Hydroeconomic analysis framework for agricultural water management

Hydroeconomic analysis framework for agricultural water management Marco P. Maneta, PhD Geosciences Department The University of Montana, Missoula marco.maneta@umontana.edu October 4, 2013

Integrated hydroeconomic analysis Objectives How do droughts impact crop mix and water use? How does agricultural change impact water availability and other water uses? How do farmers respond to water policy? What water policy maximizes the social and economic benefits of irrigated agriculture while mitigating the negative impacts on other water users M. Maneta (UM) Hydroeconomic analysis October 2013 2 / 17

Integrated hydroeconomic model Market price of crops External price of inputs: - Price of fertilizers - Price of seeds - Price of hired labor - Price of energy - Price of water Hydroclimatic model Climate Flows Crop mix Evapotranspiration GWdemand SW demand Precipitation GW available SW available Social constraints: - Available labor Agroeconomic model Physical constraints: - Available land Gross revenue Production costs Policy constraints: - Water allocation rules - Environmental flow mandates - Nitrogen export limits - Subsidies on production - Subsidies on acreage - Minimum wages Production function Risk aversion costs Optimization variables: - Crop mix and acreage - Hired and family labor used - Water applied - Amounts of seeds used - Amount of fertilizer used - Amount of pesticides used - Capital - Energy/electricity used Optimization objectives Environmental Social Farmer revenues M. Maneta (UM) Hydroeconomic analysis October 2013 3 / 17

Economic model of agricultural production max net = X j p j q j (X i,j, Params i,j ) i c i X i,j 1 2 ψ jx 2 land,j M. Maneta (UM) Hydroeconomic analysis October 2013 4 / 17

Economic model of agricultural production max net = X j q j (X i,j, Params i,j ) = τ j ( i p j q j (X i,j, Params i,j ) i β i,j X ρ i,j ) ν ρ c i X i,j 1 2 ψ jx 2 land,j M. Maneta (UM) Hydroeconomic analysis October 2013 4 / 17

Economic model of agricultural production p j q j (X i,j, Params i,j ) i ( ) ν ρ ρ q j (X i,j, Params i,j ) = τ j β i,j X i,j i max net = X j c i X i,j 1 2 ψ jx 2 land,j M. Maneta (UM) Hydroeconomic analysis October 2013 4 / 17

Economic model of agricultural production p j q j (X i,j, Params i,j ) i ( ) ν ρ ρ q j (X i,j, Params i,j ) = τ j β i,j X i,j i Alfalfa Corn crop j land. X i,j = water...... input i x i,j max net = X j c i X i,j 1 2 ψ jx 2 land,j M. Maneta (UM) Hydroeconomic analysis October 2013 4 / 17

Economic model of agricultural production max net = p j q j (X i,j, Params i,j ) c i X i,j 1 X 2 ψ jxland,j 2 j i ( ) ν ρ ρ q j (X i,j, Params i,j ) = τ j β i,j X i,j i Alfalfa Corn crop j land. X i,j = water...... input i x i,j M. Maneta (UM) Hydroeconomic analysis October 2013 4 / 17

Hydrologic engine: HEC-HMS Simulation of water availability Water distribution and availability is simulated using HEC-HMS M. Maneta (UM) Hydroeconomic analysis October 2013 5 / 17

Remote Sensing of agricultural activity Landsat, MODIS Information on crop acreage, yield and evapotranspiration M. Maneta (UM) Hydroeconomic analysis October 2013 6 / 17

Recursive hydroeconomic model calibration Satellite data assimilation stage Information gets incorporated in the model as it becomes available. The model improves with time as more information is assimilated into the model Satellite Data: - Crop Acreage - Yield - Evapotranspiration Data Assimilation Framework Ensemble Kalman Filter Hydrologic Data: - Water available - Streamflows - Water quality - Diversion points - Well fields Agroeconomic model Additional Data: - Labor data - Irrigation technology - Crop Calendar Agronomic model parameter get sequentially updated with the latest observations M. Maneta (UM) Hydroeconomic analysis October 2013 7 / 17

Ensemble Kalman Filter...or how quantity can be a substitute of quality M. Maneta (UM) Hydroeconomic analysis October 2013 8 / 17

Test run Farm in Yolo county, CA Demonstration for a farm in California 610 ac commercial farm All crops under irrigation Farmer is not water constrained Four crops (Alfalfa, wheat, corn, and tomato) Three inputs (land, water, labor) land X i,j = water labor Alfalfa Wheat Corn Toms...... M. Maneta (UM) Hydroeconomic analysis October 2013 9 / 17

Results Data assimilation stage and parameter identification β Alfalfa 0.6 0.5 0.4 0.3 0.2 0.1 0.0 B land B water B labor Wheat Corn Tomato 0.6 0.5 0.4 0.3 0.2 0.1 0.0 β 0.74 0.74 0.72 0.72 σ 0.70 0.70 σ 0.68 0.68 0.66 0.66 30 30 25 25 τ 20 20 τ 15 15 10 0 5 10 15 20 Assimilation cycles 0 5 10 15 20 Assimilation cycles 0 5 10 15 20 Assimilation cycles 0 5 10 15 20 Assimilation cycles 10 M. Maneta (UM) Hydroeconomic analysis October 2013 10 / 17

