Landscape Ecology and Ecosystem Science (LEES) Lab Department of Environmental Sciences The University of Toledo Regional Climate Change and Vegetation  Water Relations in Inner Mongolia Lessons Learned within “Effects of Land Use Change on the Energy and Water Balance of the Semi-Arid Region of Inner Mongolia, China” NASA’s NEESPI Nan Lu

Introduction The global climate has changed rapidly in the past century with the global mean temperature increased by 0.7  C (IPCC, 2007). Studies on how climate change drives changes in ecosystem processes (carbon, water, energy cycles, etc.) and their feedbacks are the current scientific frontiers (Lucier et al., 2006). It requires multiple techniques and analyses to understand these scale-dependent interactions and provide scientific foundation to policymakers.

The Northern Eurasia Earth Science Partnership Initiative (NEESPI), has been initiated by the NASA Land Use and Land Cover Change program (LULCC) to understand the feedbacks between climate, land surface processes and anthropogenic activities in Eurasia at latitudes > 40  N. Research Context of My Studies LEES Lab focused on “The Effects of Climate and Land Use Change on the Energy and Water Balance of the Semi-Arid Inner Mongolia”.

37°01’ - 53°02’ N 95°02‘ - 123°37' E Study Region: Inner Mongolia (IM) Area: 1.18 million km 2 Elevation: m Annual mean air temperature: 4˚C Annual precipitation: 308 mm Olson et al., 2001, John et al., ˚C, 450mm 2.6˚C, 350mm 6.8˚C, 200mm Sub-humid Semi-arid Arid

IM has experienced a significant land cover change during the last decades, due to climate change and anthropogenic influences related to population increase and socio- economic development (Zhang 1992; John et al. submitted). However, the changes in hydrological and energy processes in this regions under the frame of climate and land cover changes have not been well studied.

* Study 1, 2 * Study 3 * Study 4 My research as part of the proposed activities to quantify the water and energy cycles in IM (within the LEES-NASA project).

Rationale of My Dissertation To evaluate the long-term change of climate at regional, biome and local scales. To examine the spatial and temporal variability of climate extremes and the dependency on biogeographical features. To examine the dynamics of the major components of water balance and their interactions in three paired ecosystems. To evaluate the soil moisture-vegetation relationship at a large scale and develop empirical models for soil moisture downscaling. Chapter 2: Study One Chapter 3: Study Two Chapter 4: Study Three Chapter 5: Study Four Meteorological Records Spatial Interpolation Eddy- Covariance (EC) Remote Sensing Products

Climate Change in Inner Mongolia from 1955 to 2005 – Trends at Regional, Biome and Local Scales – Trends at Regional, Biome and Local Scales Study One

Introduction to Study One The rates of climate change are usually different among regions due to the varied land surface properties interacting with the climate in different ways (Meissner et al., 2003; Snyder et al., 2004; Dang et al., 2007 ). IM divides into three biomes: forest, grassland and desert (Olson et al., 2001), and each biome has different natural and anthropogenic ecology. However, how the climate change varies among the biomes in IM has not been investigated.

Objective To examine the climate changes over the past 50 years ( i.e., ) at regional, biome and local scales, with a particular focus on the differences among the biomes.

Climatic variables: Mean, max, min air temperature (T mean, T max, T min ) Diurnal temperature range (DTR) Vapor pressure deficit (VPD) Precipitation (PPT) Data source: 51 meteorological stations China Meteorological Data Sharing Service System Data analysis: Least square linear regression to examine the long-term trends T-test with repeated procedure (i.e., year repeated) to examine the differences between decadal means Methods (10) (23) (18)

Regional Climate Change and the Variations among the Biomes Year T mean T min DTRVPDPPT Region Forest Grassland Desert * means that the slope is significant. Capital letters A, B and C refers to the slope differences among biomes (p < 0.05)

Decadal Change of the Region Decade T mean (  C)T min (  C)DTR (  C) VPD (kPa)PPT (mm) (0.95)-3.0 (0.89)13.7 (0.10)0.57 (0.25)286 (0.24) (0.38)-2.5 (0.05) ↑13.2 (0.00) ↓0.58 (0.45)303 (0.21) (0.00)↑-1.5 (0.00) ↑12.8 (0.00) ↓0.59 (0.27)319 (0.51) (0.04)↑-1.3 (0.11)13.0 (0.17)0.64 (0.00) ↑290 (0.24) Arrows represent significant increasing or decreasing trends of a decade comparing to its proceeding one (p < 0.05).

