Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements
Abstract
:1. Introduction
- Simulation of long-term soil moisture dynamics and thorough validation of a process-based agroecosystem model using long-term consistent and continuous time series of daily soil water contents measured by TDR and pressure heads observed by tensiometers and an analysis of model performance.
- Identification of reasons for mismatches between simulated and measured soil water contents and pressure heads with respect to seasonal effects, hydrometeorological conditions, soil hydraulic parameters, applied measurement techniques, cultivated crop types and model assumptions.
2. Materials and Methods
2.1. Simulation Model
2.2. Description of the Test Site
2.3. Soil Hydrological Measurements
2.4. Estimation of Model Performance
2.5. Model Set Up
3. Results
3.1. Hydrometeorological Conditions
3.2. Simulated LAI, Rooting Depth, RWU, Soil Water Storage and Drainage
3.3. Soil Water Contents and Pressure Heads at Plot 1
3.4. Soil Water Contents and Pressure Heads at Plot 2
- Inadequate description of the spatial distribution of RWU in the root zone depending on crop specific maximum rooting depth and root density distribution.
- Overestimation of potential RWU by the model.
3.5. Soil Water Contents and Pressure Heads at Plot 3
4. Discussion
- Errors in RWU-calculations in the summer half years due to incorrect description of root density distribution and calculation of crop specific rooting depth by the model.
- Measurement errors of the TRIME-probes despite a quality check and correction of the measured soil water contents using gravimetry.
- Differences between TRIME and tensiometers regarding measurement scale, precision and measuring principle.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Data Availability
Appendix A
Year | Crop | Sowing (day.month.year) | Harvest (day.month.year) | Observed Yield | Simulated Yield |
---|---|---|---|---|---|
1993 | Sugar beet | 26.04.93 | 06.10.93 | 8.455 | 9.955 |
1993/1994 | Winter wheat | 15.10.93 | 29.07.94 | 4.516 | 6.150 |
1994/1995 | Winter barley | 26.09.94 | 21.07.95 | 5.652 | 7.988 |
1995/1996 | Winter rye | 02.10.95 | 21.08.96 | 6.812 | 8.423 |
1996 | Oil redish (winter catch crop) | 05.09.96 | - | - | - |
1997 | Sugar beet | 03.04.97 | 23.09.97 | 11.639 | 10.789 |
1997/1998 | Winter wheat | 08.10.97 | 27.07.98 | 3.490 | 5.120 |
1999 | Lucerne-clover-grass-mix | - | - | - | - |
2000 | Lucerne-clover-grass-mix | - | - | - | - |
2000/2001 | Winter wheat | 20.09.00 | 27.07.01 | 6.678 | 6.695 |
2001/2002 | Winter barley | 21.09.01 | 06.06.02 | - | - |
2002/2003 | Winter oilseed rape | 19.08.02 | 11.07.03 | 2.780 | 1.988 |
2003/2004 | Winter triticale | 21.09.03 | 31.07.04 | 6.360 | 8.123 |
2004/2005 | Winter rye | 23.09.04 | 02.08.05 | 6.270 | 6.195 |
2006 | Potatoes | 24.04.06 | 15.08.06 | 4.116 | 3.578 |
2007 | Sorghum | 23.05.07 | 27.09.07 | 10.112 | - |
2008 | Sorghum | 29.05.08 | 10.09.08 | 5.492 | - |
2008/2009 | Winter triticale | 24.09.08 | 23.06.09 | 9.706 | 8.408 |
2009 | Lucerne-clover-grass-mix | 03.07.09 | 25.08.09 | 3.764 | 3.212 |
2009 | “ | - | 20.10.09 | 1.155 | 2.112 |
2010 | “ | - | 26.0510 | 6.421 | 3.762 |
2010 | “ | - | 16.07.10 | 2.649 | 2.223 |
2010 | “ | - | 05.10.10 | 4.741 | 3.512 |
2011 | Lucerne-clover-grass-mix | - | 21.04.11 | - | - |
2011 | Silage maize | 02.05.11 | 14.09.11 | 23.623 | 19.989 |
201/2012 | Winter rye for green biomass | 26.09.11 | 23.05.12 | 8.461 | 5.678 |
2012 | Sorghum | 24.05.12 | 18.09.12 | 10.339 | - |
2012/2013 | Winter triticale for green biomass | 28.09.12 | 24.06.13 | 10.939 | 8.173 |
2013 | Lucerne-clover-grass-mix | 08.07.13 | 10.09.13 | 2.564 | 3.121 |
2014 | “ | - | 14.05.14 | 7.443 | 4.123 |
2014 | “ | - | 02.07.14 | 5.869 | 3.176 |
2014 | “ | - | 05.08.14 | 2.307 | 2.221 |
2014 | “ | - | 09.10.14 | 3.561 | 2.456 |
Year | Crop | Sowing (day.month.year) | Harvest (day.month.year) | Observed Yield | Simulated Yield |
---|---|---|---|---|---|
1992/1993 | Sugar beet | 26.04.93 | 06.10.93 | 9.467 | 12.123 |
1993/1994 | Winter wheat | 15.10.93 | 29.07.94 | 3.245 | 5.123 |
1994/1995 | Winter barley | 26.09.94 | 21.07.95 | 3.020 | 4.134 |
1995/1996 | Winter rye | 02.10.95 | 21.08.96 | 2.450 | 4.123 |
1996 | Yellow mustard (winter catch crop) | 05.09.96 | - | - | - |
1997 | Sugar beet | 03.04.97 | 23.09.97 | 11.762 | 11.706 |
1997/1998 | Winter wheat | 08.10.97 | 27.07.98 | 1.430 | 4.123 |
1999 | Lucerne-clover-grass-mix | - | - | - | - |
2000 | “ | - | - | - | - |
2000/2001 | Winter rye | 17.09.00 | 25.07.01 | 6.580 | 7.826 |
2002 | Peas | 11.04.02 | 16.07.02 | 2.710 | - |
2002/2003 | Winter barley | 16.09.02 | 14.07.03 | 3.600 | 6.720 |
2003/2004 | Winter oilseed rape | 20.08.03 | 23.07.04 | 4.280 | 2.315 |
2004/2005 | Winter wheat | 23.09.04 | 01.08.05 | 4.610 | 6.723 |
2006 | Silage maize | 02.05.06 | 12.09.06 | 5.820 | 6.928 |
2007 | Winter rye for green biomass | 19.09.06 | 24.04.07 | - | - |
2007 | Sorghum | 23.05.07 | 27.09.07 | 7.596 | - |
2007/2008 | Winter rye for green biomass | 02.10.07 | 28.05.08 | 5.710 | 3.935 |
2008 | Sorghum | 29.05.08 | 10.09.08 | 5.264 | - |
2008/2009 | Winter triticale | 24.09.08 | 23.06.09 | 9.365 | 8.322 |
2009 | Lucerne-clover-grass-mix | 03.07.09 | 25.08.09 | 4.391 | 3.213 |
2009 | “ | - | 20.10.09 | 1.248 | 2.111 |
2010 | “ | - | 26.05.10 | 6.374 | 3.517 |
2010 | “ | - | 15.07.10 | 2.122 | 2.213 |
2010 | “ | - | 05.10.10 | 5.166 | 3.613 |
2011 | Lucerne-clover-grass-mix | - | 21.04.11 | - | - |
2011 | Silage maize | 02.05.11 | 14.09.11 | 20.275 | 17.845 |
2011/2012 | Winter rye for green biomass | 26.09.11 | 23.05.12 | 6.760 | 3.213 |
2012 | Sorghum | 24.05.12 | 18.09.12 | 8.639 | |
2012/2013 | Winter triticale | 28.09.12 | 24.06.13 | 7.248 | 7234 |
2013 | Lucerne-clover-grass-mix | 08.07.13 | 10.09.13 | - | - |
2014 | “ | - | 14.05.14 | 5.611 | 3.231 |
2014 | “ | - | 02.07.14 | 6.052 | 3.455 |
2014 | “ | - | 05.08.14 | 2.712 | 2.221 |
2014 | “ | - | 09.10.14 | 3.259 | 2.345 |
Year | Crop | Sowing (day.month.year) | Harvest (day.month.year) | Observed Yield | Simulated Yield |
---|---|---|---|---|---|
1993 | Sugar beet | 26.04.93 | 06.10.93 | 15.427 | 13.896 |
1993/1994 | Winter wheat | 15.10.93 | 29.07.94 | 4.797 | 6.344 |
1994/1995 | Winter barley | 26.09.94 | 21.07.95 | 5.681 | 7.123 |
1995/1996 | Winter rye | 02.10.95 | 21.08.96 | 5.135 | 5.344 |
1996 | Phacelia | 05.09.96 | - | - | - |
1997 | Sugar beet | 03.04.97 | 23.09.97 | 12.419 | 11.762 |
1997/1998 | Winter wheat | 08.10.97 | 27.07.98 | 4.368 | 7.289 |
1999 | Lucerne-clover-grass-mix | - | - | - | - |
2000 | “ | - | - | - | - |
2000/2001 | Winter rye | 17.09.00 | 25.07.01 | 7.965 | 8.242 |
2002 | Potatoes | 22.04.02 | 15.08.02 | 10.382 | 7.675 |
2002/2003 | Winter wheat | 20.09.02 | 21.07.03 | 4.700 | 6.123 |
2003/2004 | Winter barley | 14.09.03 | 08.07.04 | 6.650 | 8.127 |
2004/2005 | Winter oil seed rape | 20.08.04 | 18.07.05 | 3.410 | 1.945 |
2005/2006 | Winter triticale | 23.09.05 | 18.07.06 | 4.010 | 6.981 |
2006/2007 | Winter rye for green biomass | 19.09.06 | 21.05.07 | - | - |
2007 | Sorghum | 23.05.07 | 10.09.07 | 11.231 | - |
2007/2008 | Winter rye for green biomass | 02.10.07 | 30.03.08 | - | - |
2008 | Silage maize | 24.04.08 | 05.09.08 | 10.790 | 7.981 |
2008/2009 | Winter rye for green biomass | 24.09.08 | 30.03.09 | - | - |
2009 | Silage maize | 30.04.09 | 11.09.09 | 21.979 | 17.896 |
2009/2010 | Winter rye for green biomass | 24.09.09 | 30.03.10 | - | - |
2010 | Silage maize | 30.04.10 | 18.09.10 | 15.330 | 10.234 |
2010/2011 | Winter rye for green biomass | 24.09.10 | 21.04.11 | - | - |
2011 | Silage maize | 02.05.11 | 14.09.11 | 22.684 | 17.331 |
2011/2012 | Winter rye for green biomass | 26.09.11 | 11.04.12 | - | - |
2012 | Silage maize | 25.04.12 | 17.09.12 | 21.504 | 16.123 |
2012/2013 | Winter rye for green biomass | 28.09.12 | 30.03.13 | - | |
2013 | Silage maize | 26.04.13 | 12.09.13 | 16.412 | 12.325 |
2014 | Winter rye for green biomass | 25.09.13 | 14.04.14 | - | - |
2014 | Silage maize | 30.04.14 | 18.09.14 | - | - |
References
- Bitteli, M. Measuring Soil Water Content: A Review. Hort. Technol. 2011, 21, 293–300. [Google Scholar] [CrossRef]
- Robinson, R.; Campbell, C.S.; Hopmans, J.W.; Hornbuckle, B.K.; Jones, S.B.; Knight, R.; Ogden, F.; Selker, J.; Wendroth, O. Soil moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review. Vadose Zone J. 2008, 7, 358–389. [Google Scholar] [CrossRef]
- Vereecken, H.; Huisman, J.A.; Bogena, H.; Vanderborght, J.; Vrugt, J.A.; Hopmans, J.W. On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resour. Res. 2008, 44, W00D06. [Google Scholar] [CrossRef]
- Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.-C.; Olesen, J.E.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; et al. Crop modelling for integrated assessment of risk to food production from climate change. Environ. Model. Softw. 2015, 72, 287–303. [Google Scholar] [CrossRef]
