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7-12-2018

Impacts of Agricultural Expansion (1910s-2010s) on Water Cycle in the Songneng Plain, Northeast

Lijuan Zhang

Cuizhen Wang [email protected]

Xiaxiang Li

Hongwen Zhang

Wenliang Li

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Publication Info Published in Remote Sensing, Volume 10, Issue 7, 2018, pages 1-15. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Zhang, L., Wang, C., Li, X., Zhang, H., Li, W., & Jiang, L. (2018).Impacts of Agricultural Expansion (1910s-2010s) on Water Cycle in the Songneng Plain, . Remote Sensing, 10(7), 1108. doi:10.3390/rs10071108

This Article is brought to you by the Geography, Department of at Scholar Commons. It has been accepted for inclusion in Faculty Publications by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Author(s) Lijuan Zhang, Cuizhen Wang, Xiaxiang Li, Hongwen Zhang, Wenliang Li, and Lanqi Jiang

This article is available at Scholar Commons: https://scholarcommons.sc.edu/geog_facpub/216 remote sensing

Article Impacts of Agricultural Expansion (1910s–2010s) on the Water Cycle in the Songneng Plain, Northeast China

Lijuan Zhang 1, Cuizhen Wang 2,* ID , Xiaxiang Li 3 ID , Hongwen Zhang 4, Wenliang Li 5 ID and Lanqi Jiang 6

1 Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Normal University, Harbin 150025, China; [email protected] 2 Department of Geography, University of South Carolina, Columbia, SC 29208, USA 3 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Chinese Academy of Sciences, Beijing 100101, China; [email protected] 4 Key Laboratory of Land Surface Process and Climate Change, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; [email protected] 5 Department of Geological and Atmospheric Science, Iowa State University, Ames, IA 50010, USA; [email protected] 6 Innovation and Opening Laboratory of Regional Eco-Meteorology in Northeast, China Meteorological Administration, Meteorological Academician Workstation of Heilongjiang Province, Heilongjiang Province Institute of Meteorological Sciences China, Harbin 150030, China; [email protected] * Correspondence:[email protected]

 Received: 12 June 2018; Accepted: 8 July 2018; Published: 12 July 2018 

Abstract: Agricultural expansion is one of the primary land use changes on the Earth’s surface. The in Northeast China is renowned for its Black Soil and is one of the most important agricultural regions of this country. In the last century, its population increased 20-fold and excessive areas of grassland were cultivated. Based on a series of decadal land use/land cover data sets in the plain (1910s–2010s), this study simulated the water balance in each decade using the Weather Research and Forecasting (WRF) model and assessed the water effects of centurial agricultural expansion. Six variables were simulated to explain the land-atmosphere interaction: precipitation, total evapotranspiration, canopy transpiration, canopy interception evaporation, land evaporation and land surface runoff and infiltration. Agreeing with historical climate reanalysis data, the simulated precipitation in the plain did not have a significant trend. However, the total evapotranspiration significantly increased in the study region. The canopy transpiration and interception evaporation increased and the runoff and infiltration decreased, both indicating a drought effect in soil. The drying trend varied spatially with the strongest pattern in the central plain where large areas of wetlands remain. As a consequence of agricultural expansion, the centurial drying process in the fertile Black Soil may put strong pressure on the crop productivity and food safety of this important agricultural region.

Keywords: agricultural expansion; drought effect; water cycle; WRF; the Songnen Plain

1. Introduction Anthropogenic activities play a significant role in global warming by altering land use and land cover (LULC) patterns on Earth’s surfaces [1,2]. It was reported that the LULC change had outcompeted greenhouse gases and become the largest contributor to global climate change [3,4]. Agricultural land expansion, for example, converts natural land covers to cultivated lands and is among the primary land use changes on a global scale [5]. According to the U.N. Food and Agriculture

Remote Sens. 2018, 10, 1108; doi:10.3390/rs10071108 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 1108 2 of 15

Organization (FAO), global arable lands in use increased from 1.375 million ha in 1961 to 1.562 million ha in 2005 and had a projected increase rate of 9% toward 2050 [6]. Long-term water effects of expanding cultivation have attracted attention all over the world. Land exploration in the U.S. western states since the 1800s, for example, was directly related to the desertified lands, devastated ecosystems and degraded ecological functions [7]. In historical China, several macro-scale agricultural land conversion events occurred, all of which attributed to the fluctuating decrease of humidity and rapid loss of water resources [8]. The long-term, intensive agricultural development is also believed to play a role in the depletion of civilization, for example the loss of the Ancient Loulan Kingdom to deserts in northwestern China [9]. While human beings benefit from abundant crop production, intensified cultivation may eventually be paid back by degraded lands and living environments. This change-response mechanism is a significant research question that needs to be answered for sustainable agriculture [10]. Intensive studies have been conducted to examine the climatic impacts of agricultural expansion in statistical and numerical models [11–13]. One of the most widely applied models, the Weather Research and Forecasting (WRF) model, was developed by National Center for Atmospheric Research (NCAR) under a collaborative partnership among multiple federal agencies in the United States [14]. As a mesoscale numerical weather prediction system, it has served a wide range of meteorological research and operational forecasting worldwide [15,16]. While studies generally supported the conclusion that agricultural land use change varied the temperature and precipitation patterns in regional levels [2,17], it was also recognized that the impacts were distributed heterogeneously upon local weather, soil type, historical land use and management strategies [18]. Among the limited efforts to explore the water effects of land use changes, Yu et al. [19] suggested that intensive cultivation alters the natural partition of water resources and eventually breaks the balance of water usage in the watershed. Sun et al. [20] found that the corn boom [21] in the U.S. Midwest reduced water consumption efficiency in croplands after vast areas of permanent green covers were converted to corn fields to meet bioenergy needs. In the agricultural region of Northeast China, agricultural activities have affected about 62–82% runoff [22]. The water effects of agricultural expansion involve a complex process that interacts with multiple aspects including runoff, precipitation and land-atmosphere exchanges such as evaporation and evapotranspiration. The current literature has been lacking quantitative analysis of these indicators. This study aims to simulate the impacts of long-term agricultural expansion in Northeast China on the water cycle of this region. In the late 1800s, an extensive wave of immigrants washed over the Northeast China (with the old name of Manchuria) from southern provinces such as Shandong that had undergone severe droughts and famines [19]. Since then, vast areas of natural grasslands have been cultivated in this productive Black Soil region. Taking the Songnen Plain as the experimental region, this study collects the decadal LULC data in the period from the 1910s to the 2010s, simulates the water effects of agricultural expansion using land surface models and analyzes the LULC change-response mechanism across the plain. The long-term trends of water effects extracted in this study region, which have not been thoroughly studied in the current literature, may provide compatible information for other major agricultural regions worldwide.

