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International audience; The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.

Hyporheic exchange is the interaction of river water and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic exchange has been attributed to the representation of heterogeneous subsurface properties. Our study evaluates the trade-offs between intrinsic (irreducible) and epistemic (reducible) model errors when choosing between homogeneous and highly complex subsurface parameter structures. We modeled the Steinlach River Test Site in Southwest Germany using a fully coupled surface water-groundwater model to simulate hyporheic exchange and to assess the predictive errors and uncertainties of transit time distributions. A highly parameterized model was built, treated as a "virtual reality" and used as a reference. We found that if the parameter structure is too simple, it will be limited by intrinsic model errors. By increasing subsurface complexity through the addition of zones or heterogeneity, we can begin to exchange intrinsic for epistemic errors. Thus, the appropriate level of detail to represent the subsurface depends on the acceptable range of intrinsic structural errors for the given modeling objectives and the available site data. We found that a zonated model is capable of reproducing the transit time distributions of a more detailed model, but only if the geological structures are known. An interpolated heterogeneous parameter field (cf. pilot points) showed the best trade-offs between the two errors, indicating fitness for practical applications. Parameter fields generated by multiple-point geostatistics (MPS) produce transit time distributions with the largest uncertainties, however, these are reducible by additional hydrogeological data, particularly flux measurements.

Numerical models for reactive transport can be used to estimate the breakthrough of a contaminant in a pumping well or at other receptors. However, as natural aquifers are highly heterogeneous with unknown spatial details, reactive transport predictions on the aquifer scale require a stochastic framework for uncertainty analysis. The high computational demand of spatially explicit reactive-transport models hampers such analysis, thus motivating the search for simplified estimation tools. We suggest performing an electron balance between the reactants in the infiltrating solution and in the aquifer matrix to obtain the hypothetical time of dissolved-reactant breakthrough at a receptor if the reaction with the matrix was instantaneous. This time we denote as the advective breakthrough time for instantaneous reaction (τinst ). It depends on the amount of the reaction partner present in the matrix, the mass flux of the dissolved reactant, and the stoichiometry. While the shape of the reactive-species breakthrough curve depends on various kinetic parameters, the overall timing scales with τinst . We calculate the latter by particle tracking. The effort of computing τinst is so low that stochastic calculations become feasible. We apply the concept to a two-dimensional test case of aerobic respiration and denitrification. A detailed spatially explicit reactive-transport model includes microbial dynamics. Scaling the time of local breakthrough curves observed at individual points by τinst decreased the variability of electron-donor breakthrough curves significantly. We conclude that the advective breakthrough time for instantaneous reaction is efficient in estimating the time over which an aquifer retains its degradation potential.

For decades, lumped rainfall-runoff models have been used for hydrological analysis and forecasting such as operational flood forecasting. However, the accuracy of model forecasts depends on the ability of the model to approximate the dominant hydrological processes of the catchment under consideration. These processes are site specific and, therefore, the choice of a particular model is challenging. A large number of hydrological models has been developed and applied in various regions of the world. Model choice has often been hampered in the past by technical problems such as different programming languages, different software platforms, and different input formats and requirements.The Modular Assessment of Rainfall Runoff Model Toolbox (MARRMoT) unites 46 lumped models from around the world within the same Matlab® framework with standardized inputs. The model equations have been simplified and adapted for this purpose. As a result, it is possible to test a large number of different models with comparatively little effort. The models implemented in MARRMoT vary in their structural complexity and have between 1-24 parameters and between 1-8 storages.Here MARRMoT was used in order to find a model or model ensemble suitable for the simulation of precipitation-runoff relationships in the Wairau River catchment, New Zealand. The catchment area is assumed to have predominantly homogeneous runoff-generating properties. Model input data (precipitation and potential evapotranspiration) was derived from the Virtual Climate Station Network by National Institute of Water and Atmospheric Research, NZ.In a first scenario, 42 selected models from MARRMoT were calibrated for the Wairau River catchment using 45 years of Wairau River flow data, an in-built nonlinear unconstrained optimization algorithm and the model fitness criteria Kling-Gupta-Efficiency (KGE). In two further scenarios, calibrations using the KGE with inversely transformed flows (KGEi) as well as a mixed form of the two criteria (KGEm) were realized. Model performance was further evaluated based on different performance criteria such as NSE, RMSE and R². It was demonstrated that the model ranking depends on the choice of the performance.Evaluating the model performance for the different calibration scenarios showed that a few models with very different structures performed well to reproduce the flow data. No decisive structural feature could be identified which all models have in common and which led to a good representation of the rainfall-runoff processes in the Wairau River catchment. However, the differentiated consideration of flow routing and a high degree of flexibility seem to benefit model performance. Deficits in the modeling can be seen in the discharge peaks, which are not correctly simulated by many models. The simulation of fast direct runoff with lumped models seems to be less accurate for the relatively large catchment area of ​​the Wairau River (3430 km²).Eventually, three models (GR4J, FLEX-I and HBV-96) demonstrated a high performance in all three calibration scenarios and were identified as suitable for further use in the Wairau River catchment.

