The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. This paper confronts these disciplinary barriers by advocating a synthesis of existing and missing links across the frontiers distinguishing hydrology from engineering, the social sciences and economics, Indigenous and place-based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. Specifically, we embrace ‘integrated modeling’, in both quantitative and qualitative senses, as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human-water systems. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: a) What is the modeling ‘purpose’? b) What constitutes a sound ‘boundary judgment’? and c) What are the ‘critical uncertainties’ and how do they propagate through interconnected subsystems? More broadly, we call for investigating what constitutes warranted ‘systems complexity’, as opposed to unjustified ‘computational complexity’ when representing complex natural and human-natural systems, with particular attention to interdependencies and feedbacks, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects.

In geophysics, crowdsourcing is an emerging non-traditional environmental monitoring approach that encourages contributions of data from individual citizens. Because of their reliance on undertrained citizens and imprecise low-cost sensors, crowdsourced data applications suffer from different types of noises that can deteriorate the overall monitoring accuracy. In this study, we propose a machine learning approach for automatic Crowdsourced data Quality Control (CSQC) by detecting and removing noisy data points in spatially and temporally discrete crowdsourced observations. We design a set of features from the original and interpolated rainfall data, and apply them to train and test the CSQC models based on both supervised and non-supervised machine learning algorithms. Performances of the CSQC models under various scenarios assuming no further retraining are also tested (hereafter referred to as transferability). The results based on synthetic but realistic data show that the CSQC model can significantly reduce the overall rainfall estimation error. Under the stationary assumption, CSQC models based on both supervised and unsupervised algorithms can have decent performances in noisy data identification and overall rainfall estimation error reduction; however, if the model is transferred to other cities with different rainfall structure or noise composition (without retraining), the supervised Multi-Layer Perceptrons (MLPs) turns out to be the best performing one.

Drought forecasts, particularly at seasonal scales, offer great potential for managing climate risk in water resources and agricultural systems. In this context, the importance of assessing the economic value of such forecasts and determining whether a decision-maker should adopt them cannot be overstated. Value-assessment studies often, however, ignore the dynamic aspects of forecast adoption, despite evidence from field-based studies suggesting that farmers’ forecast-adoption behavior fits the general framework of innovation diffusion, i.e. that forecast adoption is a dynamic learning process that takes place over time. In this study, we develop an agent-based model of drought forecast adoption to study the role played by heterogeneous economic and behavioral factors (i.e. risk aversion, wealth, learning rates), forecast characteristics (i.e. accuracy), and the social network structure (i.e. inter- and intra-county ties, change agents, self-reliance) in the process of forecast adoption and diffusion. We consider two learning mechanisms: learning by doing, represented by a reinforcement-learning mechanism, and learning from others, represented by a DeGroot-style opinion-aggregation model. Results show that, when social interactions between agents occur, forecast adoption follows a typical S-shaped diffusion curve. By contrast, when agents rely only on their own experience, the adoption pattern is close to linear. Our numerical experiment shows additionally that forecasts are never adopted if forecast accuracy drops below 65 percent. Finally, the proposed model also provides a flexible tool with which to test the effectiveness of extension targeting strategies in facilitating the diffusion of forecasts.

The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method for design and operations management of storage systems. It has been applied successfully in many real-world operations optimization problems (for example, the Great Lakes system and the Parambikulam-Aliyar project) and groundwater management problems. The FP method faces no curse of dimensionality unlike stochastic dynamic programming (SDP) and no need for scenarios generation as in implicit stochastic programming (ISP) methods. The paper introduces a novel implementation for the FP method by removing the need for nonlinear constraints and by decreasing the number of decision variables to just one third of its original value, significantly reducing solving time (~27 times faster than the original formulation). Additionally, new expressions derived for first and second moments of both reservoir release deficit and spill terms and the already-derived expression for second moments of reservoir storage are incorporated into the new formulation enabling the FP method to reach an improved optimality for a nonlinear objective function. The enhanced procedure is applied to solving a water reservoir operation optimization problem for a major dam in Brazil. The result comparisons made with SDP, two-stage stochastic programming and ISP along with a thorough analysis of release operation policies for both non-Gaussian correlated and Gaussian independent inflows prove the optimality of this highly numerically efficient and convenient-to-use FP method.