remote sensing

Article Grassland Phenology Response to Drought in the Canadian Prairies

Tengfei Cui *, Lawrence Martz and Xulin Guo Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada; [email protected] (L.M.); [email protected] (X.G.) * Correspondence: [email protected]; Tel.: +1-306-966-1488

Received: 25 August 2017; Accepted: 27 November 2017; Published: 4 December 2017

Abstract: Drought is a significant climatic disturbance in grasslands, yet the impact drought caused by global warming has on grassland phenology is still unclear. Our research investigates the long-term variability of grassland phenology in relation to drought in the Canadian prairies from 1982 to 2014. Based on the start of growing season (SOG) and the end of growing season (EOG) derived from Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g datasets, we found that grasslands demonstrated complex phenology trends over our study period. We retrieved the drought conditions of the prairie ecozone at multiple time scales from the 1- to 12-month Standardized Precipitation Evapotranspiration Index (SPEI). We evaluated the correlations between the detrended time series of phenological metrics and SPEIs through Pearson correlation analysis and identified the dominant drought where the maximum correlations were found for each ecozone and each phenological metric. The dominant drought over preceding months account for 14–33% and 26–44% of the year-to-year variability of SOG and EOG, respectively, and fewer water deficits would favor an earlier SOG and delayed EOG. The drought-induced shifts in SOG and EOG were determined based on the correlation between the dominant drought and the year-to-year variability using ordinary least square (OLS) method. Our research also quantifies the correlation between precipitation and the evolution of the dominant droughts and the drought-induced shifts in grassland phenology. Every millimeter (mm) increase in precipitation accumulated over the dominant periods would cause SOG to occur 0.06–0.21 days earlier, and EOG to occur 0.23–0.45 days later. Our research reveals a complex phenology response in relation to drought in the Canadian prairie grasslands and demonstrates that drought is a significant factor in the timing of both SOG and EOG. Thus, it is necessary to include drought-related climatic variables when predicting grassland phenology response to climate change and variability.

Keywords: grassland phenology; SPEI; precipitation; GIMMS NDVI3g

1. Introduction Grasslands make up about 42% of the plant cover on Earth [1] and are valued for their ecological and economic functions and services [2,3], including their regulation of the exchange of carbon between the atmosphere and biosphere [4–7]. Grasslands are vulnerable to disturbances such as grazing, fires, and drought. Drought events have occurred more frequently worldwide in recent decades [8,9] and are predicted to be even more frequent and intense under multiple future climate scenarios [10,11]. Studies suggest that drought event properties (e.g., onset, end, and duration) have a substantial effect on grassland production and biomass [12–16], the stock of soil carbon and nitrogen [17–19], species composition [20–23], and spatial distribution of grass species [24]. However, the impact drought has on the key phenophases of the growing season of grasslands, including the start of growing season (SOG) (i.e., the onset of flowering or leaf out) in spring and the end of growing season (EOG) (i.e., the onset of senescence) in autumn, is not completely understood.

Remote Sens. 2017, 9, 1258; doi:10.3390/rs9121258 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1258 2 of 21

Shifts in phenology effectively reflect the vegetation response to climate change and variability [25,26] and have a significant effect on vegetation ecosystem productivity [27–29]. Knowledge of drought impact on grassland phenology is obtained through species-specific studies. For instance, Mamolos et al. [30] recorded an earlier maximum aboveground biomass of early-season species in response to drought in a Mediterranean lowland grassland. A case study in Switzerland indicates that the full flowering of dandelion and cocksfoot grass was 11 days earlier under spring drought than the annual mean flowering [31]. Using remote sensing datasets, the response of grassland phenology to drought on larger scales have been recently investigated. For example, Glade et al. [32] detected an earlier trend of SOG from Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) of the mountainous steppe in Chile during the 2000–2013 period with increased drought severity. Tao et al. [33] extracted land surface phenology from Advanced Very High Resolution Radiometer (AVHRR) NDVI and found that preseason temperature-induced drought would cause delayed greenup and advanced senescence of the grasslands of the Northeast China Transect. The prairie ecozone is the northern extension of the Great Plains, containing most of the remaining native prairie in Canada. The prairie grasslands are a mix of mid-height grasses and short grasses, as well as a smaller amount of tall grasses [34], dominated by cool-season (C3) grasses (Wheat Grass (Agropyron spp.) and Spear Grass (Stipa spp.)) and warm-season (C4) grasses (Blue Grama Grass (Bouteloua gracilis)) [35]. The mean annual temperature and accumulated precipitation of the prairie ecozone vary from 1.5◦ to 3.5◦ and from 250 mm to 700 mm, respectively [36], and there has been a significant increase in the region-wide average of daily mean temperature and annual precipitation over the prairie ecozone since 1901 (Figure A1). Due to the high evapotranspiration and relatively low precipitation, the prairie ecozone is known for a lack of water sources for vegetation growth [37]. Drought events at various temporal scales have been observed in the prairie ecozone from instrumental records and proxy drought indices (Figure A2). The growing season droughts have recently portrayed a trend of increasing frequency [9,36]. Regarding future drought conditions, it is predicted that both warm and cool climate scenarios will result in an increase of potential evapotranspiration [24], while the precipitation over the growing season is expected to decrease [24,38]. Grasslands are a critical component of the prairie ecozone, as they preserve a variety of wildlife and provide substantial forage for livestock [39]. The drought has been a significant force for the change of production and the species composition of the prairie grasslands [40–42]. Regarding the shifts in grassland phenology during the past decades, there are few ground phenological observations of grass species in the prairie ecozone [43]. Remote sensing datasets and near-surface PhenoCam pictures are the main resources to detect the phenology changes of the prairie grasslands. Reed [44] found a later EOG and longer growing season in parts of the Canadian Prairie during 1982–2003. Li and Guo [45] found a trend of earlier SOG and delayed EOG in mixed prairie species in Grasslands National Park from 1985 to 2006. Given the increasing frequency of drought in the prairie ecozone, understanding the adaptive response of grassland phenology to drought over the past decades is crucial for predicting future shifts, as well as learning how to better manage grasslands and maintain their functionality under future climate scenarios. In this study, we addressed the following two questions on the projection of drought in grassland phenology across the prairie ecozone:

• Is drought a significant climatic factor for the temporal evolution of grassland phenology? • How have changes in precipitation influenced the grassland phenology response to drought?

