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High-resolution data on a field scale is very important for improving our understanding of hydrological processes. This is particularly the case for water-demanding agricultural production systems such as rice paddies, for which water-saving strategies need to be developed. Here we report on the application of an in situ, automatic sampling system for high-resolution data on stable isotopes of water (18O and 2H). We investigate multiple rice-based cropping systems consisting of wet rice, dry rice and maize, with a single, but distributed analytical system on a sub-hourly basis. Results show that under dry conditions, there is a clear and distinguishable crop effect on isotopic composition in groundwater. The least evaporative affected groundwater source is that of maize, followed by both rice varieties. Groundwater is primarily a mixture of irrigation and rainwater, where the main driver is irrigation water during the dry season and rainwater during the wet season. Stable isotopes of groundwater under dry season maize react rapidly on irrigation, indicating preferential flow processes via cracks and deep roots. The groundwater during the dry season under wet and dry rice fields is dominated at the beginning of the growing season mainly by the input of rainwater; later, the groundwater is more and more replenished by irrigation water. Overall, based on our data, we estimate significantly higher evaporation (63–77%) during the dry season as compared to the wet season (27–36%). We also find, for the first time, significant sub-daily isotopic variation in groundwater and surface ponded water, with an isotopic enrichment during the daytime. High correlations with relative humidity and temperature, explain part of this variability. Furthermore, the day-night isotopic difference in surface water is driven by the temperature and relative humidity; however, in groundwater, it is neither driven by these factors.

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Tightly constraint parameter ranges are seen as an important goal in constructing hydrological models, a difficult task in complex models. However, many studies show that complex models are often good at capturing the behaviour of a river. Therefore, this study explores the trade-offs between tightly constrained parameters and the ability to predict hydrological signatures, that capture the behaviour of a river. To accomplish this we built five models of differing complexity, ranging from a simple lumped model to a semi-lumped model with eight spatial subdivisions. All models are built within the same modelling framework, use the same data, and are calibrated with the same algorithm. We also consider two different methods for the potential evapotranspiration. We found that that there is a clear trade-off along the axis of complexity. While the more simple models can constrain their parameters quite well, they fail to get the hydrological signatures right. It is the other way around for the more complex models. The method of evapotranspiration only influences the parameters directly related to it. This study highlights that it is important to focus not only on parametric uncertainty. Tightly constrained parameters can be misguiding as they give credibility to oversimplified model structures.

Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they harbor highly specialized floodplain plant species depending on different inundation characteristics. Climate change will most likely alter those characteristics. This study investigates the potential impact of climate change on the inundation characteristics of a floodplain of the Rhine River in Hesse, Germany. We report on the cascading uncertainty introduced through climate projections, climate model structure, and parameter uncertainty. The established modeling framework integrates projections of two general circulation models (GCMs), three emission scenarios, a rainfall-runoff model, and a coupled surface water-groundwater model. Our results indicate large spatial and quantitative uncertainties in the simulated inundation characteristics, which are mainly attributed to the GCMs. Overall, a shift in the inundation pattern, possible in both directions, and an increase in inundation extent are simulated. This can cause significant changes in the habitats of species adapted to these highly-endangered ecosystems.