Dr. Md Shahab Alam

The NextGen Water Resources Modeling Framework (NextGen) greatly increases the flexibility and interoperability of hydrologic modeling workflows. However, deploying NextGen models remains challenging due to a complex installation process. To address this, we developed NextGen In A Box (NGIAB), an all-in-one distribution of the NextGen framework that simplifies deployment across environments using Docker and Singularity containers. NGIAB comes pre-configured with most components expected in version 4.0 of the National Water Model, which will use the NextGen framework. Additionally, NGIAB utilizes a cloud-based continuous integration/continuous deployment pipeline to automate releases. Through its ease of access and robust suite of included utilities, NGIAB promotes community engagement in hydrologic modeling and facilitates research-to-operation (R2O) pathways. In this work, we present the designs and technologies that enable these outcomes, along with studies and performance benchmarks that demonstrate NGIAB's applicability.

One technique to evaluate the performance of oil sands reclamation covers is through the simulation of long-term water balance using calibrated soil-vegetation-atmosphere transfer models. Conventional practice has been to derive a single set of optimized hydraulic parameters through inverse modelling (IM) based on short-term (<5-10 years) monitoring datasets. This approach is unable to characterize the impact of variability in the cover properties. This study utilizes IM to optimize the hydraulic properties for 12 soil cover designs, replicated in triplicate, at Syncrude's Aurora North mine site. The hydraulic parameters for three soil types (peat cover soil, coarse-textured subsoil, and lean oil sand substrate) were optimized at each monitoring site from 2013 to 2016. The resulting 155 optimized parameter values were used to define distributions for each parameter/soil type, while the progressive Latin hypercube sampling (PLHS) method was used to sample parameter values randomly from the optimized parameter distributions. Water balance models with the sampled parameter sets were used to evaluate variations in the maximum sustainable leaf area index (LAI) for five illustrative covers and quantify uncertainty associated with long-term water balance components and LAI values. Overall, the PLHS method was able to better capture broader variability in the water balance components than a discrete interval sampling method. The results also highlight that climate variability dominates the simulated variability in actual evapotranspiration and that climate and parameter uncertainty have a similar influence on the variability in net percolation.

The design of reclamation soil covers at oil sands mines in northern Alberta, Canada, has been conventionally based on the calibration of soil-vegetation-atmosphere transfer (SVAT) models against field monitoring observations collected over several years, followed by simulations of long-term performance using historical climate data. This paper evaluates the long-term water balances for reclamation covers on two oil sands landforms and three natural coarse-textured forest soil profiles using both historical climate data and future climate projections. Twenty-first century daily precipitation and temperature data from CanESM2 were downscaled based on three representative concentration pathways (RCPs) employing a stochastic weather generator [Long Ashton Research Station Weather Generator (LARS-WG)]. Relative humidity, wind speed, and net radiation were downscaled using the delta change method. Downscaled precipitation and estimated potential evapotranspiration were used as inputs to simulate soil water dynamics using physically based models. Probability distributions of growing season (April-October) actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods show that AET and NP at all sites are expected to increase throughout the twenty-first century regardless of RCP, time period, and soil profile. Greater increases in AET and NP are projected toward the end of the twenty-first century. The increases in future NP at the two reclamation covers are larger (as a percentage increase) than at most of the natural sites. Increases in NP will result in greater water yield to surface water and may accelerate the rate at which chemical constituents contained within mine waste are released to downstream receptors, suggesting these potential changes need to be considered in mine closure designs.

Intensity–Duration–Frequency (IDF) curves are typically used as a standard design tool for various engineering applications, such as storm water management systems. Warming climate, however, changes the extreme precipitation quantiles represented by the IDF curves. This study attempts to construct the future IDF curves in Saskatoon, Canada, under possible climate change scenarios. For this purpose, a stochastic weather generator was used to spatially downscale the daily projections of Global Climate Models (GCMs) from coarse grid resolution to the local point scale. The stochastically downscaled daily precipitation realizations were further disaggregated to ensembles of hourly and sub-hourly (as fine as 5-min) series using a disaggregation scheme developed based on the K-nearest neighbor (K-NN) technique. This framework was applied to construct the future IDF curves in the city of Saskatoon. The sensitivity of the K-NN disaggregation model to the number of nearest neighbors (i.e. window size) was evaluated during the baseline period (1961–1990). The optimum window size was assigned based on the performance in reproducing the historical IDF curves. By using the simulated hourly and sub-hourly precipitation series and the Generalized Extreme Value (GEV) distribution, future changes in IDF curves and associated uncertainties were quantified using a large ensemble of projections obtained from eight GCMs and three representative concentration pathways – RCP2.6, RCP4.5, and RCP8.5. The constructed IDF curves were then compared with the corresponding historical ones and the IDF curves constructed using another genetic programming-based published method. The results show that the sign and the magnitude of future variations in extreme precipitation quantiles are sensitive to the selection of GCMs and/or RCPs, which seem to get intensified toward the end of the 21st century. The quantification of uncertainties suggests that GCMs are the main contributor to the uncertainty, followed by RCPs and the downscaling method.