2. Materials and Methods

2.1. Canadian Prairie Grasslands The 1/12◦ MODIS global land cover datasets, produced by the Global Land Cover Facility (GLCF) using standard MODIS land cover type products over the 2001–2012 period [46], were used to identify grasslands in the prairie ecozone. Considering the temporal change of land cover types, Remote Sens. 2017, 9, 1258 3 of 21 Remote Sens. 2017, 9, 1258 3 of 21 totalonly of the 1350 pixels grassland classified pixels as grasslandin the prairie from ecozone 2001 through (about 86,400 2012 werekm2).retained. Grasslands As extend a result, across there the is southwesterna total of 1350 part grassland of the Canadian pixels inthe prairie prairie province ecozones: Alberta (about (AB), 86,400 Saskatchewan km2). Grasslands (SK) and extend Manitoba across (MB),the southwestern across five ecoregions: part of the Mixed Canadian Grassland, prairie provinces:Moist Mixed Alberta Grassland, (AB), Cypress Saskatchewan Upland, (SK) Fescue and Grassland,Manitoba (MB), and Aspen across fiveParkland ecoregions: (Figure Mixed 1). According Grassland, to Moist the National Mixed Grassland, Ecological Cypress Framework Upland, for Canada,Fescue Grassland, each ecoregion and Aspen is characterized Parkland (Figure by1 ).distinctive According climate, to the National soil, landform, Ecological and Framework associated for biologicalCanada, each community ecoregion as is characterizeda subdivision by of distinctive the prairie climate, ecozone soil, [47]. landform, Most of and the associated prairie grasslands biological (83%)community are in asthe a Mixed subdivision Grassland of the ecoregion, prairie ecozone which [typically47]. Most contains of the prairie species grasslands of short grasses (83%) are (e.g., in Bluethe Mixed Grama Grassland and Northern ecoregion, Wheat which Grass typically (Agropyron contains cristatum species)) ofand short mid grasses grasses (e.g., (e.g., Blue Western Grama Wheatgrassand Northern (Pascopyrum Wheat Grass smithii) (Agropyron, Needle-and-Thread cristatum)) and Grass mid (Hesperostipa grasses (e.g., comata Western) and WheatgrassJune Grass (PascopyrumKoeleria macrantha smithii)), (Table Needle-and-Thread 1). Moist Mixed Grass Grassland, (Hesperostipa which comata contains) and June8.9% Grassof the ( Koeleriagrasslands macrantha in the) (Tableprairie1 ).ecozone, Moist Mixedas usually Grassland, compos whiched of Western contains Porcupine 8.9% of the Grass grasslands (Stipa curtiseta in the prairie), Northern ecozone, and Westernas usually Wheatgrass composed of(Agropyron Western Porcupine cristatum; Grass Pascopyrum (Stipa curtiseta smithii),) Northernand Green and Needlegrass Western Wheatgrass (Nassella (viridulaAgropyron). The cristatum; remaining Pascopyrum grasslands smithii are) distributed and Green Needlegrassin Aspen Parkland (Nassella (0.2%), viridula Fescue). The remainingGrassland (2.3%),grasslands and areCypress distributed Upland in (5.6%), Aspen of Parkland which Rough (0.2%), Fescue Fescue (Festuca Grassland scabrella (2.3%),) is the and dominant Cypress Uplandspecies (5.6%),[34,35]. of which Rough Fescue (Festuca scabrella) is the dominant species [34,35].

Figure 1. GrasslandsGrasslands in in the the prairie ecozone. AB, AB, SK, SK, and and MB MB are are the the abbreviations abbreviations for for Alberta, Saskatchewan, and Manitoba, respectively.

Remote Sens. 2017, 9, 1258 4 of 21

Table 1. Characteristics of grasslands in the ecoregions of the prairie ecozone.

Grassland Grassland Ecoregions Compositional Major Grass Species Areas (km2) Proportions (%) Aspen parkland 128 0.2 Rough Fescue (Festuca scabrella) Western Porcupine Grass (Stipa cur-tiseta), Northern and Western Moist Mixed 7680 8.9 Wheatgrass (Agropyron cristatum & Pascopyrum smithii), and Green Grassland Needlegrass (Nassella viridula) Western Wheatgrass (Pascopyrum smithii), Needle-and-Thread Grass Mixed Grassland 71,744 83.0 (Hesperostipa comata), June Grass (Koeleria macrantha), Blue Grama (Bouteloua gracilis), and Northern Wheatgrass (Agropyron cristatum) Fescue Grassland 2048 2.3 Rough Fescue (Festuca scabrella) Cypress Upland 4800 5.6 Rough Fescue (Festuca scabrella)

2.2. Grassland Phenology Extraction The phenological metrics of grasslands in the prairie ecozone from 1982 to 2014 were extracted from the third-generation Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). GIMMS NDVI3g datasets were compiled from NOAA Advanced Very High Resolution Radiometer (AVHRR) data at a 15-day and 1/12◦ resolution. This dataset has improved the calibration of the original GIMMS NDVI dataset [48] using Satellite Pour L’ Observation de la Terre (SPOT) Vegetation and Sea-viewing Wide Field-of-view-Sensor (SeaWiFS) data [49]. GIMMS NDVI3g is becoming increasingly popular for use in investigating the long-term change of vegetation growth and productivity around the world [49–54]. The NDVI time series were processed in TIMESAT software (version 3.3) [55]. First, we extracted NDVI and quality flags of NDVI for grassland pixels in MATLAB (R2013b, MathWorks, Inc.: Natick, MA, USA). Second, the annual NDVI trajectory was fit to a double logistic function. The double logistic function is widely used in the extraction of satellite-based vegetation phenology [56,57]. Given the different NDVI qualities, we adapted the use of quality data in [55], setting the weights of high-quality (quality flag = 0), medium-quality (quality flag = 1), and low-quality (quality flag = 2) NDVI values to 1, 0.5, and 0.1, respectively. Since the GIMMS NDVI3g datasets do not provide the precise acquisition dates, we used the middle date of each 15-day period as the acquisition date of each NDVI value. Third, the SOG and EOG were determined as the 20% of the seasonal amplitude measured from the left and right minimum values of the annual NDVI fit, respectively [58–60]. We summarized the temporal evolution of grassland phenology by ecoregion because each ecoregion is relatively homogeneous in climate and biological diversity as a part of the entire prairie ecozone [34], while Aspen parkland was excluded because of its extremely low proportion of grasslands. For each phenological metric and each year, we examined the region-wide distribution of per-pixel onset date. Then, we employed the interquartile range rule (i.e., the onset dates greater than the 75th percentile plus 1.5 times interquartile or smaller than 25th percentile minus 1.5 times interquartile are marked as outliers) [61] to detect the outliers of onset dates. The pixels that were not marked throughout 1982–2014 were retained. We then computed the mean and standard deviation of the dates of these pixels to show the region-wide average and variability of grassland phenology.

