Representing the Integrated Water Cycle in Community Earth System Model

Representing the Integrated Water Cycle in Community Earth System Model Hong-Yi Li, L. Ruby Leung, Maoyi Huang, Nathalie Voisin, Teklu Tesfa, Mohamad Hejazi, and Lu Liu Pacific Northwest National Laboratory 1

Water in the human-earth system Water underlies and influences many important climate processes and feedbacks a leading cause of uncertainty in projecting future climate Water vapor and cloud feedback Snow-albedo feedback Aerosol-cloud interactions Carbon-water interactions Water is essential for energy systems, ecosystem services, and a wide range of life sustaining and other critical human activities Global and regional water cycles are influenced by natural processes as well as the human systems how will they co-evolve in the future? 2

Processes in the integrated water cycle Challenge: How to represent the multi-scale, dynamic interactions among the atmosphere, terrestrial, and human systems 3

Modeling the integrated water cycle Objectives Represent the dynamic interactions between the human and earth systems and their influence on the water cycle Use the models to investigate the nexus of climate, energy, water, and land under climatic and societal changes for sustainable energy and water in the future Approach Improve model scalability to address the multi-scale atmospheric and terrestrial water cycle processes Add human components (water management, water use, water demand) of water cycle processes in CESM 4

Model Structure CESM Atmospheric forcing CAM Flux Coupler GCAM CLM Surface fluxes POP Atmosphere and ocean boundary conditions RESM WRF Atmospheric forcing R-GCAM Flux Coupler Surface fluxes CLM ROMS Weather Weather River Routing/ Water Management Electric Infrastructure/ Electricity Operations 5 Land Use

CLM coupled with river routing and water management Improve and add new capabilities in Community Land Model (CLM) to represent hydrology and human water cycle interactions at multiple time and space scales Precipitation CLM Hydrology River Routing Model Coupled CLM-Routing Simulation Evaporation Transpiration Throughfall Sublimation Melt Evaporation Infiltration Surface runoff Soil Aquifer recharge Water table Saturated fraction Water Management Model Irrigation water demand Reservoir operations 6

Runoff parameterizations in LSMs CLM4 Based on TOPMODEL Assumptions: High-resolution topographic data are available; Subsurface flow is topographic driven. A quasi-steady state to approximate saturated zone dynamics; Recharge to ground water is spatially uniform; Conceptual VIC Limited assumptions: land surface, and therefore surface runoff generation, is heterogeneous; Subsurface flow is a nonlinear function of deep-layer water availability Calibration of parameters are recommended These assumptions are invalid, e.g., over flat terrain or arid regions 7

VIC as an hydrologic option in CLM To be released in Summer 2013 Li et al., 2011 8

Latent heat (W/m 2 ) Comparison of observed and simulated annual LH over MOPEX basins: CLM, CLMVIC, and other NLDAS models 120.0 Mean latent heat (2000-2007) 100.0 80.0 60.0 CLM (50.6) CLMVIC (48.1) 40.0 NOAH (57.0) VIC (62.1) 20.0 MOSAIC (80.1) 1:1 line MODIS (46.2) 0.0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 MODIS latent heat (W/m 2 ) 9

Runoff schemes affect C cycle modeling through interactions among water, energy, and C cycles Lei, H., M. Huang, L.R. Leung, et al. under review in JGR-Biogeosciences Relative difference: -20.4% (CLM4VIC CLM4) Both structural and parameter uncertainties in the runoff generation schemes can lead to large uncertainty in carbon modeling, highlighting the significant interactions among the water, energy, and carbon cycles and the need for improving hydrologic parameterizations in land surface models. 10 MODIS: 112 Pg C/year CLMVIC: 114 Pg C/year CLM4.0: 143 Pg C/year

Enhancing the CLM spatial structure Subbasins vs lat/lon grids as computational units Subgrid structure: PFT/elevation, permanent channel/floodplain Subgrid atmospheric forcing 3D radiative transfer in mountains Subbasin representation Grid cell/subbasin Landunits Columns Glacier Wetland Vegetated Lake Urban Floodplain Upland Permanent channel PFTs/Ele PFTs vation 11

Comparison of grid vs subbasin representations Land surface heterogeneity such as topography has a dominant influence on hydrological processes Using subbasins as the computational units eliminates the needs to represent the redistribution of soil moisture between units and improve the accuracy for estimating the topographic index used in the TOPMODEL parameterizations of surface and subsurface runoff Grid-based representation (CLM) Subbasin-based representation (DCLM) 1 o 0.5 o 0.25 o 0.125 o 12

The subbasin representation improves scalability Subbasin (DCLM) Grid (CLM) Runoff Latent heat flux Model skills (MAE) at different resolutions are more strongly correlated and model skill increases systematically with resolution in the subbasin representation but not the grid representation 13

Model for Scale Adaptive River Transport (MOSART) Conceptualized network Hillslope routing to account for event dynamics and impacts of overland flow on soil erosion, nutrient loading etc.; Sub-network routing: scale adaptive across different resolutions to reduce scale dependence; Main channel routing: explicit estimation of in-stream status (velocity, water depth etc). 14 (Li et al., JHM, 2013, in press) 14

Case Study: Columbia River Basin 15 Daily runoff generation from Variable Infiltration Capacity model (VIC) at 1/16 degree resolution Off-line evaluation against monthly naturalized streamflow data at selected major stations

Model for Scale Adaptive River Transport (MOSART) MOSART is more skillful in simulating streamflow compared to RTM 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 NS coeff. for monthly mean streamflow grid based representation DALLE ICEHA PRIRA CHIEF BROWN WANET CORRA ARROW Q_RTM(1/2) Q_RTM(1/4) Q_RTM(1/8) Q_RTM(1/16) Q_MOSART(1/2) Q_MOSART(1/4) Q_MOSART(1/8) Q_MOSART(1/16) Q_VIC(1/16) 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 NS coeff. for monthly mean streamflow -- subbasin based representation DALLE ICEHA PRIRA CHIEF BROWN WANET CORRA ARROW Large drainage area Small drainage area RTM(1/2) MOSART_grid(1/8) MOSART_subbasin VIC(1/16) 16

Model for Scale Adaptive River Transport (MOSART) MOSART reproduces monthly variation of channel velocity with minimum calibration 17

Model for Scale Adaptive River Transport (MOSART) Coupling MOSART to CLM4 Grid-based representation: replacing RTM with MOSART, keeping remapping and parallel algorithms Subbasin-based representation: one-one mapping between CLM and MOSART grids Supporting global multi-resolution database Hydrography parameters (flow direction, channel length and slope etc.) directly derived from high resolution DEM (1km) at 1/16, 1/8, ¼, ½, 1 and 2 degree resolutions Other parameters (channel geometry and Manning s coefficients) derived based on landcover and empirical Hydraulic Geometry relationships Global testing using NCAR benchmarking case (I-2000 ) Global run done for 1948-2004 period (spatial resolution of CLM4 0.9*1.25 degree, MOSART 0.5 degree)

Mean of simulated flow (m 3 /s) DRT estimated upstream area (km 2 ) Global testing/evaluation of CLM4-MOSART -- Evaluation with GRDC observed streamflow 1.0E+07 1.0E+06 1.0E+05 1.0E+06 1.0E+04 Preservation of upstream area Preservation of water balance 1.0E+03 1.0E+05 1.0E+02 1.0E+01 1.0E+04 1.0E+00 1.0E+04 1.0E+05 1.0E+06 1.0E+07 1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 GRDC provided upstream area (km 2 1.0E+05 1.0E+06 ) Mean of observed flow (m 3 /s) 1129 GRDC stations were geo-referenced to the new river network (drainage area error no more than 10%) NSC > 0 for 470 out of 1129 GRDC stations for monthly streamflow 19

Couple riverine biogeochemistry into MOSART Water CLM Sediment Nutrient Water column Water column Inundated area Active layer Active layer River bank River bank Hillslope routing 20 Sub-network routing Main channel routing

Water management model (Voisin et al. 2013, HESS, submitted) Designed for full coupling in an earth system models Assume no knowledge of future inflow Use generic operating rules 21

Water management model CLM Natural flow in each units WRM Regulated flow Total water demand Routing model Reservoir dependency 22

Natural and regulated flow Combining flood control and irrigation objectives in operating rules best capture the observed regulated flow in the Columbia river basin 23

Reservoir storage and supply deficit Reservoir storage is only reproduced using operating rules that combine flood control and irrigation priorities At the American Falls, supply deficit is related to groundwater use 24

Integrating with IAM: Water market and water demand Land use Water demand for various sectors driven by energy demand, GDP, and agricultural land demand is simulated by IAM (GCAM) GCAM also solves the energy market, land market, and water market simultaneously to establish prices and shares by source Water demand from sectors other than agriculture WRM CLM Natural flow in each units Regulated flow Routing model 25

Summary On hydrologic modeling and model scalability: Subbasin representation offers some advantage in scalability The new river routing model (MOSART) represents hillslope, tributary, and channel routing and works well across scales A new CLM subgrid structure is being developed to account for subgrid PFT, elevation, and inundation On representing the dynamic interactions between human and earth systems: Developed a coupled system including CLM, river routing, and water management to represent irrigation water use and water management Ongoing research to represent water demand, water use, and water market using the coupled CESM - GCAM Global implementation and evaluation underway 26