On this page a reference list is given of SWAP user manuals and scientific studies which employed SWAP or one of its predecessors.


1.    General reference to SWAP

2.    On the use of SWAP

3.    Soil water flow

4.    Evapotranspiration

5.    Irrigation management

6.    Drainage conditions

7.    Surface water management

8.    Plant growth

9.    Soil water extraction by roots

10. Soil moisture indicators for natural vegetations

11. Salinization

12. Solute transport

13. Soil water flow as affected by soil spatial heterogeneity

14. Sensitivity analysis

15. Regional analysis

16. Integration with other models


1       General reference to SWAP   (Back to top)

Kroes, J.G., J.C. van Dam, R.P. Bartholomeus, P. Groenendijk, M. Heinen, R.F.A. Hendriks, H.M. Mulder, I. Supit, P.E.V. van Walsum, 2017. SWAP version 4; Theory description and user manual. Wageningen, Wageningen Environmental Research, Report 2780.  https://library.wur.nl/WebQuery/wurpubs/fulltext/416321

Heinen, M., M. Mulder, J. van Dam, R. Bartholomeus, Q. de Jong van Lier, J. de Wit, A. de Wit, M. Hack – ten Broeke. 2024. SWAP 50 years: Advances in modelling soil-water-atmosphere-plant interactions. Agricultural Water Management 298, 108883. https://doi.org/10.1016/j.agwat.2024.108883

Van Dam, J.C., P. Groenendijk, R.F.A. Hendriks and J.G. Kroes, 2008. Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone Journal, 7, 640-653.  https://doi.org/10.2136/vzj2007.0060

Groenendijk, P., Boogaard, H., Heinen, M., Kroes, J., Supit, I., & Wit, A. De., 2016. Simulation of nitrogen-limited crop growth with SWAP / WOFOST. Report 2721. Alterra Rapport, 2721.  https://edepot.wur.nl/400458

Kroes, J.G., J.G. Wesseling and J.C. Van Dam, 2000. Integrated modelling of the soil-water-atmosphere-plant system using the model SWAP 2.0 an overview of theory and an application. Hydrological Processes, 14, 1993-2002.  https://onlinelibrary.wiley.com/doi/10.1002/1099-1085(20000815/30)14:11/12%3C1993::AID-HYP50%3E3.0.CO;2-%23/abstract

Van Dam, J.C., 2000. Field-scale water flow and solute transport. SWAP model concepts, parameter estimation, and case studies. PhD-thesis, Wageningen University, The Netherlands.  https://edepot.wur.nl/121243

2       On the use of SWAP   (Back to top)

Kroes, J.G., J.C. van Dam, P. Groenendijk, R.F.A. Hendriks and C.M.J. Jacobs, 2008. SWAP version 3.2. Theory description and user manual. Alterra-report 1649, 262 pp, Alterra, Research Institute, Wageningen, The Netherlands.  https://edepot.wur.nl/39776

Kroes, J.G. and J.C. van Dam (eds), 2003. Reference Manual SWAP version 3.0.3. Alterra-report 773, 211 pp, Alterra, Research Institute, Wageningen, The Netherlands.  https://edepot.wur.nl/35471

Huygen, J., J.C. van Dam, J.G. Kroes, 2000. Swap Graphical User Interface, User Manual, version January 2000, Alterra, Wageningen.

Kroes, J.G., J.C. van Dam, J. Huygen, and R.W. Vervoort, 1999. User’s Guide of SWAP version 2.0. Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment. Technical Document 48, Alterra Green World Research, Wageningen, Report 81, Department of Water Resources, Wageningen University, 127 p.  https://edepot.wur.nl/222773

Van Dam, J.C., J. Huygen, J.G. Wesseling, R.A. Feddes, P. Kabat, P.E.V. van Walsum, P. Groenendijk and C.A. van Diepen, 1997. Theory of SWAP version 2.0. Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment. Report 71, Subdep. Water Resources, Wageningen University, Technical document 45, Alterra Green World Research, Wageningen.  https://edepot.wur.nl/222782

3       Soil water flow   (Back to top)

Alves Rodrigues Pinheiro E., & Nunes M.R. (2023). Long-term agro-hydrological simulations of soil water dynamic and maize yield in a tillage chronosequence under subtropical climate conditions. Soil and Tillage Research, 229, art. no. 105654. https://doi.org/10.1016/j.still.2023.105654

Rab M.A., Nasta P., Armindo R.A., Beverly C.R., Reynolds W.D., & Romano N. (2023). Empirical equations for estimating field capacity in dryland cropping soils of southeastern Australia. Soil and Tillage Research, 234, art. no. 105816. https://doi.org/10.1016/j.still.2023.105816

Auwalu A., & Abdulkareem J.H. (2023). Plant Morphological Structures as Influenced by Soil Hydraulic Properties: A Review. Communications in Soil Science and Plant Analysis, 54 (21), pp. 2915 - 2938. https://doi.org/10.1080/00103624.2023.2253848

Turek M.E., De Jong van Lier Q., & Armindo R.A. (2022). Parameterizing field capacity as the upper limit of available water in bucket-type hydrological models. Computers and Electronics in Agriculture, 194, art. no. 106801. https://doi.org/10.1016/j.compag.2022.106801

Inforsato L., & de Jong van Lier Q. (2021). Polynomial functions to predict flux-based field capacity from soil hydraulic parameters. Geoderma, 404, art. no. 115308. https://doi.org/10.1016/j.geoderma.2021.115308

Mirbabaei S.M., Shabanpour M., van Dam J., Ritsema C., Zolfaghari A., & Khaledian M. (2021). Observation and simulation of water movement and runoff in a coarse texture water repellent soil. Catena, 207, art. no. 105637. https://doi.org/10.1016/j.catena.2021.105637

Mulder, M., Meijninger, W., & Hack-ten Broeke, M. (2021). Validatie Waterwijzer Landbouw; vergelijking modelresultaten Groen- monitor, GRAM en HELP. STOWA Rapport 2021-48. https://edepot.wur.nl/556066

Wang, X., Gao, R., & Yang, X. (2021). Responses of soil moisture to climate variability and livestock grazing in a semiarid Eurasian steppe. Science of the Total Environment, 781. https://doi.org/10.1016/j.scitotenv.2021.146705

Kokoreva A.A., Dembovetskiy A.V., Ezhelev Z.S., Bolotov A.G., Stepanenko V.M., Shishkin K.V., & Abramyan I.A. (2021). Simulating water transport in porous media of urban soil. IOP Conference Series: Earth and Environmental Science, 862 (1), art. no. 012042. https://doi.org/10.1088/1755-1315/862/1/012042

Turek, M. E., de Jong van Lier, Q., & Armindo, R. A. (2020). Estimation and mapping of field capacity in Brazilian soils. Geoderma, 376(June), 114557. https://doi.org/10.1016/j.geoderma.2020.114557

Kroes, J.G. (Joop), 2018. Soil hydrological modelling and sustainable agricultural crop production at multiple scales. PhD thesis. Wageningen University, The Netherlands. https://doi.org/https://doi.org/10.18174/458526

Kroes, J., Supit, I., Van Dam, J., Van Walsum, P., Mulder, M., 2018. Impact of capillary rise and recirculation on simulated crop yields. Hydrol. Earth Syst. Sci. 1–31. https://doi.org/10.5194/hess-2017-223

Leer-Groen, C. T. (Charlotte) van der. (2017). Water use in agriculture. MSc-thesis, TU Delft. Delft. https://www.oecd.org/environment/wateruseinagriculture.htm

Massop, H. T. L., Kroes, J. G., Vroon, H. R. J., & Mulder, H. M., 2014. Pilot SWAP berekening droogteschade. Vergelijking droogteschadeberekening volgens SWAP met de TCGB-tabel voor de waterwinning Vierlingsbeek. Rapport 2600. Alterra Rapport, 2600. In Dutch.  https://library.wur.nl/WebQuery/wurpubs/484563

Moene, A.F., and J.C. van Dam, 2014. Transport in the Atmosphere-Vegetation-Soil Continuum. Cambridge University Press, New York, 436 p.

Miegel, K., Bohne, K., Wessolek, G., 2013. Prediction of long-term groundwater recharge by using hydropedotransfer functions. Int. Agrophysics 27, 31–37. https://doi.org/10.2478/v10247-012-0065-z

Pollacco, J. A. P., & Mohanty, B. P., 2011. Uncertainties of Water Fluxes in SVAT Models Inverting Surface Soil Moisture and Evapotranspiration Retrieved from Remote Sensing. Vadose Zone Journal.  https://doi.org/10.2136/vzj2011.0167

Bonfante, A., Basile, A., Manna, P., & Terribile, F., 2011. Use of Physically Based Models to Evaluate USDA Soil Moisture Classes. Soil Science Society of America Journal, 75(1), 181.  https://doi.org/10.2136/sssaj2009.0403

Romano, N., Palladino, M., & Chirico, G. B. (2011). Parameterization of a bucket model for soil-vegetation-atmosphere modeling under seasonal climatic regimes. Hydrology and Earth System Sciences, 15(12), 3877–3893. https://doi.org/10.5194/hess-15-3877-2011

Bonfante, A., Basile, A., Acutis, M., De Mascellis, R., Manna, P., Perego, A., & Terribile, F., 2010. SWAP, CropSyst and MACRO comparison in two contrasting soils cropped with maize in Northern Italy. Agricultural Water Management, 97(7), 1051–1062.  https://doi.org/10.1016/j.agwat.2010.02.010

Jin, H., Zhongfu, W., & Shengxiu, G., 2011. Simulation of water dynamics of farmland in the piedmont plain of the Taihang Mountains in the North China Plain. Procedia Engineering, 12, 66–73.  https://doi.org/10.1016/j.proeng.2011.05.012

Baroni, G., Facchi, A., Gandolfi, C., Ortuni, B., Horeschi, D., & Van Dam, J. C., 2010. Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity. Hydrology and Earth System Sciences, 14, 251–270.  https://www.hydrol-earth-syst-sci.net/14/251/2010/

Groenendijk, P., van den Eertwegh, G. A. P.H., 2004. Drainage-water travel times as a key factor for surface water contamination. In: Feddes et al. (2004): Unsaturated-zone Model. Progress, Challenges Appl. 145, chapter 5.

Feddes, R.A., G.H. de Rooij and J.C. van Dam (editors), 2004. Unsaturated zone modeling. Progress, challenges and applications. Wageningen UR Frontis Series, volume 6. Kluwer academic publishers, Dordrecht, the Netherlands, 364 p.

Eitzinger, J., Trnka, M., Hösch, J., Žalud, Z., & Dubrovský, M., 2004. Comparison of CERES , WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modelling, 171, 223–246.  https://doi.org/10.1016/j.ecolmodel.2003.08.012

Feddes, R. A., & Raats, P. A. C., 2004. Parameterizing the soil - water - plant root system. In Wageningen Frontis Series (Vol. 6, pp. 95–141). https://doi.org/10.1016/0022-1694(76)90017-2

Kroes, J.G., J.G. Wesseling and J.C. Van Dam, 2000. Integrated modelling of the soil-water-atmosphere-plant system using the model SWAP 2.0 an overview of theory and an application. Hydrological Processes, 14, 1993-2002 (2000).  https://onlinelibrary.wiley.com/doi/10.1002/1099-1085(20000815/30)14:11/12%3C1993::AID-HYP50%3E3.0.CO;2-%23/abstract

Van Dam, J.C., and R.A. Feddes, 2000. Simulation of infiltration, evaporation and shallow groundwater levels with the Richards' equation. Journal of Hydrology, 233: 72-85.  https://doi.org/10.1016/S0022-1694(00)00227-4

Jong, R. de, and A. Bootsma, 1997. Estimates of water deficits and surpluses during the growing season in Ontario using the SWATRE model. Canadian Journal Soil Science, 77: 285-294.

Abenney-Mickson, S., A. Yomota, and T. Miura, 1997. Water balance of field plots planted with soybean and pumpkin. Transactions ASAE, 40: 899-909.

Hack-ten Broeke, M. J. D., & Hegmans, J. H. B. M., 1996. Use of soil physical characteristics from laboratory measurements or standard series for modelling unsaturated water flow. Agricultural Water Management, 29, 201–213.  https://ac.els-cdn.com/0378377495011900/1-s2.0-0378377495011900-main.pdf?_tid=66c1ec0a-51b0-11e7-9c30-00000aacb360&acdnat=1497520554_88b55e47497bfce0a4439debb3bfe651

Qureshi, A.S., and S.A. Hussain, 1996. Soil water simulations in a lysimeter. In ‘Sustainability of irrigated agriculture: crop-water-environment models’, R. Ragab, D.E. El-Quosy, B.J. van den Broek and L.S. Pereira (Eds.), Proc. ICID congress, Sept. 1996, Cairo, Egypt, p. 163-174.

Bastiaanssen, W.G.M., J. Huygen, J.K. Schakel, and B.J. van den Broek, 1996. Modelling the soil-water-crop-atmosphere system to improve agricultural water management in arid zones (SWATRE). In ‘Dutch experience in irrigation water management modelling’, B.J. van den Broek (Ed.), Report 123, Alterra Green World Research, Wageningen, p. 13-30.

Beekma, J., T.J. Kelleners, T.M. Boers, and Z.I. Raza, 1995. Applications of SWATRE to evaluate drainage of an irrigated field in the Indus Plain, Pakistan. In ‘Crop-water-simulation models in practice.’ Proc. ICID Congress 1993, The Hague, The Netherlands, p. 141-160.

Clemente, R.S., R. de Jong, H.N. Hayhoe, W.D. Reynolds and M. Hares, 1994. Testing and comparisons of three unsaturated soil water flow models. Agricultural Water Management, 25: 135-152.  https://ac.els-cdn.com/0378377494900418/1-s2.0-0378377494900418-main.pdf?_tid=a15360a6-51b0-11e7-b59f-00000aacb35d&acdnat=1497520652_71498202f80da7d5a221d15f364e1bd8

Feddes, R. A., Kabat, P., Bakel, P. J. T. Van, Bronswijk, J., & Halbertsma, J., 1988. Modelling soil water dynamics in the unsaturated zone - state of the art. Journal of Hydrology, 100, 69–111.  https://doi.org/10.1016/0022-1694(88)90182-5

Belmans, C., Wesseling, J. G., & Feddes, R. A., 1983. Simulation model of the water balance of a cropped soil: SWATRE. Journal of Hydrology, 63, 271–286.  https://doi.org/10.1016/0022-1694(83)90045-8

Feddes, R. A., Kowalik, P. J., & Zaradny, H., 1978. Simulation of field water use and crop yield. Simulation monographs. Retrieved from https://edepot.wur.nl/168026

4       Evapotranspiration   (Back to top)

Ali, A., Al-Mulla, Y. A., Charabi, Y., Al-Wardy, M., & Al-Rawas, G. (2021). Use of multispectral and thermal satellite imagery to determine crop water requirements using SEBAL, METRIC, and SWAP models in hot and hyper-arid Oman. Arabian Journal of Geosciences, 14(7).


Gelsinari, S., Pauwels, V. R. N., Daly, E., Van Dam, J., Uijlenhoet, R., Fewster-Young, N., & Doble, R. (2021). Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling. Hydrology and Earth System Sciences, 25(4), 2261–2277. https://doi.org/10.5194/hess-25-2261-2021

Wang, X., Li, L., Ding, Y., Xu, J., Wang, Y., Zhu, Y., Wang, X., & Cai, H. (2021). Adaptation of winter wheat varieties and irrigation patterns under future climate change conditions in Northern China. Agricultural Water Management, 243. https://doi.org/10.1016/j.agwat.2020.106409

Waqas, M. M., Hussain Shah, S. H., Awan, U. K., Waseem, M., Ahmad, I., Fahad, M., Niaz, Y., & Ali, S. (2020). Evaluating the impact of climate change on water productivity of maize in the semi-arid environment of Punjab, Pakistan. Sustainability (Switzerland), 12(9). https://doi.org/10.3390/su12093905

Van Den Eertwegh, G., Bartholomeus, R., De Louw, P., Witte, F., Van Dam, J., Van Deijl, D., … De Wit, J. (2019). Droogte in zandgebieden van Zuid-, Midden- en Oost-Nederland. Retrieved from https://library.wur.nl/WebQuery/wurpubs/558370

Hess, A.J., 2017. Rain Garden Evapotranspiration Accounting. PhD thesis, Villanova Pennsylvania. Villanova University.

Bartholomeus, R. P., Stagge, J. H., Tallaksen, L. M., & Witte, J. P. M., 2015. Sensitivity of potential evaporation estimates to 100 years of climate variability. Hydrology and Earth System Sciences, 19, 997–1014.  https://doi.org/10.5194/hess-19-997-2015

Monaco, E., Bonfante, A., Alfieri, S. M., Basile, A., Menenti, M., & De Lorenzi, F., 2014. Climate change, effective water use for irrigation and adaptability of maize: A case study in southern Italy. Biosystems Engineering, 1–18.  https://doi.org/10.1016/j.biosystemseng.2014.09.001

Liu, M., Bárdossy, A., Li, J., & Jiang, Y. (2012). Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns through radiation and wind. Hydrology and Earth System Sciences, 16(2), 357–373. https://doi.org/10.5194/hess-16-357-2012

Minacapilli, M., Agnese, C., Blanda, F., Cammalleri, C., Ciraolo, G., D’Urso, G., Iovino, M., Pumo, D., Provenzano, G., Rallo, G., 2009. Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models. Hydrology and Earth System Sciences, 13(7), 1061–1074. https://doi.org/10.5194/hess-13-1061-2009

Kamble, B., & Irmak, A., 2008. Assimilating Remote Sensing-Based ET into SWAP Model for Improved Estimation of Hydrological Predictions. Paper 38. Civil Engineering Faculty Publications., 38.  https://doi.org/10.1109/IGARSS.2008.4779530

Droogers, P., 2000. Estimating actual evapotranspiration using a detailed agro-hydrological model. Journal of Hydrology, 229: 50-58.  https://doi.org/10.1016/S0022-1694(99)00198-5

Kite, G.W., P. Droogers, 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. Journal of Hydrology, 229: 3-18.  https://doi.org/10.1016/S0022-1694(99)00195-X

5       Irrigation management   (Back to top)

Wang B., van Dam J., Yang X., Ritsema C., Du T., & Kang S. (2023). Reducing water productivity gap by optimizing irrigation regime for winter wheat-summer maize system in the North China Plain. Agricultural Water Management, 280, art. no. 108229. https://doi.org/10.1016/j.agwat.2023.108229

Li P., & Ren L. (2023). Evaluating the differences in irrigation methods for winter wheat under limited irrigation quotas in the water-food-economy nexus in the North China Plain. Agricultural Water Management, 289, art. no. 108497. https://doi.org/10.1016/j.agwat.2023.108497

Lee T., Jang W.S., Chun B., Ahmad M.J., Jung Y., Kim J., & Shin Y. (2023). Development of irrigation schedule and management model for sustaining optimal crop production under agricultural drought. Paddy and Water Environment, 21 (1), pp. 31 - 45. https://doi.org/10.1007/s10333-022-00911-9

Li P., & Ren L. (2022). Assessing the feasibility of sprinkler irrigation schemes at the regional scale using a distributed agro-hydrological model. Journal of Hydrology, 610, art. no. 127917. https://doi.org/10.1016/j.jhydrol.2022.127917

