Large rivers often present a river–lake–delta system, with a wide range of temporal and spatial scales of the flow due to the combined effects of human activities and various natural factors, e.g., river discharge, tides, climatic variability, droughts, floods. Numerical models that allow for simulating the flow in these river–lake–delta systems are essential to study them and predict their evolution under the impact of various forcings. This is because they provide information that cannot be easily measured with sufficient temporal and spatial detail. In this study, we combine one-dimensional sectional-averaged (1D) and two-dimensional depth-averaged (2D) models, in the framework of the finite element model SLIM, to simulate the flow in the Mahakam river–lake–delta system (Indonesia). The 1D model representing the Mahakam River and four tributaries is coupled to the 2D unstructured mesh model implemented on the Mahakam Delta, the adjacent Makassar Strait, and three lakes in the central part of the river catchment. Using observations of water elevation at five stations, the bottom friction for river and tributaries, lakes, delta, and adjacent coastal zone is calibrated. Next, the model is validated using another period of observations of water elevation, flow velocity, and water discharge at various stations. Several criteria are implemented to assess the quality of the simulations, and a good agreement between simulations and observations is achieved in both calibration and validation stages. Different aspects of the flow, i.e., the division of water at two bifurcations in the delta, the effects of the lakes on the flow in the lower part of the system, the area of tidal propagation, are also quantified and discussed.

@en

SLIM is an unstructured mesh, finite element model of environmental and geophysical fluid flows, which is being improved to simulate fine-grained sediment transport in riverine and marine water systems. A 2D depth-averaged version of the model is applied to the Mahakam Delta (Borneo, Indonesia), the adjacent ocean, and three lakes in the central part of the Mahakam River catchment. The 2D code is coupled to a 1D section-averaged model for the Mahakam River and four tributaries. The coupled 2D/1D model is mainly aimed at simulating fine-grained sediment transport in the riverine and marine water continuum of the Mahakam River system. Using the observations of suspended sediment concentration (SSC) at five locations in the computational domain, the modelling parameters are first determined in a calibration step, for a given
period of time. A validation step is then performed using data related to another period of time. It is concluded that the coupled 2D/1D model
reproduces very well the observed suspended sediment distribution within the delta. The spatial distribution of sediment concentration in the delta
and its temporal variation are also discussed.@en

Rainfall retrieval using weather radar relies on power functions between radar reflectivity Z and rain rate R. The nonlinear nature of these relations complicates the comparison of rainfall estimates employing reflectivities measured at different scales. Transforming Z into R using relations that have been derived for other scales results in a bias and added uncertainty. We investigate the sensitivity of Z-R relations to spatial and temporal aggregation using high-resolution reflectivity fields for five rainfall events. Existing Z-R relations were employed to investigate the behavior of aggregated Z-R relations with scale, the aggregation bias, and the variability of the estimated rain rate. The prefactor and the exponent of aggregated Z-R relations systematically diverge with scale, showing a break that is event-dependent in the temporal domain and nearly constant in space. The systematic error associated with the aggregation bias at a given scale can become of the same order as the corresponding random error associated with intermittent sampling. The bias can be constrained by including information about the variability of Z within a certain scale of aggregation, and is largely captured by simple functions of the coefficient of variation of Z. Several descriptors of spatial and temporal variability of the reflectivity field are presented, to establish the links between variability descriptors and resulting aggregation bias. Prefactors in Z-R relations can be related to multifractal properties of the rainfall field. We find evidence of scaling breaks in the structural analysis of spatial rainfall with aggregation. Key Points Nonlinear rainfall retrieval relations are susceptible to aggregation bias High-resolution radar data can help to constrain aggregation bias Aggregation bias can become of the same order as that of intermittent sampling@en