been reduced in the form of heavy rain, and the intensity of the heavy rain has been increasing [49]. In recent years, extreme hydrological events in all regions show an increasing trend. Floods mainly reduce vegetation through physical damage, and droughts reduce vegetation mainly through physiological damage. Based on the results of the net land-use change and exchange change and the land-use conversion matrix from 1980 to 2010, it is possible to know the conversion probability and direction of land use. Cultivated land, forest and grassland are frequently transformed into each other, while artificial surface is not easily transformed into other land types, and have strong expansion [50,51]. Under natural conditions, the probability of land use conversion is also one of the reasons for the difference in the spatial distribution of land use.

5.2 Impact of future climate on land use/cover

Compared with 2010, the spatial pattern of land use has changed in the future (Year 2030). We used the secondary watershed to make spatial statistics on the land-use pattern, and the results show that the distribution patterns of a forest, grassland, and cultivated land significantly change in the source and downstream region. The spatial distribution of areas with significant changes in the secondary watershed, shown in Figure 10. In the RCP 2.6 scenario, the area of cultivated land use in the upstream is reduced by more than 30%, while that is less than 10% in the RCP 4.5 and RCP 8.5 scenario. Whereas the area of grassland in downstream is reduced by more than 30% under the RCP 2.6 scenario but less than 10% under 10% under the RCP 4.5 and RCP 8.5 scenarios. According to the analysis of climatic conditions, the major factor for the different land-use patterns in upstream and downstream is precipitation. In the future, human activities will aim to minimize the disturbance of the natural environment and carry out green development [52]. Precipitation and temperature play the important roles in the growth of vegetation [53]. Therefore, precipitation and temperature will become the main factors affecting vegetation in the future. The risk of drought disaster risk in the Huang-Huai-Hai river basin is mainly reflected in the growing season precipitation in the entire region, the frequency of drought events, and the regulation and storage effect of the underlying surface on precipitation [54]. The higher risk areas are concentrated in the upper reaches of the basin. The risk of flood disaster risk in the Huang-Huai-Hai river basin is mainly reflected in the extreme precipitation in the entire basin and the regulation and storage effect of the underlying surface on precipitation [55]. The higher risk areas are concentrated in the lower reaches of the basin. The risk of drought and flood events in the basin in the future will have an impact on land use. At the same time, the pattern of land-use change in the upstream was different from that in the downstream. The upstream is dominated by the reduction of cultivated land, and the downstream is dominated by the reduction of grassland. That is because precipitation in the upstream will decrease 30 mm and that will increase about 40 mm in the downstream in the future. There is a large area of farmland upstream of the whole basin, which is affected not only by rainwater but also by artificial irrigation from the Yellow River. Therefore, with the participation of human activities, the area of cultivated land in the future may not be reduced much.

6 Conclusions

LUCC is affected by humans and climate change. By analyzing the historical changes in land use, the land-use changes after the 1990s had been dramatic, mainly reflected in the reduction of cultivated land and the expansion of artificial land. The social reason was mainly due to the increase in population, and the climatic reason was the increase in temperature. At the same time, this paper also studied the spatial distribution of land use in future climate scenarios. The changes in the upstream and downstream regions were the most obvious. The obvious changes in land use types were cultivated tland, grassland, and forest. The major factor was precipitation.

This article analyzes the causes of LUCC from the perspective of climatic conditions. The spatial distribution of land use in future climate conditions was also predicated. This result can also provide data support for future research on land use under future climate conditions.

Acknowledgments

This study was supported by the National Key Research and Development Project (Grant No. 2017YFA0605004; 2016YFA0601503) and the National Science Fund for Distinguished Young Scholars (Grant No. 51725905 and 51879275) and the National Natural Science Foundation of China (No. 51809002).

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Received: 2020-03-28
Revised: 2020-10-22
Accepted: 2020-10-29
Published Online: 2020-11-19

© 2020 Jianwei Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2020-0112
Schlagwörter für diesen Artikel
LUCC; marcochains; FLUS; climate change; Huang-Huai-Hai river basin
Creative Commons
BY 4.0