Spatial Variability Spatial Variability Solid circle means the trend is not significant (p > 0.05); open circle of different sizes means the differences in the rate of changes. b) Rate of change in T min (˚C) /10yr – – – 1.00 d) Rate of change in VPD (kPa) /10yr – – – a) Rate of change in T mean (˚C) /10yr – – – 0.71 c) Rate of change in DTR (˚C) /10yr – – – -0.90

Conclusions IM has changed to a warmer and drier environment over the period of , with grassland and desert biomes experiencing stronger changes as compared to the forest biome. The changes in the climate varied significantly by location and over time.

Temporal and Spatial Variability of Climate Extremes in Inner Mongolia from 1955 to 2005 Study Two

Introduction to Study Two Climate extremes are often the most sensitive measures of climate change (IPCC 2001). Climate extremes can produce much stronger influences on ecological, societal and economic processes than means do (Katz et al., 1992; Beniston and Stephenson, 2004). However, our knowledge of the temporal and spatial variations in climate extremes is still not as conclusive as mean climate conditions.

Objectives To evaluate the variations in the climate extremes in time and space in IM. 1.To detect the differences in the long-term trends of climate extremes among the three biomes (i.e., forest, grassland and desert); 2.To examine the inter-decadal variations and shifts in space; 3.To explore the dependency of the spatiotemporal changes on geographical features such as latitude, longitude, and elevation.

Methods Extreme Climate Indices Frich et al. (2002) Abbr.DefinitionUnitSeason of a Year Extreme Temp Indices (ETI) ETR Extreme temperature range (intra-annual) : difference between the highest and lowest temperatures of a year CC summer & winter anomaly FD Frost days: No. of days (d) with absolute minimum temperature <0  C dwinter extreme low GSL Growing season length: period between when T mean >5  C for >5 days and T mean 5 days dspring & fall anomaly WN Warm night: No. of days with T min > 90 th percentile of daily minimum temperature dnighttime extreme low HWDI Heat wave duration index: maximum period > 5 consecutive days with T max above 5  C compared to daily T max normal days ddaytime extreme high Extreme Precp Indices (EPI) CDD Consecutive dry days: maximum number of consecutive dry days (R day < 1 mm) ddry season RR1 No. of precipitation days (precipitation ≥ 1 mm/day)d wet season SDII Simple daily intensity index: annual total of daily precipitation ≥ 1 mm / RR1 mm/d R5d Maximum 5 day precipitation (total)mm R75 Wet days: no. of days when daily precipitation exceeding the 1955– th percentile d

Statistical Analysis Least square linear regression to examine the long-term changes. T-test with repeated procedure to examine the differences in the indices among decadal means. Repeated regression analysis to examine the relationships between the magnitudes/trends of the indices and geographical features. Spatial interpolations in selected indices using the method of regularized spline with tension (RST).

Temporal Changes at Regional and Biome Scales - ETI Year ETRFDGSLWNHWDI R F G D B B A B A A A A A B A A A A A Region Forest Grassland Desert *means that the slope is significant. Capital letters A, B and C indicate the slope differences among biomes (p < 0.05)

Temporal Changes at Regional and Biome Scales - EPI Year R F G D CDDRR1SDIIR5dR75 Region Forest Grassland Desert *means that the slope is significant (p < 0.05).

