Investigating summer flow paths in a Dutch agricultural field using high frequency direct measurements

Delsman, Joost; Waterloo, M. J.; Groen, Michel; Groen, J.; Stuyfzand, Pieter J.

doi:10.1016/j.jhydrol.2014.10.058

Cited by 23 publications

(19 citation statements)

References 43 publications

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“… (a) Precipitation and evapotranspiration (mm/d), (b–g) jointly conditioned model result (green, solid line denotes median, shaded area 5–95% prediction interval), heads‐only‐conditioned model result (black, solid line denotes median, shaded area 5–95% prediction interval), and observations (red) for: (b) head in piezometer 5a (for location see Figure 1), (c) head beneath ditch, (d) tile drain exfiltration, (e) ditch in/exfiltration, (f) EC25 of tile drain exfiltration, and (g) EC25 of ditch exfiltration. Shaded red areas around measured values in Figures 9e and 9g are the 25–75% percentiles of Monte Carlo uncertainty estimates [ Delsman et al ., ]. Note that the ditch exfiltration peak of 10 September 2013 was likely caused by infiltration‐excess overland flow [ Delsman et al ., ] and was therefore excluded from the conditioning process.…”

Section: Resultsmentioning

confidence: 99%

Global sampling to assess the value of diverse observations in conditioning a real‐world groundwater flow and transport model

Delsman

Winters

Vandenbohede

et al. 2016

Water Resources Research

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The use of additional types of observational data has often been suggested to alleviate the ill-posedness inherent to parameter estimation of groundwater models and constrain model uncertainty. Disinformation in observational data caused by errors in either the observations or the chosen model structure may, however, confound the value of adding observational data in model conditioning. This paper uses the global generalized likelihood uncertainty estimation methodology to investigate the value of different observational data types (heads, fluxes, salinity, and temperature) in conditioning a groundwater flow and transport model of an extensively monitored field site in the Netherlands. We compared model conditioning using the real observations to a synthetic model experiment, to demonstrate the possible influence of disinformation in observational data in model conditioning. Results showed that the value of different conditioning targets was less evident when conditioning to real measurements than in a measurement error-only synthetic model experiment. While in the synthetic experiment, all conditioning targets clearly improved model outcomes, minor improvements or even worsening of model outcomes was observed for the real measurements. This result was caused by errors in both the model structure and the observations, resulting in disinformation in the observational data. The observed impact of disinformation in the observational data reiterates the necessity of thorough data validation and the need for accounting for both model structural and observational errors in model conditioning. It further suggests caution when translating results of synthetic modeling examples to real-world applications. Still, applying diverse conditioning data types was found to be essential to constrain uncertainty, especially in the transport of solutes in the model

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Section: Resultsmentioning

confidence: 99%

Global sampling to assess the value of diverse observations in conditioning a real‐world groundwater flow and transport model

Delsman

Winters

Vandenbohede

et al. 2016

Water Resources Research

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“…Moreover, they concluded that the fresh/saline ratio of the drainage water will change from zero to the infiltration/upward seepage ratio. However, as shown by others (De Vos et al, 2002;Delsman et al, 2014;Eeman et al, 2012), seasonal variations of infiltration and plant root withdrawal of fresh water will cause temporal fluctuations of the thickness of the lenses and the fresh-saline ratio of the drainage water.…”

Section: Current Progressmentioning

confidence: 97%

Critical knowledge gaps and research priorities in global soil salinity

Hopmans

Qureshi

Kisekka

et al. 2021

Advances in Agronomy

230

105

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“…study. On the other hand, the Schermer polder (surface level 4.14-3.86 m BSL, water depth average depth, water depth 5 m BSL, salinity concentration variation in the ditches 700-7700 g/m 3 (Delsman et al, 2014b)) is representative of polders where the main salinity input derives from shallow saline groundwater, viz. exfiltrating towards ditches and tile drains (Fig.…”

Section: Introductionmentioning

confidence: 99%

Optimal salinity and water level control of water courses using Model Predictive Control

Aydin

Tian

Delsman

et al. 2019

Environmental Modelling & Software

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Worldwide, delta areas are under stress due to climate change. With rising sea levels and decreasing freshwater availability, surface water salinization due to groundwater exfiltration is expected to increase in these low-lying areas. To counteract surface water salinization, freshwater diverted from rivers is used to flush agricultural ditches. In this paper, we demonstrate a Model Predictive Control (MPC) scheme to control salinity and water levels in a water course while minimizing freshwater usage. A state space description of the discretized De Saint Venant and advection-dispersion equations for water and salt transport, respectively, is used as the internal model of the controller. The developed MPC scheme is tested using groundwater exfiltration data from two different representative Dutch polders. The tests demonstrate that water levels and salinity concentrations can successfully be controlled within set limits while minimizing the freshwater used. Software and data availabilityThe developed MPC scheme is implemented in MATLAB. Please contact the corresponding author for further information and availability. Year first available: March 2018. Software required: MATLAB.

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Investigating summer flow paths in a Dutch agricultural field using high frequency direct measurements

Cited by 23 publications

References 43 publications

Global sampling to assess the value of diverse observations in conditioning a real‐world groundwater flow and transport model

Global sampling to assess the value of diverse observations in conditioning a real‐world groundwater flow and transport model

Critical knowledge gaps and research priorities in global soil salinity

Optimal salinity and water level control of water courses using Model Predictive Control

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