Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning

Transmission losses are the loss in the flow volume of a river as water moves downstream. These losses provide crucial ecosystem services, particularly in ephemeral and intermittent river systems. Transmission losses can be quantified at many scales using different measurement techniques. One of the most common methods is differential gauging of river flow at two locations. An alternative method for non-perennial rivers is to replace the downstream gauging location by visual assessments of the wetted river length on satellite images. The transmission losses are then calculated as the flow gauged at the upstream location divided by the wetted river length. We used this approach to estimate the transmission losses in the Selwyn River (Canterbury, New Zealand) using 147 satellite images collected between March 2020 and May 2021. The location of the river drying front was verified in the field on six occasions and seven differential gauging campaigns were conducted to ground-truth the losses estimated from the satellite images. The transmission loss point data obtained using the wetted river lengths and differential gauging campaigns were used to train an ensemble of random forest models to predict the continuous hourly time series of transmission losses and their uncertainties. Our results show that the Selwyn River transmission losses ranged between 0.25 and 0.65 m³ s‾¹ km‾¹ during most of the 1-year study period. However, shortly after a flood peak the losses could reach up to 1.5 m³ s‾¹ km‾¹. These results enabled us to improve our understanding of the Selwyn River groundwater-surface water interactions and provide valuable data to support water management. We argue that our framework can easily be adapted to other ephemeral rivers and to longer time series

Sorption and transformation of the reactive tracers resazurin and resorufin in natural river sediments

Resazurin (Raz) and its reaction product resorufin (Rru) have increasingly been used as reactive tracers to quantify metabolic activity and hyporheic exchange in streams. Previous work has indicated that these compounds undergo sorption in stream sediments. We present laboratory experiments on Raz and Rru transport, sorption, and transformation, consisting of 4 column and 72 batch tests using 2 sediments with different physicochemical properties under neutral (pH=7) and alkaline (pH=9) conditions. The study aimed at identifying the key processes of reactive transport of Raz and Rru in streambed sediments and the experimental setup best suited for their determination. Data from column experiments were simulated by a travel-time-based model accounting for physical transport, equilibrium and kinetic sorption, and three first-order reactions. We derived the travel-time distributions directly from the breakthrough curve (BTC) of the conservative tracer, fluorescein, rather than from fitting an advective-dispersive transport model, and inferred from those distributions the transfer functions of Raz and Rru, which provided conclusive approximations of the measured BTCs. The most likely reactive transport parameters and their uncertainty were determined by a Markov chain–Monte Carlo approach. Sorption isotherms of both compounds were obtained from batch experiments. We found that kinetic sorption dominates sorption of both Raz and Rru, with characteristic timescales of sorption in the order of 12 to 298 min. Linear sorption models for both Raz and Rru appeared adequate for concentrations that are typically applied in field tracer tests. The proposed two-site sorption model helps to interpret transient tracer tests using the Raz–Rru system.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.hydrology-and-earth-system-sciences.net/home.html

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

Using an integrated hydrological model to estimate the usefulness of meteorological drought indices in a changing climate

Droughts are serious natural hazards, especially in semi-arid regions. They are also difficult to characterize. Various summary metrics representing the dryness level, denoted drought indices, have been developed to quantify droughts. They typically lump meteorological variables and can thus directly be computed from the outputs of regional climate models in climate-change assessments. While it is generally accepted that drought risks in semi-arid climates will increase in the future, quantifying this increase using climate model outputs is a complex process that depends on the choice and the accuracy of the drought indices, among other factors. In this study, we compare seven meteorological drought indices that are commonly used to predict future droughts. Our goal is to assess the reliability of these indices to predict hydrological impacts of droughts under changing climatic conditions at the annual timescale. We simulate the hydrological responses of a small catchment in northern Spain to droughts in present and future climate, using an integrated hydrological model calibrated for different irrigation scenarios. We compute the correlation of meteorological drought indices with the simulated hydrological time series (discharge, groundwater levels, and water deficit) and compare changes in the relationships between hydrological variables and drought indices. While correlation coefficients linked with a specific drought index are similar for all tested land uses and climates, the relationship between drought indices and hydrological variables often differs between present and future climate. Drought indices based solely on precipitation often underestimate the hydrological impacts of future droughts, while drought indices that additionally include potential evapotranspiration sometimes overestimate the drought effects. In this study, the drought indices with the smallest bias were the rainfall anomaly index, the reconnaissance drought index, and the standardized precipitation evapotranspiration index. However, the efficiency of these drought indices depends on the hydrological variable of interest and the irrigation scenario. We conclude that meteorological drought indices are able to identify years with restricted water availability in present and future climate. However, these indices are not capable of estimating the severity of hydrological impacts of droughts in future climate. A well-calibrated hydrological model is necessary in this respect

Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data

In the past two decades significant progress has been made toward the application of inverse modeling to estimate the water retention and hydraulic conductivity functions of the vadose zone at different spatial scales. Many of these contributions have focused on estimating only a few soil hydraulic parameters, without recourse to appropriately capturing and addressing spatial variability. The assumption of a homogeneous medium significantly simplifies the complexity of the resulting inverse problem, allowing the use of classical parameter estimation algorithms. Here we present an inverse modeling study with a high degree of vertical complexity that involves calibration of a 25 parameter Richards'-based HYDRUS-1D model using in situ measurements of volumetric water content and pressure head from multiple depths in a heterogeneous vadose zone in New Zealand. We first determine the trade-off in the fitting of both data types using the AMALGAM multiple objective evolutionary search algorithm. Then we adopt a Bayesian framework and derive posterior probability density functions of parameter and model predictive uncertainty using the recently developed differential evolution adaptive metropolis, DREAMZS adaptive Markov chain Monte Carlo scheme. We use four different formulations of the likelihood function each differing in their underlying assumption about the statistical properties of the error residual and data used for calibration. We show that AMALGAM and DREAMZS can solve for the 25 hydraulic parameters describing the water retention and hydraulic conductivity functions of the multilayer heterogeneous vadose zone. Our study clearly highlights that multiple data types are simultaneously required in the likelihood function to result in an accurate soil hydraulic characterization of the vadose zone of interest. Remaining error residuals are most likely caused by model deficiencies that are not encapsulated by the multilayer model and can not be accessed by the statistics and likelihood function used. The utilization of an explicit autoregressive error model of the remaining error residuals does not work well for the water content data with HYDRUS-1D prediction uncertainty bounds that become unrealistically large

Analysis of long-term groundwater storage trends in the Wairau aquifer, New Zealand

The Wairau Aquifer covers a small proportion of the Wairau catchment in the Marlborough District of New Zealand just prior to the river discharging into the sea. The aquifer is almost exclusively recharged by surface water from the Wairau River and serves as the major drinking water resource for Blenheim and the surrounding settlements on the Wairau Plain. Because a small but constantly declining trend in aquifer levels and spring flows have been observed over the past decades, it has been made a high priority by the Marlborough District Council to better understand the limits and the mechanics of the recharge mechanism. While previous research efforts have been centred at water budgets during low-flow conditions and steady-state modelling, this study aims at understanding the dynamics of river-groundwater exchange fluxes using information of Wairau river flows at three new gauging stations, time series of groundwater observations, spring flows and qualitative (soft-)knowledge. Both qualitative and quantitative observations were integrated into a transient numerical MODFLOW model and simulations were conducted with the calibrated model for a 20-year time period. The gravels of the Wairau aquifer are highly conductive with estimated lateral conductivity values exceeding 1km per day. Although there is also evidence for anisotropy of the aquifer materials, it was found that river recharge at the upper slopes of the Wairau aquifer was consistently happening under perched conditions. In addition, exchange fluxes seem to have a functional relationship with river discharge only under low flow conditions while the exchange fluxes appear to be capped at about 16-20 m³/s for medium and large river flows. Therefore, the Wairau aquifer storage seems to be vulnerable more to the occurrence and duration of extreme low flow periods. To analyse this further, we have analysed the frequency and re-occurrence of low flow periods from the Wairau river record and found that the days of flow below a critical threshold in a given year have increased in recent years. To link the river flow record to large-scale climatic drivers, we analysed the precipitation record from several rainfall stations in the Wairau catchment as well as daily time series of precipitation data from the National Institute of Water and Atmospheric Research (NIWA) virtual climate station (VCS) network. The areal annual precipitation totals calculated from the VCS station data show a clear decline of precipitation since 1960. Shorter precipitation records from weather stations in the hilly ranges of the Wairau catchment seem to confirm the trend, while data from stations in the valleys or the Wairau Plains doesn't support the trend. The decline in areal precipitation and the corresponding increase in low flow periods of the Wairau river flows have a strong correspondence to the long-term trend in Wairau aquifer water levels, but other factors such as changes in the river bed morphology could also contribute. The reason for the decline of precipitation in the Wairau catchment is not yet known

Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession

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

Physically based coupled model for simulating 1D surface-2D subsurface flow and plant water uptake in irrigation furrows. II: Model test and evaluation