Results Reproduction of baseline observations Probability Probability Probability 0.035 0.030 0.025 0.020 0.015 0.010 0.005 Alfalfa 0.05 0.04 0.03 0.02 0.01 Wheat 0.000 0.00 0.00 0.000 120 140 160 180 200 220 240 140 150 160 170 180 190 200 210 220 70 80 90 100 110 120 130 140 50 100 150 200 250 300 Land (ac.) Land (ac.) Land (ac.) Land (ac.) 0.0035 0.008 0.008 0.0045 0.0030 0.007 0.007 0.0040 0.0025 0.006 0.006 0.0035 0.005 0.005 0.0030 0.0020 0.0025 0.004 0.004 0.0015 0.0020 0.003 0.003 0.0010 0.0015 0.002 0.002 0.0010 0.0005 0.001 0.001 0.0005 0.0000 0.000 0.000 0.0000 200 400 600 800 1000 1200 1400 250 300 350 400 450 500 550 600 650 700 200 250 300 350 400 450 500 550 600 650 0 200 400 600 800 1000 1200 Water (cf/ac) Water (cf/ac) Water (cf/ac) Water (cf/ac) 0.0012 0.007 0.005 0.00020 0.0010 0.0008 0.0006 0.0004 0.0002 0.006 0.005 0.004 0.003 0.002 0.001 0.06 0.05 0.04 0.03 0.02 0.01 0.004 Corn 0.020 0.015 0.010 0.005 Tomato 0.0000 0.000 0.000 0.00000 500 1000 1500 2000 2500 3000 3500 4000 300 400 500 600 700 800 900 1000 200 300 400 500 600 700 800 900 1000 0 5000 10000 15000 20000 25000 Labor(hrs) Labor(hrs) Labor(hrs) Labor(hrs) 0.003 0.002 0.001 0.00015 0.00010 0.00005 M. Maneta (UM) Hydroeconomic analysis October 2013 11 / 17

Results Reproduction of baseline observations Probability Probability Probability 0.035 0.030 0.025 0.020 0.015 0.010 0.005 Alfalfa 0.05 0.04 0.03 0.02 0.01 Wheat 0.000 0.00 0.00 0.000 120 140 160 180 200 220 240 140 150 160 170 180 190 200 210 220 70 80 90 100 110 120 130 140 50 100 150 200 250 300 Land (ac.) Land (ac.) Land (ac.) Land (ac.) 0.0035 0.008 0.008 0.0045 0.0030 0.007 0.007 0.0040 0.0025 0.006 0.006 0.0035 0.005 0.005 0.0030 0.0020 0.0025 0.004 0.004 0.0015 0.0020 0.003 0.003 0.0015 0.0010 0.002 0.002 0.0010 0.0005 0.001 0.001 0.0005 0.0000 0.000 0.000 0.0000 200 400 600 800 1000 1200 1400 250 300 350 400 450 500 550 600 650 700 200 250 300 350 400 450 500 550 600 650 0 200 400 600 800 1000 1200 Water (cf/ac) Water (cf/ac) Water (cf/ac) Water (cf/ac) 0.0012 0.007 0.005 0.00020 0.0010 0.0008 0.0006 0.0004 0.0002 0.006 0.005 0.004 0.003 0.002 0.001 0.06 0.05 0.04 0.03 0.02 0.01 0.004 Corn 0.020 0.015 0.010 0.005 Tomato 0.0000 0.000 0.000 0.00000 500 1000 1500 2000 2500 3000 3500 4000 300 400 500 600 700 800 900 1000 200 300 400 500 600 700 800 900 1000 0 5000 10000 15000 20000 25000 Labor(hrs) Labor(hrs) Labor(hrs) Labor(hrs) 0.003 0.002 0.001 0.00015 0.00010 0.00005 M. Maneta (UM) Hydroeconomic analysis October 2013 12 / 17

Results Simulation of scenarios Test drive: New water allocation rules that results in: - Scenario 1: 30% reduction in water available - Scenario 2: 50% reduction in water available M. Maneta (UM) Hydroeconomic analysis October 2013 13 / 17

Results Impact of a reduced access to water M. Maneta (UM) Hydroeconomic analysis October 2013 14 / 17

Results Summary of impacts Baseline 30% reduction 50% reduction Water available 2300 1610 1150 Water used 2060 1610 1150 Shadow value $0.0 $9.00 $25.3 % loss net rev -2.76-11.3 % change hiring -11.7-28.9 M. Maneta (UM) Hydroeconomic analysis October 2013 15 / 17

Conclusions Hydroeconomic models can be a valuable tool to inform policy and water management M. Maneta (UM) Hydroeconomic analysis October 2013 16 / 17

Conclusions Hydroeconomic models can be a valuable tool to inform policy and water management Coupled with remote sensing in a data assimilation framework permits operationalization Hydroeconomic models may help develop water markets Assimilation of frequent RS data permits the detection of gradual changes in farming practices M. Maneta (UM) Hydroeconomic analysis October 2013 16 / 17

Conclusions Hydroeconomic models can be a valuable tool to inform policy and water management Coupled with remote sensing in a data assimilation framework permits operationalization Hydroeconomic models may help develop water markets Assimilation of frequent RS data permits the detection of gradual changes in farming practices Impact of water shortage on rural economies is not proportional to shortage amounts M. Maneta (UM) Hydroeconomic analysis October 2013 16 / 17

THANK YOU M. Maneta (UM) Hydroeconomic analysis October 2013 17 / 17