Effective soil hydraulic and transport properties!!!!!

Transcription

1 Parameter Identification of LargeScale SpatiallyVariable Vadose Zone Properties using Global Optimization: 5year Reconstruction of Salinity Changes in the SJV Jan Hopmans University of California, Davis, CA Gerrit Schoups and Chuck Young University of California, Davis, CA Jasper Vrugt University of Amsterdam, Netherlands Ken Tanji and Wesley Wallender University of California, Davis, CA San Joaquin Valley, SJV Spatial Scale Vadose Zone Flow & Transport Spatial & Time scales Dynamics? Plotinfiltration Core soil water retention Field scaleirrigation Pore scale immobile versus mobile soil water Characteristic Time Scale Watershedwater tables and stream flow/drainage Effective soil hydraulic and transport properties!!!!! San Joaquin Valley: Irrigated Agriculture 1

2 San Joaquin Valley: Irrigated Agriculture San Joaquin Valley: Issues Agricultural sustainability is threatened shallow water tables soil and groundwater salinization limits on drainage discharge from agriculture (selenium in Kesterson wetlands) Management options Groundwater pumping (subsidence, salinization of production wells) Drainage reuse Increase irrigation efficiency San Joaquin Valley: Issues Objectives Develop a subsurface water flow and salt transport model capable of predicting longterm impact of water and land management decisions on hydrologic system Groundwater levels, drainage Soil and groundwater salinization 5year reconstruction (simulation) of observed changes in groundwater levels, and soil and groundwater salinity at the regional scale 2

3 Regional flow domain (Belitz et al., 1993) of western San Joaquin Valley Regional VariablySaturated Flow and Transport Interfans Alluvial fans 1,4 km 2 13 Water Districts (WD) Optimization of regional variablysaturated water flow model 1,4 km 2 of western San Joaquin Valley to reconstruct historical soil salinity in the San Joaquin Valley for a 57year period Regional flow domain (Belitz et al., 1993) of western San Joaquin Valley 3DSimulation Model Beta version (MODFLOWBased) 1,4 km 2 13 Water Districts (WD) An Effective Numerical Approach for MultiScale Conjunctive Modeling of SurfaceGround Water Flow and Solute Transport 3

4 Model Calibration Procedure Shuffled Complex Evolution Metropolis (SCEM) Identify model calibration parameters; Simulate soil hydrology using MODHMS,; Optimize model parameters using SCEM, by matching observed with measured groundwater tables, groundwater pumping and drainage for 12year period; Use optimized parameters to simulate 5 year soil and shallow groundwater salinity; SCE Vrugt, Gupta, Bouten & Sorooshian WRR 23 Global optimizers Controlled random search Competitive population evolution Complex shuffling SCEM Result: Most likely parameter set, including its uncertainty, from posterior probability distribution Numerical grid for MODHMS MODHMS: Variablysaturated flow equation Horizontally Study area discretized into.5x.5 mi grid cells, i.e. typical field size Vertically Rootzone (2 m): 7 layers, each.3 m Semiconfined aquifer: 1 layers, variable thickness up to Corcoran clay K x k xx rw h + K x y k yy rw h + K y z k zz rw Sw Swr 1 Se = = for ψ < γ 1 S β wr 1 + ( αψ ) h Sw h W = n + SwSs z t t k = S b rw e Hydraulic parameters, estimated from neural network analysis, using Rosetta 4

5 Hydraulic properties Boundary conditions Rootzone (2 m) Estimated for each soil type using Rosetta Semiconfined aquifer (> 2 m) Spatial distribution of grid cell coarse fractions, f c α Districtscale data (annual): crop acreages, surface water deliveries Downscale to grid cells: (1) random crop assignment, (2) cellbycell annual water balance Irrigation requirement ETc R IR = Max, IE gs Determines K h, K v and S f K h,v ω = [ K ( )] ω f + K f 1 ω Sf = Sf, CfC+ Sf, F( 1 fc) C C F 1 C S f determines retention or yield (uniform values for α and β) Surface water applied Ground water applied SWd SW = IR IR GW = Max, d Scale to district quantities { IR SW DW} Available data Soil texture, irrigated area and Crop ET Calibration data Annual Drain water for BWD Annual Groundwater pumping for WWD Annual Groundwater table across region Initial condition Irrigated Area (%) Year 5

