ABSTRACT
Hydrologic models enable users to predict, monitor, and study the movement of water within the water cycle. As computing power grows ever more ubiquitous and inexpensive, the range of plausible uses for hydrologic models also grows. Hydrologic modeling and capacity building across the African continent are a natural fit. Hydrologic models hold the promise of improving forecasts and monitoring of droughts and flooding, which are together among the most devastating and most common environmental disasters to afflict African nations. Hydrologic models also permit governments, nongovernmental organizations (NGOs), and citizens to plan around water surpluses and water shortages. In the agricultural realm, model outputs like soil moisture can guide irrigation decisions, while surveys to collect soil properties cannot only increase the fidelity of hydrologic model outputs but also inform the choice of which crop to plant, where, and at what time. The choice of hydrologic model requires brief consideration. Models can be statistical (stochastic), in which the model inputs and outputs are linked via mathematical equations or statistical relationships, or physical, where the modeler attempts to characterize the physical processes linking the inputs and outputs; these can also be described via the terms “empirical” model and “conceptual” model, respectively. Models can also be characterized by whether they use lumped parameters (where the model settings are the same across the entire modeled domain) or distributed parameters (where the model settings vary from model cell to model cell). These parameters, or model settings, can be thought of as tuning knobs. Consider a stereo where turning the base or treble knob alters the output sound. In a hydrologic model, changing a parameter changes the model output: streamflow, soil moisture, or some other modeled variable of interest.