A physically based seasonal furrow model was developed, which comprises three modules: The one-dimensional surface flow , the two-demensional suvsurface flow, and a crop model. The modeling principle of these modules, their simulatneous coupling, and the solution strategies were described in a companion paper (Wöhling and Shmitz 2007). In the current contribution, we present the model testing with experimental data from five real-scale laboratory experiments [Hubert-Engels Laboratory (HEL)], two field experiments in Kharagpur, Eastern India (KGP), one literature data set [Flowell-wheel (FW) ]and data from three irrigation during a corn growing season in Montpellier., Southern France [Lavalette experiments (LAT)]. The simulated irrigation advance times match well with the observations of the HEL, FW and KGP experiments, which is confirmed by coefficients of determination R2 >>0.99 and coefficients of efficiency Ce > 0.7. Predicted recession times also match with observations of HEL runs, however, the values of R2> 0.9 and Ce >0.6 are lower for predicted recession times as coimpared to predicted advance times. In contrast to other experiments in the study, advance times are under predicted for experiments in France. The established soil hydraulic parameters for this site lead to an underestimation of the actual initial infiltration capability of soil. In the long-term simulation, however, the overall change in soil moisture storage is correctly predicted by the model and calculated yield of 12.8 t/ha is in very good agreement with the observations (12.7 t/ha). We evaluated the sensitivity of the input parameters with regards to predicted advance time and runoff in both a 26.4 m long furrow and a long 360 m long furrow. The analysis reveald that calculated runoff is four to five times more sensitive to the inlet flow rate than to infiltration parameters. Furrow geometry parameters are most sensitive to calculated adavance times in the short furrow with low infiltration opportunity time, whereas the inflow rate and infiltration parameters are more sensitive to calculated advance times in the long furrow with larger infiltration opportunity time

Simplification error analysis for groundwater predictions with reduced order models

Groundwater resource management often requires detailed numerical models that make calibration and predictive uncertainty analysis computationally challenging. Reduced order models (ROMs) alleviate the computational burden but can potentially lead to predictive bias and underestimation of uncertainty. A paired model approach has previously been proposed to estimate the predictive uncertainty of models compared to highly complex, synthetic realities. This approach is modified to analyze and compare the simplification error for groundwater predictions of a real-world numerical MODFLOW model of the Wairau Plains Aquifer in the Marlborough Region of New Zealand. Two different ROM types were applied in this study to predict groundwater heads, spring discharge and river–groundwater exchange fluxes: (1) a drastically simplified MODFLOW model, and (2) artificial neural networks (ANNs). The different ROMs exhibit very different patterns of bias and magnitude of model simplification error. The method accurately captures the simplification error for most predictions by the MODFLOW model, but underestimates the error for predictions highly dependent on the variability of the complex model. The simplified MODFLOW model shows significant parameter surrogacy and non-optimality of simplification, thus questioning the adherence to expert-knowledge based parameter limits. For predictions where historic data sets are available, ANNs provide superior predictive power. However, they cannot be applied to predictions of data types and locations not contained in the calibration data set. For those predictions, simplified numerical models can be applied with varying degree of accuracy

Robust data worth analysis with surrogate models

Highly detailed physically based groundwater models are often applied to make predictions of system states under unknown forcing. The required analysis of uncertainty is often unfeasible due to the high computational demand. We combine two possible solution strategies: (1) the use of faster surrogate models; and (2) a robust data worth analysis combining quick first-order second-moment uncertainty quantification with null-space Monte Carlo techniques to account for parametric uncertainty. A structurally and parametrically simplified model and a proper orthogonal decomposition (POD) surrogate are investigated. Data worth estimations by both surrogates are compared against estimates by a complex MODFLOW benchmark model of an aquifer in New Zealand. Data worth is defined as the change in post-calibration predictive uncertainty of groundwater head, river-groundwater exchange flux, and drain flux data, compared to the calibrated model. It incorporates existing observations, potential new measurements of system states (“additional” data) as well as knowledge of model parameters (“parametric” data). The data worth analysis is extended to account for non-uniqueness of model parameters by null-space Monte Carlo sampling. Data worth estimates of the surrogates and the benchmark suggest good agreement for both surrogates in estimating worth of existing data. The structural simplification surrogate only partially reproduces the worth of “additional” data and is unable to estimate “parametric” data, while the POD model is in agreement with the complex benchmark for both “additional” and “parametric” data. The variance of the POD data worth estimates suggests the need to account for parameter non-uniqueness, like presented here, for robust results