Spatial Variation of Trends - ETI Circle size indicates the magnitude of the rate; black diamond indicates a significant change at p< ↑, 51↓ 51↑, 0↓ 49↑, 2↓ 36↑, 15↓

Spatial Variation of Trends - EPI Circle size indicates the magnitude of the rate; black diamond indicates a significant change at p< ↑, 36↓ 20↑, 31↓ 31↑, 20↓ 14↑, 37↓ 24↑, 27↓

Geographical Influences on Climate Extremes Magnitude vs. longitude, latitude & elevation Trend vs. longitude, latitude & elevation * p< 0.05, ** p<0.001, *** p< Longitude gradient (from east to west): the warm and dry extremes increased; the cold and wet extremes decreased. Latitude gradient (from south to north): warm extremes decreased; cold extreme increased. PPT days increased and PPT density decreased. Elevation: similar to latitude

Spatial Interpolation - Decadal Means ETI EPI

Conclusions The hot extremes have increased and the cold extremes have decreased in IM in the past 50 years. The most significant changes occurred in the grassland and desert biomes. The dry or wet extremes had no significantly changes in the region, with high temporal and spatial variability and inconsistent differences among the biomes. With increasing longitude, the climate was getting warmer and drier; with increasing latitude or elevation, the climate was getting colder. The precipitation days increased but precipitation density decreased. The trends in the extreme indices were mostly independent of the geographical gradients.

Potential Effects of Climate Change on the Ecosystems in IM The warming and drying climate may affect ecosystems in various aspects in IM, such as reducing vegetation production and crop yield (Hou et al., 2008), reducing biodiversity (John et al., 2008) and aggravating desertification (Gao et al., 2003). Ecosystem processes (land cover change) and climate feedbacks: For example, a positive feedback between the warming-drying climate and decrease in ecosystem carbon storage.

Evapotranspiration and Soil Moisture Dynamics in Three Paired Ecosystems in Semi-arid Inner Mongolia Study Three

Introduction to Study Three In semi-arid and arid regions, evapotranspiration (ET) is the dominant component of water balance (Kurc and Small, 2004; Huxman et al., 2005). Precipitation pulses control the dynamics of ET and the physiological responses of plants (Noy-Meir, 1973; Schwinning and Sala, 2004).

Cultivation and grazing are the two representative anthropogenic disturbances in IM. Trees are naturally distributed only in the scattered areas with shallow groundwater in the semi-arid IM, but poplars were planted as fast-growing woods to combat desertification in IM. The disturbances (or land cover change) are expected to alter ET, vertical distribution of soil water, and ET-soil water interactions due to the changes in species composition, vegetation cover and soil properties (Grayson and Western, 1998; Zhang and Schilling, 2006).

Objectives To evaluate the effects of three types of anthropogenic disturbances on: 1.the magnitude and temporal dynamics of ET; 2.the interaction between ET and soil water content; 3.the relative contribution of soil water storage (  S) from different soil layers to ET. I hypothesize that cultivation, grazing and tree plantation have significant influences on the water cycles due to the changes in vegetation and soil properties.

Ecosystem-based Observations in Three Paired Sites Xilinhot Fenced Grassland (Xf) Xilinhot Grazed Grassland (Xd) X Fenced in 1999 Kubuqi Poplar Plantation (Kp) Kubuqi Shrubland (Ks) K Planted in 2003 Duolun Cropland (Dc) Duolun Grassland (Ds) D Reclaimed from 1970s Disturbed vs. natural

Methods Latent heat flux (LE), net radiation (Rn): EC system Soil heat flux (G): HFT-3 heat plates Air temperature (Ta) and relative humidity (Rh): HMP45AC probes Precipitation (PPT): TE525 tipping bucket rain gauge Wind speed (u): propeller anemometer (CSI) Volumetric water content (VWC): EasyAC50 probes (at 0-10, 10-20, 20-30, cm) Leaf Area Index (LAI): portable area meter FAO Penman-Monteith equation: Water balance: PPT = ET +  S + R or PPT – ET =  S + R R – water residual