Groundwater is a major resource for drinking water supply and irrigation of crops in many parts of the world. Many groundwater aquifers are already fully allocated, but the demand is projected to increase further while, concurrently, climate change may cause more variability in the natural supply. This poses enormous challenges for the future management of groundwater resources and a paradigm shift from traditional, threshold-based management strategies to more flexible, adaptive management strategies.For that purpose, operational forecasting tools are required that predict future states of groundwater aquifers under various scenarios and to predict the risk of critical states which would have adverse effects either for the environment or for water users, or both.The mathematical description of the complex interactions particularly of shallow, unconfined river-fed aquifers typically requires the use of spatially explicit numerical models. These are, however, not suitable for operational forecasting due to lengthy run-times and extensive data requirements. This also poses strong limitations with respect to predictive uncertainty analysis – which should be an integral part of predictive management tools. Model simplification or model surrogates are the method of choice to circumvent the problem.An operational forecasting tool is presented here to predict groundwater heads and groundwater storage in the unconfined Wairau Plain Aquifer in Marlborough, New Zealand, during flow recession times. The tool uses low-complexity “eigenmodels” to describe groundwater flow and to provide an early warning for critical groundwater storage levels to the Marlborough District Council, which manages the groundwater resource. These critical levels have been approached more frequently during the past years when the natural recession of groundwater storage in summer is exacerbated by groundwater abstraction to satisfy the irrigation water demand of the Plain’s viticulture.The forecasting tool requires, amongst others, daily forecasts of Wairau River flows because the river is the major recharge source for the aquifer. Flow forecasts and their uncertainty are computed i) by using a master recession curve for predictions during flow recession times and ii) by a lumped rainfall-runoff model for times of aquifer storage recovery. This allows a broad evaluation of forecasting scenarios. The tool has been tested and is operational for recession times (worst-case scenario predictions; Wöhling & Burbery, 2020). The rainfall-runoff model performs reasonably well in predicting river flows and correspondingly in predicting groundwater storage recovery for historic data (hindcasting). The 30-day predictive uncertainty bounds generally cover the observations of river flows, groundwater levels and aquifer storage. The predictive accuracy of the tool largely depends on the predictive accuracy of the drivers, particularly of the areal estimates of precipitation that drives the rainfall-runoff model and the river-groundwater exchange function that describes aquifer recharge rates. References Wöhling T, Burbery L (2020). Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession. Science of the Total Environment, 747, 141220, doi: 10.1016/j.scitotenv.2020.141220.