2.3. Drought and Precipitation for Grasslands in the Prairie Ecozone The 0.5◦ gridded dataset of Standardized Precipitation Evapotranspiration Index (SPEI), computed based on the cumulative climatic water balance (i.e., the difference between precipitation (P) and potential evapotranspiration (PET)) [62], was extracted from Global SPEI database v2.4 [63]. This was used to characterize the drought events at multiple time scales (TS) in the prairie ecozone, from 1982 to 2014. Being a standardized index, the SPEI is comparable across space and time with a mean value of 0 and a standard deviation of 1. SPEI at the time scale of i months (i ranges from 1 to 48) represents the cumulative water balance over the preceding i months with respect to the long-term mean. A positive Remote Sens. 2017, 9, 1258 5 of 21

SPEI indicates wet conditions, while a negative SPEI represents dry conditions. Compared to the common drought indices calculated at a fixed time scale, such as the Palmer Drought Severity Index (PDSI) [64] and the self-calibrating Palmer Drought Severity Index (SC-PDSI) [65], SPEI has the advantage that it is capable of identifying drought events at different time scales [62]. Relative to the Standardized Precipitation Index (SPI), a widely used multiscalar drought index based on the changes of precipitation [66], SPEI is more suited for monitoring drought events under global warming because it considers the impacts of both precipitation and temperature on climatic water balance by including PET [62]. To understand how drought conditions change in relation to precipitation over time, we downloaded the high-resolution gridded datasets of monthly precipitation from Climate Research Unit (CRU) TS v. 3.24 [67] over our study period. Given the different spatial resolutions of climate and satellite datasets, SPEI and P were interpolated from 0.5◦ to 1/12◦ for the 1982–2014 period to match the GIMMS NDVI3g [68] and averaged for grasslands in each ecoregion.

2.4. Statistics We assessed the sensitivities of grassland phenology to drought conditions by applying Pearson correlation analysis to the time series of region-wide average phenological metrics and monthly SPEI. We confined the periods of drought events that may affect the onset of SOG or EOG to the preceding 12 months. For instance, if the typical SOG (i.e., the mean of the region-wide average SOG during 1982–2014) is 110 in the day of the year (DOY) unit, then the time series of SPEI for all subperiods (in months) between May and April will be included. The long-term trends in the original time series need to be removed to avoid the spurious correlation caused by the trend components [69]. Given our long study period (33 years), over which the trend of either SPEI or phenological metrics may be stochastic, we used the first differences of the original time series (i.e., a sequence of the differences between two consecutive elements of the original time series of SPEI or phenological metrics), rather than the residuals from the simple linear regression, as the detrended time series [70,71]. For each ecoregion and each phenological metric, we obtained three statistical variables of the correlation between SPEI and phenology, which are the values of the correlation coefficient (r), adjusted r-squared value (Adjrsq), and associated significance (p). We recorded the time scale (TS) and month of the year (MOY) of each significant drought event (p < 0.05) and identified the dominant drought event (DSPEI) where the maximum correlation was found. Based on the MOY and TS of DSPEI, we identified the dominant cumulative period over which the cumulative water balance accounted for the most variability of grassland phenology and computed the amount of precipitation accumulated over the dominant cumulative period (DCP) for each ecoregion and each phenological metric. For example, if two-month SPEI for April is the DSPEI, then the associated dominant cumulative period will be March–April, and the DCP will be the precipitation accumulated over March–April. The impacts of DCP on the grassland phenology response to drought were evaluated through a three-step process. First, we evaluated the effect of precipitation anomaly on the drought in the prairie grasslands. The amounts of DCP were standardized to show the deviations of annual DCP amounts of their mean from 1982 to 2014. We tested the correlation between DSPEI and standardized DCP and recorded the values of r, Adjrsq, and p. Second, we retrieved the drought-induced shifts in grassland phenology from the year-to-year variability (i.e., the time series of first-differenced SOG and EOG). This step was conducted through linear regression modeling, where the first-differenced SOG or EOG (response variable) was regressed against the first-differenced DSPEI (explanatory variable). This was done using the ordinary least square (OLS) method. The fitted values of the response variable were the estimate of drought-induced shifts in grassland phenology. Third, we assessed the relationship between DCP and the drought-induced shifts in grassland phenology. The time series of DCP was detrended using the first differencing approach. A simple linear regression model was built upon the first-differenced DCP (explanatory variable) and drought-induced shifts in grassland phenology (response variable), and the values of Adjrsq and the regression coefficient (slope of the fit line) were recorded for each phenological metric and ecoregion. Remote Sens. 2017, 9, 1258 6 of 21

Remote Sens. 2017, 9, 1258 6 of 21 3. Results and Discussion 3. Results and Discussion 3.1. Long-Term Means and Interannual Changes of Grassland Phenology in the Prairie Ecozone from 1982–2014 We3.1. calculatedLong-Term Means the long-term and Interannual means Changes over of 1982–2014Grassland Phenology for the in SOG the Prairie and EOGEcozone to from display 1982–2014 the spatial variabilityWe of calculated grassland the phenology long-term inmeans the prairieover 1982–201 ecozone4 for (Figure the SOG2). and The EOG mean to SOGdisplay of the grasslands spatial in the prairievariability ecozone of grassland from 1982–2014 phenology ranges in the prairie from mid-Marchecozone (Figure (76 2). in The DOY) mean to earlySOG of May grasslands (127 in in DOY). Mostthe of theprairie grasslands ecozone allfrom over 1982–2014 the prairie ranges ecozone from mid-March (82%) start (76 to growin DOY) in to April, early several May (127 small in DOY). grassland patchesMost (17%) of the in Mixedgrasslands Grassland all over and the Cypressprairie ecozone Upland (82%) start tostart grow to grow earlier in in April, March several while small about 1% of grasslandsgrassland withpatches late (17%) SOG in inMixed May Grassland are dotted and about Cypress Mixed Upland Grassland start to grow and earlier Moist in Mixed March Grassland. while about 1% of grasslands with late SOG in May are dotted about Mixed Grassland and Moist Mixed The mean EOG of grasslands is between 270 and 333 in DOY. More than half of grasslands in the Grassland. The mean EOG of grasslands is between 270 and 333 in DOY. More than half of grasslands prairie ecozone (57%) stop photosynthetic activity in November, while the growing season of the rest in the prairie ecozone (57%) stop photosynthetic activity in November, while the growing season of of grasslandsthe rest of ends grasslands in early ends October in early or October late September. or late September.

(a)

(b)

Figure 2. Long-term means of the start of growing season (SOG) (a) and the end of growing season Figure 2. Long-term means of the start of growing season (SOG) (a) and the end of growing season (EOG) (b) of grasslands in the prairie ecozone over 1982–2014. (EOG) (b) of grasslands in the prairie ecozone over 1982–2014. The interannual variation in grassland phenology is displayed by the time series of region-wide Theaverage interannual SOG and variationEOG for each in grassland ecoregion phenology(Figure 3). We is displayed tested whether by the the time temporal series evolution of region-wide of average SOG and EOG for each ecoregion (Figure3). We tested whether the temporal evolution of these phenological metrics followed a consistent trend (i.e., advanced or delayed). Based on the regression Remote Sens. 2017,, 9,, 12581258 7 of 21

these phenological metrics followed a consistent trend (i.e., advanced or delayed). Based on the coefficients,regression coefficients, the SOG of the Mixed SOG Grassland of Mixed andGrassl Cypressand and Upland Cypress occurred Upland earlier occurred during earlier 1982–2014, during while,1982–2014, in Moist while, Mixed in Moist Grassland Mixed and Grassland Fescue and Grassland, Fescue SOGGrassland, was delayed. SOG was In delayed. comparison, In comparison, the EOG of allthe four EOG ecoregions of all four tendedecoregions to be tended later as to time be later progressed. as time progressed. These trends, These except trends, for the except one infor the the SOG one ofin Fescuethe SOG Grassland, of Fescue areGrassland, not significant are not ( psignificant> 0.05). (p > 0.05). The inconsistent interannual changes from the timetime series of phenological metrics (Figure 33)) indicate that most grassland pixels did not present significantsignificant trends in either SOG or EOG over our study period. This contradicts the results from boreal and continental biomes [[72]72] but isis consistentconsistent with the previous findings findings obtained obtained from from the the grassl grasslandsands in in North North America America [44,73]. [44,73 ].The The evolutions evolutions of ofgrassland grassland phenology phenology are are complex. complex. For Forexample, example, in the in theMixed Mixed Grassland, Grassland, which which contains contains 83% 83% of ofgrasslands grasslands of ofthe the prairie prairie ecozone, ecozone, no no consistent consistent trend trend in in SOG SOG is is found found over over 1982–2014 1982–2014 (Figure (Figure 33).). However, therethere are are two two significant significant advanced advanced subtrends subtrends (1982–1990 (1982–1990 (r = (r− 0.86,= −0.86,p = p 0.003), = 0.003), and and 1991–2001 1991– (r2001 = − (r0.71, = −0.71,p = 0.014)) p = 0.014)) within within this period this period (Figure (Figure A3). Similar A3). Similar phenology phenology variability variability over a longover perioda long wasperiod documented was documented recently recently from from the grassland the grassland ecosystems ecosystems in Thein The Ordos Ordos [74 [74],], and and on on thethe Tibetan Plateau [[75],75], where the moisture conditions in relation to precipitation (rainfall and snowfall) played noticeable roles roles in in governing governing the the interannual interannual variation variation in ingrassland grassland phenology. phenology. Climate Climate change change has hasbeen been widely widely accepted accepted as the as themain main force force for forshifts shifts in vegetation in vegetation phenology, phenology, and and this this research research all allsuggests suggests that that moisture moisture condition condition has has a acritical critical impact impact on on vegetation vegetation growth growth in water-limitedwater-limited ecosystems [[7373–‒7575].]. Therefore, it is worthworth consideringconsidering environmentalenvironmental factorsfactors suchsuch asas waterwater deficitdeficit in addition to temperature,temperature, when evaluatingevaluating the climate-inducedclimate-induced change in grasslandgrassland phenologyphenology andand predicting the futurefuture phenologyphenology ofof semi-arid-to-aridsemi-arid-to-arid grassland ecosystems (e.g., the grasslands in the prairie ecozone) in relation to to climate climate ch changeange and and variability variability under under global global warming. warming.

(a)

Figure 3. Cont.

Remote Sens. 2017, 9, 1258 8 of 21 Remote Sens. 2017, 9, 1258 8 of 21

(b)

FigureFigure 3.3. Temporal evolutionsevolutions ofof region-wideregion-wide averageaverage SOGSOG (aa)) andand EOGEOG ((b) of the prairie grasslands. TheThe redred dotsdots denotedenote thethe region-wideregion-wide averageaverage phenologicalphenological metricsmetrics whilewhile thethe blueblue errorerror barsbars areare oneone standardstandard deviationdeviation ofof thethe region-wideregion-wide averages.averages. TheThe dasheddashed lineslines areare thethe linearlinear trendlinestrendlines ofof thethe timetime seriesseries ofof region-wideregion-wide averageaverage phenologicalphenological metrics.metrics. TheThe equationsequations ofof linearlinear trendlinestrendlines areare displayeddisplayed aboveabove graphs.graphs. TheThe valuesvalues ofof pp are presentedpresented toto describedescribe thethe statisticalstatistical significancesignificance ofof thethe linearlinear trendstrends atat thethe significancesignificance levellevel ofof 0.05.0.05.

3.2.3.2. CorrelationsCorrelations betweenbetween GrasslandGrassland PhenologyPhenology andand PrecedingPreceding DroughtDrought EventsEvents TheThe typicaltypical region-wideregion-wide MOYsMOYs ofof thethe phenologicalphenological metricsmetrics ofof grasslandsgrasslands areare thethe samesame forfor allall fourfour ecoregions—thatecoregions—that is,is, thethe growinggrowing seasonseason startsstarts inin AprilApril andand endsends inin November.November. BasedBased onon previousprevious literature,literature, moisturemoisture conditionsconditions ofof thethe precedingpreceding yearyear significantlysignificantly influenceinfluence thethe vegetationvegetation activityactivity overover thethe currentcurrent yearyear inin thethe water-limitedwater-limited regionsregions [[7766–78].–78]. Hence,Hence, wewe confinedconfined thethe periodsperiods ofof droughtdrought conditionsconditions thatthat possibly possibly affect affect the SOGthe andSOG EOG and to EOG May–April, to May–April, and December–November, and December–November, respectively. Forrespectively. each phenological For each metricphenological and each metric ecoregion, and each there ecoregion, is a total there of 78 is different a total of time 78 seriesdifferent of SPEItime (i.e.,series twelve of SPEI 1-month (i.e., twelve SPEIs, 1-month eleven 2-month SPEIs, SPEIs,eleven ...2-month, and oneSPEIs, 12-month …, and SPEI), one 12-month representing SPEI), all droughtrepresenting events all at drought 1- to 12-month events at time 1- to scales 12-mon overth thetime preceding scales over 12 the months. preceding 12 months. TheThe values of of r rbetween between SOG SOG and and SPEI SPEI range range from from−0.59 −to0.59 0.4 over to 0.4 the over prairie the ecozone, prairie while ecozone, the whilesignificant the significantcorrelations correlations are all negative are all negative(Figure 4). (Figure The 4SOG). The of SOGMoist of Mixed Moist MixedGrassland Grassland is only issignificantly only significantly related to related the two-month to the two-month SPEI for Apr SPEIil for(Figure April 4a), (Figure which4 a),corresponds which corresponds to the cumulative to the cumulativeclimatic water climatic balance water from balanceMarch to from April. March The SOG to April. of Fescue The grassland SOG of Fescue is strongly grassland related is to strongly climatic relatedwater balance to climatic in February water balance (one-month in February SPEI (one-month for February) SPEI and for February)the cumulative and the water cumulative balance waterfrom balancepreceding from August preceding to April August (nine-month to April (nine-monthSPEI for April), SPEI the for former April), of the which former has ofa greater which hasinfluence a greater on influenceSOG (Figure on SOG4c). In (Figure comparison,4c). In comparison,more drought more condit droughtions are conditions significantly are related significantly to the relatedSOG of toMixed the SOGGrassland. of Mixed Among Grassland. these SPEIs, Among the thesenine-month SPEIs, SPEI the nine-month for April, which SPEI corresponds for April, which to the corresponds climatic water to balance accumulated from preceding August through April, has the greatest impact on SOG of Mixed Grassland (Figure 4b). Similarly, we recorded the maximum correlation for Cypress Upland between

Remote Sens. 2017, 9, 1258 9 of 21 the climatic water balance accumulated from preceding August through April, has the greatest impact on SOGRemote of MixedSens. 2017 Grassland, 9, 1258 (Figure4b). Similarly, we recorded the maximum correlation for9 Cypress of 21 Uplandthe between cumulative the water cumulative balance over water the balancepreceding over June–preceding the preceding December June–preceding period and the December SOG among period and thethe SOG significant among correlations the significant at mult correlationsiple time scales at (Figure multiple 4d). time scales (Figure4d).

FigureFigure 4. The 4.correlations The correlations (Adjrsq (Adjrsq and and r) r) between between SOG SOG andand preceding drought drought conditions conditions for forMoist Moist Mixed Grassland (a); Mixed Grassland (b); Fescue Grassland (c); and Cypress Upland (d). These Mixed Grassland (a); Mixed Grassland (b); Fescue Grassland (c); and Cypress Upland (d). These graphs graphs are generated based on the values of r and Adjrsq, that is, the colours and black arrows are generated based on the values of r and Adjrsq, that is, the colours and black arrows illustrate of the illustrate of the variability of the r (red: positive; blue: negative) and Adjrsq (the arrowhead points variabilitytowards of thegreater r (red: Adjrsq, positive; and the blue: length negative) of arrow andindicates Adjrsq the (themagnitude arrowhead of variability) points towardsas the month greater Adjrsq,of andthe year the length (MOY) of (x arrow-axis) and indicates time scale the magnitude(y-axis) of Standardized of variability) Precipitation as the month Evapotranspiration of the year (MOY) (x-axis)Index and time(SPEI) scale change, (y-axis) respectively. of Standardized The black Precipitation dashed lines Evapotranspiration outline the regions Index of (SPEI)significant change, respectively.correlations The black(p < 0.05). dashed The lines greatest outline significant the regions correlation of significant is marked correlations with a white(p triangle,< 0.05). Thewhich greatest is significantpinpointed correlation by the is white marked arrow. with The a whiter of the triangle, greatest significant which is pinpointed correlation byis presented the white by arrow. the text The in r of the greatestthe white significant box. correlation is presented by the text in the white box.

The values of r between EOG and SPEI range from −0.45 and 0.7, while the significant The values of r between EOG and SPEI range from −0.45 and 0.7, while the significant correlations correlations are all positive (Figure 5). The EOG of Moist Mixed Grassland is significantly related to are allSPEI positive for August (Figure (TS5 ).= 1–5, The and EOG 8–12 of months), Moist Mixed September Grassland (TS = 2–5, is and significantly 9–12 months), related October to SPEI(TS = for August3–5, (TS and = 11–12 1–5, andmonths), 8–12 and months), November September (TS = 5 months), (TS = 2–5, and and is heavily 9–12months), influenced October by the climatic (TS = 3–5, and 11–12water months), balance in and August November (one-month (TS SPEI = 5 months),for August) and (Figure is heavily 5a). We influenced obtained similar by the distributions climatic water balance in August (one-month SPEI for August) (Figure5a). We obtained similar distributions of Remote Sens. 2017, 9, 1258 10 of 21

Remote Sens. 2017, 9, 1258 10 of 21 significant correlations for the other three ecoregions. The greatest influence on the EOG of Mixed of significant correlations for the other three ecoregions. The greatest influence on the EOG of Mixed Grassland and Cypress Uplands are both from the climatic water balance of August (Figure5b,d), Grassland and Cypress Uplands are both from the climatic water balance of August (Figure 5b,d), while the cumulative water balance from August to September has the greatest impact on the EOG of while the cumulative water balance from August to September has the greatest impact on the EOG Fescueof Grassland Fescue Grassland (Figure (Figure5c). 5c).