SiteSoil type Bulk density (g cm-3) * Dominant speciesTa (˚C)VPD (kPa)LAI max DsChestnut 1.38 Stipa Krylovii, Artemisia frigida DcChestnut 1.24 Triticum aestivum KsSandy soil - Artermisia sp KpSand - Populus sp XfChestnut 1.22 Stipa grandis, Leymus chinensis XdChestnut 1.33 Stipa grandis, Artemisia frigida Site Characteristics * upper 20 cm of soil

Seasonal Changes of PPT, ET and Water Yield R 2 Xf: Xd: PPT (mm) PPT-ET (mm) ET (mm) R 2 Ds: Dc: R 2 Ks: Kp:

Cumulative PPT, ET (mm) Date Cumulative PPT, ET and  S SitePPTETPPT-ETET/PPT Ds Dc Ks Kp Xf Xd Cumulative  S, R (mm) Date

Effects of VWC on ET and ET/PET Soil layer (cm) AllNRAllNRAllNRAllNRAllNRAllNR Ds ETDc ETKs ETKp ETXf ETXd ET n/a Ds ET/PETDc ET/PETKs ET/PETKp ET/PETXf ET/PETXd ET/PET n/a All: all observations included, NR: observations during rainy periods excluded

Relative Contribution of ∆S in Soil Vertical Profile to ET ∆S from 0-10, 10-20, 20-30, cm soil contributed varied percents of water to total ET at different site: –Ds: 40%, 24%, 6%, and 0% (66%) –Dc: 15%, 10%, 5% and 11% (42%) –Ks: 16%, 15%, 6% and 0% (37%) –Kp: 3%, 0%, 0% and 0% (3%) –Xf : 38% (>38%) –Xd: 27% (>27%)

Correlation Between Root Biomass and Relative Contribution of ∆S to ET R 2 =0.72, p<0.01

Pattern of water flow through root system during day and night (Caldwell, 1988). Hydraulic Lift Hypothesis

Conclusions Cultivation and grazing tended to decrease ecosystem ET of the growing season due to the decreased ∆S in the upper soil layers where the roots were mainly distributed. Poplar plantation increased ET most probably because the poplars accessed the groundwater by the deep roots. Changes in growing length and LAI also accounted for the ET difference between sites.

Downscaling AMSR Soil Moisture Using MODIS Indices in Semi-arid Inner Mongolia Study Four

Introduction to Study Four Spatial variability of available soil moisture (Ms) is the key factor influencing vegetation distribution, ecosystem structure, function and diversity (Grayson et al., 1997; Yeakley et al., 1998; Baudena et al., 2007). The precision of current spatial models to simulate carbon, energy and water fluxes are mostly poor due to the lack of spatial Ms data. The errors in Ms estimations contributed substantial uncertainties to model output (Xiao et al., 1997; Zhang et al., 2009).

Conventional Methods Point measurements: predominantly developed for applications in agriculture to understand field-scale soil water dynamics, such as time-domain reflectometry (TDR) techniques. Remote sensing technology: developed for understanding the hydrology of land–surface–atmosphere interactions, especially at river basin, continental, and global scales (Kerr et al., 2001).

Gaps in Ms Database The current techniques of Ms measurement have limitations in providing sufficient spatial resolution or coverage of intermediate scales (Qiu et al., 2000; William et al., 2003; English et al., 2005). It is pertinent to bridge between the Ms measurements and data requirements in ecosystem and regional studies. Advanced Microwave Scanning Radiometer - EOS (AMSR-E) (C band, 6.9 GHz):  Global coverage  Spatial resolution of 25 km

Objectives (1) To evaluate the relationship between AMSR-E derived Ms and MODIS-derived indices in three land use/cover (LULC) types in semi-arid IM; (2) To investigate the capability of MODIS products (500 m or 1000 m) as proxies of AMSR Ms so that finer-resolution Ms can be estimated