Rising concentrations of dissolved organic carbon (DOC) in inland waters are observed and investigated intensely in the last decades. The development of adaptive measures requires the forecasting of DOC-exports from catchments. Since DOC is exported from river catchments along hydrological pathways it is evident that the investigation of runoff generation, retention and travel times along flow paths are important to quantify DOC-loads and to develop a forecast model.To gain comprehensive insights in runoff formation and DOC export in a small forested catchment in the Bavarian Forest National Park we apply a nested multi-tracer approach, combining experimental and analytical methods with the aim to develop a hydrological forecast model which is able to reproduce the dominant mobilization- and export processes of DOC in forested mountain catchments. The use of multiple tracers combines different approaches to determine source areas, flow paths and retention times of runoff water in catchments. Stable isotopes (d2H, d18O) are suitable as natural tracers to estimate contributions from precipitation to stream discharge. With the additional use of geochemical tracers (e.g. DOC, SiO2) contributions from groundwater and the organic and mineral soil horizons can be estimated. Combined with a nested approach these analyses can be conducted on different spatial scales, enabling the development of scalable prognostic models of runoff formation in catchments.To complement the limited information from historic data sets we instrumented two hill transects to observe lateral contributions from hill slopes and to investigate potential preferential flow paths. Water samples from stream-, soil-, ground- and precipitation water were collected during two flood events and analysed for stable isotopes and chemical compositions. To support the nested approach, the sampling sites were chosen at strategical sites within the catchment, including the instrumented hill transects and the stream network from the creek to the catchment outlet.Preliminary results of stable isotope analysis show, that after dry periods nearly no event water seems to contribute to runoff formation, whereas after wet periods the proportions can be up to 40 %. A strongly delayed reaction of the groundwater was observed which suggests that deep groundwater is not contributing to stream flow, but a possible mobilization of pre-event water in the riparian zone was observed as a response to precipitation events.A likely major source of DOC is in the organic soil horizons due to storage and degradation of organic material. This is supported by higher DOC-concentrations in the soil water from these horizons. In how far residence times, precipitation intensities and flow paths activation from different source areas influence concentration peaks of DOC in the stream will be analysed in the next steps.The results of the recent field campaign help to identify the dominant processes of runoff generation and DOC mobilization on different temporal and spatial scales and for different antecedent system states. The data and insights gained from the field campaign will be used to develop and calibrate process models for hypothesis testing and further analyses to eventually develop a forecast model for DOC mobilization.

The identification of origin, flow paths and transit times of water in catchments is an important component for process-based model development for runoff prediction. Hydrological studies offer, combined with isotope data, the possibility to quantify interactions between different compartments in catchments. In the context of this work it is examined to what extent event sampling of precipitation, streamflow, soil water and groundwater and the evaluation of their isotopic ratios δ2H and δ18O enable complex hydrological process investigations in the small forested mountain catchment of the river Große Ohe in the Bavarian Forest National Park. Within this study process analyses are carried out on small scales, e.g. runoff formation on hill slopes and on catchment scale as integrative process analysis. The water samples were collected during a small flood event and analysed for the isotope ratios δ2H and δ18O using a Picarro. A hydrograph separation was carried out through a comprehensive evaluation of the concentration profiles during the event. In combination with further hydrological and soil hydrological observations possible areas of origin and retention times of the water were determined. A strongly delayed reaction of the groundwater was observed which suggests that groundwater is not contributing to stream flow during a flood event, but a possible mobilization of pre-event water in the riparian zone can be observed as a response to precipitation events. The knowledge gained hereby is the basis for further process analysis and model development.

Excessive groundwater pumping due to immense agricultural, industrial and municipal demand poses a major threat of aquifer depletion in many areas around the world. The impact of climate change on the global hydrological cycle has further exacerbated the situation. Accurate and reliable prediction of long-term aquifer balance terms is a key prerequisite to manage groundwater sustainably. To deal with uncertainties of such predictions, lumped (conceptual) hydrological models could help with their computational speed that allows for Monte-Carlo simulation. Compared to more complex models, lumped models are fast, lean on data requirement and capable to quantify uncertainty. However, lumped models are mainly designed to simulate river discharge only, not aquifer storage. Even the standard practice for calibrating lumped hydrological models only includes river discharge, as data on groundwater storage is not directly accessible. In this study, we hypothesize that we can extend the HBV model by additional water budget and groundwater storage terms, and calibrated it on both groundwater storage data and discharge data. Then, we test whether its predictions of groundwater storage levels withstand validation tests. To avoid problems with unavailability of data for calibration and validation in a first proof of concept, we build a virtual reality with a MODFLOW-based model, driven with synthetic weather data over a period of more than 50 years. For rigorous testing, we cast calibration into the framework of Bayesian parameter inference, and validate with metrics that assess the appropriateness of the Bayesian prediction distribution of groundwater storage. We test our idea in the Wairau Plain aquifer, New Zealand. Poor understanding of recharge mechanisms and hence declining groundwater levels are the major hindrance for sustainable groundwater management in our study area. We pay specific attention to river-groundwater exchange processes, to the forecast of aquifer storage dynamics, and to groundwater depletion in a hypothetical, persistent draught. The purpose is to provide a proof of concept whether lumped models can be adapted and made suitable to predict declining groundwater resources up to full depletion, as an uncertainty-aware decision support system for sustainable management.