- Asseng, S.; Ewert, F.; Martre, P.; Rötter, R.P.; Lobell, D.B.; Cammarano, D.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; et al. Rising temperatures reduce global wheat production. Nat. Clim. Chang. 2015, 5, 143–147. [Google Scholar] [CrossRef]
- Bassu, S.; Brisson, N.; Durand, J.L.; Boote, K.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; et al. How do various maize crop models vary in their responses to climate change factors? Glob. Chang. Biol. 2014, 20, 2301–2320. [Google Scholar] [CrossRef] [PubMed]
- Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.; Antle, J.M.; Nelson, G.C.; Porter, C.; Janssen, S.; et al. The agricultural model intercomparison and improvement project (AgMIP): Protocols and pilot studies. Agric. For. Met. 2013, 170, 166–182. [Google Scholar] [CrossRef]
- White, J.W.; Hunt, L.A.; Boote, K.J.; Jones, J.W.; Koo, J.; Kim, S.; Porter, C.H.; Wilkens, P.W.; Hoogenboom, G. Integrated description of agricultural field experiments and production: The ICASA version 2.0 data standards. Comput. Electron. Agric. 2013, 96, 1–12. [Google Scholar] [CrossRef]
- Kersebaum, K.-C.; Boote, K.J.; Jorgenson, J.S.; Nendel, C.; Bindi, M.; Frühauf, C.; Gaiser, T.; Hoogenboom, G.; Kollas, C.; Olesen, J.E.; et al. Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environ. Model. Softw. 2015, 72, 402–417. [Google Scholar] [CrossRef]
- Bitteli, M. Measuring Soil Water Potential for Water Management in Agriculture: A Review. Sustainability 2010, 2, 1226–1251. [Google Scholar] [CrossRef]
- Vereecken, H.; Huisman, J.A.; Pachepsky, Y.; Montzka, C.; van der Kruk, J.; Bogena, H.; Weihermuller, L.; Herbst, M.; Martinez, G.; Vanderborght, J. On the spatio-temporal dynamics of soil moisture at the field scale. J. Hydrol. 2014, 516, 76–96. [Google Scholar] [CrossRef]
- Brocca, L.; Ciabatta, L.; Massari, C.; Camici, S.; Tarpanelli, A. Soil Moisture for Hydrological applications: Open Questions and New Opportunities. Water 2017, 9, 140. [Google Scholar] [CrossRef]
- Poltoradnev, M.; Ingwersen, J.; Streck, Th. Spatial and Temporal Variability of Soil Water Content in Two Regions of Southwest Germany during a Three-Year Observation Period. Vadose Zone J. 2016. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; et al. The international soil moisture network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 2011, 15, 1675–1698. [Google Scholar] [CrossRef]
- Smith, A.B.; Walker, J.P.; Western, A.W.; Young, R.I.; Ellett, K.M.; Pipunic, R.C.; Grayson, R.B.; Siriwidena, L.; Chiew, F.H.S.; Richter, H. The Murrumbidgee Soil Moisture Monitoring Network Data Set. Water Resour. Res. 2012, 48, W07701. [Google Scholar] [CrossRef]
- Schelde, K.; Ringgaard, R.; Herbst, M.; Thomsen, A.; Friborg, T.; Søgaard, H. Comparing Evapotranspiration Rates Estimated from Atmospheric Flux and TDR Soil Moisture Measurements. Vadose Zone J. 2011, 10, 78–83. [Google Scholar] [CrossRef]
- Deb, S.K.; Manoj, K.S.; Mexal, J.G. Numerical Modeling of Water Fluxes in the Root Zone of a Mature Pecan Orchard. Soil Sci. Soc. Am. J. 2011, 75, 1667–1680. [Google Scholar] [CrossRef]