FigureFigure 5. The 5. correlations The correlations (Adjrsq (Adjrsq and and r) betweenr) between EOG EOG and and precedingpreceding drought drought conditions conditions for forMoist Moist Mixed Grassland (a); Mixed Grassland (b); Fescue Grassland (c); and Cypress Upland (d). The graphs Mixed Grassland (a); Mixed Grassland (b); Fescue Grassland (c); and Cypress Upland (d). The graphs and annotations are generated the same way as Figure 4. and annotations are generated the same way as Figure4. SPEI is a superior index for illustrating the deviations of climatic water balance about the normal SPEIconditions is a superior in both indexspatial forand illustrating temporal dimensions. the deviations It explains of climatic not only water the severity balance of about water thedeficits normal conditions(by the in value both of spatial SPEI) andbut the temporal types of dimensions. usable water Itsources explains that not the onlywater the deficits severity affect of (by water the time deficits (by thescale value of SPEI). of SPEI) Soil but water the content types ofis usablethe main water water sources source that controls the water grassland deficits growth affect (byand the time scaleproductivity of SPEI). [79], Soil and water its change content is usually is the mainmonitored water using source the thatSPEI controlscalculated grassland at short time growth scales and [62]. Hence, it is not surprising to find that the SOG and EOG of grasslands are closely related to the productivity [79], and its change is usually monitored using the SPEI calculated at short time scales [62]. one- to two-month SPEI (Figure 4a,c and Figure 5a–d). This is consistent with the finding from Hence,Mongolian it is not surprising steppes [80]. to Based find that on the the values SOG andof r of EOG these of correlations, grasslands we are can closely conclude related that to more the soil one- to two-monthwater SPEIcontent (Figure in late4 a,cwinter–early and Figure spring5a–d). will This favor is consistentan earlier SOG with of the grasslands finding in from Moist Mongolian Mixed steppesGrassland [80]. Based and onFescue the values Grassland, of r ofand these more correlations, soil water in welate cansummer–early conclude that fall will more lead soil to water a delayed content in late winter–early spring will favor an earlier SOG of grasslands in Moist Mixed Grassland and Fescue Grassland, and more soil water in late summer–early fall will lead to a delayed EOG of grasslands in all Remote Sens. 2017, 9, 1258 11 of 21 four ecoregions. On the other hand, we noted that the SOG of Mixed Grassland and Cypress Upland is most influenced by SPEI at nine and seven months, respectively (Figure4b,d), which are considerably longer than those for Moist Mixed Grassland and Fescue Grassland. Abatzoglou et al. [81] found that SPEI at medium time scales (6–10 months) are strongly correlated with the cumulative water-year streamflow. The largest contributor to the surface streamflow on the prairie ecozone is the melt of snowpack in March and April [82], so we may suspect that the spring snowpack melt is the main usable water source for the SOG of Mixed Grassland and Cypress Upland. As a result, sufficient water supply from spring melt would lead to an advanced SOG given the negative value of r (Figure4b,d). This difference in the time scale of DSPEI may also be a result of different adaptabilities to drought disturbance in relation to the aridity of each ecoregion. Plants at biomes with different aridity degrees have different physiological mechanisms to tolerate water deficits at various time scales [68]. A recent study even pointed out that the semi-arid regions are more sensitive to climatic extremes than the arid and humid regions by studying the shifts in vegetation phenology and productivity in southeastern Australia [83]. The prairie ecozone is a semi-arid-sub humid region with a minor difference in aridity index (AI: the ratio of mean annual precipitation to mean annual potential evapotranspiration [84]) among the grasslands of four ecoregions (Figure A4). The effect of the aridity index on the different responses of grassland phenology to drought (e.g., the different time scales of dominant drought and the associated r values in (Figures4 and5) in the prairie ecozone would be worth investigating in the future to better understand the impact of drought on grassland ecosystems under global warming.

3.3. Drought-Induced Shifts in Grassland Relation to Phenology and Their Precipitation Given the maximum correlations between grassland phenology and preceding drought conditions (Table2), drought is a moderate climatic factor to the temporal evolutions of the grassland phenology of the prairie ecozone, accounting for 14–33% and 26–44% of the year-to-year variability of SOG and EOG, respectively, over the 1982–2014 period.

Table 2. The maximum correlations between grassland phenology (i.e. the start of growing season (SOG) and the end of growing season (EOG)) and Standardized Precipitation Evapotranspiration Index (SPEI) for each ecoregion of the prairie ecozone.

Moist Mixed Grassland Fescue Grassland Mixed Grassland Cypress Upland r Adjrsq r Adjrsq r Adjrsq r Adjrsq SOG −0.40 * 0.14 −0.41 * 0.14 −0.59 *** 0.33 −0.50 ** 0.23 EOG 0.57 *** 0.31 0.68 *** 0.44 0.60 *** 0.34 0.53 ** 0.26 * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.

In general, the drought-induced year-to-year variability of SOG/EOG is consistent with the whole year-to-year variability of SOG/EOG, in terms of the direction of the variability (Figures6 and7). However, we observed several outliers in the time series. For instance, the SOG of Mixed Grassland in 2011 is later than that in 2010, whereas the increase of SPEI from 2010 to 2011 favors an earlier SOG in 2011 (Figure6c). This difference implies that drought may not always be the critical climatic factor that determines the direction of the year-to-year variability of grassland phenology. Some researchers have investigated the relative importance of different climate drivers, including temperature and precipitation, on grassland phenology. However, no universally accepted dominant climatic factor has been found. Zhu and Meng [74] found that spring precipitation is the most dominant climatic variable for the SOG of grasslands in the Ordos, while Yu et al. [76] found that growing season precipitation did not have an impact as big as increasing temperatures had on the SOG of grasslands on the Tibetan Plateau. Hence, it is reasonable to assume that the temporal evolutions of the SOG and EOG of the Canadian prairie grasslands are the result of the integrated impact of multiple climatic variables and the most dominant variable affecting the direction and magnitude of the year-to-year variability of the SOG and EOG from 1982 to 2014 could change annually. Remote Sens. 2017, 9, 1258 12 of 21 Remote Sens. 2017, 9, 1258 12 of 21 Remote Sens. 2017, 9, 1258 12 of 21

Figure 6. The year-to-year variability of SOG (unfilled bars) and the drought-induced shifts in SOG FigureFigure 6. The 6. year-to-yearThe year-to-year variability variability of of SOG SOG (unfilled (unfilled bars) and and the the drought-induced drought-induced shifts shifts in SOG in SOG (red lines marked with red circles) for Moist Mixed Grassland (a); Fescue Grassland (b); Mixed (red lines(red lines marked marked with with red red circles) circles) for for Moist Moist Mixed Mixed GrasslandGrassland (a (a);); Fescue Fescue Grassland Grassland (b); (bMixed); Mixed Grassland (c); and Cypress Upland (d). * indicates that the signs of the drought-induced year-to-year GrasslandGrassland (c); and (c); and Cypress Cypress Upland Upland (d ().d). * indicates* indicates thatthat the signs signs of of the the drought-induced drought-induced year-to-year year-to-year variability of SOG and the whole year-to-year variability of SOG are different. * indicates p < 0.05, ** variability of SOG and the whole year-to-year variability of SOG are different. * indicates p < 0.05, ** variabilityindicates of SOGp < 0.01, and and the *** whole indicates year-to-year p < 0.001. variability of SOG are different. * indicates p < 0.05, ** indicatesindicatesp < p 0.01,< 0.01, and and *** *** indicates indicates p << 0.001. 0.001.