The common practice of infrequent (e.g., monthly) stream water quality sampling for state of the environment monitoring may, when combined with high resolution stream flow data, provide sufficient information to accurately characterise the dominant nutrient transfer pathways and predict annual catchment yields. In the proposed approach, we use the spatially lumped catchment model StreamGEM to predict daily stream flow and nitrate concentration (mg L −1 NO 3 -N) in four contrasting mesoscale headwater catchments based on four years of daily rainfall, potential evapotranspiration, and stream flow measurements, and monthly or daily nitrate concentrations. Posterior model parameter distributions were estimated using the Markov Chain Monte Carlo sampling code DREAM ZS and a log-likelihood function assuming heteroscedastic, t-distributed residuals. Despite high uncertainty in some model parameters, the flow and nitrate calibration data was well reproduced across all catchments (Nash-Sutcliffe efficiency against Log transformed data, NSL, in the range 0.62–0.83 for daily flow and 0.17–0.88 for nitrate concentration). The slight increase in the size of the residuals for a separate validation period was considered acceptable (NSL in the range 0.60–0.89 for daily flow and 0.10–0.74 for nitrate concentration, excluding one data set with limited validation data). Proportions of flow and nitrate discharge attributed to near-surface, fast seasonal groundwater and slow deeper groundwater were consistent with expectations based on catchment geology. The results for the Weida Stream in Thuringia, Germany, using monthly as opposed to daily nitrate data were, for all intents and purposes, identical, suggesting that four years of monthly nitrate sampling provides sufficient information for calibration of the StreamGEM model and prediction of catchment dynamics. This study highlights the remarkable effectiveness of process based, spatially lumped modelling with commonly available monthly stream sample data, to elucidate high resolution catchment function, when appropriate calibration methods are used that correctly handle the inherent uncertainties.

Low-land alluvial gravel aquifers are formed from, and tend to be recharged, by rivers. These interconnected river - groundwater systems can be highly dynamic with groundwater levels following the seasonality of the hydrological regime of the river. The associated groundwater resources are regularly under stress during summer periods when abstractive demand is high and recharge is low. Predicting lead-times for critical groundwater levels allows for a more flexible and adaptive groundwater management. An eigenmodel approach is proposed here as a way of making such predictions, fast and efficiently. The eigenmodel is a mathematical concept that represents the hydraulic function of a groundwater aquifer as a set of conceptual linear reservoirs, arranged in-series. River recharge, land surface recharge, and groundwater abstraction for irrigation are considered as model forcings. The eigenmodel approach is demonstrated on three wells of the unconfined Wairau Aquifer in the Marlborough District of New Zealand, which are used for water resources management. Individual eigenmodels were calibrated to historic data and predictive uncertainty bounds were determined by Markov chain Monte Carlo sampling. Hindcasting of past recession periods showed a low predictive error of the models and a good coverage of the predictive uncertainty bounds. The main advantage of the approach is a 4-orders of magnitude higher computational efficiency compared to a numerical benchmark model. This allows for probabilistic simulation in operational forecasting of groundwater levels. The framework is implemented as a web application for 30-day operational forecasts that comprises automatic data downloads and model input generation, stochastic simulation, uncertainty estimation, visualization, and daily updates on a website.