Artikel in diesem Heft

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  22. Estimation of permeability and saturation based on imaginary component of complex resistivity spectra: A laboratory study
  23. Distribution characteristics of typical geological relics in the Western Sichuan Plateau
  24. Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning
  25. A new methodological approach (QEMSCAN®) in the mineralogical study of Polish loess: Guidelines for further research
  26. Displacement and deformation study of engineering structures with the use of modern laser technologies
  27. Virtual resolution enhancement: A new enhancement tool for seismic data
  28. Aeromagnetic mapping of fault architecture along Lagos–Ore axis, southwestern Nigeria
  29. Deformation and failure mechanism of full seam chamber with extra-large section and its control technology
  30. Plastic failure zone characteristics and stability control technology of roadway in the fault area under non-uniformly high geostress: A case study from Yuandian Coal Mine in Northern Anhui Province, China
  31. Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image
  32. Soil carbon stock and nutrient characteristics of Senna siamea grove in the semi-deciduous forest zone of Ghana
  33. Carbonatites from the Southern Brazilian platform: I
  34. Seismicity, focal mechanism, and stress tensor analysis of the Simav region, western Turkey
  35. Application of simulated annealing algorithm for 3D coordinate transformation problem solution
  36. Application of the terrestrial laser scanner in the monitoring of earth structures
  37. The Cretaceous igneous rocks in southeastern Guangxi and their implication for tectonic environment in southwestern South China Block
  38. Pore-scale gas–water flow in rock: Visualization experiment and simulation
  39. Assessment of surface parameters of VDW foundation piles using geodetic measurement techniques
  40. Spatial distribution and risk assessment of toxic metals in agricultural soils from endemic nasopharyngeal carcinoma region in South China
  41. An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level
  42. Microscopic mechanism of sandstone hydration in Yungang Grottoes, China
  43. Importance of traditional landscapes in Slovenia for conservation of endangered butterfly
  44. Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale
  45. The influence of river training on the location of erosion and accumulation zones (Kłodzko County, South West Poland)
  46. Multi-temporal survey of diaphragm wall with terrestrial laser scanning method
  47. Functionality and reliability of horizontal control net (Poland)
  48. Strata behavior and control strategy of backfilling collaborate with caving fully-mechanized mining
  49. The use of classical methods and neural networks in deformation studies of hydrotechnical objects
  50. Ice-crevasse sedimentation in the eastern part of the Głubczyce Plateau (S Poland) during the final stage of the Drenthian Glaciation
  51. Structure of end moraines and dynamics of the recession phase of the Warta Stadial ice sheet, Kłodawa Upland, Central Poland
  52. Mineralogy, mineral chemistry and thermobarometry of post-mineralization dykes of the Sungun Cu–Mo porphyry deposit (Northwest Iran)
  53. Main problems of the research on the Palaeolithic of Halych-Dnister region (Ukraine)
  54. Application of isometric transformation and robust estimation to compare the measurement results of steel pipe spools
  55. Hybrid machine learning hydrological model for flood forecast purpose
  56. Rainfall thresholds of shallow landslides in Wuyuan County of Jiangxi Province, China
  57. Dynamic simulation for the process of mining subsidence based on cellular automata model
  58. Developing large-scale international ecological networks based on least-cost path analysis – a case study of Altai mountains
  59. Seismic characteristics of polygonal fault systems in the Great South Basin, New Zealand
  60. New approach of clustering of late Pleni-Weichselian loess deposits (L1LL1) in Poland
  61. Implementation of virtual reference points in registering scanning images of tall structures
  62. Constraints of nonseismic geophysical data on the deep geological structure of the Benxi iron-ore district, Liaoning, China
  63. Mechanical analysis of basic roof fracture mechanism and feature in coal mining with partial gangue backfilling
  64. The violent ground motion before the Jiuzhaigou earthquake Ms7.0
  65. Landslide site delineation from geometric signatures derived with the Hilbert–Huang transform for cases in Southern Taiwan
  66. Hydrological process simulation in Manas River Basin using CMADS
  67. LA-ICP-MS U–Pb ages of detrital zircons from Middle Jurassic sedimentary rocks in southwestern Fujian: Sedimentary provenance and its geological significance
  68. Analysis of pore throat characteristics of tight sandstone reservoirs
  69. Effects of igneous intrusions on source rock in the early diagenetic stage: A case study on Beipiao Formation in Jinyang Basin, Northeast China
  70. Applying floodplain geomorphology to flood management (The Lower Vistula River upstream from Plock, Poland)
  71. Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM
  72. Morphodynamic conditions of heavy metal concentration in deposits of the Vistula River valley near Kępa Gostecka (central Poland)
  73. Accuracy and functional assessment of an original low-cost fibre-based inclinometer designed for structural monitoring
  74. The impacts of diagenetic facies on reservoir quality in tight sandstones
  75. Application of electrical resistivity imaging to detection of hidden geological structures in a single roadway
