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. SWAP version 4; Theory description and user manual. Wageningen, Wageningen Environmental Research, Report 2780. 244 pp.; 57 fig.; 17 tab.; 312 ref. SWAP 4 simulates transport of water, solutes and heat in the vadose zone. It describes a domain from the top of canopy into the groundwater which may be in interaction with a surface water system. The program has been developed by Wageningen Environmental Research and Wageningen University, and is designed to simulate transport processes at field scale and during entire growing seasons. This is a new release with recent developments on atmosphere, soil water and crop growth interactions.This manual describes the theoretical background, model use, input requirements and output tables. • Acquisition, duplication and transmission of this publication is permitted with clear acknowledgement of the source.• Acquisition, duplication and transmission is not permitted for commercial purposes and/or monetary gain.• Acquisition, duplication and transmission is not permitted of any parts of this publication for which the copyrights clearly rest with other parties and/or are reserved.Wageningen Environmental Research assumes no liability for any losses resulting from the use of the research results or recommendations in this report. Wageningen Environmental Research Report 2780 | ISSN 1566-7197Photo cover: The picture on the front cover shows SWAP's core processes in the soil below a grass vegetation positioned in a rural area with different land uses. ContentsPreface 7 and hence the dynamics of light interception. During crop development a part of the living biomass dies due to senescence (Chapter 7).Grass growth is special: it is perennial, very sensitive to nitrogen, and grass is either grazed or mowed. Therefore SWAP includes a separate WOFOST module for grass, which simulates these special grass features (Chapter 7).SWAP simulates transport of salts, pesticides and other solutes that can be described with basic physical relations: convection, diffusion, dispersion, root uptake, Freundlich adsorption and first order decomposition. In case of advanced pesticide transport, including volatilization and kinetic adsorption, SWAP can be used in combination with PEARL. In case of advanced transport of nitrogen and phosphorus, SWAP can be used in combination with ANIMO or Soil-N (Chapter 8).SWAP may simulate soil temperature analytically, using an input sine function at the soil surface and the soil thermal diffusivity. In the numerical approach, SWAP takes into account the influence of soil moisture on soil heat capacity and soil thermal conductivity. The top boundary condition may include air temperatures or soil surface temperatures (Chapter 9).The snow module calculates the accumulation and melting of a snowpack when the air temperature is below a threshold value. The water balance of the snow pack includes storage, incoming snow and rain and outgoing melting and sublimation. Melting may occur due to air temperature rise or heat release from rainfall. When a snowpack is p...

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A : ME, model effi ciency coeffi cient.S S : V Z M Well-conceived and detailed simula on of soil-moisture processes is a prerequisite for accurate watershed-scale modeling of water quan ty and quality processes. For this purpose, Richards' equa on (and its extensions) is the conceptually preferable op on. Applying the equa on on the watershed scale, however, may overstretch available computer resources. At the other extreme, methods based on lumping are oversimplifi ed. Approaches are therefore needed that are effi cient and just accurate enough, and that provide the required detail in the ver cal column. We have developed a quasi-steady-state model that uses a sequence of steady-state water content profi les for performing dynamic simula ons. The appropriate profi les are-for each me level-selected on the basis of water balances at the aggregate scale of control volumes. The groundwater coupling scheme involves an itera on cycle for the phrea c storage coeffi cient. In the postprocessing stage, the values of state variables obtained using the coupled model are disaggregated, thus delivering pressure heads, moisture contents, and fl uxes at the detailed scale of compartments of a Richards-type model. The plausibility of the simplifi ed approach was tested by comparing its results to those of a Richards-type model. The results appear promising for at least three-quarters of the area of the Netherlands with a shallow groundwater eleva on (within 2 m of the soil surface) and a thin root zone (<0.5 m thick). Customizing the modeling method used to the situa on conserves computa onal resources, allowing more room for doing sensi vity analyses. This could be instrumental for quan fi ca on of model reliability.

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions.