Search Results (6)

Despite the widespread use of distributed hydrological models for operational forecasting, climate change impact assessment, and large ensemble experiments, their computational performance and scalability are rarely systematically and reproducibly quantified. This lack of explicit information limits researchers’ and practitioners’ ability to anticipate runtimes, allocate computational resources efficiently, and design feasible modeling experiments. To address this gap, a general methodological framework was developed to assess computational scalability by varying spatial and temporal resolutions, input/output gauge densities, and hardware configurations. This framework was evaluated using the TETIS v9.1 ecohydrological model as a case study. Runtimes were systematically recorded, and a Random Forest regression model was trained to predict computational performance based exclusively on user-defined configuration variables. Model robustness was further assessed through a Monte Carlo uncertainty analysis. The results reveal clear scaling patterns: spatial resolution and output-gauge density exert the strongest influence on runtime, while temporal resolution shows nonlinear effects that depend on catchment size. The predictive tool achieved high accuracy for large hydrological simulations, with increased uncertainty limited to extremely short runtimes on high-speed processors. This study introduces a transferable framework to support efficient experimental design and operational hydrological modeling and provides the first reproducible characterization of TETIS computational scalability. Full article

Advanced wastewater management systems are necessary to effectively direct severely contaminated initial rainwater runoff to the treatment facility only when pollutant concentrations are elevated during the initial flush event, thereby reducing the risk of water pollution caused by urban drainage systems. This necessitates the implementation of intelligent decision-making systems, forecasting, and monitoring. However, conventional “deterministic” forecasts are inadequate for making informed decisions in the presence of uncertainty regarding future values, despite the fact that a variety of modeling techniques have been employed to predict total suspended solids at specific locations. The literature contains a number of “probabilistic” forecasting approaches that take into account uncertainty. Among them, this paper proposes the Model Conditional Processor (MCP), which is well-known in hydrological, hydraulic, and climatological fields, to forecast the predictive probability density of total suspended solids based on one or more deterministic predictions. This is intended to address the issue. The decision to divert the first flush is subsequently guided by the predictive density and probabilistic thresholds. The effective implementation of the MCP approach is demonstrated in a real case study that is part of the USGS’s extensive and long-term stormwater monitoring initiative, based on observations of a real stormwater drainage system. The results obtained confirm that probabilistic approaches are suitable instruments for enhancing decision-making. Full article

This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources. Full article

Low-quality input data (such as sparse rainfall gauges, low spatial resolution soil type and land use maps) have limited the application of physically-based distributed hydrological models in operational practices in many data-sparse regions. It is necessary to quantify the uncertainty in the deterministic forecast results of distributed models. In this paper, the TOPographic Kinematic Approximation and Integration (TOPKAPI) distributed model was used for deterministic forecasts with low-quality input data, and then the Hydrologic Uncertainty Processor (HUP) was used to provide the probabilistic forecast results for operational practices. Results showed that the deterministic forecasts by TOPKAPI performed poorly in some flood seasons, such as the years 1997, 2001 and 2008, despite which the overall accuracy of the whole study period 1996–2008 could be acceptable and generally reproduced the hydrological behaviors of the catchment (Lushi basin, China). The HUP model can not only provide probabilistic forecasts (e.g., 90% predictive uncertainty bounds), but also provides deterministic forecasts in terms of 50% percentiles. The 50% percentiles obviously improved the forecast accuracy of selected flood events at the leading time of one hour. Besides, the HUP performance decayed with the leading time increasing (6, 12 h). This work revealed that deterministic model outputs had large uncertainties in flood forecasts, and the HUP model may provide an alternative for operational flood forecasting practices in those areas with low-quality data. Full article

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales. Full article

Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accounting for inflow forecast uncertainty in a sub-basin of Huaihe River, China. The Xinanjiang model was used to provide deterministic flood forecasts, and was combined with the Hydrologic Uncertainty Processor (HUP) to quantify reservoir inflow uncertainty in the probability density function (PDF) form. Furthermore, the PDFs of reservoir water level (RWL) and the risk rate of RWL exceeding a defined safety control level could be obtained. Results suggested that the median forecast (50th percentiles) of HUP showed better agreement with observed inflows than the Xinanjiang model did in terms of the performance measures of flood process, peak, and volume. In addition, most observations (77.2%) were bracketed by the uncertainty band of 90% confidence interval, with some small exceptions of high flows. Results proved that this framework of risk analysis could provide not only the deterministic forecasts of inflow and RWL, but also the fundamental uncertainty information (e.g., 90% confidence band) for the reservoir flood routing calculation. Full article