- Marković, M.; Filipović, V.; Legović, T.; Josipović, M.; Tadić, V. Evaluation of different soil water potential by field capacity threshold in combination with a triggered irrigation module. Soil Water Resour. 2015, 10, 164–171. [Google Scholar] [CrossRef]
- Zhang, K.; Bosch-Serra, A.D.; Boixadera, J.; Thompson, A.J. Investigation of Water Dynamics and the Effect of Evapotranspiration on Grain Yield of Rainfed Wheat and Barley under a Mediterranean Environment: A Modelling Approach. PLoS ONE 2015, 10, e0131360. [Google Scholar] [CrossRef]
- Karandish, F.; Šimunek, J. A comparison of numerical and machine-learning modeling of soil water ontent with limited input data. J. Hydrol. 2016, 543, 892–909. [Google Scholar] [CrossRef]
- Kelleners, T.J.; Koonce, J.; Shillito, R.; Dijkema, J.; Berli, M.; Young, M.H.; Frank, J.M.; Massman, W.J. Numerical Modeling of Coupled Water Flow and Heat Transport in soil and snow. Soil Sci. Soc. Am. J. 2016, 80, 247–263. [Google Scholar] [CrossRef]
- Chandler, D.G.; Seyfried, M.S.; McNamara, J.P.; Hwang, K. Inference of Soil Hydrologic Parameters from Electronic Soil Moisture Records. Front. Earth Sci. 2017, 5, 25. [Google Scholar] [CrossRef]
- Sipek, V.; Tesar, M. Year-round estimation of soil moisture content using temporally variable soil hydraulic parameters. Hydrol. Process. 2017, 31, 1438–1452. [Google Scholar] [CrossRef]
- Hua, W.; Wang, C.; Chen, G.; Yang, H.; Zhai, Y. Measurement and Simulation of Soil Water Contents in an Experimental Field in Delta Plain. Water 2017, 9, 947. [Google Scholar] [CrossRef]
- Wegehenkel, M.; Mirschel, W.; Wenkel, K.O. Predictions of soil water and crop growth dynamics using the agroecosystem models THESEUS and OPUS. J. Plant Nutr. Soil Sci. 2004, 167, 736–744. [Google Scholar] [CrossRef]
- Van Ittersum, M.K.; Leffelaar, P.A.; van Keulen, H.; Kropff, M.J.; Bastiaans, L.; Goudriaan, J. On approaches and applications of the Wageningen crop models. Eur. J. Agron. 2003, 18, 201–234. [Google Scholar] [CrossRef]
- Penman, H.L. Evaporation: An introductory survey. Neth. J. Agric. Sci. 1956, 4, 9–29. [Google Scholar]
- Frère, M.; Popov, G.F. Agrometeorological Crop Monitoring and Forecasting; FAO Plant Production and Protection Paper 17; FAO: Rome, Italy, 1979. [Google Scholar]
- Ritchie, J.T. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 1972, 8, 1204–1212. [Google Scholar] [CrossRef]
- Koitzsch, R.; Günter, R. Modell zur ganzjährigen Simulation der Verdunstung und der Bodenfeuchte landwirtschaftlicher Kulturen. Archiv für Acker-und Pflanzenbau und Bodenkunde 1990, 24, 717–725. [Google Scholar]
- Ten Berge, H.F.M.; Metselaar, K.; Jansen, M.J.W.; San Agustin, E.M.; Woodhead, T. The Sawah riceland hydrology model. Water Resour. Res. 1995, 31, 2721–2731. [Google Scholar] [CrossRef]
- Van Genuchten, M. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 1980, 44, 892–898. [Google Scholar] [CrossRef]
- Mualem, Y. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 1976, 12, 513–522. [Google Scholar] [CrossRef]