Methods Excluded grids of cropland cover > 60% & NDVI > 0.5 (Jackson, 2002). LULCNDVIEVINDSVILST 1 km 25 km Class definition: X > 50% of the grid area

Statistical Analysis ANOVA with repeated procedure to test Ms differences among LULC types. Non-intercept linear regression analysis between Ms and the grid-mean EVI and NDSVI (VI). Paired t-test for testing the differences of Ms-VI regression slopes. Multivariate stepwise regression: Ms = f (EVI, EVI sd, NDSVI, NDSVI sd, LST, LST sd )

07/22/04 06/18/04 05/11/04 04/28/04 …… Three Ms images were randomly selected for each month (one for every ten days) from April to October in 2004 (21 in total); NDVI, EVI, NDSVI and LST products were selected according to the dates of Ms. Data Selection

Ms in Three LULC Types G – grassland S – shrubland C – cropland SeasonSpringSummerFall Land coverGSCGSCGSC N Ms0.11 b 0.09 c 0.12 a 0.11 b 0.09 c 0.14 a 0.10 b 0.07 c 0.12 a Ms sd Ms max Ms min

Ms – VI Relationship K’: non-intercept slope of the linear regression between Ms and (a) EVI and (b) NDSVI

Effects of Temperature on K’ Kg’(EVI): 36% (p=0.005) Ks’(EVI): 9% (p=0.30) Kc’(EVI): 20% (p=0.04)

Empirical Models SeasonSpringSummerFall Land coverParameterPartial R 2 ParameterPartial R 2 ParameterPartial R 2 Grassland NDSVI0.34NDSVI0.26NDSVI0.31 LST0.05EVI0.09LST0.08 EVI sd 0.03 Total R Total R Total R Shrubland LST0.11NDSVI0.14NDSVI0.40 EVI0.05LST0.04 LST sd 0.06 Total R Total R Total R Cropland NDSVI0.42NDSVI0.25NDSVI0.24 LST0.05EVI0.04LST0.03 LST0.05 Total R Total R Total R

Conclusions The Ms-EVI relationship varied over the growing season and among the LULC types. The Ms-NDSVI relationship was relatively constant; and NDSVI appeared to be the primary predictor of surface Ms for all three LULC types. The empirical models for predicting Ms using MODIS indices were plausible, which provided an insight to estimate finer- resolution Ms at a large spatial scale.

Summary: Lessons Learned from the Studies * Study 1, 2 * Study 3 * Study 4

Acknowledgements This research was conducted as part of the Northern Eurasia Earth Science Program Initiative (NEESPI) and supported by the National Aeronautics and Space Administration (NASA) and the US-China Carbon Consortium (USCCC). Collaboration institution: Institute of Botany, Chinese Academy of Science (IBCAS). Advisor: Dr. Jiquan Chen Committee members: Dr. Daryl Moorhead, Dr. Scott Heckathorn, Dr. Kevin Czajkowski, Dr. Asko Noormets and Dr. Ge Sun. Fellow lab mates: especially Dr. Burkhard Wilske, Ranjeet, Jessica, Jianye, Mike, Rachel and Gwen. Dr. James Harrell, Dr. Christine Mayer, Dr. Ann Krause, Dr. Elliot Tramer, Dr. Daryl Dwyer, Lisa, Dan, Malak, Zach, Chongfeng and Haiqiang. My family.

Thanks!

Spatial Changes by Decade ETI EPI ETR FD GSL R75 SDII R5d

R John, J Chen, N Lu, et al submitted “Land cover / land use change in Inner Mongolia: ” Land Use and Land Cover Change in IM

Microwave Remote Sensing Ms Passive microwave signal offers several advantages over other methods for remote sensing Ms (Draper et al., 2009).  The long wavelength can penetrate through cloud cover, haze and dust.  It has a direct relationship with Ms through the soil dielectric constant.  It has a reduced sensitivity to land surface roughness.