2. Materials and Methodology

2.1. Study Region The Songnen Plain is located in the Nenjiang River Basin, Northeast China with a geographic extent of 42◦300–51◦200N and 121◦400–128◦300E, covering an area of 0.18 million km2 (Figure1). It is a huge alluvial plain deposited from the Songhuajiang River and Nenjiang River [20]. The plain has a gentle, flat terrain with an elevation of 100–200 m decreasing from north to south. In a temperate continental monsoon climate, it has clear seasonality with cold winters and wet summers influenced by the southeast Monsoon. Temperature varies from −24 ◦C in January to 26 ◦C in July. Annual precipitation has a range of 350–700 mm, most of which occurs in the growing season from May Remote Sens. 2018, 10, 1108 3 of 15

Remote Sens. 2018, 10, x FOR PEER REVIEW 3 of 15 to September. Northeast China is well known for its unique, highly productive Black Soil [23,24], in whichSeptember. the Songnen Northeast Plain China (our is study well known region), for Sanjiang its unique, Plain highly and theproductive Liaohe Black Plain Soil compose [23,24] the, in three which the Songnen Plain (our study region), and the compose the three primary plains from north to south. Historically converted from vast areas of grasslands since the primary plains from north to south. Historically converted from vast areas of grasslands since the 1800s, croplands dominate the plains with major crop types of corn, soybean and spring wheat. 1800s, croplands dominate the plains with major crop types of corn, soybean and spring wheat.

Figure 1. The Songnen Plain (red polygon) in the Basin, Northeast China. The right Figure 1. The Songnen Plain (red polygon) in the Songhua River Basin, Northeast China. The right figure is an elevation map of the basin. figure is an elevation map of the basin. 2.2. Data Sets 2.2. Data Sets The 100-year LULC products are the primary data inputs of this study. The study region can be Thefully 100-yearcovered by LULC 30 Landsat products tiles are (path the 2 primary6–30 and datarow 11 inputs6–121) of that this have study. been The available study regionsince the can be fully1970s. covered With by a 30 rich Landsat set of Landsat tiles (path MSS, 26–30 TM and and ETM+ row 116–121)imagery, thatthe decadal have been LULC available maps in sincethe 1970s, the 1970s. With1980s, a rich 1990, set of 2000s Landsat and MSS, 2010s TM were and classified ETM+ at imagery, a 30-m thegrid decadal size in previous LULC maps projects in the[24] 1970s,. While 1980s, classification accuracies in earlier decades cannot be assessed due to a lack of ground-truth data, the 1990, 2000s and 2010s were classified at a 30-m grid size in previous projects [24]. While classification overall accuracy of the 2010s LULC map reached 85% using 400 ground-truth points randomly accuracies in earlier decades cannot be assessed due to a lack of ground-truth data, the overall accuracy collected from the meter-scale Google Earth imagery in adjacent years. The LULC map in the 1950s of thewas 2010s digitized LULC from map the reachedhistorical 85% 1:300,000,000 using 400 land ground-truth use maps of Northeast points randomly China published collected in 1959 from the meter-scale[25]. Information Google Earth on the imagery study region in adjacent was very years. limited The before LULC the founding map in theof the 1950s New was China digitized in 1949. from the historicalThrough our 1:300,000,000 previous efforts, land usethe mapsdocumentation of Northeast of historical China publishedforest distributions, in 1959 [25population]. Information and on the studysettlement region clusters was very were limited collected before and the the LULC founding maps ofin the1910s New and China 1930s were in 1949. extracted Through in alignment our previous efforts,with the river documentation channel patterns of historicaland cultivation forest indices. distributions, More details population about the and process settlement can be clustersfound in were collected[13]. Using and the the LULCsatellite maps-classified in 1910s LULCand maps 1930s as a reference, were extracted all historical in alignment LULC maps with in the river 1910s, channel patterns1930s and and cultivation1950s were geo indices.-referenced More and details re-sampled about to the30-m process spatial resolution can be found by previous in [13 projects.]. Using the Given the coarse resolution of digitized maps, these resampled 30-m outputs inevitably contained satellite-classified LULC maps as a reference, all historical LULC maps in the 1910s, 1930s and 1950s high uncertainties in calculating land use changes. However, the uncertainties were less dominant were geo-referenced and re-sampled to 30-m spatial resolution by previous projects. Given the coarse for our model simulation using large grid sizes (described in the next section). To generalize the resolutionproducts, of digitized the LULC maps, types thesein each resampled decade were 30-m grouped outputs into inevitably the same contained class set: developed high uncertainties lands in calculating(urban landand rural use changes.development), However, agricultural the uncertainties lands (dryland were and less paddy dominant fields), for grasslands, our model forests, simulation usingwetlan largeds grid and sizes water. (described The centurial in thechange next of section). agricultural To lands generalize is our primary the products, interest. the LULC types in each decadeEnvironmental were grouped data intosuch theas the same digital class elevation set: developed model (DEM) lands were (urban downloaded and rural from development), the U.S. agriculturalGeological lands Survey (dryland Data Clearinghouse. and paddy fields), Data grasslands,on the monthly forests, precipitation wetlands and and tempera water.ture The incenturial the change1910s of– agricultural2010s came from lands the is European our primary Reanalysis interest. Interim (ERA-Interim) data at a grid size of 0.5° × Environmental0.5°, which served data as the such validation as the source digital for elevation the model modelsimulation (DEM) in this were study. downloaded The 6-h National from the Centers for Environmental Prediction (NCEP) global forecast system (GFS) final (FNL) operational U.S. Geological Survey Data Clearinghouse. Data on the monthly precipitation and temperature in global analysis data were adopted to parameterize the planetary boundary layer for WRF model the 1910s–2010s came from the European Reanalysis Interim (ERA-Interim) data at a grid size of simulation [26]. 0.5◦ × 0.5◦, which served as the validation source for the model simulation in this study. The 6-h

National Centers for Environmental Prediction (NCEP) global forecast system (GFS) final (FNL) operational global analysis data were adopted to parameterize the planetary boundary layer for WRF model simulation [26]. Remote Sens. 2018, 10, 1108 4 of 15

2.3. Approaches Land use patterns in the study region and their decadal changes in the 1910s–2010s are first examined. The water effects of these changes to the study region are then evaluated via model simulation. Common mesoscale weather models often use a grid resolution ranging from 10 to 100 km [27] and model simulation reaches higher accuracies with finer grid sizes [28]. Limited by computation power in this study, we decided to group the study region into a 30 km × 30 km grid network. There is a total of 94 by 89 grids across the region. Within each grid, the percentage of coverage of each LULC class in a decade is calculated from the LULC data sets. For agricultural land use, its trajectory of percentage changes in the past 100 years represents the process of historical expansion in this grid. This study applied the WRF model to simulate the land-atmosphere interactions in the study region. The land surface model, NOAH Multi-Parameterization (NAOH-MP) (Niu et al., 2011), was selected within the WRF simulation. Overall, two categories of input variables were considered for WRF model simulation: land surface characteristics that are represented by topography, land use/land cover types, soil, leaf area index (LAI) and albedo, and so forth.; and atmospheric conditions described by air temperature, precipitation, water vapor, wind speed and air pressure, and so forth. [14]. When water balance is of the major interest, the coupled WRF/NOAH model can be partitioned as [29]:

PRE = TET + RI (1) TET = CE + IE + LE where the term PRE represents the precipitation, TET denotes the total evapotranspiration and RI is the surface runoff and infiltration in the soil layer. The CE is the canopy transpiration, IE is the interception evaporation from the vegetation layer and LE is the evaporation from the land surface. As described in the NOAH-MP model [24], in a water cycle, the downward precipitation (PRE) balances with the upward total evapotranspiration (TET), runoff on land surface and infiltration downward. With a lack of detailed soil texture properties in such a large region, we combined the runoff and infiltration as a single variable (RI) in this study. The TET is composed of canopy transpiration (CE), vegetation interception evaporation (IE) and land evaporation (LE). These variables define the water cycle and can be simulated in the WRF model. Meanwhile, since this study examined the impacts of agricultural land use change on the water cycle, we set a fixed boundary layer of climatic condition represented by the 5-year averages of climatic variables over the period from 1 January 2008 to 31 December 2012. These truthing data were retrieved from the FNL Reanalysis products. All WRF simulations in this study were based on this fixed climatic condition and therefore, the influences of climatic change in the past century were excluded from the analysis. In our experimental design, the 1910s were set as an initial stage that represents the original land-atmosphere status before agricultural expansion in the study region and all other decades (1930s, 1950s, 1970s, 1980s, 1990s, 2000s, 2010s) represent stages undergoing various expansion. In each decade, all water balance variables in Equation (1) were simulated in a monthly interval at each WRF grid within the growing season (May–September). To reduce seasonal effects, the off-season simulations were not processed. In each decade, their deviations against the 1910s (e.g., ∆PRE and ∆TET) at each grid were calculated. Their spatial and temporal patterns were then compared to explore the LULC change-induced variations of water balance in different stages of LULC change. Finally, the LULC-based WRF simulations were compared with the drought index to evaluate the water effects of agricultural expansion. In this study, we utilized the widely applied Palmer Drought Severity Index (PDSI) that is rooted on the supply-demand concept of water balance [30]. The PDSI is generally in a range of −6 to 6, with negative values indicating dry spells while positive values denote wet spells. Using readily available temperature and precipitation data to estimate relative dryness, the PDSI has been reasonably successful at quantifying long-term agricultural drought [31]. The exclusion of off-season months in this study reduced the snow/ice effects on its calculation. Remote Sens. 2018, 10, 1108 5 of 15

RemoteTherefore, Sens. 2018 it is, 10 a, x fairly FOR PEER good REVIEW tool for exploring the drought effects in the study region. Here5 of we 15 calculated the PDSI from the ERA Reanalysis data in the study region and compared their 30-year 30trends-year in trends the periods in the periods from 1901–1930 from 190 (initial1–1930 stage)(initial and stage) from and 1981–2010 from 198 (post-stage).1–2010 (post- Thesestage). spatialThese spatialpatterns patterns serve as serve good as reference good reference about the about variations the variations of drought of drought status in status the past in the century. past century.

3. Results and Discussion

3.13.1.. The Centurial LULC Change (1910s (1910s–2010s)–2010s) The Songnen Plain is one of the mostmost productiveproductive agricultural plains in China.China. It is primarily composed of of croplands, croplands, grasslands grasslands and and forests forests that that currently currently cover cover more more than 80% than of 80% the study of the region study [24]region. Areal [24]. coverage Areal coverage of the three of the classes three in classes different in decades different is decades summar isized. summarized. In the 1910s, In grasslands the 1910s, 2 dominatedgrasslands the dominated region in thea total region area of in 0.1 a totalmillion area km of2, followed 0.1 million by croplands km , followed in 0.05 by million croplands km2 and in 2 2 forests0.05 million in 0.02 km millionand km forests2. As in shown 0.02 millionin Figure km 2,. croplands As shown dramatically in Figure2, croplandsincreased from dramatically 1910s to 2 1970sincreased at a from rate 1910sof 0.03 to million 1970s at km a rate2/10a of 0.03(p < million0.05), with km /10athe unit (p < 10a 0.05), representing with the unit a 10a10-year representing period. Correspondingly,a 10-year period. Correspondingly, grasslands decreased grasslands at a similar decreased rate in at the a similar 1910s– rate1970 ins. Since the 1910s–1970s. the 1930s, croplands Since the 1930s,have become croplands the dominant have become LULC the type dominant in the study LULC region. type in Since the studyChina’s region. Reform Since and China’sOpening Reform policy inand 1978, Opening its economy policy inhas 1978, dramatically its economy switched has dramatically to industrialization switched and toindustrialization other financial forms and other[32]. Therefore,financial forms agricultural [32]. Therefore, activities slowed agricultural down activities and the total slowed area down of croplands and the became total area relatively of croplands stable 2 afterbecame the relatively 1970s. By stable the after2010s, the croplands 1970s. By cover the 2010s, a total croplands area of cover 0.13 milliona total area km2 of while 0.13 million grasslands km 2 whiledecrease grasslandsd to 0.015 decreased million km to2 0.015. Forests million have km much. Forests less coverage have much than less the coverage other two than classes the otherand have two notclasses revealed and have significant not revealed change significant in the past change century. in the Figure past 2 century. shows Figurethat the2 showsLULC thatchanges the LULCin the studychanges region in the primarily study region occurred primarily in the period occurred from in the the 1910s period–1970 froms and the the 1910s–1970s major land and conversion the major is fromland conversion grasslands is to from croplands, grasslands the to so croplands,-called agricultural the so-called expansion. agricultural Total expansion. areas (×10 Total4 km areas2) of 4 2 conversion(×10 km ) of of all conversion land cover of alltypes land from cover the types 1910s from to 2010s the 1910s are listed to 2010s in Table are listed 1. Within in Table the1 .century, Within 2 morethe century, than 600 more million than km 6002 millionof grasslands km of were grasslands converted were to convertedcroplands, to confirming croplands, that confirming agricultural that expansionagricultural in expansion the study in region the study is mainly region from is mainly grasslands. from grasslands. Table 1 also Table shows1 also that shows croplands, that croplands, forests andforests grass andlands grasslands are the arethree the primary three primary classes classesin the region. in the region.This study This only study examines only examines these three these LULC three typesLULC and types their and contributions their contributions to water to balance water balance across acrossthe plain. the plain.

Figure 2.2. Trajectories of areal coverage of the three primary land use and land cover (LULC)(LULC) classes (grassland, cropland, forest) inin thethe 1910s–2010s.1910s–2010s.

Table 1. Total area of conversion from the 1900s to 2010s (unit of 104 km2). Note there are no developed lands in the 1910s. The three primary classes are highlighted in bold.