- BGR-SGD (Bundesanstalt für Geowissenschaften und Rohstoffe–Staatliche Geologische Dienste) (Ed.) Bodenkundliche Kartieranleitung KA5; Schweizerbart Science Publishers: Hannover, Germany, 2005; 438p. [Google Scholar]
- Schindler, U.; Müller, L.; Eulenstein, F. Investigations in the discharge out of the root zone at sandy arable soils in pleistocene landscapes. Arch. Agron. Soil Sci. 1995, 41, 161–169. [Google Scholar] [CrossRef]
- FAO. World Reference Base for soil Resources; Food and Agriculture Organization of the United Nations: Rome, Italy, 2015; 203p. [Google Scholar]
- Mirschel, W.; Barkusky, D.; Kersebaum, K.-C.; Laacke, L.; Luzi, K.; Rosner, G.; Wenkel, K.-O. Field data set of different cropping systems for agro-ecosystem modelling from Müncheberg, Germany. Open Data J. Agric. Res. 2018, 4, 1–8. [Google Scholar] [CrossRef]
- Wegehenkel, M.; Luzi, K.; Mirschel, W.; Sowa, D.; Barkusky, D. 22-years time series of observed daily soil water contents and pressure heads under rainfed conditions from agricultural field plots at the Experimental Station Müncheberg, Germany. Open Data J. Agric. Res. 2019. under review. [Google Scholar]
- Willmott, C.J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 1982, 64, 1309–1313. [Google Scholar] [CrossRef]
- Ghezzehei, T. Errors in determination of soil water content using time domain reflectometry caused by soil compaction around waveguides. Water Resour. Res. 2008, 44, W08451. [Google Scholar] [CrossRef]
- Graeff, T.; Zehe, E.; Schlaeger, S.; Morgner, M.; Bauer, A.; Becker, R.; Creutzfeldt, B.; Bronstert, A. A quality assessment of Spatial TDR soil moisture measurements in homogenous and heterogeneous media with laboratory experiments. Hydrol. Earth Syst. Sci. 2010, 14, 1007–1020. [Google Scholar] [CrossRef]
- Yu, L.; Zeng, Y.; Su, Z.; Cai, H.; Zheng, Z. The effect of different evapotranspiration methods on portraying soil water dynamics and ET partitioning in a semi-arid environment in Northwest China. Hydrol. Earth Syst. Sci. 2016, 20, 975–990. [Google Scholar] [CrossRef]
Horizon | Depth from to (cm) | Sand (%) | Clay (%) | Silt (%) | Organic Carbon (%) | Bulk Density (g cm−3) |
---|---|---|---|---|---|---|
Plot 1 | ||||||
Ap | 0–30 | 90 | 7 | 3 | 0.45 | 1.45 |
Ael | 30–60 | 90 | 5 | 5 | 0.26 | 1.50 |
Bt | 60–90 | 80 | 12 | 8 | 0.10 | 1.55 |
C1 | 90–120 | 90 | 4 | 6 | - | - |
C2 | 120–200 | 90 | 3 | 7 | - | - |
Plot 2 | ||||||
Ap | 0–30 | 85 | 5 | 10 | 0.45 | 1.45 |
Ael | 30–90 | 90 | 5 | 5 | 0.26 | 1.50 |
Bt1 | 90–130 | 80 | 12 | 8 | 0.10 | 1.55 |
Bt2 | 130–170 | 80 | 10 | 10 | - | - |
C | 170–200 | 90 | 5 | 5 | - | - |
Plot 3 | ||||||
Ap | 0–30 | 85 | 6 | 9 | 0.45 | 1.45 |
Ael | 30–100 | 90 | 5 | 5 | 0.26 | 1.50 |
Bt1 | 100–110 | 81 | 13 | 7 | 0.10 | 1.55 |
Bt2 | 110–200 | 80 | 11 | 9 | - | - |
θs (cm3 cm−3) | θr (cm3 cm−3) | n | α (cm−1) | Ksat (cm day−1) | |
---|---|---|---|---|---|
Plot 1 | |||||
Ap | 0.38 | 0.03 | 2.013 | 0.021 | 92 |
Ael | 0.32 | 0.03 | 2.179 | 0.027 | 162 |
