An Integrated Catchment Model Applied to Chile, IIV Region : Bridging scales in irrigation efficiency

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1 An Integrated Catchment Model Applied to Chile, IIV Region : Bridging scales in irrigation efficiency Thorsten Arnold (University of Hohenheim, Germany)

2 Outline Objectives Contents Motivation: Research project and study region Context: Integration Model Framework Hydrolgogical model component WaSiM-ETH Model realization Conceptual difficulties (irrigation efficiency) The extended irrigation module Conclusion

3 CGIAR Challenge Program on Food & Water: Integrating Governance & Modeling (in irrigation agriculture areas) Analysis of multi-stakeholder governance structure Identification of stakeholders' problems, policy options and criteria for evaluating these policy options Integrated modeling system (MpMAS & WaSiM-ETH) Policy Pilot Studies in cooperation with stakeholders Consolidation and extension Present and visualize outputs of modeling systems in a form that is useful for the stakeholders Aim: Develop a planning support tool Demonstrate use of planning support tool within the planning process

4 VII Region of Chile (Talca Linares Parral) Maule catchment Mediterranean Climate Putagan, Ancoa, Achibueno and Longaví sub basins. Dry Summer & Rainy Winter Intensive irrigation (surface water) Water right and potential for trade in WR

5 The research area R Rio Rio Lo aga P ut Rio nc om illa ari n Rio Ach Rio ibueno Anc oa Rio Liguay Rio Longa ví

6 Integrated modeling of water, water use and impact on and impact of water users

7 From software-centered coupling..

8 ... to an Integrated Model System. Fully coupled model (MpMas + WaSiM-ETH) Multi-agent system (MpMas + EDIC) Few-agent model (MILP sensitivity analysis + Census data ) Water model (WaSiM-Eth extended: Hydrology + Irrigation) Sector routing model EDIC extended Hydrological model (WaSiM-Eth) Single-agent model (MILP sensitivity analysis) Integrated relational data base Irrigation model (Field balance model)

9 Models, data & flows Maps of ETpot, ETreal, etc From model: From data: Spatial Spatial allocation of allocation agricultural activities Water rights registries GIS database Topography Catchments Irrigation sectors Soil map Rivers and canals Land ownership... River flow Time Series Census data Survey 1996 Census 1996 Survey 2004/5 More Tables Sector X Activity Meteorlogical Time series Hydrological Time series Activity ActivityID FK CropID FK - SoilID Crop Crop ID FK WasimLU... Data Table FK IrrigTechID FK Labor FK Investments... Data Table Labor costs DataTable Investments Data Table More MPMAS Decision Matrix Objective OFV Function Coefficients Constraints Land use Irrigation EDIC Semi-distributed hydrological model, Irrigation module WaSiM-ETH Dynamic coupling Lumped routing model Vectors of river flows Technical Coefficients WASIM Land use DataTable Soil properties DataTable Irrigation Method DataTable Product Prices FK Crop / T.S. InputSeries Costs Time FK - Activity Time Series

10 The WaSiM-ETH model

11 WaSiM-ETH

12 WaSiM-ETH V2 Water flow and balance Simulation Model Distributed model Modular structure Mixture of physically based and conceptual approaches Irrigation module Input data Spatial data Temporal data

13 Wasim Viewer

14 Study region: Conceptualization, r le -S u al au loz M Me am MAULE R. So rp Zo na R R. y po Lle Lo ng -M el. Longaví R. a u g Li Melado R. Achibueno R. ia Ancoa R. r le ob R Canal Melado LONCOMILLA R. Putagán R.. R i ar Bureo Cree k Melado Canal External Inflows To Digua Reservoir Calibration Point

15 Study region:..., Data, Digital Elevation Models Rio Lo nc om i lla (Uribe, Arnold, 2008) Use and type of Soils ri Ra o i a nr nco aga Put io A o R i R ibueno Rio AchRio Liguay Irrigation Areas Rio Longa ví Properties and Water Right

16 Study region: subcatchment delineation,... (Uribe, Arnold, 2008)

17 Study region:... treatment of water rights and abstractions,...

18 and problems with original irrigation module... bugs, but also systematic problems occured... Drip irrigation lost to surface evaporation! In dry conditions, too often rivers restricting (irrigation water restriction)... but too much water in rivers!?!... and ET underestimated real Lack of data Numerical instabilities with daily time steps

19 Aim: Field level data Plant water requirement (with / without effective precipitation) Irrigation efficiency Irrigation water availability (from water right registries) Land use should be consistent with basin-level measurements River flows... at daily time steps, numerically stable.