  76. Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study
  77. The genesis model of carbonate cementation in the tight oil reservoir: A case of Chang 6 oil layers of the Upper Triassic Yanchang Formation in the western Jiyuan area, Ordos Basin, China
  78. Disintegration characteristics in granite residual soil and their relationship with the collapsing gully in South China
  79. Analysis of surface deformation and driving forces in Lanzhou
  80. Geochemical characteristics of produced water from coalbed methane wells and its influence on productivity in Laochang Coalfield, China
  81. A combination of genetic inversion and seismic frequency attributes to delineate reservoir targets in offshore northern Orange Basin, South Africa
  82. Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data
  83. DTM-based analysis of the spatial distribution of topolineaments
  84. Spatiotemporal variation and climatic response of water level of major lakes in China, Mongolia, and Russia
  85. The Cretaceous stratigraphy, Songliao Basin, Northeast China: Constrains from drillings and geophysics
  86. Canal of St. Bartholomew in Seča/Sezza: Social construction of the seascape
  87. A modelling resin material and its application in rock-failure study: Samples with two 3D internal fracture surfaces
  88. Utilization of marble piece wastes as base materials
  89. Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines
  90. Rigidity of “Warsaw clay” from the Poznań Formation determined by in situ tests
  91. Numerical simulation for the effects of waves and grain size on deltaic processes and morphologies
  92. Impact of tourism activities on water pollution in the West Lake Basin (Hangzhou, China)
  93. Fracture characteristics from outcrops and its meaning to gas accumulation in the Jiyuan Basin, Henan Province, China
  94. Impact evaluation and driving type identification of human factors on rural human settlement environment: Taking Gansu Province, China as an example
  95. Identification of the spatial distributions, pollution levels, sources, and health risk of heavy metals in surface dusts from Korla, NW China
  96. Petrography and geochemistry of clastic sedimentary rocks as evidence for the provenance of the Jurassic stratum in the Daqingshan area
  97. Super-resolution reconstruction of a digital elevation model based on a deep residual network
  98. Seismic prediction of lithofacies heterogeneity in paleogene hetaoyuan shale play, Biyang depression, China
  99. Cultural landscape of the Gorica Hills in the nineteenth century: Franciscean land cadastre reports as the source for clarification of the classification of cultivable land types
  100. Analysis and prediction of LUCC change in Huang-Huai-Hai river basin
  101. Hydrochemical differences between river water and groundwater in Suzhou, Northern Anhui Province, China
  102. The relationship between heat flow and seismicity in global tectonically active zones
  103. Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia
  104. M-GAM method in function of tourism potential assessment: Case study of the Sokobanja basin in eastern Serbia
  105. Dehydration and stabilization of unconsolidated laminated lake sediments using gypsum for the preparation of thin sections
  106. Agriculture and land use in the North of Russia: Case study of Karelia and Yakutia
  107. Textural characteristics, mode of transportation and depositional environment of the Cretaceous sandstone in the Bredasdorp Basin, off the south coast of South Africa: Evidence from grain size analysis
  108. One-dimensional constrained inversion study of TEM and application in coal goafs’ detection
  109. The spatial distribution of retail outlets in Urumqi: The application of points of interest
  110. Aptian–Albian deposits of the Ait Ourir basin (High Atlas, Morocco): New additional data on their paleoenvironment, sedimentology, and palaeogeography
  111. Traditional agricultural landscapes in Uskopaljska valley (Bosnia and Herzegovina)
  112. A detection method for reservoir waterbodies vector data based on EGADS
  113. Modelling and mapping of the COVID-19 trajectory and pandemic paths at global scale: A geographer’s perspective
  114. Effect of organic maturity on shale gas genesis and pores development: A case study on marine shale in the upper Yangtze region, South China
  115. Gravel roundness quantitative analysis for sedimentary microfacies of fan delta deposition, Baikouquan Formation, Mahu Depression, Northwestern China
  116. Features of terraces and the incision rate along the lower reaches of the Yarlung Zangbo River east of Namche Barwa: Constraints on tectonic uplift
  117. Application of laser scanning technology for structure gauge measurement
  118. Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia
  119. Evolution of the Bystrzyca River valley during Middle Pleistocene Interglacial (Sudetic Foreland, south-western Poland)
  120. A 3D numerical analysis of the compaction effects on the behavior of panel-type MSE walls
  121. Landscape dynamics at borderlands: analysing land use changes from Southern Slovenia
  122. Effects of oil viscosity on waterflooding: A case study of high water-cut sandstone oilfield in Kazakhstan
  123. Special Issue: Alkaline-Carbonatitic magmatism
  124. Carbonatites from the southern Brazilian Platform: A review. II: Isotopic evidences
  125. Review Article
  126. Technology and innovation: Changing concept of rural tourism – A systematic review
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Heruntergeladen am 5.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2020-0112/html?lang=de
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