Study region The Lerma catchment, a small (7.3 km 2 ) sub-catchment of the Ebro Basin in northern Spain. Study focus The Lerma catchment underwent a monitored transition to irrigated agriculture, using water from outside the catchment, between 2006 and 2008. This transition has successfully been simulated using the partial-differential-equation-based model HydroGeoSphere, simulating coupled evapotranspiration, surface water, and groundwater flow in the catchment. We use the calibrated model to study how irrigation practices influence the response of the Lerma catchment to the climate change projected for northern Spain. We consider four different irrigation scenarios: no irrigation, present irrigation, climate-adapted irrigation with current crops, and adapted irrigation for crops requiring less water. The climate scenarios are based on four regional climate models and two downscaling methods. New hydrological insight The simulated catchment responses to climate change show clear differences between the irrigation scenarios. In future climate, groundwater levels and base flows decrease more when irrigation is present than without irrigation, because groundwater levels and base flow in present climate are already at low levels without irrigation. In contrast, annual peak discharges increase more in non-irrigated cases than in irrigated cases. Irrigation increases water availability and an associated rise in potential evapotranspiration results in higher actual evapotranspiration during summer. In non-irrigated scenarios, by contrast, actual evapotranspiration in summer is controlled by precipitation and thus decreases in future climate.

New Zealand's gravel-bed rivers have deposited coarse, highly conductive gravel aquifers that are predominantly fed by river water. Managing their groundwater resources is challenging because the recharge mechanisms in these rivers are poorly understood and recharge rates are difficult to predict, particularly under a more variable future climate. To understand the river-groundwater exchange processes in gravel-bed rivers, we investigate the Wairau Plain Aquifer using a three-dimensional groundwater flow model which was calibrated using targeted field observations, "soft" information from experts of the local water authority, parameter regularization techniques, and the model-independent parameter estimation software PEST. The uncertainty of simulated river-aquifer exchange flows, groundwater heads, spring flows, and mean transit times were evaluated using Null-space Monte-Carlo methods. Our analysis suggests that the river is hydraulically perched (losing) above the regional water table in its upper reaches and is gaining downstream where marine sediments overlay unconfined gravels. River recharge rates are on average 7.3 m3 /s, but are highly dynamic in time and variable in space. Although the river discharge regularly hits 1000 m3 /s, the net exchange flow rarely exceeds 12 m3 /s and seems to be limited by the physical constraints of unit-gradient flux under disconnected rivers. An important finding for the management of the aquifer is that changes in aquifer storage are mainly affected by the frequency and duration of low-flow periods in the river. We hypothesize that the new insights into the river-groundwater exchange mechanisms of the presented case study are transferable to other rivers with similar characteristics.

Interactions between the soil, the vegetation, and the atmospheric boundary layer require close attention when predicting water fluxes in the hydrogeosystem, agricultural systems, weather, and climate. However, land-surface schemes used in large-scale models continue to show deficiencies in consistently simulating fluxes of water and energy from the subsurface through vegetation layers to the atmosphere. In this study, the multiphysics version of the Noah land-surface model (Noah-MP) was used to identify the processes, which are most crucial for a simultaneous simulation of water and heat fluxes between land surface and the lower atmosphere. Comprehensive field data sets of latent and sensible heat fluxes, ground heat flux, soil moisture, and leaf area index from two contrasting field sites in South-West Germany are used to assess the accuracy of simulations. It is shown that an adequate representation of vegetation-related processes is the most important control for a consistent simulation of energy and water fluxes in the soil-plant-atmosphere system. In particular, using a newly implemented submodule to simulate root growth dynamics has enhanced the performance of Noah-MP. We conclude that further advances in the representation of leaf area dynamics and root/soil moisture interactions are the most promising starting points for improving the simulation of feedbacks between the subsoil, land surface and atmosphere in fully coupled hydrological and atmospheric models.