6 CALIBRATION PARAMETERSREGIONAL MODEL Historical Surface Water and Groundwater Use Parameter Description Units Min Max Prior Distrib f Kc Scaling factor K coarse.5 5 Calibration Period K F Hydraulic conductivity fine fraction m/yr.3 1. log values S y,c S y,f K Corc f ET Specific yield coarse fraction Specific yield fine fraction Hydraulic conductivity Corcoran clay ET correction coefficient m/yr log values Water depth (m) Groundwater Surface water IE shallow IE deep Maximum irrigation efficiency Minimum irrigation efficiency Year d e C d Drain depth Drain conductance m 1/yr drought Comparison of simulated with measured shallow water table Comparison of measured with simulated groundwater pumping in Westland s water district Number of grid cells with a shallow water table (< 2 m) Year Longterm trend of gradual increase in shallow water table area due to shift in irrigation water supply from pumped groundwater to imported surface water Decline in shallow water table in drought periods of late seventies and early nineties. Groundwater pumping (m/yr) Year Shows effect of droughts on groundwater pumping 6

7 Comparison of measured with simulated drainage in Broadview Water District, since drains were installed Spatiallydistributed leaching rates: Timeaveraged mean Standard deviation Drainage (m/yr) Year Shows effect of early nineties drought Next Step: Reconstruct Historical Soil and Groundwater salinity from 1942 Role of salt chemistry Couple MODHMS with UNSATCHEM and simulate simultaneous transport of 7 major ions: Ca, Mg, Na, K, HCO 3, SO 4 and Cl using 3D CDE Significant amounts of gypsum present; Gypsum acts as a source/sink of salts through mineral dissolution/precipitation; Nonsaline Na/Cl canal water turns into Na/SO4 saline drainwater; Dissolution of gypsum into Ca and SO4; Ca replaces Na on exchange complex (clay). 7

8 Role of salt chemistry Reconstruct Ground Water Table from 1952 Observed Relative Error (%) Average rootzone salinity after 1 years chemistry important Simulated 5 M M1 M2 M3 M4 M5 Level of model complexity Comparison of observed and simulated water table depth maps; brown: > 3 m, red: 163 m, yellow: 716 m, green: 37 m, light blue: 2 3 m, blue: < 2 m. Hydrology and salinity dynamics from Soil Chemistry 8

9 Soil Chemistry Irrigation Water salinities Type TDS SAR * Ca Mg Na Units mg/l (meq/l).5 meq/l meq/l meq/l DeltaMendota Canal (DMC) Na/Cl California Aqueduct Na/Cl Groundwater (subcorcoran) Na/SO 4 1, Rainfall Na/Cl San Joaquin River floodplain deposits north of Mendota Na/HCO Kings River floodplain deposits south of Mendota Na/HCO Panoche Creek Na/SO Reconstruct Root zone salinity dynamics Root zone salinity reconstruction Average root zone Shallow groundwater (6 m depth) Deep groundwater (26 m) TDS (mg/l) > 1,24 7,68 1,24 5,12 7,68 2,56 5,12 1,28 2,56 < 1,28 EC e (ds/m) > < 2 9

10 Regional trends in soil salinity Root zone salinity: 1992 Observed Simulated Initial high soil salinity Switch from gw to surface water droughts Shallow wt effect Continuous Gypsum dissolution TDS (mg/l) > 1,24 7,68 1,24 5,12 7,68 2,56 5,12 1,28 2,56 < 1,28 EC e (ds/m) > < 2 Shallow water table TDS: 1985 Predicted longterm trends TDS (mg/l) > 1,24 7,68 1,24 5,12 7,68 2,56 5,12 1,28 2,56 < 1,28 EC e (ds/m) > < 2 Observed Simulated Regional water table rises, as irrigation water is switched from local groundwater to imported canal water; Initial salt leaching, followed by increase in soil salinity because of development of shallow water table; Shallow groundwater salinity dynamics follows soil salinity trends, e.g. droughts; No changes in deep groundwater salinity; Salt chemistry (gypsum) was highly relevant. 1

11 Salinity Reconstruction TDS (mg/l) > 1,24 7,68 1,24 5,12 7,68 2,56 5,12 1,28 2,56 < 1,28 EC e (ds/m) > < 2 11