K. O. Asante

Global‐scale flow routing using a source‐to‐sink algorithm

In this paper, the development and global application of a new approach to large-scale river routing is described. It differs from previous methods by the extent to which the information content of high-resolution global digital elevation models is exploited in a computationally efficient framework. The model transports runoff directly from its source of generation in a land model cell to its sink on a continental margin or in an internally draining basin (and hence is referred to as source-to-sink routing) rather than from land cell to land cell (which we call cell-to-cell routing). It advances the development of earlier source-to-sink models by allowing for spatially distributed flow velocities, attenuation coefficients, and loss parameters. The method presented here has been developed for use in climate system models, with a specific goal of generating hydrographs at continental margins for input into an ocean model. However, the source-to-sink approach is flexible and can be applied at any space-time scale and in a number of other types of large-scale hydrological and Earth system models. Hydrographs for some of the worlds major river basins resulting from a global application, as well as hydrographs for the Nile River from a more detailed application, are discussed.

Developing a Flood Monitoring System From Remotely Sensed Data for the Limpopo Basin

This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administrations Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administrations infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting streamflow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information

A Catchment‐Based Hydrologic and Routing Modeling System with explicit river channels

In this paper, we present a macroscale hydrologic modeling system with an explicit representation of storage and movement of water in river channels and floodplains. The overall modeling system, called the Catchment-Based Hydrologic and Routing Modeling System (CHARMS), is composed of a land surface model and a river routing model that operate on a network of hydrologic catchments (or watersheds). The land surface model in CHARMS is based on the National Center for Atmospheric Research Community Land Model. The river routing model in CHARMS generates river discharge by transporting runoff generated by the catchment-based CLM through the river network. The routing model uses information on channel cross-section geometry, derived from the 90 m Shuttle Radar Topography Mission digital elevation model, to simulate river discharge and the associated flow depth and inundation width. CHARMS was implemented over the Wabash River basin in the central United States (drainage area 72282 km2), and simulated streamflow was validated using daily observations. Simulated flow depth and inundation extent generally followed seasonal variations in observed flooding and droughts. Limitations of some of the assumptions and scaling factors used in this study and the issues that need to be addressed for a continental- or global-scale implementation of CHARMS are discussed. This paper serves as the foundation for a catchment-based, global land surface modeling framework that could incorporate spatiotemporal variations in surface water bodies, as well as satellite measurements of these variations.

Establishing an operational waterhole monitoring system using satellite data and hydrologic modelling: Application in the pastoral regions of East Africa

Timely information on the availability of water and forage is important for the sustainable development of pastoral regions. The lack of such information increases the dependence of pastoral communities on perennial sources, which often leads to competition and conflicts. The provision of timely information is a challenging task, especially due to the scarcity or non-existence of conventional station-based hydrometeorological networks in the remote pastoral regions. A multi-source water balance modelling approach driven by satellite data was used to operationally monitor daily water level fluctuations across the pastoral regions of northern Kenya and southern Ethiopia. Advanced Spaceborne Thermal Emission and Reflection Radiometer data were used for mapping and estimating the surface area of the waterholes. Satellite-based rainfall, modelled run-off and evapotranspiration data were used to model daily water level fluctuations. Mapping of waterholes was achieved with 97% accuracy. Validation of modelled water levels with field-installed gauge data demonstrated the ability of the model to capture the seasonal patterns and variations. Validation results indicate that the model explained 60% of the observed variability in water levels, with an average root-mean-squared error of 22%. Up-to-date information on rainfall, evaporation, scaled water depth and condition of the waterholes is made available daily in near-real time via the Internet (http://watermon.tamu.edu). Such information can be used by non-governmental organizations, governmental organizations and other stakeholders for early warning and decision making. This study demonstrated an integrated approach for establishing an operational waterhole monitoring system using multi-source satellite data and hydrologic modelling.