[1] Knowledge about the strength and travel times of hyporheic exchange is vital to predict reactive transport and biogeochemical cycling in streams. In this study, we outline how to perform and analyze stream tracer tests using pulse injections of fluorescein as conservative and resazurin as reactive tracer, which is selectively transformed to resorufin when exposed to metabolically active zones, presumably located in the hyporheic zone. We present steps of preliminary data analysis and apply a conceptually simple mathematical model of the tracer tests to separate effects of in-stream transport from hyporheic exchange processes. To overcome the dependence of common parameter estimation schemes on the initial guess, we derive posterior parameter probability density functions using an adaptive Markov chain Monte Carlo scheme. By this, we can identify maximum-likelihood parameter values of in-stream transport, strength of hyporheic exchange, distribution of hyporheic travel times as well as sorption and reactivity coefficients of the hyporheic zone. We demonstrate the approach by a tracer experiment at River Goldersbach in southern Germany (60 L/s discharge). In-stream breakthrough curves were recorded with online fluorometers and jointly fitted to simulations of a one-dimensional reactive transport model assuming an exponential hyporheic travel-time distribution. The findings show that the additional analysis of resazurin not only improved the physical basis of the modeling, but was crucial to differentiate between surface transport and hyporheic transient storage of stream solutes. Parameter uncertainties were usually small and could not explain parameter variability between adjacent monitoring stations. The latter as well as a systematic underestimation of the tailing are due to structural errors of the model, particularly the exponential hyporheic travel-time distribution. Mean hyporheic travel times were in the range of 12 min, suggesting that small streambed structures dominate hyporheic exchange at the study site.

Sustainable water quality management requires a profound understanding of water fluxes (precipitation, run-off, recharge, etc.) and solute turnover such as retention, reaction, transformation, etc. at the catchment or landscape scale. The Water and Earth System Science competence cluster (WESS, http://www.wess.info/ ) aims at a holistic analysis of the water cycle coupled to reactive solute transport, including soil–plant–atmosphere and groundwater–surface water interactions. To facilitate exploring the impact of land-use and climate changes on water cycling and water quality, special emphasis is placed on feedbacks between the atmosphere, the land surface, and the subsurface. A major challenge lies in bridging the scales in monitoring and modeling of surface/subsurface versus atmospheric processes. The field work follows the approach of contrasting catchments, i.e. neighboring watersheds with different land use or similar watersheds with different climate. This paper introduces the featured catchments and explains methodologies of WESS by selected examples.

Simulating soil moisture dynamics in coarse-textured alluvial soils under high evaporative demand and infrequent rainfall challenges both characterization and numerical methods. For this study, three geomorphically young disturbed soils in the central Mojave Desert were characterized using direct, indirect, and inverse parameter estimation methods for numerical simulations using HYDRUS. Observations of soil water content (θ) and matric head ( h ) were used to formulate our multiobjective framework optimized using the AMALGAM algorithm. Both the direct and indirect parameterization methods reproduced θ but proved inadequate for h data. Sensitivity analysis found the parameters related to the dry end of the soil hydraulic function—including residual water content (θ r ) and pore-size distribution ( n) —most sensitive but often conflicting between the two criteria while soil temperature was effectively simulated but insensitive. At the sites, a large dynamic range of h (1–400 m) was accompanied by a relatively small range of θ (0.04–0.10 m3 m−3), limiting our ability to resolve the wet-end of the hydraulic functions. Direct parameterization from infiltrometer data and indirect pedotransfer functions had good agreement with θ but largely underestimated h . Pareto analysis determined the trade-off between fitting both criteria and revealed model structure inadequacies as well as bifurcations into parameter populations, favoring either θ or h error minimization. The Pareto solutions compensated for the underestimation of h by forcing the α and n parameters to uncharacteristically low values for such coarse textures. This compensation kept the water capacity sufficiently high at lower θ to maintain conductivity toward the surface. These results are likely a consequence of either the disturbed soil system or hysteresis between measurements of θ and h.