Figure 7. The year-to-year variability of EOG (unfilled bars) and the drought-induced shifts in EOG FigureFigure 7. The 7. year-to-yearThe year-to-year variability variability of of EOG EOG (unfilled (unfilled bars) and and the the drought-induced drought-induced shifts shifts in EOG in EOG (red lines marked with red circles) for Moist Mixed Grassland (a); Fescue Grassland (b); Mixed (red lines(red lines marked marked with with red red circles) circles) for for Moist Moist Mixed Mixed GrasslandGrassland (a (a);); Fescue Fescue Grassland Grassland (b); (bMixed); Mixed Grassland (c); and Cypress Upland (d). * indicates that the signs of the drought-induced year-to-year GrasslandGrassland (c); and (c); and Cypress Cypress Upland Upland (d ().d). * indicates* indicates thatthat the signs signs of of the the drought-induced drought-induced year-to-year year-to-year variability of EOG and the whole year-to-year variability of EOG are different. * indicates p < 0.05, ** variabilityvariability of EOG of EOG and and the the whole whole year-to-year year-to-year variability of of EOG EOG are are different. different. * indicates * indicates p < 0.05,p <** 0.05, indicates p < 0.01, and *** indicates p < 0.001. ** indicatesindicatesp < p 0.01,< 0.01, and and *** *** indicates indicates p << 0.001. 0.001. In a comparison of the time series of DSPEI and DCP, we highlighted the influence of In a comparison of the time series of DSPEI and DCP, we highlighted the influence of precipitation on the drought conditions that dominate the changes of grassland phenology of the Inprecipitation a comparison on the of the drought time seriesconditions of DSPEI that domi and DCP,nate the we changes highlighted of grassland the influence phenology of precipitation of the prairie ecozone. That is, DCP explains 58–93% and 86–93% of the variability of DSPEI for SOG and on theprairie drought ecozone. conditions That is, that DCP dominate explains 58–93% the changes and 86–93% of grassland of the variability phenology of DSPEI of the for prairie SOG ecozone.and

That is, DCP explains 58–93% and 86–93% of the variability of DSPEI for SOG and EOG of grasslands of the prairie ecozone, respectively (Figures8 and9). We further quantified the impact of precipitation on the drought-induced shifts in grassland phenology. Our results show that DCP is negatively related Remote Sens. 2017, 9, 1258 13 of 21 Remote Sens. 2017, 9, 1258 13 of 21 Remote Sens. 2017, 9, 1258 13 of 21 EOG of grasslands of the prairie ecozone, respectively (Figure 8 and 9). We further quantified the EOG of grasslands of the prairie ecozone, respectively (Figure 8 and 9). We further quantified the impact of precipitation on the drought-induced shifts in grassland phenology. Our results show that impact of precipitation on the drought-induced shifts in grassland phenology. Our results show that toDCP the drought-induced is negatively related shifts to in the SOG, drought-induced and every mm shifts increase in inSOG, DCP and causes every 0.06–0.21 mm increase days ofin advanceDCP DCP is negatively related to the drought-induced shifts in SOG, and every mm increase in DCP incauses SOG (Figure 0.06–0.21 10). days EOG, of onadvance the other in SOG hand, (Figure is positively 10). EOG, related on the to other DCP, hand, and withis positively every mm related increase to causes 0.06–0.21 days of advance in SOG (Figure 10). EOG, on the other hand, is positively related to ofDCP, and EOG with is delayed every mm by increase 0.23–0.45 of days DCP, (Figure EOG is 11 delayed). by 0.23–0.45 days (Figure 11). DCP, and with every mm increase of DCP, EOG is delayed by 0.23–0.45 days (Figure 11).

Figure 8. The correlation between the dominant drought event (DSPEI) (unfilled bars) and the FigureFigure 8. 8The. The correlation correlation betweenbetween thethe dominantdominant dr droughtought event event (DSPEI) (DSPEI) (unfilled (unfilled bars) bars) and and the the standardized precipitation accumulated over the dominant cumulative period (DCP) (blue solid standardizedstandardized precipitation precipitation accumulated accumulated overover thethe dominant cumulative cumulative period period (DCP) (DCP) (blue (blue solid solid lines) for the SOG of grasslands of Moist Mixed Grassland (a); Fescue Grassland (b); Mixed Grassland lines)lines) for for the the SOG SOG of of grasslands grasslands of of Moist Moist MixedMixed GrasslandGrassland ( (aa);); Fescue Fescue Grassland Grassland (b ();b );Mixed Mixed Grassland Grassland (c); and Cypress Grassland (d). *** indicates that p < 0.001. (c);(c and); and Cypress Cypress Grassland Grassland (d ().d). *** *** indicates indicates thatthat pp << 0.001.

Figure 9. The correlation between DSPEI (unfilled bars) and standardized DCP (blue solid lines) for Figure 9. The correlation between DSPEI (unfilled bars) and standardized DCP (blue solid lines) for Figurethe EOG 9. The of the correlation grasslands between of Moist DSPEI Mixed (unfilled Grassland bars) (a); andFescue standardized Grassland (b DCP); Mixed (blue Grassland solid lines) (c);for the EOG of the grasslands of Moist Mixed Grassland (a); Fescue Grassland (b); Mixed Grassland (c); theand EOG Cypress of the Upland grasslands (d). *** of indicates Moist Mixed p < 0.001. Grassland (a); Fescue Grassland (b); Mixed Grassland (c); and Cypress Upland (d). *** indicates p < 0.001. d p and Cypress Upland ( ). *** indicates < 0.001.

Remote Sens. 2017, 9, 1258 14 of 21 Remote Sens. 20172017,, 99,, 12581258 1414 ofof 2121

Figure 10. Influence of precipitation on the adaptive shifts in SOG to drought of the grasslands of FigureFigure 10. 10Influence. Influence of of precipitation precipitation onon thethe adaptive sh shiftsifts in in SOG SOG to to drought drought of of the the grasslands grasslands of of Moist Mixed Grassland (a); Fescue Grassland (b); Mixed Grassland (c); and Cypress Upland (d). *** MoistMoist Mixed Mixed Grassland Grassland ((aa);); Fescue Grassland Grassland (b ();b );Mixed Mixed Grassland Grassland (c); and (c); andCypress Cypress Upland Upland (d). *** ( d). indicates p < 0.001. ***indicates indicates pp < <0.001. 0.001.

FigureFigureFigure 11. 11.11.Influence InfluenceInfluence of ofof precipitation precipitationprecipitation ononon thethe adaptiveadaptive shiftsshifts shifts inin in EOGEOG EOG toto to droughtdrought drought ofof of thethe the grasslandsgrasslands grasslands ofof of MoistMoist Mixed Mixed Grassland Grassland ((aaa););); Fescue Fescue GrasslandGrassland Grassland ((b (););b );MixedMixed Mixed GrasslandGrassland Grassland ((cc);); and (andc); andCypressCypress Cypress UplandUpland Upland ((d).). ****** ( d). ***indicatesindicates indicates ppp << < 0.0010.001 0.001.

