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This folder contains MATLAB codes required for the UWFS analysis.

Runoff characteristics of wetland-dominated landscapes are highly influenced by the heterogeneity of wetland properties such as contributing areas, local storage deficits, and degree of connection to the wetland network. Field observations indicate the fill-and-spill process is a common and controlling process in wetland-dominated landscapes in which wetlands receive water until a threshold is satisfied and then release water to the next water bodies in a basin. The upscaled probabilistic fill-and-spill algorithm provided here aims to take the understanding from observation of the fill-and-spill process in individual wetlands at local scales to estimate the response of thousands of wetlands to precipitation or snowmelt events at a larger scale. For this purpose, a novel derived distribution approach is implemented using the probabilistic characteristics of wetlands, i.e., deficit and concentrating factor probability distribution functions. The method proposed here is a generalization of the Probability Distributed Model (PDM) and Xinanjiang probabilistic runoff models with the inclusion of the effects of wetland contributing area and cascading networks. The analytical solution of this upscaled fill-and-spill processes has been compared with a Monte Carlo solution and then implemented in RAVEN, a semi distributed hydrological modelling framework. The accuracy and ability of the model simulation has been tested on ten watersheds in the Qu'Appelle River Basin in Saskatchewan, Canada. The promising simulation results obtained from manual and automated calibration shows strength of the proposed upscaled fill-and-spill algorithm in simulation of low gradient landscapes.

Fill-and-spill hydrology, where landscape storage features such as bogs, lakes, prairie sloughs, or surface depressions impound and then dynamically release water after a deficit is filled, has received increased attention in recent years. In systems dominated by fill-and-spill, the contributing runoff area is a function of both local storage deficit and the degree and nature of connectivity between storage features. Here, a closed-form analytical upscaled probabilistic event model of runoff response from thousands of bog cascades in a wetland complex is developed and demonstrated. The efficient mathematical model represents the individual wetland contributing area, runoff coefficient, and pre-event deficit of each bog as probability distributions that may be estimated via a combination of spatial analysis and field observation.The model is here used to explore the impacts of cascade depth, network branching ratio, local contributing area, and deficit distribution on runoff response. The upscaling results provide insight into the critical runoff characteristics and emergent behaviour of watersheds typified by fill-and-spill hydrology and clarify the role of ‘gatekeeper’ storage features at large scales and for systems with shallow cascade depth. The mathematical solution is found to be a generalization of the well-known PDM (Probability Distributed Model) and Xinanxiang probabilistic runoff models for the specific case where network depth is one and contributing area of each storage feature is zero, and therefore can be readily generalized to support simulation of classical rainfall-runoff responses in heterogeneous landscapes. The results of the model enable exploration of scaling and distribution effects upon catchment runoff in basins influenced by fill-and-spill hydrology.