A model is presented for simulating one-dimensional advective dispersive solute transport in the vadose zone. The finite-volume, mixing-cell model uses drainage flux intervals as the index variable, which are calculated by a soil water balance model. The modelling approach considers solute transport from two different regions as well as a slow and a fast transport domain in each region as parallel transport processes. The model is applied to breakthrough curves of Cl(-) and Br(-) measured at different locations and different depths in the volcanic vadose zone of the Tutaeuaua subcatchment of Lake Taupo, New Zealand, following a dual tracer application. Estimates of transport parameter and model predictive uncertainty were derived using the differential evolution adaptive metropolis, DREAM(ZS) adaptive Markov chain Monte Carlo algorithm, a formal Bayesian likelihood function, observed leachate volumes, and Cl(-) breakthrough curves. The model was subsequently evaluated using Br(-) breakthrough curves from the dual tracer experiment and a previously conducted Br(-) tracer-only experiment. Uncertainty bounds derived by this MCMC method simultaneously capture the observed Br(-) and Cl(-) breakthrough curves and corresponding drainage volumes. Results suggest that the slow transport domain properties are relatively similar for different locations in the vadose zone and that the variability in contaminant fluxes is predominantly driven by structural variability of the vadose zone causing lateral flow.

summary A comprehensive description of water flow in environmental and agricultural systems requires an account of both surface and subsurface pathways. We present a new model which combines a 1D overland flow model and the 2D subsurface flow HYDRUS-2D model, and uses the multi-objective global search method AMALGAM for inverse parameter estimation. Furthermore, we present data from bench-scale flow experiments which were conducted with two 5-m long replicate soil channels. While rainfall was applied, surface runoff was recorded at the downstream end of the soil channel, subsurface drainage waters were sampled at three positions equally spaced along the channels, and pressure heads were recorded at five depths. The experimental observations were used to evaluate the performance of our modeling system. The complexity of the modeling approach was increased in three steps. First, only runoff and total drainage were simulated, then drainage flows from individual compartments were additionally evaluated, and finally a surface crust and immobile soil water were also considered. The results showed that a good match between measured and observed surface runoff and total drainage does not guarantee accurate representation of the flow process. An inspection of the Pareto results of different multiobjective calibration runs revealed a significant trade-off between individual objectives, showing that no single solution existed to match spatial variability in the flow. In spite of the observed crust formation, its consideration in the more complex model structure did not significantly improve the fit between the model and measurements. Accounting for immobile water regions only slightly improved the fit for one of the two replicate soil channels. Discrepancies between relatively complex model simulations and seemingly simple soil channel experiments suggest the presence of additional unknowns, such as heterogeneity of the soil hydraulic properties. Nevertheless, with its versatile subsurface options and powerful inverse method, the model system shows promise for studying hillslope flow problems involving both surface runoff and subsurface flow.

The hydraulic characteristics of the plough pan of paddy fields provide continuous ponding conditions during the growing season and control the water use efficiency in wet rice production. Its saturated hydraulic conductivity Ks, however, exhibits a large spatiotemporal variability as a consequence of a highly dynamic soil structure involving temporary shrinkage cracks. Water flow through the earthen bunds surrounding the fields further contributes to the uncertainty in water flux calculations. The objective of this study was to develop a simple deterministic model with stochastic elements (‘PADDY-FLUX’) for depiction of deep percolation, and to assess the effect of different water management scenarios on percolation in two channel command areas. Darcy's law is used as the fundamental equation for water flow calculations with the ponding water depth h as a time-dependent variable. Flux uncertainty is estimated by a Monte-Carlo-type implementation. Ks is treated as a random variable of a bimodal probability density function (PDF), which is the weighted sum of two Gaussian PDFs (accounting for a matrix and a preferential flow domain). The weighing factor α is a function of h, reflecting an increasing risk for preferential flow situations after desiccation and the development of shrinkage cracks. Under-bund percolation is calculated using transfer functions. The results demonstrate that percolation losses increase in the following order: continuous soil saturation < continuous flooding (CF) < mid-season drainage and intermittent irrigation (MD + II) < mid-season drainage and continuous flooding. The bunds contribute up to 54 and 17% to total fluxes under CF and MD + II, respectively. Preferential water fluxes are responsible for the major part of water losses as soon as desiccation causes the formation of shrinkage cracks. As a conclusion, continuous soil saturation should be promoted as the least water-intensive irrigation regime, while intermittent irrigation is recommended only in case that irreversible shrinkage cracks have already developed. Copyright © 2010 John Wiley & Sons, Ltd.