Remote Sens. 2017, 9, 1258 15 of 21

Precipitation and potential evapotranspiration are the two variables used to determine the anomaly of climatic water balance [62]. Compared to potential evapotranspiration, which is estimated from temperature, sun hours, solar radiation, etc. [85], the amount of precipitation is easy to measure and often paralleled with temperature when assessing the grassland phenology response to climate change and variability [73,76,86,87]. Some studies have explored the underlying mechanism of the impact of precipitation on vegetation phenology and state that spring rainfall may trigger an increase in nutrient levels for the start of vegetation growth [88]. Our research investigates the impact of precipitation on grassland phenology from a drought-related perspective, accounting for the influence of precipitation on the shortage of different water sources for the start and end of the growing season of prairie grasslands. First, we verified that the interannual variation in precipitation is a dominant contributing factor to the temporal pattern of drought in the prairie ecozone at various time scales, with different ending months (Figures8 and9). It is notably difficult to identify the meteorological cause of drought in this area [37]. Second, we interpreted the impact of precipitation on drought-induced shifts in grassland phenology by determining the directions of the correlations between DCP and drought-induced shifts in SOG and EOG. We also determined the rates of change of the drought-induced shifts related to DCP. Hence, the interaction between precipitation and other climatic variables related to the drought conditions should be considered when assessing the impact of precipitation on grassland ecosystem and may help understand how precipitation triggers the shifts in the SOG and EOG of grasslands.

4. Conclusions Our research investigates the temporal evolution of grassland phenology in relation to preceding droughts in the semi-arid-to-sub humid Canadian prairies from 1982 to 2014. The SOG and EOG of the prairie grasslands were extracted from GIMMS NDVI3g, and the results showed that most prairie grasslands did not demonstrate consistent changes in either SOG or EOG over our study period. We evaluated the impact of drought conditions (SPEI) on the interannual variation in SOG and EOG. Through the correlation analysis between SOG/EOG and all drought events over the preceding 12 months, we found that the preceding droughts are moderately significant factors for the interannual changes of grassland phenology, accounting for 14–33% and 26–44% of the year-to-year variability of SOG and EOG respectively, and fewer water deficits would favour an earlier SOG and delayed EOG. Precipitation is a critical climatic factor in determining the severity of drought. Based on the strong correlations between the DCP and DSPEI, we concluded that the temporal changes of DCP dominated the climatology of DSPEI and thus the drought-induced shifts in SOG and EOG. While the increasing temperature under global warming is considered the most critical climatic driver to alter the land surface phenology, it is interesting to retrieve inconsistent long-term phenology response related to drought and precipitation from the semiarid-to-sub humid grasslands. In addition, we identified significant subtrends across the time series that do not contain overall significant trends (Figure A3) and revealed the different dominant drought events for grassland phenology among different ecoregions (Figures4 and5). To understand the complex grassland phenology response to climate change, future work is needed to investigate the correlation between phenological metrics of prairie grasslands and climatic variables, including temperature, precipitation, and drought, over shorter periods (e.g., a decade) for each ecoregion.

Acknowledgments: This work is supported by the Department of Geography & Planning and College of Graduate and Postdoctoral Studies of the University of Saskatchewan, the Canadian Natural Science and Engineering Research Council (NSERC) Discovery Grant: Hydrological thresholds in depression storage and contributing area for low-relief landscapes and NSERC project: Integrating measures of grassland function using Remote Sensing. Author Contributions: Xulin Guo and Lawrence Martz designed the research. Xulin Guo contributed the data processing and analysis. Tengfei Cui processed the data, and conceived and wrote the manuscript. Conflicts of Interest: The authors declare no conflicts of interest. Remote Sens. 2017, 9, 1258 16 of 21

Remote Sens. 2017, 9, 1258 16 of 21 AppendixRemote Sens. A 2017, 9, 1258 16 of 21 Appendix A Appendix A

FigureFigure A1. A1.Evolution Evolution of of climatic climatic variables variables ofof thethe prairieprairie ecozone ecozone from from 1901 1901 to to2014. 2014. The The blue blue dots dots Figure A1. Evolution of climatic variables of the prairie ecozone from 1901 to 2014. The blue dots denotedenote the the region-wide region-wide average average ofof daily mean mean temperature temperature (a) (anda) and annual annual precipitation precipitation (b) while (b) the while denotegrey horizontal the region-wide bars are average one standard of daily deviation mean temperature of the region-wide (a) and annualaverages. precipitation The changes (b )of while climatic the the grey horizontal bars are one standard deviation of the region-wide averages. The changes of greyvariables horizontal over the bars study are one period standard (1982–2014) deviation are ofoutlin the region-wideed with the redaverages. rectangles. The changes The black of climaticand red climatic variables over the study period (1982–2014) are outlined with the red rectangles. The black and variablesdashed lines over display the study the trendsperiod of(1982–2014) climatic variables are outlin overed 1901–2014with the red and rectangles. 1982–2014, The respectively. black and Thered red dashed lines display the trends of climatic variables over 1901–2014 and 1982–2014, respectively. dashedtemperature lines displayand precipitation the trends dataof climatic of the variables prairie ecozone over 1901–2014 are extracted and 1982–2014, from the Climaterespectively. Research The The temperatureUnit (CRU) TS and and v. 3.24precipitation precipitation high-resolution data data of ofgridded the the prairie prairie datasets. ecozone ecozone are are extracted extracted from from thethe Climate Climate Research Research UnitUnit (CRU) (CRU) TS v.TS 3.24 v. 3.24 high-resolution high-resolution gridded gridded datasets. datasets.

Figure A2. The region-wide average 1-, 3-, 6-, 12-, 18-, and 24-month SPEI of the prairie ecozone from FigureFigure1901 A2. to A2.The2014. The region-wide Negative region-wide SPEIs average average (red bars) 1-, 1-, 3-, indicates3-, 6-, 6-, 12-,12-, dry 18-, periods and 24-month 24-month while positive SPEI SPEI of ofSPEIsthe the prairie (blue prairie ecozonebars) ecozone indicate from from 19011901wet to 2014. periods. to 2014. Negative NegativeThe SPEIs SPEIs SPEIs over (red the(red bars)study bars) indicates periodindicates (1982–2014) drydry periods are while whileoutlined positive positive with SPEIs the SPEIs green (blue (blue rectangle. bars) bars) indicate indicate wet periods.wet periods. The The SPEIs SPEIs over over the the study study period period (1982–2014) (1982–2014) are outlined outlined with with the the green green rectangle. rectangle.

Remote Sens. 2017, 9, 1258 17 of 21 Remote Sens. 2017, 9, 1258 17 of 21 Remote Sens. 2017, 9, 1258 17 of 21

Figure A3. Significant trends in the temporal evolution of SOG of the grasslands in Mixed Grassland FigureFigure A3 A3.. SignificantSignificant trends trends in in the the temporal temporal evolution evolution of of SOG SOG of of the the grasslands grasslands in in Mixed Mixed Grassland Grassland from 1982 to 2014. fromfrom 1982 1982 to to 2014. 2014.

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