20 Field scale: Processes & data mm every 5-10 days (rotation) Micro-scale topography of surface Standing water (puddles) affect water flows Soil properties & management practices relevant Consequences: I. High variability II. Difficulty to obtain data III. Difficulty to model (see Hansen 2007) IV. How to consider micro-meso gap?

21 Conceptualization of some modeling challenges

22 I Basin / Watershed Internal Reuse RI Sector 1 Return flow RF Internal Reuse RI Sector 2 O Q= I O 1 rf 1 ri

23 I Basin / Watershed Internal Reuse RI Sector 1 Return flow RF Internal Reuse RI Sector 2 O Hydrological Field model scale level scale Q= I O 1 rf 1 ri

24 Irrigation water & Water abstractions from rivers m3/sec Water applied at field level Water abstracted from river Farm-level Basin-level data data Data scale gap Internal reuse Qeff = I O 1 r eff scale Surface runoff Return flow Lateral (base) flow Groundwater replenishment Evapotranspiration 1 ha field Grid / HRU Sub catchment Cyclic reuse bellow smallest hydrologically modeled scale Basin scale

25 Numerical sensitivity: Water-limited production when using field-level data m3/sec River limitation: 6.5 m3/s = Irrigat. water abstraction Request: 10 m3/s Proportional reduction, Runoff factor 0.65 Drought and impact on plant water availability Groundwater recharge Runoff Lateral gw Lateral gw ET ET 1 ha field Grid / HRU Sub catchment Basin scale

26 ... Application of water: Pulsing or continuous? 50 mm for two days, 3 times a month (300 mm) 10 mm / day (300 mm)

27 Comparison Continuous (conceptual) real-world representation Pulsed irrigation (physical) Parameterization difficult: Spatial heterogeneity Non-constant soil porosity Soil surface (Infiltration / runoff) Percolation Parametrization of surface losses, runoff components, lateral flows Resolution sensitive and scale-variant (numerics of time steps and soil layerthickness) Rotational schemes (data-based or calibrated parameters?)

28 The extended Wasim Irrigation module

29 Solution Effective value reduction by 1.8 mm/day equivalent to 15 % area increase Daily time steps irrigable land! Application of (Estimation using EDIC model) effective water quantity to reduce numerical errors (non-physical) parametrization of surface runoff share, percolation

30 Direct injections of irrigation water Sprinkler share Topsoil share Runoff share Precipitation Transpiration Inter- from roots ception Surface Evaporation Infiltration Surface runoff Root zone Percolation share Capillary Richard's rise Equation Percolation Saturated zone Aquifer recharge (groundwater model)

31 Wettening process

32 ,5 0,45 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0 Soil water content, 1/1l] Original model Soil moisture profile with furrow irrigation Daily irrigation with 27 mm during first weeks in january Modified model [date]

33 Further improvements... bugs... Special case handlings... Reference-surface with Kc factors The river irrigation restrictions Combined surface-ground water modelling

34 Calibration & Validation

35 Indicative model results

36 Some results... With best calibration, 13% of the applied irrigation water returns as surface flows, 10% as base flow 13% internal reuse correction of farm-level data 5% losses (deep aquifer?) Ground water interaction surface water irrigation elevates the ground water table by m after winters with strong rainfalls, difference temporarily decreases to meters GW-table back to natural level if 17% of the agricultural area shifted to gw-abstractions (shallow wells)

37 Conclusion Integration is learning Model integration revealed conceptual gaps between models and scales Thesis: Simple is beautiful For sub basin scale modeling, conceptual treatment (parametrization) of irrigation efficiency necessary and preferable Source code verified Modified WaSiM-ETH Irrigation module robust Surface water Groundwater interactions Ground water irrigation with threshold, watershedlevel management advisable

38 Discussion Improved ground water representation (GW-model) advisable Scale issues for integrating field models and basin models? Publication? How to integrate irrigation module into WaSiM-ETH source code?

39 The End! Thank you,

40 Irrigation water & Effective efficiency m3/sec Farm-level Basin-level data data Effective water applied = at field level Water abstracted from river Cyclic reuse bellow smallest hydrologically modeled scale Scale gap ha field Grid / HRU Sub catchment Basin scale