1900s Cropland Forest Grassland Water Wetland 2010s Cropland 34,827.73 25,469.41 62,876.58 988.54 6951.86 Forest 1589.51 18,332.04 6271.71 179.38 1431.92 Grassland 2368.49 3620.72 15,184.79 807.71 3516.76 Water 803.99 746.14 2298.71 2963.71 2409.82 Wetland 0.36 0.32 0.69 0.28 5718.10 Developed land 0.185 0.122 0.225 0.07 0.06

Remote Sens. 2018, 10, 1108 6 of 15

Table 1. Total area of conversion from the 1900s to 2010s (unit of 104 km2). Note there are no developed lands in the 1910s. The three primary classes are highlighted in bold.

1900s Cropland Forest Grassland Water Wetland 2010s Cropland 34,827.73 25,469.41 62,876.58 988.54 6951.86 Forest 1589.51 18,332.04 6271.71 179.38 1431.92 Grassland 2368.49 3620.72 15,184.79 807.71 3516.76 Water 803.99 746.14 2298.71 2963.71 2409.82 Wetland 0.36 0.32 0.69 0.28 5718.10 Developed land 0.185 0.122 0.225 0.07 0.06 Remote Sens. 2018, 10, x FOR PEER REVIEW 6 of 15

Percentage covers of the three primary LULC classes in all WRF grids were extracted. Their spatial Percentage covers of the three primary LULC classes in all WRF grids were extracted. Their patterns and the grassland-cropland conversions are displayed in Figure3. Croplands in the 1910s spatial patterns and the grassland-cropland conversions are displayed in Figure 3. Croplands in the were mostly clustered in the southeast of the plain. By the 1930s, the extensive area of grasslands in 1910s were mostly clustered in the southeast of the plain. By the 1930s, the extensive area of the eastern plain was converted to croplands. Croplands in other decades continued to expand across grasslands in the eastern plain was converted to croplands. Croplands in other decades continued to the plain at the expense of grassland. As seen in the 1950s map, an exception remained in the central expand across the plain at the expense of grassland. As seen in the 1950s map, an exception remained plain where agricultural expansion was less intensive. Checking with historical maps and Google in the central plain where agricultural expansion was less intensive. Checking with historical maps Earth imagery, this area has higher coverage of water surfaces and wetlands that are not suitable for and Google Earth imagery, this area has higher coverage of water surfaces and wetlands that are not cultivation. Forests grow in relatively high elevations along the north and northeast ends of the region suitable for cultivation. Forests grow in relatively high elevations along the north and northeast ends (refer to the DEM in Figure1) and did not reveal apparent change over the 100 years. Poor soil quality of the region (refer to the DEM in Figure 1) and did not reveal apparent change over the 100 years. in mountainous areas may prevent the conversion of forests to croplands. Poor soil quality in mountainous areas may prevent the conversion of forests to croplands.

Figure 3.3. Spatial patterns and dynamics of the three LULC classes in the 1910s–2010s.1910s–2010s. Maps in the 1980s, 1990s and 2000s are not shown here because of their visual similarities with the 1970s and 2010s.

3.2. The WRF-Simulated Water Effects 3.2. The WRF-Simulated Water Effects

3.2.1. WRF Model Validation To test the validity of the WRF model in the study region, the simulated PRE and TET results were compared with the ERA Reanalysis data in the recent 5-year5-year period (2008–2012).(2008–2012). For both variables, the monthly average values of all grids across the study region were calculated.calculated. For both PRE (Figure(Figure 44a)a) andand TET (Figure(Figure 44c),c), thethe WRFWRF simulationsimulation revealsreveals thethe fluctuationfluctuation ofof thethe ERAERA datadata inin all years. In their scatterplots, the WRF si simulatedmulated precipitation ( (FigureFigure 44b)b) isis significantlysignificantly correlatedcorrelated with the ERA precipitation with a Pearson’s correlation coefficient of 0.93 (p < 0.001) and that of the simulated TET (Figure 4d) is 0.88 (p < 0.001). For both variables, the WRF simulated outputs are generally lower than the ERA data. Since this study was merely interested in the 100-year dynamics of water effects from agricultural expansion, other environmental and socio-economic changes such as urbanization, increased population and intensified cropping activities are purposely excluded from the model simulation. This explains the absolute differences between the modeled and ERA Reanalysis in Figure 4a (PRE) and Figure 4c (TET). Figure 5 indicates that the WRF model is able to pick up the inter-annual trends of the land- atmosphere water cycle in the study region. Spatial distributions of the WRF simulations and ERA data in the growing season, averaged in May–September, were also compared (Figure 5). For the two variables, both the model simulation (Figure 5a,c) and ERA Reanalysis (Figure 5b,d) reveal an increasing trend from the west to the east.

Remote Sens. 2018, 10, 1108 7 of 15

with the ERA precipitation with a Pearson’s correlation coefficient of 0.93 (p < 0.001) and that of the simulated TET (Figure4d) is 0.88 ( p < 0.001). For both variables, the WRF simulated outputs are generally lower than the ERA data. Since this study was merely interested in the 100-year dynamics of water effects from agricultural expansion, other environmental and socio-economic changes such as urbanization, increased population and intensified cropping activities are purposely excluded from the model simulation. This explains the absolute differences between the modeled and ERA Reanalysis in Figure4a ( PRE) and Figure4c ( TET). Figure5 indicates that the WRF model is able to pick up the inter-annual trends of the land-atmosphere water cycle in the study region. Spatial distributions of the WRF simulations and ERA data in the growing season, averaged in Remote Sens. 2018, 10, x FOR PEER REVIEW 7 of 15 May–September, were also compared (Figure5). For the two variables, both the model simulation (Figure5a,c) and ERA Reanalysis (Figure5b,d) reveal an increasing trend from the west to the east. The WRF simulated results extract more details at local scales, for example higher TET in the The WRF simulated results extract more details at local scales, for example higher TET in the northwest northwestof the study of the region study (Figure region5a) ( andFigure higher 5a)PRE andin high the northeaster PRE in (Figure the northeast5c). For the (Figure ERA data, 5c). the For spatial the ERA data,patterns the spatial of TET patterns(Figure 5ofb) andTET PRE(Figure(Figure 5b)5 d)and are PRE smoother, (Figure with 5d) one are possible smoother, reason with being one that possible the reasonERA being Reanalysis that data the are ERA coarse-resolution Reanalysis data global are products coarse from-resolution spatial interpolation global products of meteorological from spatial interpolationrecords, while of meteorological the stations in therecords, plain arewhile limited. the Takingstations all in grids the intoplain consideration, are limited. theTaking grid-level all grids into correlation consideration coefficients, the grid between-level the correlation WRF simulated coefficients and ERA Reanalysisbetween the data WRF reach 0.90 simulated (p < 0.001) and for ERA ReanalysisTET and data 0.55 reach (p < 0.001) 0.90 for(p 

