Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with the highest resolution in the parameter search space, while the least sensitive ones are initially explored with the coarsest resolution. This is followed by an optimization‐based iterative calibration procedure consisting of a series of subtasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each subtask, the parameter ranges within each group are systematically refined from their previously estimated ranges, which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization‐based approach was performed using a quasi‐synthetic double‐model setup experiment to calibrate 134 parameters and two well‐known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land–atmosphere coupling, and (v) hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

Hydraulic fracturing generates large volumes of produced water, and treatment of produced water may be necessary for disposal or reuse.

The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main existing frameworks for the determination of the models' initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a hybrid algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search) which is targeted at non-linear high-resolution problems and that brings together ideas from particle filters (PFs), four-dimensional variational methods (4D-Var), evolutionary Pareto optimization, and kernel density estimation in a unique way. Streamflow forecasting experiments were conducted to test which specific configurations of OPTIMISTS led to higher predictive accuracy. The experiments were conducted on two watersheds: the Blue River (low resolution) using the VIC (Variable Infiltration Capacity) model and the Indiantown Run (high resolution) using the DHSVM (Distributed Hydrology Soil Vegetation Model). By selecting kernel-based non-parametric sampling, non-sequential evaluation of candidate particles, and through the multi-objective minimization of departures from the streamflow observations and from the background states, OPTIMISTS was shown to efficiently produce probabilistic forecasts with comparable accuracy to that obtained from using a particle filter. Moreover, the experiments demonstrated that OPTIMISTS scales well in high-resolution cases without imposing a significant computational overhead. With the combined advantages of allowing for fast, non-Gaussian, non-linear, high-resolution prediction, the algorithm shows the potential to increase the efficiency of operational prediction systems.

Expert water distribution system design methodologies have recently challenged the performance of well-established metaheuristic approaches. By predetermining the pressure values of these systems, the design process has become almost straightforward in terms of a much reduced number of hydraulic simulations needed. However, these algorithms have only been used to solve the simplified least-cost problem. Moreover, they have been developed after the assumption that pressure distribution is sufficient to determine the hydraulic behavior of these systems. This article shows that flow values in looped networks cannot be completely determined by pressure and that these can also be assigned by the designer to follow different criteria. Three different methods were developed and tested showing how flow predetermination can be used to handle additional objectives such as network reliability.