Bt | 0.38 | 0.07 | 2.147 | 0.028 | 30 |
C1, C2 | 0.32 | 0.03 | 2.379 | 0.027 | 162 |
Plot 2 | |||||
Ap | 0.39 | 0.03 | 2.013 | 0.021 | 92 |
Ael | 0.32 | 0.03 | 2.179 | 0.027 | 162 |
Bt1, Bt2 | 0.39 | 0.07 | 2.147 | 0.028 | 30 |
C | 0.32 | 0.03 | 2.379 | 0.027 | 162 |
Plot 3 | |||||
Ap | 0.39 | 0.03 | 2.013 | 0.021 | 92 |
Ael | 0.32 | 0.03 | 2.379 | 0.027 | 162 |
Bt1, Bt2 | 0.39 | 0.07 | 2.147 | 0.028 | 30 |
PV_200 cm (mm) | FC_200 cm (mm) | WP_200 cm (mm) | |
---|---|---|---|
Plot 1 | 678 | 238 | 65 |
Plot 2 | 710 | 266 | 81 |
Plot 3 | 724 | 267 | 88 |
Soil Compartment | Number of Data Pairs (1993–2014 = 8035 Potential Data Pairs Measured Versus Simulated Daily Soil Water Content) | IA | R2 | RMSD (cm3 cm−3) |
---|---|---|---|---|
Plot 1 | ||||
0–30 cm | 6900 | 0.82 | 0.61 | 0.02 |
30–60 cm | 7409 | 0.84 | 0.63 | 0.02 |
60–90 cm | 7667 | 0.45 | 0.10 | 0.04 |
90–120 cm | 5565 | 0.78 | 0.55 | 0.02 |
120–150 cm | 5551 | 0.72 | 0.50 | 0.02 |
Plot 2 | ||||
0–30 cm | 6720 | 0.78 | 0.58 | 0.03 |
30–60 cm | 6658 | 0.83 | 0.62 | 0.02 |
60–90 cm | 6724 | 0.71 | 0.44 | 0.04 |
90–120 cm | 5915 | 0.52 | 0.12 | 0.04 |
120–150 cm | 6778 | 0.42 | 0.12 | 0.04 |
Plot 3 | ||||
0–30 cm | 6720 | 0.86 | 0.66 | 0.02 |
30–60 cm | 7101 | 0.84 | 0.63 | 0.02 |
60–90 cm | 7221 | 0.82 | 0.61 | 0.02 |
90–120 cm | 5890 | 0.82 | 0.65 | 0.02 |
120–150 cm | 5590 | 0.71 | 0.53 | 0.02 |
Measurement Depth | Number of Data Pairs (1993–2014 = 8035 Potential Data Pairs Measured Versus Simulated Daily Pressure Head) | IA | R2 | RMSD (hPa) |
---|---|---|---|---|
Plot 1 | ||||
30 cm | 2569 | 0.78 | 0.50 | 120 |
60 cm | 3985 | 0.51 | 0.20 | 140 |
90 cm | 6408 | 0.30 | 0.10 | 200 |
120 cm | 4756 | 0.11 | 0.11 | 122 |
150 cm | 5437 | 0.11 | 0.11 | 110 |
Plot 2 | ||||
30 cm | 2592 | 0.79 | 0.56 | 141 |
60 cm | 2767 | 0.45 | 0.18 | 144 |
90 cm | 6938 | 0.33 | 0.10 | 102 |
120 cm | 5356 | 0.67 | 0.21 | 31 |
150 cm | 6137 | 0.78 | 0.67 | 20 |
Plot 3 | ||||
30 cm | 2276 | 0.77 | 0.48 | 121 |
60 cm | 2554 | 0.28 | 0.11 | 136 |
90 cm | 6663 | 0.35 | 0.11 | 125 |
120 cm | 4826 | 0.74 | 0.45 | 27 |
150 cm | 5322 | 0.76 | 0.53 | 17 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wegehenkel, M.; Luzi, K.; Sowa, D.; Barkusky, D.; Mirschel, W. Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements. Water 2019, 11, 989. https://doi.org/10.3390/w11050989
Wegehenkel M, Luzi K, Sowa D, Barkusky D, Mirschel W. Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements. Water. 2019; 11(5):989. https://doi.org/10.3390/w11050989
Chicago/Turabian StyleWegehenkel, Martin, Karin Luzi, Dieter Sowa, Dietmar Barkusky, and Wilfried Mirschel. 2019. "Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements" Water 11, no. 5: 989. https://doi.org/10.3390/w11050989
APA StyleWegehenkel, M., Luzi, K., Sowa, D., Barkusky, D., & Mirschel, W. (2019). Simulation of Long-Term Soil Hydrological Conditions at Three Agricultural Experimental Field Plots Compared with Measurements. Water, 11(5), 989. https://doi.org/10.3390/w11050989