FigureFigure 4. Comparison 4. Comparison of ofthe the Weather Weather Research Research and Forecasting Forecasting (WRF) (WRF simulated) simulated and and European European ReanalysisReanalysis (ERA (ERA)) Reanalysis Reanalysis of of precipitation precipitation ( a()a and) and total total evapotranspiration evapotranspiration (c) in 2008–2012.(c) in 2008 (–b2012.–d) are (b–d) are scatterplotsscatterplots ofof precipitationprecipitation (PRE (PRE) and) and total total evapotranspiration evapotranspiration (TET), (TET respectively.), respectively. 
Assuming the ERA Reanalysis as truthing data in the period from 2008 to 2012, Figures4 and5 indicate that the WRF model successfully extracted the spatial patterns and temporal dynamics of the land-atmosphere process in the study region. It was then used to examine the centurial dynamics of water balance following the intensive agricultural expansion.
Figure 5. The spatial patterns of total evapotranspiration and precipitation: the WRF-modeled (a,c) and ERA Re-analysis (b,d). Each grid value is a 5-year average in the growing season (May– September). 
Assuming the ERA Reanalysis as truthing data in the period from 2008 to 2012, Figures 4 and 5 indicate that the WRF model successfully extracted the spatial patterns and temporal dynamics of 
Remote Sens. 2018, 10, x FOR PEER REVIEW 7 of 15 
The WRF simulated results extract more details at local scales, for example higher TET in the northwest of the study region (Figure 5a) and higher PRE in the northeast (Figure 5c). For the ERA data, the spatial patterns of TET (Figure 5b) and PRE (Figure 5d) are smoother, with one possible reason being that the ERA Reanalysis data are coarse-resolution global products from spatial interpolation of meteorological records, while the stations in the plain are limited. Taking all grids into consideration, the grid-level correlation coefficients between the WRF simulated and ERA Reanalysis data reach 0.90 (p < 0.001) for TET and 0.55 (p < 0.001) for PRE. 
Figure 4. Comparison of the Weather Research and Forecasting (WRF) simulated and European Remote Sens. 2018Reanalysis, 10, 1108 (ERA) Reanalysis of precipitation (a) and total evapotranspiration (c) in 2008–2012. (b–d) 8 of 15 are scatterplots of precipitation (PRE) and total evapotranspiration (TET), respectively. 
Figure 5. The spatial patterns of total evapotranspiration and precipitation: the WRF-modeled (a,c) Figure 5.andThe ERA spatial Re-analysis patterns (b, ofd). total Each evapotranspiration grid value is a 5-year and average precipitation: in the growing the WRF-modeled season (May– (a,c) and Remote Sens. 2018, 10, x FOR PEER REVIEW 8 of 15 ERA Re-analysisSeptember). ( b,d). Each grid value is a 5-year average in the growing season (May–September). the land-atmosphere process in the study region. It was then used to examine the centurial dynamics Assuming the ERA Reanalysis as truthing data in the period from 2008 to 2012, Figures 4 and 5 3.2.2. Waterof water Effects balance of Agriculturalfollowing the intensive Expansion agricultural expansion. indicate that the WRF model successfully extracted the spatial patterns and temporal dynamics of For 3.2.2. the Water plain-wide Effects of trends, Agricultural the precipitation Expansion deviations (∆PRE) do not show a significant trend in a linear trend analysis (Figure6a). Since the climatic variation was excluded by fixing the climatic For the plain-wide trends, the precipitation deviations (ΔPRE) do not show a significant trend boundaryin a layers linear tr forend all analysis simulations, (Figure 6 Figurea). Since6a the indicates climatic variation that the was centurial excluded agricultural by fixing the expansion climatic does not haveboundary a significant layers effectfor all onsimulations, precipitation Figure plainwide. 6a indicates that However, the centurial it results agricultural in a significantly expansion does increasing trend ofnot∆TET have(p a< significant 0.01) with effect a rate on of precipitation3.4 mm/10a plainwide. (Figure6b). How Forever, both it variables, results in thea significantly∆PRE and ∆TET becomeincreasing relatively trend stable of ΔTET after (p the < 0.01) 1970s. with It a rate is reasonable of 3.4 mm/10a because (Figure land6b). For conversion both variables, from the grasslands ΔPRE to croplandsand was ΔTET saturated become relatively (as shown stable in Figureafter the2). 1970s. It is reasonable because land conversion from grasslands to croplands was saturated (as shown in Figure 2). 
Figure 6. Trends of the plain-wide and grid-level deviations (against the 1910s) of the WRF-simulated Figure 6.precipitationTrends of ( thea,c) plain-wideand total evapotranspiration and grid-level (b deviations,d) in growing (against season. the In (a 1910s),b), theof dashed the WRF-simulated lines are precipitationthe linear (a, ccorrelation) and total lines. evapotranspiration In (c,d), only grids with (b,d significant) in growing trends season. are colored. In (a ,b), the dashed lines are the linear correlation lines. In (c,d), only grids with significant trends are colored. The grid-level centurial trends of the PRE and TET deviations from the 1910s were also mapped in the study region. A linear trend analysis was performed and only the grids with statistically significant trends were mapped in Figure 6c,d. Although Figure 6a does not show a significant ΔPRE trend plainwide, its grid-level trends in Figure 6c reveal a statistically strong decrease in the central plain in a rate of 5–10 mm/10a, where agricultural expansion was actually less intensified (as shown in Figure 3). For spatial patterns of the ΔTET (Figure 6d), trends in both directions are observed. Similar to the ΔPRE distributions in Figure 6c, the ΔTET in Figure 6d also shows decreasing trends in the central plain. Different from the mono-directional ΔPRE patterns, however, the ΔTET shows statistically significant two-way changes. While the central plain also has a decreasing trend (red grids in Figure 6d), other areas all over the study region reveal weak yet statistically increasing trends (the blue grids in Figure 6d). In comparison with the land use maps in Figure 3, areas with increasing ΔTET have undergone intensive agricultural expansion in the past 100 years. It is reasonable to conclude that the total evapotranspiration increased after the permanent grass covers were replaced by annual crops. The significant decrease of local precipitation in the less disturbed central plain may reflect the localized drying effects by agricultural expansion. The vegetation-related variables of water balance are the CE and IE. In Figure 7, their plain-wide decadal deviations against the 1910s are statistically significant, reaching a rate of 3.6 mm/10a for the ΔCE (Figure 7a) and 0.6 mm/10a for the ΔIE (Figure 7b). The absolute amount of change to the CE is 
Remote Sens. 2018, 10, 1108 9 of 15