Alavi, S. A., Naseri, A. A., Ritzema, H., van Dam, J., & Hellegers, P. (2022). A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Agricultural Water Management, 271. https://doi.org/10.1016/j.agwat.2022.107741

do Nascimento, F. A. L., da Silva, A. J. P., Freitas, F. T. O., Fernandes, R. D. M., & Veimrober Junior, L. A. (2022). Sensors and frequencies of soil water content measurement affecting agro-hydrological simulations and irrigation management. Computers and Electronics in Agriculture, 194. https://doi.org/10.1016/j.compag.2022.106763

Xue J., Li X., Chen J., Zheng X., & Chen X. (2021). Suitable Irrigation Scheduling for Spring Maize under Different Annual Precipitation Patterns in Hetao Irrigation District. Journal of Irrigation and Drainage, 40 (6), pp. 80 - 87. https://doi.org/10.13522/j.cnki.ggps.2020466

Anupoju, V., Kambhammettu, B. V. N. P., & Regonda, S. K. (2021). Role of Short-Term Weather Forecast Horizon in Irrigation Scheduling and Crop Water Productivity of Rice. Journal of Water Resources Planning and Management, 147(8). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001406

Ismail, H., Kamal, M. R., bin Abdullah, A. F., & bin Mohd, M. S. F. (2020). Climate-Smart Agro-Hydrological Model for a Large Scale Rice Irrigation Scheme in Malaysia. Applied Sciences, 10(11), 3906. https://doi.org/10.3390/app10113906

Pan, Y., Yuan, C., & Jing, S. (2020). Simulation and optimization of irrigation schedule for summer maize based on swap model in saline region. International Journal of Agricultural and Biological Engineering, 13(3), 117-122. https://doi.org/10.25165/j.ijabe.20201303.5218

da Silva, A. J. P., Pinheiro, E. A. R., & de Jong van Lier, Q. (2020). Determination of soil hydraulic properties and its implications for mechanistic simulations and irrigation management. Irrigation Science, 38(3), 223–234. https://doi.org/10.1007/s00271-020-00664-5

da Silva, A. J. P., de Jong van Lier, Q., & Coelho, E. F. (2019). Time Stable Representative Position determination as affected by the considered part of an irrigation cycle. Computers and Electronics in Agriculture, 157, 281-287. https://doi.org/10.1016/j.compag.2019.01.002

Polinova, M., Salinas, K., Bonfante, A., & Brook, A. (2019). Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields. Remote Sensing. https://doi.org/10.3390/rs11182127

Xu, X., Jiang, Y., Liu, M., Huang, Q., & Huang, G. (2019). Modeling and assessing agro-hydrological processes and irrigation water saving in the middle Heihe River basin. Agricultural Water Management, 211(October 2018), 152–164. https://doi.org/10.1016/j.agwat.2018.09.033

Bonfante, A., Monaco, E., Manna, P., De Mascellis, R., Basile, A., Buonanno, M., … Brook, A. (2019). LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study. Agricultural Systems, 176(April), 102646. https://doi.org/10.1016/j.agsy.2019.102646

Jiang, J., Feng, S., Ma, J., Huo, Z., & Zhang, C. (2016). Irrigation management for spring maize grown on saline soil based on SWAP model. Field Crops Research, 196, 85–97. https://doi.org/10.1016/j.fcr.2016.06.011

Jiang, Y., Xu, X., Huang, Q., Huo, Z., & Huang, G., 2016. Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model. Agricultural Water Management, 178, 76–88. https://doi.org/10.1016/j.agwat.2016.08.035

Xue, J., & Ren, L., 2016. Evaluation of crop water productivity under sprinkler irrigation regime using a distributed agro-hydrological model in an irrigation district of China. Agricultural Water Management, 178, 350–365. https://doi.org/10.1016/j.agwat.2016.10.003

Jiang, Y., Xu, X., Huang, Q., Huo, Z., & Huang, G. (2015). Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model. Agricultural Water Management, 147, 67–81. https://doi.org/10.1016/j.agwat.2014.08.003

Liu, L., Cui, Y., & Luo, Y. (2013). Integrated modeling of conjunctive water use in a canal-well irrigation district in the lower Yellow River Basin, China. Journal of Irrigation and Drainage Engineering, 139(9), 775–784. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000620

Rallo, G., Agnese, C., Minacapilli, M., & Provenzano, G., 2012. Comparison of SWAP and FAO Agro-Hydrological Models to Schedule Irrigation of Wine Grapes. Journal of Irrigation and Drainage Engineering, 138(7), 581–591.  https://doi.org/10.1061/(ASCE)IR.1943-4774.0000435

Ma, Y., Feng, S., Huo, Z., & Song, X., 2011. Application of the SWAP model to simulate the field water cycle under deficit irrigation in Beijing, China. Mathematical and Computer Modelling, 54(3–4), 1044–1052.  https://doi.org/10.1016/j.mcm.2010.11.034

Wang, D., & Cai, X., 2009. Irrigation Scheduling—Role of Weather Forecasting and Farmers’ Behavior. Journal of Water Resources Planning and Management, 135(5), 364.  https://doi.org/10.1061/(ASCE)0733-9496(2009)135:5(364)

Vazifedoust, M., Vandam, J., Feddes, R., & Feizi, M., 2008. Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management, 95(2), 89–102.  https://doi.org/10.1016/j.agwat.2007.09.007

Blanda, F., Provenzano, G., Rallo, G., Minacapilli, M., & Agnese, C., 2008. Mediterranean environment assessing agro-hydrological models to schedule irrigation for crops of Mediterranean environment. Options Méditerranéennes, 284, 275–284.

Singh, R., Van Dam, J., & Feddes, R., 2006. Water productivity analysis of irrigated crops in Sirsa district, India. Agricultural Water Management, 82(3), 253–278.  https://doi.org/10.1016/j.agwat.2005.07.027

Bessembinder, J., Leffelaar, P., Dhindwal, A., & Ponsioen, T., 2005. Which crop and which drop, and the scope for improvement of water productivity. Agricultural Water Management, 73(2), 113–130.  https://doi.org/10.1016/j.agwat.2004.10.004

Dam, J.C. van, and R.S. Malik (Eds.), 2003. Water productivity of irrigated crops in Sirsa district, India. Integration of remote sensing, crop and soil models and geographical information systems. WATPRO final report, including CD-ROM.  https://www.futurewater.nl/downloads/2003_VanDam_WatPro.pdf

Hack-ten Broeke, M. J. D., 2001. Irrigation management for optimizing crop production and nitrate leaching on grassland. Agricultural Water Management, 49, 97–114.  https://doi.org/10.1016/S0378-3774(00)00141-4

Droogers, P., W.G.M. Bastiaanssen, M. Beyazgül, Y. Kayam, G.W. Kite, H. Murray-Rust, 2000. Distributed agro-hydrological modeling of an irrigation system in western Turkey. Agricultural Water Management 43: 183-202.  https://doi.org/10.1016/S0378-3774(99)00055-4

Groot, W.J.M. de, and M.J.D. Hack-ten Broeke, 1999. Verification of irrigation planning with the hydrological model SWAP2.0. Report 661, Alterra Green World Research, Wageningen, 80 p. (in Dutch).

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6       Drainage conditions   (Back to top)

De Wit, J.A., van Huijgevoort, M.H.J., van Dam, J.C., van den Eertwegh, G.A.P.H, van Deijl, D., Ritsema, C.J.,  Bartholomeus, R.P., 2024. Hydrological consequences of controlled drainage with subirrigation. J. Hydrol. 628, 130432. https://doi.org/10.1016/j.jhydrol.2023.130432

Xue J., & Ren L. (2023). Simulating the impact of subsurface pipe drainage systems on crop water productivity at a regional scale in the upper Yellow River Basin. Irrigation and Drainage. https://doi.org/10.1002/ird.2887

Waqas, M. M., Shah, S. H. H., Awan, U. K., Arshad, M., & Ahmad, R. (2019). Impact of climate change on groundwater fluctuation, root zone salinity and water productivity of sugarcane: A case study in lower Chenab canal system of Pakistan. Pakistan Journal of Agricultural Sciences, 56(2), 443-450. https://doi.org/10.21162/PAKJAS/19.7194

Eeman, S., 2017. Dynamics of rainwater lenses on upward seeping saline groundwater. Wageningen University, PhD thesis.  https://edepot.wur.nl/390156

Xu, X., Sun, C., Qu, Z., Huang, G., Ramos, T. B., & Huang, G., 2015. Groundwater recharge and capillary rise in irrigated areas of the upper yellow river basin assessed by an agro-hydrological model Irrigation and Drainage 64, 587-599.  https://doi.org/10.1002/ird.1928

Pinto, V.M., K. Reichardt, J.C. van Dam, Q. de Jong van Lier, I.P. Bruno, A. Durigon, D. Dourado-Neto, R.P. Bortolotto, 2015. Deep drainage modeling for a fertigated coffee plantation in the Brazilian savanna. Agricultural Water Management 148, 130-140.  https://dx.doi.org/10.1016/j.agwat.2014.09.029

Bennett, S. J., Bishop, T. F. a., & Vervoort, R. W., 2013. Using SWAP to quantify space and time related uncertainty in deep drainage model estimates: A case study from northern NSW, Australia. Agricultural Water Management, 130, 142–153.  https://doi.org/10.1016/j.agwat.2013.08.020

Xu, X., Huang, G., Sun, C., Pereira, L. S., Ramos, T. B., Huang, Q., & Hao, Y., 2013. Assessing the effects of water table depth on water use, soil salinity and wheat yield: Searching for a target depth for irrigated areas in the upper Yellow River basin. Agricultural Water Management, 125, 46–60.  https://doi.org/10.1016/j.agwat.2013.04.004

