Integrating partitioned evapotranspiration data into hydrologic models: vegetation parameterization and uncertainty quantification of simulated plant water use

Accurate simulation of plant water use across agricultural ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. Recently, the flux variance similarity approach has been developed to partition ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations for vegetation parameterization. We used ET observations from 5 eddy covariance towers located in the San Joaquin Valley, California, to parameterize orchard crops in an integrated land surface – groundwater model. We find that a simple approach of selecting the best parameter sets based on ET and T performance metrics works best at these study sites. Selecting parameters based on performance relative to observed ET creates an uncertainty of 27% relative to the observed value. When parameters are selected using both T and ET data, this uncertainty drops to 24%. Similarly, the uncertainty in potential groundwater recharge drops from 63% to 58% when parameters are selected with ET or T and ET data, respectively. Additionally, using crop type parameters results in similar levels of simulated ET as using site-specific parameters. Different irrigation schemes create high amounts of uncertainty and highlight the need for accurate estimates of irrigation when performing water budget studies.

Validation and Projections of Climate Characteristics in the Saginaw Bay Watershed, MI, for Hydrologic Modeling Applications

Fundamental differences in the nature of climate and hydrologic models make coupling of future climate projections to models of watershed hydrology challenging. This study uses the NCAR Weather Research and Forecast model (WRF) to dynamically downscale climate simulations over the Saginaw Bay Watershed, MI and prepare the results for input into semi-distributed hydrologic models. One realization of the bias-corrected NCAR CESM1 model's RCP 8.5 climate scenario is dynamically downscaled at a spatial resolution of 3 km by 3 km for the end of the twenty-first century and validated based on a downscaled run for the end of the twentieth century in comparison to ASOS and NWS COOP stations. Bias-correction is conducted using Quantile Mapping to correct daily maximum and minimum temperature, precipitation, and relative humidity for use in future hydrologic model experiments. In the Saginaw Bay Watershed the end of the twenty-first century is projected to see maximum and minimum average daily temperatures warming by 5.7 and 6.3°C respectively. Precipitation characteristics over the watershed show an increase in mean annual precipitation (average of +14.3 mm over the watershed), mainly due to increases in precipitation intensity (average of +0.3 mm per precipitation day) despite a decrease in frequency of −10.7 days per year. The projected changes have substantial implications for watershed processes including flood prediction, erosion, mobilization of non-point source and legacy contaminants, and evapotranspirative demand, among others. We present these results in the context of usefulness of the downscaled and bias corrected data for semi-distributed hydrologic modeling.

Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”

Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...]

Integrated Hydrologic Modelling of Groundwater-Surface Water Interactions in Cold Regions

Groundwater-surface water (GW-SW) interaction, as a key component in the cold region hydrologic cycle, is extremely sensitive to seasonal and climate change. Specifically, the dynamic change of snow cover and frozen soil bring additional challenges in observing and simulating hydrologic processes under GW-SW interactions in cold regions. Integrated hydrologic models are promising tools to simulate such complex processes and study the system behaviours as well as its responses to perturbations. The cold region integrated hydrologic models should be physically representative and fully considering the thermal-hydrologic processes under snow cover variations, freeze-thaw cycles in frozen soils and GW-SW interactions. Benchmarking and integration with scarce field observations are also critical in developing cold region integrated hydrologic models. This review summarizes the current status of hydrologic models suitable for cold environment, including distributed hydrologic models, cryo-hydrogeologic models, and fully-coupled cold region GW-SW models, with a specific focus on their concepts, numerical methods, benchmarking, and applications across scales. The current research can provide implications for cold region hydrologic model development and advance our understanding of altered environments in cold regions disturbed by climate change, such as permafrost degradation, early snow melt and water shortage.

RavenR v2.1.4: an open source R package to support flexible hydrologic modelling

In recent decades, advances in the flexibility and complexity of hydrologic models has enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and in the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complimentary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs, and these tools may be useful even for non-Raven users. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing reality checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project, and will provide a useful tool to new and experienced users of Raven alike.

EVALUATING HYDROLOGIC MODEL PERFORMANCE OF GLOBAL AND LOCAL WEATHER DATA INPUTS

Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

A global open-source database of flood-protection levees on river deltas (openDELvE)

Flood-protection levees have been built along rivers and coastlines globally. Current datasets, however, are generally confined to territorial boundaries (national datasets) and are not always easily accessible, posing limitations for hydrologic models and assessments of flood hazard. Here we present our work to develop a single, open-source global river delta levee data environment (openDELvE) which aims to bridge a data deficiency by collecting and standardising global flood-protection levee data for river deltas. In openDELvE we have aggregated data from national databases as well as data stored in reports, maps, and satellite imagery. The database identifies the river delta land areas that the levees have been designed to protect, and where additional data is available, we record the extent and design specifications of the levees themselves (e.g., levee height, crest width, construction material) in a harmonised format. openDELvE currently contains 5,089 km of levees on deltas, and 44,733.505 km2 of leveed area in 1,601 polygons. For the 152 deltas included in openDELvE, on average 19 % of their habitable land area is confined by verifiable flood-protection levees. Globally, we estimate that between 5 % and 54 % of all delta land is confined by flood-protection levees. The data is aligned to the recent standards of Findability, Accessibility, Interoperability and Reuse of scientific data (FAIR) and is open-source. openDELvE is made public on an interactive platform (www.opendelve.eu), which includes a community-driven revision tool to encourage inclusion of new levee data and continuous improvement and refinement of open-source levee data.