The grid-level centurial trends of the PRE and TET deviations from the 1910s were also mapped in the study region. A linear trend analysis was performed and only the grids with statistically significant trends were mapped in Figure6c,d. Although Figure6a does not show a significant ∆PRE trend plainwide, its grid-level trends in Figure6c reveal a statistically strong decrease in the central plain in a rate of 5–10 mm/10a, where agricultural expansion was actually less intensified (as shown in Figure3). For spatial patterns of the ∆TET (Figure6d), trends in both directions are observed. Similar to the ∆PRE distributions in Figure6c, the ∆TET in Figure6d also shows decreasing trends in the central plain. Different from the mono-directional ∆PRE patterns, however, the ∆TET shows statistically significant two-way changes. While the central plain also has a decreasing trend (red grids in Figure6d), other areas all over the study region reveal weak yet statistically increasing trends (the blue grids in Figure6d). In comparison with the land use maps in Figure3, areas with increasing ∆TET have undergone intensive agricultural expansion in the past 100 years. It is reasonable to conclude that the total evapotranspiration increased after the permanent grass covers were replaced by annual crops. The significant decrease of local precipitation in the less disturbed central plain may reflect the localized drying effects by agricultural expansion. The vegetation-related variables of water balance are the CE and IE. In Figure7, their plain-wide decadal deviations against the 1910s are statistically significant, reaching a rate of 3.6 mm/10a for the ∆CE (FigureRemote 7Sens.a) and2018, 10 0.6, x FOR mm/10a PEER REVIEW for the ∆IE (Figure7b). The absolute amount of change9 to of the15 CE is much higher than the IE because the CE is a continuous physiological process of vegetation along its growth.much The higherIE is mainlythan the effectiveIE because duringthe CE is rainfall. a continuous Grasses physiological have relatively process of lowervegetation canopy along coverits and growth. The IE is mainly effective during rainfall. Grasses have relatively lower canopy cover and green biomassgreen biomass than annualthan annual crops. crops. Conversion Conversion ofof grasslands to tocroplands croplands thus thuscreates creates stronger stronger effects effects on the CEon thethan CE the thanIE the. IE. 
Figure 7. Trends of the plain-wide and grid-level deviations (against the 1910s) of the WRF-simulated Figure 7.canopyTrends transpiration of the plain-wide (a,c) and interception and grid-level evaporation deviations (b,d) in (against growing the season. 1910s) In ( ofa,b the), the WRF-simulated dashed canopylines transpiration are the linear (a correlation,c) and interception lines. In (c,d evaporation), only grids with (b ,significantd) in growing trends season. are colored. In (a,b), the dashed lines are the linear correlation lines. In (c,d), only grids with significant trends are colored. Given the intensified cultivation all over the region, the grid-level ΔCE (Figure 7c) and ΔIE (Figure 7d) have mono-directionally increased in the past 100 years. Studies have measured that the GivenLAI of the grasslands intensified (with cultivation an average around all over 1.62) the is region,much lower the than grid-level annual ∆cropsCE (Figuresuch as spring7c) and ∆IE (Figure7wheatd) have (2.82) mono-directionally and corn (3.49) that are increased commonly in grown the past in northern 100 years. China Studies [33]. More have than measured a two-fold that the LAI of grasslandsincrease of the (with LAI an therefore average dramatically around 1.62) enhances is much the lower vegetation than transpiration annual crops and such interception as spring wheat (2.82) andevaporation corn (3.49) in the that canopy are commonly layer. The CE grown in the canopy in northern layer is China mostly [absorbed33]. More from than water a two-fold in the soil increase of the LAIand thereforethe IE blocks dramatically water availability enhances in the the soil. vegetation Therefore, transpirationthe dramatic increase and interception of the CE and evaporation LE indicates the increased water usage accompanying agricultural expansion. In the growing season, in in the canopy layer. The CE in the canopy layer is mostly absorbed from water in the soil and the IE grids with significant centurial trends, the ΔCE and ΔIE spread across the study region, although the blocks waterabsolute availability amounts of ΔIE in the are soil.generally Therefore, lower than the that dramatic of ΔCE. increase of the CE and LE indicates the The soil-related variables of water balance are the RI and LE. Their plain-wide deviations against the 1910s statistically decreased at a rate of −4.4 mm/10a for the ΔRI (Figure 8a) and −0.8 mm/10a for the ΔLE (Figure 8b). In the 1910s–2010s, the RI decreased 34.2 mm and the LE decreased 6.4 mm. While the LE decrease indicates less water loss from land evaporation, the decrease of RI dramatically outcompetes the decrease of the LE, also indicating a drying trend of soil moisture in the study region. In their grid-level spatial distributions, water loss in the central plain is apparent from the decreasing trends of both ΔRI (Figure 8c) and ΔLE (Figure 8d). Especially in the central plain where large areas of wetlands remain nowadays, a drying trend is suggested by a strong decrease of the RI. This is in agreement with the spatial patterns in Figures 6 and 7 above. 
Remote Sens. 2018, 10, 1108 10 of 15 increased water usage accompanying agricultural expansion. In the growing season, in grids with significant centurial trends, the ∆CE and ∆IE spread across the study region, although the absolute amounts of ∆IE are generally lower than that of ∆CE. The soil-related variables of water balance are the RI and LE. Their plain-wide deviations against the 1910s statistically decreased at a rate of −4.4 mm/10a for the ∆RI (Figure8a) and −0.8 mm/10a for the ∆LE (Figure8b). In the 1910s–2010s, the RI decreased 34.2 mm and the LE decreased 6.4 mm. While the LE decrease indicates less water loss from land evaporation, the decrease of RI dramatically outcompetes the decrease of the LE, also indicating a drying trend of soil moisture in the study region. In their grid-level spatial distributions, water loss in the central plain is apparent from the decreasing trends of both ∆RI (Figure8c) and ∆LE (Figure8d). Especially in the central plain where large areas of wetlands remain nowadays, a drying trend is suggested by a strong decrease of the RI. This is in agreement with the spatial patterns in Figures6 and7 above. Remote Sens. 2018, 10, x FOR PEER REVIEW 10 of 15 
Figure 8. Trends of the plain-wide and grid-level deviations (against the 1910s) of the WRF-simulated Figure 8. RI Trends(a,c) and of land the plain-wide evaporation and (b,d)grid-level in growing deviations season. In (against (a,b), the the dashed 1910s) lines of arethe the WRF-simulated linear RI (a,c) andcorrelation land evaporation lines. In (c,d), ( obnly,d) grids in growing with significant season. trends In (a, bar),e thecolored. dashed lines are the linear correlation lines. In (c,d), only grids with significant trends are colored. The three figures above demonstrate the increased water usage in crop canopies (Figure 7) and decreased water availability in the soil (Figure 8), resulting in an enhanced total water loss in this Theagricultural three figures region above (Figure demonstrate 6). These changes the increasedare jointly attributed water usage to the in drought crop canopies effects across (Figure the 7) and decreasedstudy water region. availability Geographically, in the the soilcentral (Figure plain 8reveals), resulting the highest in an sensitivity enhanced of water total effects. water In loss this in this agriculturalarea, the region decreased (Figure precipitation6). These directly changes reflects are a jointly drying trend attributed affected to by the the droughtexpanded effectscultivation. across the study region.The temporal Geographically, trends in Figures the central7 and 8 show plain that reveals the CE the and highest RI have sensitivitythe largest amount of water of changes effects. In this area, theindecreased the 1910s–2010s. precipitation Using these directly two variables reflects as a drying examples, trend their affected spatial distributions by the expanded in different cultivation. decades are simulated in the WRF model (Figure 9). Along with the conversion of grasslands into The temporal trends in Figures7 and8 show that the CE and RI have the largest amount of changes croplands, the CE increase and RI decrease are apparent. Spatially, the CE increases all over the region in the 1910s–2010s.but the decrease Using of the RI these is more two dramatic variables in the as central examples, plain. Therefore, their spatial these two distributions variables explai in differentn decadesdifferent are simulated mechanisms in theof water WRF loss; model one is (Figure in the vegetation9). Along layer with and the the conversionother is on the of soil grasslands surface. into croplands,Both the variablesCE increase effectively and revealRI decrease the drying are trend apparent. in the plain. Spatially, the CE increases all over the region but the decrease of the RI is more dramatic in the central plain. Therefore, these two variables explain different mechanisms of water loss; one is in the vegetation layer and the other is on the soil surface. Both variables effectively reveal the drying trend in the plain.
Figure 9. The centurial patterns of the canopy transpiration (CE) and runoff and infiltration (RI) in the study region. Maps in the 1980s, 1990s and 2000s are not shown because of their visual similarities with the 1970s and 2010s. 
Remote Sens. 2018, 10, x FOR PEER REVIEW 10 of 15 
Figure 8. Trends of the plain-wide and grid-level deviations (against the 1910s) of the WRF-simulated RI (a,c) and land evaporation (b,d) in growing season. In (a,b), the dashed lines are the linear correlation lines. In (c,d), only grids with significant trends are colored. 
The three figures above demonstrate the increased water usage in crop canopies (Figure 7) and decreased water availability in the soil (Figure 8), resulting in an enhanced total water loss in this agricultural region (Figure 6). These changes are jointly attributed to the drought effects across the study region. Geographically, the central plain reveals the highest sensitivity of water effects. In this area, the decreased precipitation directly reflects a drying trend affected by the expanded cultivation. The temporal trends in Figures 7 and 8 show that the CE and RI have the largest amount of changes in the 1910s–2010s. Using these two variables as examples, their spatial distributions in different decades are simulated in the WRF model (Figure 9). Along with the conversion of grasslands into croplands, the CE increase and RI decrease are apparent. Spatially, the CE increases all over the region but the decrease of the RI is more dramatic in the central plain. Therefore, these two variables explain Remote Sens. 2018, 10, 1108 11 of 15 different mechanisms of water loss; one is in the vegetation layer and the other is on the soil surface. Both variables effectively reveal the drying trend in the plain. 
Figure 9. The centurial patterns of the canopy transpiration (CE) and runoff and infiltration (RI) in the Figure 9.studyThe region. centurial Maps patterns in the 1980s, of the 1990s canopy and 2000s transpiration are not shown (CE) becauseand runoff of their and visual infiltration similarities (RI) in the study region.with the Maps 1970s and in the 2010s. 1980s, 1990s and 2000s are not shown because of their visual similarities with the 1970s and 2010s.
3.3. Comparison with Drought Index
The PDSIRemote of Sens. the 2018 study, 10, x FOR region PEER REVIEW was extracted to compare with the drought effects identified11 of 15 in this study. Two stages3.3. Comparison of the with last Drought century Index are considered: the initial stage (1901–1930) and the post-stage (1981–2010) afterThe agricultural PDSI of the study expansion. region was Overall, extracted the to compare 30-year wi averageth the drought PDSI effects distributions identified in in the initial stage indicatethis slightly study. Two dry stages conditions of the last across century the are plain considered: (Figure the 10initiala). Thestage southeast(1901–1930) ofand the the plain,post- where the expansion startedstage (198 (as1–2010 shown) after inagricultural Figure 3expansion.), is drier Overall, than the other 30-year areas. average Agricultural PDSI distributions expansion in the actually initial stage indicate slightly dry conditions across the plain (Figure 10a). The southeast of the plain, slowed downwhere after the reaching expansion saturationstarted (as shown in the in Figure 1970s. 3), Inis drier the post-stagethan other areas. PDSI Agricultural map (Figure expansion 10b), the west of region becomesactually slowed slightly down wetter after reaching and thesaturation east in is the drier. 1970s. The In the relatively post-stage PDSI low map PDSI (Figure values 10b), across the plain, however,the west indicate of region a becomes mild climate slightly wetter in both and stages.the east is Temporally, drier. The relatively the PDSIlow PDSI trends valuesin across each stage are examined viathe a plain, linear however, trend indicate analysis a mild and climate tested in at both a significance stages. Temporally, interval the PDSI of 0.05. trends In in Figureeach stage 10 c, opposite are examined via a linear trend analysis and tested at a significance interval of 0.05. In Figure 10c, trends in theopposite west andtrends east in the of west the and plain east are of the observed plain are observed in the initialin the initial stage stage but but only only the the drying trends in the northwesttrends are in significant. the northwest Thisare significant. is reasonable This is reasonable because because agricultural agricultural expansion expansion was still still in in its early stage in thisits period. early stage In thein this post-stage, period. In the the post drying-stage, trendsthe drying become trends become stronger stronger across across the the plain plain (Figure 10d) (Figure 10d) and more grids with significant drying trends are observed in both the northwest and and more gridsthe southea withst. significant drying trends are observed in both the northwest and the southeast.
Figure 10. The 30-year average Palmer Drought Severity Index (PDSI) in 1901–1930 (a) and 1981–2010 Figure 10. The 30-year average Palmer Drought Severity Index (PDSI) in 1901–1930 (a) and 1981–2010 (b), (b), in which a negative value represents drier and a positive one means wetter conditions. In their in which a negative30-year linear value trends represents in 1901– drier1930 ( andc) and a positive1981–2010 one(d), black means dots wetter mark the conditions. grids with Insignificant their 30-year linear trends in 1901–1930trends. (c) and 1981–2010 (d), black dots mark the grids with significant trends.
The PSDI considers the interaction of land surface and climatic conditions. As shown in Figure 5, the modeled and ERA Reanalysis data confirm that the precipitation decreases from west to east. Both Figures 10b,c show apparent transect lines splitting the west and east, which may be explained by the climatic patterns of the region. Although the PSDI grids with significant trends in Figure 10c,d do not exactly comply with the drying trends identified in this study, the widespread PSDI patterns support our results that the Songnen Plain has become drier in the past 100 years, although the drying pace may be slow given the low PDSI values in both stages. The PDSI captures the basic climatic 
Remote Sens. 2018, 10, 1108 12 of 15
The PSDI considers the interaction of land surface and climatic conditions. As shown in Figure5, the modeled and ERA Reanalysis data confirm that the precipitation decreases from west to east. Both Figure 10b,c show apparent transect lines splitting the west and east, which may be explained by the climatic patterns of the region. Although the PSDI grids with significant trends in Figure 10c,d do not exactly comply with the drying trends identified in this study, the widespread PSDI patterns support our results that the Songnen Plain has become drier in the past 100 years, although the drying pace may be slow given the low PDSI values in both stages. The PDSI captures the basic climatic effects on drought through changes in potential evapotranspiration due to land expansion. It has to be noted, however, this simple index has some key limitations and is less accurate than the more advanced drought indices such as the Standardized Precipitation Index (SPI) [34] and the Standardized Precipitation Evapotranspiration Index (SPEI) [35]. When advanced data become available, we will explore these advanced indices in explaining the drought effects in the study region. The annual (year-long) and growing-season climatic variables in the two periods were further compared (Table2). The ANOVA analysis shows that the precipitation and precipitation days do not have significant differences between 1901–1930 and 1981–2010. However, there is a significant increase for both annual and growing-season water vapor pressure between the two periods. The annual evaporation is also significantly increased, probably due to decreased vegetation cover after harvesting. These significant changes in Table2 also agree with our WRF-model simulation that the study region has become drier.
Table 2. The ANOVA comparison of climatic variables in the Songnen Plain between 1901–1930 and 1981–2010 in two intervals: annual (year-long) and growing-season.
1901~1930 1981~2010 Variance Analysis (t Value) Climatic Variables Annual Growing-Season Annual Growing-Season Annual Growing-Season Precipitation (mm) 495.133 426.315 516.636 442.125 −1.047 −0.867 evaporation (mm) 791.055 566.148 821.472 574.073 −3.046 ** −1.037 precipitation days (d) 84.122 56.059 85.267 56.166 −0.811 −0.095 Water vapor pressure (hpa) 7.483 14.310 7.783 14.671 −4.152 ** −2.432 * ** denotes p < 0.01; * denotes p < 0.05.
The drought effects simulated via the WRF model in this study agree with field observations in past studies of the region. From the observations of 30 meteorological stations in 1961–2013, Zheng et al. [36] found an upward trend of average drought index in the Songnen Plain and the annually increasing number of medium-scale droughts indicated the enhanced drought severity in this region. Our results are also supported by the observed warming and drying trend since the 1960s [37], although some studies claimed that the precipitation change was not significant in the past 50 years [38]. Our major findings about the water effects of agricultural expansion are also in agreement with past model-based studies in other regions. For example, similar to the increased evaporation and water vapor pressure in Table2, Douglas et al. [ 39] found an increase of vapor fluxes due to agricultural land use change and irrigation in the Indian Monsoon Belt. Using over 1500 estimates of annual evapotranspiration worldwide, Sterling et al. [2] reported a dramatic decrease of terrestrial evapotranspiration due to the loss and degradation of wetlands. This finding also agrees with our simulated results of total evapotranspiration in Figure6b, where the wetland-dominated central plain has undergone significant TET decrease in the past century. Most WFR model efforts in past studies have focused on the climatic responses such as temperature, precipitation and carbon flux. While agricultural expansion is integrated in their simulations, the simulated outputs are inevitably affected by climatic change in global energy cycles, which heavily contaminate the impacts specifically from land use change [40]. In a uniquely different view angle, our study simulated the water balance between the land-atmosphere interactions in which the LULC change is the primary driver of the region. Therefore, our results better explain the centurial changes to the water cycle caused by agricultural expansion. In the 30-km grid scale of our Remote Sens. 2018, 10, 1108 13 of 15
WRF model, all crop types are treated equally as croplands in the simulation. Actually, crops in the study region (e.g., corn, soybean and winter wheat) contain different canopy structures and green biomass. Intensified cropping activities and management strategies further enlarge their biophysical heterogeneities. With a high-resolution (km-scale) WRF model and advanced computer power, it is possible to simulate individual crop types and management activities to better quantify the water effects of land cultivation and conversion. One assumption in this study was to fix the climatic boundary layers with the 5-year average data to remove the effects of weather dynamics in the model simulation. While the simulated precipitation and evapotranspiration are noticeably lower than the Reanalysis data (as shown in Figure4), our model fairly matches the 5-year trajectories that the Reanalysis data explain. It is good enough for this study as we only examine the impacts of areal expansion of agricultural lands. To better understand the water effects of land use change, more detailed, human-induced modifications should be considered, for example irrigation and fertilizing intensities. These changes do not affect the planting acreage but may dramatically change water usage and land-atmosphere interaction. Moreover, drought effects of agricultural expansion may also be related to the decreased total area of waterbodies. Although not investigated here, past studies have reported that water coverage in the Songnen Plain has decreased 14% since 1986 [24]. Although intensified water usage in the region is probably the major contributor to water volume decrease, the shrinking water resource from runoff and ground water infiltration may also play a role and deserves further investigation. Due to limited sources of soil data in such a large region, this study does not separate these two water variables. When the extent and volume of water bodies across the region are available, the variation of runoff and infiltration could be separately examined. In our future research, we will address these LULC-related changes in the WRF model to better quantify the drought effects of agricultural intensification. Finally, the Songnen Plain is one of three Black Soil Belts in the world. Soil organic matter in the plain has dropped from 4–6% in the 1960s to 0.8–4.7% in the 2000s [41]. It has been commonly recognized that intensified cultivation plays a major role in soil degradation. Studies also reported that droughts amplified the decrease of organic matter in soil [42]. Identifying the drought effects of agricultural expansion across the plain, this study suggests the urgent need for Black Soil conservation for sustainable development of this important agricultural region.
4. Conclusions This study explored the spatiotemporal dynamics of centurial agricultural expansion in the Songnen Plain, Northeast China and assessed its impacts on the land-atmosphere water cycle via WRF model simulation. Primary findings include: (1) Agricultural lands in the plain were primarily converted from the historically predominant grasslands and were expanded from 28% of the plain in the 1910s to 72% in the 2010s. (2) The WRF model simulations showed that the plain-wide precipitation did not change significantly in the past 100 years but agricultural expansion resulted in a dramatic increase in canopy transpiration (at an average rate of 36 mm/10a) and vegetation interception evaporation (6 mm/10a) and a decrease in surface runoff and infiltration in soil (−44 mm/10a) and land surface evaporation (−8 mm/10a). All these aspects jointly attributed to the increased evapotranspirition across the plain. (3) This study reveals a drought effect of centurial agricultural expansion in the Songnen Plain, which is supported by the stronger decreasing trends of the Palmer Index since the 1980s. The drying trend in its Black Soil raises alarm about the sustainability of this famous Black Soil Belt.
Author Contributions: L.Z. designed the research framework and drafted the manuscript; C.W. completed the manuscript and made major revision; X.L. contributed to model simulation and data analysis; H.Z., W.L. and L.J. collected the data and provided preliminary processing for model inputs. Remote Sens. 2018, 10, 1108 14 of 15
Funding: This research is funded by the National Natural Science Foundation of China (No. 1771067 and No. 41371397). Conflicts of Interest: The authors declare no conflict of interest.
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