Akker, J. J. H. van den, Hendriks, R. F. A., Hoving, I. E., Meerkerk, B., Houwelingen, K. Van, Kleef, J. Van, Toorn, A. van den., 2013. Pilot onderwaterdrains Krimpenerwaard. Alterra-rapport 2466.  https://edepot.wur.nl/280068

Verma, A. K., Gupta, S. K., & Isaac, R. K. (2010). Long-term Use of Saline Drainage Waters for Irrigation in Subsurface Drained Lands : Simulation Modelling with SWAP. Journal of Agriculture Engineering, 47(3), 15–23. https://www.indianjournals.com/ijor.aspx?target=ijor:joae&volume=47&issue=3&article=003

Cirkel, D. G., Witte, J.-P. M., & van der Zee, S.E.A.T.M., 2010. Estimating seepage intensities from groundwater level time series by inverse modelling: A sensitivity analysis on wet meadow scenarios. Journal of Hydrology, (385), 132–142.  https://doi.org/10.1016/j.jhydrol.2010.02.009

Peters, E., Bier, G., van Lanen, H. A. J., & Torfs, P. J. J. F., 2006. Propagation and spatial distribution of drought in a groundwater catchment. Journal of Hydrology, 321(1–4), 257–275.  https://doi.org/10.1016/j.jhydrol.2005.08.004

Wang, X., Hollanders, P. H. J., Wang, S., & Fang, S., 2004. Effect of field groundwater table control on water and salinity balance and crop yield in the Qingtongxia Irrigation District, China. Irrigation and Drainage, 53(3), 263–275.  https://doi.org/10.1002/ird.117

Qureshi, A. S., & Feddes, R. A. (2000). Evaluating Drainage Design Parameters for the Fourth Drainage Project, Pakistan by using SWAP Model: Part II – Modeling Results. Irrigation and Drainage Systems, 14(1–4), 281–299. https://doi.org/10.1023/A

Sarwar, A., W.M.G. Bastiaanssen, Th.M. Boers, and J.C. van Dam, 2000. Evaluating drainage design parameters for the fourth drainage project, Pakistan by using the SWAP model: Part 1: Calibration. Irrigation and Drainage Systems, 14, 257-280.

Sarwar, A. (2000). A transient model approach to improve on-farm irrigation and drainage in semi-arid zones. PhD thesis, Wageningen UR. https://library.wur.nl/WebQuery/wurpubs/fulltext/197426

Massop, H.T.H.L., T. Kroon, P.J.T. van Bakel, W.J. de Lange, A. van der Giessen, M.J.H. Pastoors, and J. Huygen, 2000. Hydrology for STONE; schematization and parameterization. Report 38, Serie Milieu Planbureau 9, Alterra Green World Research, Wageningen, 101 p. (in Dutch).

Knotters, M., and J.G. de Gooijer, 1999. TARSO modeling of water table depths. Water Resources Research, 35: 695-705.

Bierkens, M.F.P., 1998. Modeling water table fluctuations by means of a stochastic differential equation. Water Resources Research, 34: 2485-2499.

Knotters, M., and P.E.V. van Walsum, 1997. Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component. Journal of Hydrology, 197: 25-46.  https://www.sciencedirect.com/science/journal/00221694/197

7       Surface water management   (Back to top)

Schipper, P., Groenendijk, P., Eekeren, N. van, Zanen, M. van, Rozemeijer, J., Jansen, G., & Swart, B., 2015. Goede grond voor een duurzaam watersysteem. STOWA rapport 2015-19. In Dutch.  https://library.wur.nl/WebQuery/wurpubs/489383

Bierkens, M.F.P., P.J.T. van Bakel, and J.G. Wesseling, 1999. Comparison of two modes of surface water control using a soil water model and surface elevation data. Geoderma, 89: 149-175.

Spieksma, J.F.M., J.M. Schouwenaars, and J. Blankenburg, 1996. Combined modelling of groundwater table and open water level in raised mires. Nordic Hydrology, 27: 231-246.

Massop, H.Th.L., J.M.P.M. Peerboom, and H.C. van Vessem, 1994. Effects of measures to alleviate desiccation at rural estate ‘De Wildenborch’. Report 342, Alterra Green World Research, Wageningen, 128 p. (in Dutch).

8       Plant growth   (Back to top)

Zhao, Y., Mao, X., Shukla, M. K., & Li, S. (2020). Modeling soil water-heat dynamic changes in seed-maize fields under film mulching and deficit irrigation conditions. Water (Switzerland), 12(5). https://doi.org/10.3390/W12051330

Zhao, Y., Mao, X., & Shukla, M. K. (2020). A modified SWAP model for soil water and heat dynamics and seed–maize growth under film mulching. Agricultural and Forest Meteorology, 292–293(October), 108127. https://doi.org/10.1016/j.agrformet.2020.108127

Pinto, V. M., van Dam, J. C., de Jong van Lier, Q., & Reichardt, K. (2019). Intercropping simulation using the SWAP model: Development of a 2x1D algorithm. Agriculture (Switzerland), 9(6). https://doi.org/10.3390/agriculture9060126

Hu, S., Shi, L., Huang, K., Zha, Y., Hu, X., Ye, H., & Yang, Q. (2019). Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crops Research, 232(December 2018), 49–61. https://doi.org/10.1016/j.fcr.2018.12.009

Landschreiber, L., 2019. Quantifying the effect of vegetation and land use on soil hydrological functioning along a regional transect in southern Africa. PhD thesis, Univ. Hamburg. https://ediss.sub.uni-hamburg.de/volltexte/2019/9817/pdf/Dissertation.pdf

Bonfante, A., Terribile, F., Bouma, J., 2019. Refining physical aspects of soil quality and soil health when exploring the effects of soil degradation and climate change on biomass production: An Italian case study. Soil 5, 1–14. https://doi.org/10.5194/soil-5-1-2019

WerkgroepWaterwijzerLandbouw, 2018. Waterwijzer landbouw: instrumentarium voor kwantificeren van effecten van waterbeheer en klimaat op landbouwproductie. STOWA Rapport 2018-48. https://www.stowa.nl/sites/default/files/assets/PUBLICATIES/Publicaties%202018/STOWA%202018-48%20WWL%20defversie.pdf

Mokhtari, A., Noory, H., & Vazifedoust, M. (2018). Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model. Agricultural and Forest Meteorology, 250–251(December 2017), 159–170. https://doi.org/10.1016/j.agrformet.2017.12.250

Bonfante, A., Monaco, E., Langella, G., Mercogliano, P., Bucchignani, E., Manna, P., & Terribile, F. (2018). A dynamic viticultural zoning to explore the resilience of terroir concept under climate change. Science of The Total Environment, 624, 294–308. https://doi.org/10.1016/j.scitotenv.2017.12.035

Schwantes, A.P., 2017. Agricultural resource efficiency and reduction of impacts under landuse and climate change scenarios in Brazil. University of São Paulo “ Luiz de Queiroz ” College of Agriculture. PhD thesis. https://www.utwente.nl/en/et/wem/research/projects/finished/brazil/

Hu, S., Shi, L., Zha, Y., Williams, M., & Lin, L. (2017). Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system. Journal of Hydrology, 555, 812–831. https://doi.org/10.1016/j.jhydrol.2017.10.061

Heinen, M., Mulder, M., Walvoort, D., Bartholomeus, R., Stofberg, S., & Broeke, M. H. (2017). Praktijktoets Waterwijzer Landbouw in pilotgebieden De Raam en Vecht. STOWA rapport 2017-44.

Bonfante, A., Impagliazzo, A., Fiorentino, N., Langella, G., Mori, M., & Fagnano, M., 2017. Supporting local farming communities and crop production resilience to climate change through giant reed ( Arundo donax L.) cultivation: An Italian case study. Science of The Total Environment, 601–602, 603–613.  https://doi.org/10.1016/j.scitotenv.2017.05.214

Hack-ten-Broeke, M. J. D., Kroes, J. G., Bartholomeus, R. P., Dam, J. C. Van, Wit, A. J. W. De, Supit, I., Walvoort, D. J. J., Bakel, P. J. T. van, Ruijtenberg, R., 2016. Quantification of the impact of hydrology on agricultural production as a result of too dry , too wet or too saline conditions. Soil, 2, 391–402.  https://doi.org/doi:10.5194/soil-2-391-2016

Kroes, J., Groenendijk, P., Supit, I., Wit, A. de, Abelleyra, D. d., & Vero, S. R., 2016. Disentangle mechanisms of nitrogen and water availability on soybean yields. In F. Ewert, K. J. Boote, R. P. Rötter, P. Thorburn, & C. Nendel (Eds.), iCROPM2016. Crop Modelling for Agriculture and Food Security under Global Change. 15-17 March 2016, Berlin, Germany (Vol. 1, pp. 294–295).  https://doi.org/10.1017/CBO9781107415324.004

Kroes, J., Reidsma, P., Heinen, M., Oel, P. van, Feenstra, J., Dannenburg, M., & Ryan, A.-M., 2016. Final EURO-AGRIWAT conference Water Footprint of agricultural products : progress , challenges and solutions 7-9 March 2016 Wageningen. Book of Abstracts. In Water Footprint of agricultural products: progress, challenges and solutions.  https://edepot.wur.nl/380126

Kersebaum, K. C., Kroes, J., Gobin, A., Takác, J., Hlavinka, P., Trnka, M., Ventrella, D., Giglio, L., Ferrise, R., Moriondo, M., Marta, A. D., Luo, Q., Eitzinger, J., Mirschel, W., Weigel, H. J., Manderscheid, R., Hoffmann, M., Nejedlik, P., Iqbal, M. A., Hösch, J., 2016. Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat. Water, 8(12), 571.  https://doi.org/10.3390/w8120571

De Jong van Lier, Q., O. Wendroth and J.C. van Dam, 2015. Prediction of winter wheat yield with the SWAP model using pedotransfer functions: an evaluation of sensitivity, parameterization and prediction accuracy. Agricultural Water Management 154, 29-42.  https://dx.doi.org/10.1016/j.agwat.2015.02.011

Bonfante, A., Monaco, E., Alfieri, S. M., De Lorenzi, F., Manna, P., Basile, A., & Bouma, J., 2015. Climate Change Effects on the Suitability of an Agricultural Area to Maize Cultivation: Application of a New Hybrid Land Evaluation System. Advances in Agronomy, 133.  https://doi.org/10.1016/bs.agron.2015.05.001

Bonfante, A., & Bouma, J., 2015. The role of soil series in quantitative land evaluation when expressing effects of climate change and crop breeding on future land use. Geoderma, 259–260, 187–195.  https://doi.org/10.1016/j.geoderma.2015.06.010

Hao, F., Chen, S., Ouyang, W., Shan, Y., & Qi, S., 2013. Temporal rainfall patterns with water partitioning impacts on maize yield in a freeze-thaw zone. Journal of Hydrology.  https://doi.org/10.1016/j.jhydrol.2013.02.008

Kroes, J. G., & Supit, I., 2011. Impact analysis of drought, water excess and salinity on grass production in The Netherlands using historical and future climate data. Agriculture, Ecosystems & Environment, 144(1), 370–381.  https://doi.org/10.1016/j.agee.2011.09.008

Droogers, P., J.C. van Dam, J. Hoogeveen and R. Loeve, 2004. Adaptation strategies to climate change to sustain food security. In ‘Climate change in contrasting river basins’, CABI publisher, London, p. 49-74.

Li, K.Y., J.B. Boisvert, and R. de Jong, 1999. An exponential root water uptake model. Canadian Journal of Soil Science, 79: 333-343.

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Neut, D. van der, J.C. van Dam, and R.A. Feddes, 1995. Effects of higher surface water levels in ‘De Hoeksche Waard’. An evaluation of yield reductions of potatoes and sugar beets at 4 drainage depths during 42 years. Report 48, Subdep. Water Resources, Wageningen University, 69 p. In Dutch.

Broek, B.J. van den, and P. Kabat, 1995. SWACROP: dynamic simulation model of soil water and crop yield applied to potatoes. In ‘Modeling and parameterization of the Soil-Plant-Atmospere System. A comparison of potato growth models’, P. Kabat, B. Marshall, B.J. van den broek, J. Vos and H. van Keulen (Eds.), Wageningen Press, p. 299-334.

9       Soil water extraction by roots   (Back to top)

De Jong van Lier, Q., de Melo, M.L.A., Pinheiro, E.A.R., 2024. Stochastic analysis of plant available water estimates and soil water balance components simulated by a hydrological model. Vadose Zone J., e20306. https://doi.org/10.1002/vzj2.20306

de Melo M.L.A., de Jong van Lier Q., Cichota R., Pollacco J.A.P., Fernández-Gálvez J., & Pahlow M. (2023). Sensitivity analysis of land and water productivities predicted with an empirical and a process-based root water uptake function. Journal of Hydrology, 626, art. no. 130241. https://doi.org/10.1016/j.jhydrol.2023.130241

Inforsato L., & de Jong van Lier Q. (2021). Polynomial functions to predict flux-based field capacity from soil hydraulic parameters. Geoderma, 404, art. no. 115308. https://doi.org/10.1016/j.geoderma.2021.115308

de Melo, M. L. A., & de Jong van Lier, Q. (2021). Revisiting the Feddes reduction function for modeling root water uptake and crop transpiration. Journal of Hydrology, 603(PB), 126952. https://doi.org/10.1016/j.jhydrol.2021.126952

Hamada, K., Inoue, H., Mochizuki, H., Miyamoto, T., Asakura, M., & Shimizu, Y. (2021). Effect of hardpan on the vertical distribution of water stress in a converted paddy field. Soil and Tillage Research, 214. https://doi.org/10.1016/j.still.2021.105161

Wang, X., Cai, H., Zheng, Z., Yu, L., Wang, Z., & Li, L. (2020). Modelling root water uptake under deficit irrigation and rewetting in Northwest China. Agronomy Journal, 112(1), 158-174. https://doi.org/10.1002/agj2.20043

Hamada, K., Inoue, H., Mochizuki, H., Asakura, M., Shimizu, Y., & Takemura, T. (2020). Evaluating maize drought and wet stress in a converted Japanese paddy field using a SWAP model. Water (Switzerland), 12(5). https://doi.org/10.3390/w12051363

Pinheiro, E. A. R., de Jong van Lier, Q., & Šimůnek, J. (2019). The role of soil hydraulic properties in crop water use efficiency: A process-based analysis for some Brazilian scenarios. Agricultural Systems, 173 (December 2018), 364–377. https://doi.org/10.1016/j.agsy.2019.03.019

de Jong van Lier, Q., 2017. Field capacity, a valid upper limit of crop available water? Agricultural Water Management, 193, 214–220. https://doi.org/10.1016/j.agwat.2017.08.017

Bonfante, A., Alfieri, S. M., Albrizio, R., Basile, A., De Mascellis, R., Gambuti, A., Giorio, P., Langella, G., Manna, P., Monaco, E., Moio, L., Terribile, F. (2017). Evaluation of the effects of future climate change on grape quality through a physically based model application: a case study for the Aglianico grapevine in Campania region, Italy. In: Agricultural Systems, 152, 100–109. https://doi.org/10.1016/j.agsy.2016.12.009

Marcos Alex dos Santos, Quirijn de Jong van Lier, Jos C. van Dam, Andre Herman Freire Bezerra, 2017. Benchmarking test of empirical root water uptake models. Hydrology and Earth System Sciences, 21, 473-493, 2017.  https://www.hydrol-earth-syst-sci.net/21/473/2017/

de Jong van Lier, Q., van Dam, J. C., Durigon, A., Dos Santos, M. A., & Metselaar, K., 2013. Modeling Water Potentials and Flows in the Soil–Plant System Comparing Hydraulic Resistances and Transpiration Reduction Functions. Vadose Zone Journal, 12(3).  https://doi.org/10.2136/vzj2013.02.0039

Willigen, P. De, Dam, J. C. van, Javaux, M., & Heinen, M., 2012. Root Water Uptake as Simulated by Three Soil Water Flow Models. Vadose Zone Journal.  https://doi.org/10.2136/vzj2012.0018

De Jong Van Lier, Q., Dam, J. C. Van, & Metselaar, K., 2009. Root Water Extraction under Combined Water and Osmotic Stress. Soil Science Society of America Journal, 73(3), 862–875.  https://doi.org/10.2136/sssaj2008.0157

Bartholomeus, R. P., Witte, J. M., Bodegom, P. M. Van, Dam, J. C. Van, & Aerts, R., 2008. Critical soil conditions for oxygen stress to plant roots : Substituting the Feddes-function by a process-based model. Journal of Hydrology.  https://doi.org/10.1016/j.jhydrol.2008.07.029

De Jong Van Lier, Q., Dam, J. C. Van, Metselaar, K., Jong, R. De, & Duijnisveld, W. H. M., 2008. Macroscopic Root Water Uptake Distribution Using a Matric Flux Potential Approach. Vadose Zone Journal, 7(3), 1065–1078.  https://doi.org/10.2136/vzj2007.0083

De Jong van Lier, Q., K. Metselaar and J.C. van Dam, 2006. Root water extraction and limiting soil hydraulic conditions estimated by numerical simulation. Vadose Zone Journal 5, 1264-1277.  https://doi.org/10.2136/vzj2006.0056

Hupet, F., J.C. van Dam and M. Vanclooster, 2004. Impact of within-field variability of soil hydraulic functions on transpiration and crop yields: a numerical study. Vadose Zone Journal 3: 1367-1379.  https://doi.org/10.2136/vzj2004.1367

Hupet, F., S. Lambot, R.A. Feddes, J.C. van Dam, M. Vanclooster, 2003. Estimation of root water uptake parameters by inverse modeling with soil water content data. Water Resources Research, Vol. 39, No. 11, 1312.  https://doi.org/10.1029/2003WR002046

Li, K. Y., Boisvert, J. B., & Jong, R. De., 1999. An exponential root-water-uptake model. Canadian Journal of Soil Science.

10    Soil moisture indicators for natural vegetations   (Back to top)

Taufik, M., Veldhuizen, A. A., Wösten, J. H. M., & van Lanen, H. A. J. (2019). Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma, 347, 160-169. https://doi.org/10.1016/j.geoderma.2019.04.001

Taufik, M., Setiawan, B.I., Van Lanen, H.A.J., 2018. Increased fire hazard in human-modified wetlands in Southeast Asia. Ambio. https://doi.org/10.1007/s13280-018-1082-3

Bartholomeus, R.P., Witte, J.P.M., van Bodegom, P.M., van Dam, J.C., de Becker, P., Aerts, R., 2012. Process-based proxy of oxygen stress surpasses indirect ones in predicting vegetation characteristics. Ecohydrology 5, 746–758. https://doi.org/10.1002/eco.261

Bartholomeus, R. P., Witte, J. P. M., Van Bodegom, P. M., Van Dam, J. C., & Aerts, R., 2011. Climate change threatens endangered plant species by stronger and interacting water-related stresses. Journal of Geophysical Research: Biogeosciences, 116(4), 1–14.

Ordoñez, J.C., Van Bodegom, P.M., Witte, J.P.M., Bartholomeus, R.P., Van Dobben, H.F., Aerts, R., 2010. Leaf habit and woodiness regulate different leaf economy traits at a given nutrient supply. Ecology 91, 3218–3228. https://doi.org/10.1890/09-1509.1 https://doi.org/10.1029/2011JG001693

Witte, J.P.M., Meuleman, A.F.M., Schaaf, S. van der, Raterman, B., 2004. Eco-hydrology and biodiversity, in: Unsaturated-Zone Modeling. Progress, Challenges and Applications. pp. 301–329.

Jansen, P.C., J. Runhaar, J.P.M. Witte, and J.C. van Dam, 2000. Wetness indication of grass vegetations in relation to the moisture status of soils. Report 057, Alterra Green World Research, Wageningen, 59 p. (in Dutch)

Runhaar, J., J.P.M. Witte, and P.H. Verburg, 1997. Groundwater level, moisture supply, and vegetation in The Netherlands. Wetlands, 17: 528-538.

11    Salinization   (Back to top)

Bhuyan M.I., Supit I., Mia S., Mulder M., & Ludwig F. (2023). Effect of soil and water salinity on dry season boro rice production in the south-central coastal area of Bangladesh. Heliyon, 9 (8), art. no. e19180. https://doi.org/10.1016/j.heliyon.2023.e19180

Maleki Tirabadi M.S., Banihabib M.E., & Randhir T.O. (2022). An integrated framework for simultaneously modeling primary and secondary salinity at a watershed scale. Journal of Hydrology, 612, art. no. 128171. https://doi.org/10.1016/j.jhydrol.2022.128171

Li, P., & Ren, L. (2021). Evaluating the saline water irrigation schemes using a distributed agro-hydrological model. Journal of Hydrology, 594. https://doi.org/10.1016/j.jhydrol.2020.125688

Yuan, C. (2021). Simulation soil water-salt dynamics in saline wasteland of Yongji Irrigation Area in Hetao Irrigation District of China. Water Supply, 21(6), 2681-2690. https://doi.org/10.2166/ws.2020.299

Eberhard, J., van Schaik, N. L. M. B., Schibalski, A., & Gräff, T. (2020). Simulating future salinity dynamics in a coastal marshland under different climate scenarios. Vadose Zone Journal, 19(1). https://doi.org/10.1002/vzj2.20008

Yuan, C., Feng, S., Huo, Z., & Ji, Q. (2019). Simulation of saline water irrigation for seed maize in arid northwest China based on SWAP model. Sustainability (Switzerland), 11(16). https://doi.org/10.3390/su11164264

Yuan, C. F., Feng, S. Y., Wang, J., Huo, Z. L., & Ji, Q. Y. (2018). Effects of irrigation water salinity on soil salt content distribution, soil physical properties and water use efficiency of maize for seed production in arid Northwest China. International Journal of Agricultural and Biological Engineering, 11(3), 137–145. https://doi.org/10.25165/j.ijabe.20181103.3146

Mulder, H. M., Bakel, P. J. T. van, Vos, A. de, Straten, G. van, Heinen, M., & Kroes, J. G., 2018. SWAP-WOFOST toepassing op Zilt Proefbedrijf Texel. STOWA Rapport 2018-01.

Hassanli, M., Ebrahimian, H., Mohammadi, E., Rahimi, A., & Shokouhi, A. (2016). Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models. Agricultural Water Management, 176, 91–99. https://doi.org/10.1016/j.agwat.2016.05.003

Kumar, P., Sarangi, A., Singh, D. K., Parihar, S. S., & Sahoo, R. N., 2015. Simulation of salt dynamics in the root zone and yield of wheat crop under irrigated saline regimes using SWAP model. Agricultural Water Management, 148, 72–83.  https://dx.doi.org/10.1016/j.agwat.2014.09.014

Verma, A.K., Gupta, S.K., Isaac, R.K., 2014. Calibration and Validation of SWAP to Simulate Conjunctive Use of Fresh and Saline Irrigation Waters in Semi-Arid Regions. In: Environ. Model. Assess. 19, 45–55. https://doi.org/10.1007/s10666-013-9379-x

Oster, J. D., Letey, J., Vaughan, P., Wu, L., & Qadir, M., 2012. Comparison of transient state models that include salinity and matric stress effects on plant yield. Agricultural Water Management, 103, 167–175.  https://doi.org/10.1016/j.agwat.2011.11.011

Jiang, J., Feng, S., Huo, Z., Zhao, Z., & Jia, B., 2011. Application of the SWAP model to simulate water–salt transport under deficit irrigation with saline water. Mathematical and Computer Modelling, 54(3–4), 902–911.  https://doi.org/10.1016/j.mcm.2010.11.014

Ben-Asher, J., J.C. van Dam, R.A. Feddes, and R.K. Jhorar, 2006. Irrigation of grapevines with saline water II Mathematical simulation of vine growth and yield. Agricultural Water Management, 83, 22-29.  https://doi.org/10.1016/j.agwat.2005.11.006

Su, N., Bethune, M., Mann, L., & Heuperman, A., 2005. Simulating water and salt movement in tile-drained fields irrigated with saline water under a Serial Biological Concentration management scenario. Agricultural Water Management, 78(3), 165–180.  https://doi.org/10.1016/j.agwat.2005.02.003

Smets, S.M.P., M. Kuper, J.C. van Dam, and R.A. Feddes, 1997. Salinization and crop transpiration of irrigated fields in Pakistan’s Punjab. Agricultural Water Management, 35: 43-60.  https://doi.org/10.1016/S0378-3774(97)00031-0

Feddes, R.A., and J.C. van Dam, 1997. Modelling of water flow and salt transport for irrigation management and drainage design. In ‘Water management, salinity and pollution control towards sustainable irrigation in the Meditterranean region’, Int. Conf., Sept. 1997, Bari, Italy, p. 145-179.

12    Solute transport   (Back to top)

Sun X., Li Y., Heinen M., Ritzema H., Hellegers P., & van Dam J. (2023). Fertigation Strategies to Improve Water and Nitrogen Use Efficiency in Surface Irrigation System in the North China Plain. Agriculture (Switzerland), 13 (1), art. no. 17. https://doi.org/10.3390/agriculture13010017

Sabzzadeh I., & Alimohammadi S. (2023). Spatiotemporal Simulation of Nitrate, Phosphate, and Salinity in the Unsaturated Zone for an Irrigation District West of Iran Using SWAP-ANIMO Model. Journal of Hydrologic Engineering, 28 (1), art. no. 04022037. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002231

Pinto, V. M., Bruno, I. P., de Jong van Lier, Q., Dourado-Neto, D., & Reichardt, K., 2017. Environmental benefits of reducing N rates for coffee in the Cerrado. Soil and Tillage Research, 166, 76–83.  https://doi.org/10.1016/j.still.2016.10.006

Ter Horst, M.M.S., Wipfler, E.L., Adriaanse, P.I., Boesten, J.J.T.I., Fait, G., Wenjuan, L., Chuanjiang, T., 2014. Development of scenarios for environmental risk assessment procedures of pesticides in China. Alterra report 2559, Wageningen

Janssen, G. M. C. M., Fraters, B., Boumans, L. J. M., & Vrijhoef, A., 2013. Onderzoek naar vervangend rekenmodel om weersinvloeden op nitraatconcentraties te berekenen. Vergelijking van de modellen ONZAT, HYDRUS-1D en SWAP.  www.rivm.nl/bibliotheek/rapporten/680717033.pdf

Bonten, L. T. C., Kroes, J. G., Groenendijk, P., & van der Grift, B., 2012. Modeling diffusive Cd and Zn contaminant emissions from soils to surface waters. Journal of Contaminant Hydrology, 138–139, 113–122.  https://doi.org/10.1016/j.jconhyd.2012.06.008

Van Den Berg, F., Tiktak, A., Heuvelink, G. B. M., Burgers, S. L. G. E., Brus, D. J., de Vries, F., … Kroes, J. G., 2012. Propagation of uncertainties in soil and pesticide properties to pesticide leaching. Journal of Environmental Quality, 41(1), 253–61.  https://doi.org/10.2134/jeq2011.0167

Visser, A., Kroes, J., T H van Vliet, M., Blenkinsop, S., Fowler, H. J., & Broers, H. P., 2012. Climate change impacts on the leaching of a heavy metal contamination in a small lowland catchment. Journal of Contaminant Hydrology, 127, 47–64.  https://doi.org/10.1016/j.jconhyd.2011.04.007

Kroes, J., & Roelsma, J., 2007. Simulation of water and nitrogen flows on field scale : application of the SWAP – ANIMO model for the Müncheberg data set. In K. Ch. Kersebaum et al. (eds.), Modelling Water and Nutrient Dynamics in Soil–Crop Systems, 111–128. (pp. 111–128). Springer.

Marinov, D., Querner, E., & Roelsma, J. (2005). Simulation of water flow and nitrogen transport for a Bulgarian experimental plot using SWAP and ANIMO models. Journal of Contaminant Hydrology, 77(3), 145–164. https://doi.org/10.1016/j.jconhyd.2004.12.004

Peters, L., & Griffioen, J., 2005. Poriewaterstroming en -transport op perceelschaal. Quasi-2D versus 2D benadering. Stromingen, 11(2). In Dutch.

Feddes, R.A., and J.C. van Dam, 2002. Modelling water flow and solute transport for horticultural and environmental management. Acta Horticulturae, number 573, 107-117.

Hack-ten Broeke, M.J.D., 2000. Nitrate leaching from dairy farming on sandy soils. Case studies for experimental farm De Marke. PhD-thesis, Wageningen University, 145 p.  https://edepot.wur.nl/199017

Hack-ten Broeke, M. J. D., Schut, A. G. T., & Bouma, J., 1999. Effects on nitrate leaching and yield potential of implementing newly developed sustainable land use systems for dairy farming on sandy soils in the Netherlands. Geoderma, 217–235.

Hack-ten Broeke, M. J. D., & Putten, A. H. J. Van Der., 1997. Nitrate leaching affected by management options with respect to urine-affected areas and groundwater levels for grazed grassland. Agriculture , Ecosystems and Environment, 66, 197–210.

Groen, K.P., 1997. Pesticide leaching in polders. Field and model studies on cracked clays and loamy sand. PhD-thesis, Wageningen University, 296 p.  https://library.wur.nl/WebQuery/wurpubs/fulltext/193568

13     Soil water flow as affected by soil spatial heterogeneity   (Back to top)

Phankamolsil Y., Rittima A., Teerapunyapong P., Surakit K., Tabucanon A.S., Sawangphol W., Kraisangka J., Talaluxmana Y., & Vudhivanich V. (2022). Comparative assessment of groundwater recharge estimation using physicalbased models and empirical methods in Upper Greater Mae Klong Irrigation Project, Thailand. Agriculture and Natural Resources, 56 (4), pp. 737 - 750. https://doi.org/10.34044/J.ANRES.2022.56.4.08

Tenreiro, T. R., García-Vila, M., Gómez, J. A., Jimenez-Berni, J. A., & Fereres, E. (2020). Water modelling approaches and opportunities to simulate spatial water variations at crop field level. Agricultural Water Management, 240(May), 106254. https://doi.org/10.1016/j.agwat.2020.106254

Turek, M. E., de Jong van Lier, Q., & Armindo, R. A. (2020). Estimation and mapping of field capacity in Brazilian soils. Geoderma, 376(June), 114557. https://doi.org/10.1016/j.geoderma.2020.114557

Bonfante, A., Basile, A., & Bouma, J. (2020). Exploring the effect of varying soil organic matter contents on current and future moisture supply capacities of six Italian soils. Geoderma, 361. https://doi.org/10.1016/j.geoderma.2019.114079

Faúndez Urbina, C. A., van Dam, J. C., Hendriks, R. F. A., van den Berg, F., Gooren, H. P. A., & Ritsema, C. J. (2019). Water flow in soils with heterogeneous macropore geometries. Vadose Zone Journal, 18(1). https://doi.org/10.2136/vzj2019.02.0015

Szymkiewicz, A., Savard, J., & Jaworska-Szulc, B. (2019). Numerical analysis of recharge rates and contaminant travel time in layered unsaturated soils. Water (Switzerland), 11(3), 1–13. https://doi.org/10.3390/w11030545

Wright, A. (2018). The effect of land surface hydrological process representation on drought prediction, at a range of spatio-temporal scales. PhD thesis. Univ. of Reading, UK. Reading.

Bonfante, A., Sellami, M. H., Abi Saab, M. T., Albrizio, R., Basile, A., Fahed, S., Giorio, P., Langella, G., Monaco, E., Bouma, J. (2017). The role of soils in the analysis of potential agricultural production: A case study in Lebanon. In: Agricultural Systems, 156(May), 67–75. https://doi.org/10.1016/j.agsy.2017.05.018

Nasta, P., & Romano, N. (2016). Use of a flux-based field capacity criterion to identify effective hydraulic parameters of layered soil profiles subjected to synthetic drainage experiments. Water Resources Research, 52(1), 566–584. https://doi.org/10.1002/2015WR016979

Wiesner, S., Gröngröft, A., Ament, F., & Eschenbach, A. (2016). Spatial and temporal variability of urban soil water dynamics observed by a soil monitoring network. Journal of Soils and Sediments, 16(11), 2523–2537. https://doi.org/10.1007/s11368-016-1385-6

van Schaik, N.L.M.B., A. Bronstert, S.M. de Jong, V.G. Jetten, J.C. van Dam, C.J. Ritsema, and S. Schnabel, 2013. Process-based modelling of a headwater catchment in semi-arid conditions: the influence of macropore flow. Hydrological Processes.  https://doi.org/10.1002/hyp.10086

Chirico, G. B., Medina, H., & Romano, N. (2010). Functional evaluation of PTF prediction uncertainty: An application at hillslope scale. Geoderma, 155(3–4), 193–202. https://doi.org/10.1016/j.geoderma.2009.06.008

Schaik, N.L.M.B., R.F.A. Hendriks, and J.C. van Dam, 2010. Parameterization of macropore flow using dye-tracer infiltration patterns in the SWAP model. Vadose Zone Journal, 9, 95-106.  https://doi.org/10.2136/vzj2009.0031

Crescimanno, G., & Garofalo, P., 2005. Application and Evaluation of the SWAP Model for Simulating Water and Solute Transport in a Cracking Clay Soil. Soil Science Society of America Journal, 69, 1943–1954.  https://doi.org/10.2136/sssaj2005.0051

G. Kramers, J.C. van Dam, C.J. Ritsema, F. Stagnitti, K. Oostindie, and L.W. Dekker, 2005. A new modeling approach to simulate preferential flow and transport in water repellent porous media: Parameter sensitivity, and effects on crop growth and solute leaching. Australian Journal of Soil Research, 43, 371-382.  https://doi.org/10.1071/SR04098

Ritsema, C. J., van Dam, J. C., Dekker, L. W., & Oostindie, K., 2005. A new modelling approach to simulate preferential flow and transport in water repellent porous media: Model structure and validation. Australian Journal of Soil Research, 43(3), 361.  https://doi.org/10.1071/SR05054

Ritsema, C.J., J.C. van Dam, L.W. Dekker and K. Oostindie, 2001. Principles and modeling of flow and transport in water repellent surface layers, and consequences for management. International Turfgrass Society Research Journal, 9, 3-11.

Kelleners, T.J., J. Beekma, and M.R. Chaudhry, 1999. Spatially variable soil hydraulic properties for simulation of field-scale solute transport in the unsaturated zone. Geoderma, 92: 199-215.

Kabat, P., R.W.A. Hutjes, and R.A. Feddes, 1997. The scaling characteristics of soil parameters: from plot scale heterogeneity to subgrid parameterization. Journal of Hydrology, 190: 363-396.

Finke, P.A., J.H.M. Wösten, and J.G. Kroes, 1996. Comparing two approaches of characterizing soil map unit behaviour in solute transport. Soil Science Society of America Journal, 60: 200-205.

Dam, J.C. van, J.H.M. Wösten and A. Nemes, 1996. Unsaturated soil water movement in hysteretic and water repellent soils. Journal of Hydrology, 184, 153-173.  https://doi.org/10.1016/0022-1694(95)02996-6

Wösten, J.H.M., P.A. Finke, and M.J.W. Jansen, 1995. Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics. Geoderma, 66: 227-237.

Vos, E. C., & Kooistra, M. J., 1994. The effect of soil structure differences in a silt loam soil under various farm management systems on soil physical properties and simulated land qualities. Agriculture , Ecosystems and Environment, 51(65), 227–238.

Boers, Th.M., 1994. Rain water harvesting in arid and semi-arid zones. PhD-thesis, Wageningen University, 133 p.  https://edepot.wur.nl/74476

Feddes, R.A., G.H. de Rooij, J.C. van Dam, P. Kabat, P. Droogers, and J.N.M. Stricker, 1993. Estimation of regional effective soil hydraulic parameters by inverse modelling. In ‘Water flow and solute transport in soils: modelling and application’, D. Russo and G. Dagan (Eds.), Springer Verlag, Berlin, p. 211-231.

Feddes, R.A., M. Menenti, P. Kabat, and W.G.M. Bastiaanssen, 1993. Is large scale inverse modelling of unsaturated flow with areal average evaporation and surface soil moisture as estimated from remote sensing feasible? Journal of Hydrology, 143: 125-152.

14     Sensitivity analysis   (Back to top)

Lei, G., Zeng, W., Jiang, Y., Ao, C., Wu, J., & Huang, J. (2021). Sensitivity analysis of the SWAP (Soil-Water-Atmosphere-Plant) model under different nitrogen applications and root distributions in saline soils. Pedosphere, 31(5), 807-821. https://doi.org/10.1016/S1002-0160(21)60038-3

Pinheiro, E. A. R., & De Jong Van Lier, Q. (2021). Propagation of uncertainty of soil hydraulic parameterization in the prediction of water balance components: A stochastic analysis in kaolinitic clay soils. Geoderma, 388, 114910. https://doi.org/10.1016/j.geoderma.2020.114910

Faúndez Urbina, C. A., van den Berg, F., van Dam, J. C., Tang, D. W. S., & Ritsema, C. J. (2020). Parameter sensitivity of SWAP–PEARL models for pesticide leaching in macroporous soils. Vadose Zone Journal, 19(1), 1–14. https://doi.org/10.1002/vzj2.20075

Wang, X., Cai, H., Li, L., & Wang, X. (2020). Estimating soil water content and evapotranspiration of winter wheat under deficit irrigation based on SWAP model. Sustainability, 12(22), 1–29. https://doi.org/10.3390/su12229451

Oliveira, T. C. (2019). Variability of soil hydraulic properties and its impact on agro-hydrological model predictions. PhD thesis, Univ. Sao Paulo. Sao Paulo.

Wesseling, J. G. J., Kroes, J. G. J., Oliveira, T. T. C., & Damiano, F. F. (2019). The impact of sensitivity and uncertainty of soil physical parameters on the terms of the water balance : some case studies with default R packages . Part I : Theory , methods and case descriptions. Computers and Electronics in Agriculture.

Wesseling, J. G. J., Kroes, J. G. J., Oliveira, T. T. C., & Damiano, F. F. (2019). The impact of sensitivity and uncertainty of soil physical parameters on the terms of the water balance : some case studies with default R packages . Part II : Results and discussion. Computers and Electronics in Agriculture, 1–19.

Sedaghatdoost, A., Ebrahimian, H., & Liaghat, A. (2019). An Inverse Modeling Approach to Calibrate Parameters for a Drainage Model with Two Optimization Algorithms on Homogeneous/Heterogeneous Soil. Water Resources Management, 33(4), 1383–1395. https://doi.org/10.1007/s11269-019-2191-x

Xu, X., Sun, C., Huang, G., & Mohanty, B. P. (2016). Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model. Environmental Modelling and Software, 83, 88–102. https://doi.org/10.1016/j.envsoft.2016.05.013

Mulder, M.H., J. Kroes, I.S., 2016. Uncertainty and global sensitivity analysis of actual evapotranspiration and crop Yield using SWAP-WOFOST, in: ICROPM2016. Crop Modelling for Agriculture and Food Security under Global Change. 15-17 March 2016, Berlin, Germany. pp. 294–295. https://communications.ext.zalf.de/sites/crop-modelling/SiteCollectionDocuments/Book_of_Abstracts.pdf

Shafiei, M., Ghahraman, B., Saghafian, B., Davary, K., Pande, S., & Vazifedoust, M. (2014). Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region. Agricultural Water Management, 146, 324–334. https://doi.org/10.1016/j.agwat.2014.09.008

Rienzner, M., Facchi, A., Maria, S.C. De, Gandolfi, C., 2013. The use of the SCEM-UA optimization algorithm in the calibration of the physically based hydrological model SWAP, in: EGU 2013. p. 13255.

Wanders, N., Karssenberg, D., Bierkens, M., Parinussa, R., de Jeu, R., van Dam, J., & de Jong, S., 2012. Observation uncertainty of satellite soil moisture products determined with physically-based modeling. Remote Sensing of Environment, 127(October 2011), 341–356.  https://doi.org/10.1016/j.rse.2012.09.004

Droogers, P., Van Loon, A., & Immerzeel, W. W., 2008. Quantifying the impact of model inaccuracy in climate change impact assessment studies using an agro-hydrological model. Hydrology and Earth System Sciences, 12(2), 669–678.  https://doi.org/10.5194/hess-12-669-2008

Wesseling, J.G., J.G. Kroes, and K. Metselaar, 1998. Global sensitivity analysis of the Soil-Water-Atmosphere-Plant (SWAP) model. Report 160, Alterra Green World Research, Wageningen, 67 p.

Finke, P.A., J.H.M. Wösten, and M.J.W. Jansen, 1996. Effects of uncertainty in major input variables on simulated soil behaviour. Hydrological Processes, 10: 661-669.

15    Regional analysis   (Back to top)

Jiang, Y., Xiong, L., Xu, Z., & Huang, G. (2021). A simulation-based optimization model for watershed multi-scale irrigation water use with considering impacts of climate changes. Journal of Hydrology, 598(April), 126395. https://doi.org/10.1016/j.jhydrol.2021.126395

Mokhtari A., Noory H., Balkhi A., & Alaghmand S. (2021). Comparison of Three Different Satellite-Based Approaches for Aboveground Biomass Estimation. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 89 (1), pp. 33 - 47. https://doi.org/10.1007/s41064-020-00134-9

Weenink, E. (2020). Climate change impacts on maize and potato yields in the Achterhoek & Twente. An exploratory study analysing projected trends and the potential of a shift in the cultivation period as an adaptation measure. MSc-thesis, Water Systems and Global Change Group, Wageningen University, The Netherlands.

Hack-ten Broeke, M. J. D., Mulder, H. M., Bartholomeus, R. P., van Dam, J. C., Holshof, G., Hoving, I. E., Walvoort, D. J. J., Heinen, M., Kroes, J. G., van Bakel, P. J. T., Supit, I., de Wit, A. J. W., & Ruijtenberg, R. (2019). Quantitative land evaluation implemented in Dutch water management. Geoderma, 338, 536-545. https://doi.org/10.1016/j.geoderma.2018.11.002

Li, P., & Ren, L. (2019). Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agro-hydrological model: I. Parameter sensitivity analysis, calibration and model validation. Journal of Hydrology, 574(2), 497–516. https://doi.org/10.1016/j.jhydrol.2019.04.053

Li, P., & Ren, L. (2019). Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agro-hydrological model: II. Scenario simulation and analysis. Journal of Hydrology, 574(2), 715–732. https://doi.org/10.1016/j.jhydrol.2019.03.034

Kroes, J., van Dam, J., Supit, I., de Abelleyra, D., Verón, S., de Wit, A., Boogaard, H., Angelini, M., Damiano, F., Groenendijk, P., Wesseling, J., Veldhuizen, A., 2019. Agrohydrological analysis of groundwater recharge and land use changes in the Pampas of Argentina. Agric. Water Manag. 213, 843–857. https://doi.org/10.1016/j.agwat.2018.12.008

Huang, X., Yu, C., Fang, J., Huang, G., Ni, S., Hall, J., Zorn, C., Huang, X., Zhang, W., 2018. A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer. Comput. Electron. Agric. 154, 400–410. https://doi.org/10.1016/j.compag.2018.07.027

Kroes, J., Boogaard, H., Yan, N., Zhang, M., Groenendijk, P., Supit, I., & Wit, A. de., 2016. Impact analyses of land use changes on soil nitrogen and crop water productivity in the delta of the Huanghe river. EO-BAR symposium, 16-17 May 2016, Beijing, China. In International Symposium on Earth Observation for One Belt.  https://doi.org/10.1016/j.fcr.2012.11.005

Noory, H., van der Zee, S. E. a. T. M., Liaghat, A.-M., Parsinejad, M., & van Dam, J. C., 2011. Distributed agro-hydrological modeling with SWAP to improve water and salt management of the Voshmgir Irrigation and Drainage Network in Northern Iran. Agricultural Water Management, 98(6), 1062–1070.  https://doi.org/10.1016/j.agwat.2011.01.013

Xu, X., Huang, G., Qu, Z., Huang, G., 2011. Regional scale model for simulating soil water flow and solute transport processes- GSWAP. Trans. Chinese Soc. Agric. Eng. 27, 58–63.

Singh, U. K., Ren, L., & Kang, S., 2010. Simulation of soil water in space and time using an agro-hydrological model and remote sensing techniques. Agricultural Water Management, 97(8), 1210–1220.  https://doi.org/10.1016/j.agwat.2010.03.002

Vazifedoust, M., J.C. van Dam, W.G.M. Bastiaanssen and R.A. Feddes, 2009. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, 30, 2523-2545.  https://dx.doi.org/10.1080/01431160802552769

Kroes, J. G., Schaap, J. D., Bolt, F. J. E. Van Der, Wolleswinkel, R. J. L.-, Roelsma, J., Schoumans, O. F., Leenders, T. P. V. T., 2008. Systeemanalyse voor het stroomgebied van de Krimpenerwaard. Fase 3 Monitoring Stroomgebieden Stroomgebieden. Wageningen. In Dutch.

Van Dam, J. C., Singh, R., Bessembinder, J. J. E., Leffelaar, P. A., Bastiaanssen, W. G. M., Jhorar, R. K., Kroes, J. G., Droogers, P., 2006. Assessing Options to Increase Water Productivity in Irrigated River Basins Using Remote Sensing and Modelling Tools. Water Resources Development, 22(1), 115–133.  https://doi.org/10.1080/07900620500405734

Singh, R., Kroes, J. G., Dam, J. C. Van, & Feddes, R., 2006. Distributed ecohydrological modelling to evaluate the performance of irrigation system in Sirsa district , India : I . Current water management and its productivity. Journal of Hydrology, 329, 692–713.  https://doi.org/10.1016/j.jhydrol.2006.03.037

Singh, R., R.K. Jhorar, J.C. van Dam, and R.A. Feddes, 2006. Distributed ecohydrological modelling to evaluate the irrigation system performance in Sirsa district. II. Impact of alternative water management scenarios. Journal of Hydrology, 329, 714-723.  https://doi.org/10.1016/j.jhydrol.2006.03.016

Van Dam, J.C., and R.S. Malik (Eds), 2003. Water productivity of irrigated crops in Sirsa district, India. Integration of remote sensing, crop and soil models and geographical information systems. WATPRO final report, including CD-ROM. ISBN 90-6464-864-6. 173 p.  https://www.futurewater.nl/downloads/2003_VanDam_WatPro.pdf

Jhorar, R.K., W.M.G. Bastiaanssen, R.A. Feddes, and J.C. van Dam, 2002. Inversely estimating soil hydraulic functions using evapotranspiration fluxes. Journal of Hydrology, 258, 198-213.  https://doi.org/10.1016/S0022-1694(01)00564-9

Feddes, R.A., G.H. de Rooij, J.C. van Dam, P. Kabat, P. Droogers and J.N.M. Stricker, 1993. Estimation of regional effective soil hydraulic parameters by inverse modelling. In 'Water flow and solute transport in soils: modelling and application' (Ed. D Russo and G Dagan), Springer Verlag.

16    Integration with other models   (Back to top)

Abdi A., Kapourchal S.A., Vazifedoust M., & Rezaei M. (2022). A novel satellite-based methodology for retrieving specific leaf area of rice (hashemi cultivar) at field scale. Environmental Engineering and Management Journal, 21 (12), pp. 2093 - 2102. https://doi.org/10.30638/eemj.2022.185

Li Y., Liu L., & Sun S. (2022). Study on Soil Water and Salt Information Model of Digital Farmland. Proceedings - 2022 International Conference on Virtual Reality, Human-Computer Interaction and Artificial Intelligence, VRHCIAI 2022, pp. 123 - 127. https://doi.org/10.1109/VRHCIAI57205.2022.00028

Liu, Y., Zeng, W., Ao, C., Lei, G., Wu, J., Huang, J., Gaiser, T., & Srivastava, A. K. (2022). Optimization of winter irrigation management for salinized farmland using a coupled model of soil water flow and crop growth. Agricultural Water Management, 270. https://doi.org/10.1016/j.agwat.2022.107747

Farmaha, B. S., Pritpal, S., & Bijay, S. (2021). Spatial and temporal assessment of nitrate-n under rice-wheat system in riparian wetlands of punjab, north-western india. Agronomy, 11(7). https://doi.org/10.3390/agronomy11071284

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