Embedded Files
Unprecedented rainfall events increase the magnitude of design storms
Climate change, driven by human activities and increasing greenhouse gas emissions, is pushing Earth's climate toward a warmer state, as evidenced by long-term observations. The frequency and intensity of unprecedented rainfall events have increased in recent years, underscoring the urgent need to revise design storms and depth-duration frequency (DDF) curves to better adapt to and mitigate the impacts of climate change. This study used a serial type of stochastic rainfall generator (SRG) that is capable of simulating daily rainfall series by embedding unprecedented events to study extreme precipitation scenarios under the changing climate. By perturbing values of power law tuning parameters in the SRG model, we developed thirty-six precipitation scenarios, some of which directly correlate with the current climate change scenario, while others represent very extreme conditions. High-performance computing is employed to run the computationally intensive SRG for simulating thirty-six scenarios across the entire Indian region. These simulated scenarios were analyzed to prepare rainfall return level maps and DDF curves. The findings reveal substantial increases in rainfall return levels across all frequencies when unprecedented events are considered, with pronounced impacts in coastal, northeastern, and Himalayan regions. The spatial pattern of simulated extreme precipitation was consistent across all generated scenarios from SRG irrespective of the return periods. Minimal spatial uncertainty in return level estimates across climate zones is observed which confirms the robustness of the SRG model and spatial clusters of extreme rainfall are identified irrespective of SRG being a point model. The analysis in this study based on SRG simulated climate change scenarios offers crucial insights for revising design storms and for devising climate resilience and flood management strategies.
Identification of dominant hydrologic processes using sensitivity analysis
A sequential sensitivity analysis using the Efficient Elementary Effect (EEE) method is employed to select the sensitive parameters for each model structure considered. This allowed to capture the dominant hydrological process of the catchment under study, irrespective of the model structure choice. The study is carried out in the Netravathi basin of Karnataka, India. The findings pertaining to dominant processes obtained from this study are in alignment with the existing literature, thereby affirming the efficacy of SUMMA in modeling the hydrological processes specific to Indian conditions. Notably, this is the first-time application of SUMMA to Indian basins. Identifying the most sensitive parameters of the highly parameterized model, SUMMA, also helped to scale down the dimensionality of the problem in terms of computational demand and complexity. By giving precedence to parameters with the most substantial influence also help to mitigate the challenge of parameter equifinality. Furthermore, the selection of sensitive parameters eases model calibration.
Statistical assessment of the impact of subjective hydrological modeling decisions on flood simulations
The process of setting up a hydrological model requires the modeler to make various subjective decisions. These decisions include the choices like selecting the model’s structure, discretizing the space, representing the forcing data spatially, determining the metrics for assessing performance during calibration and many more. The influence of these subjective modeling decisions is investigated in turn in a standardized framework to understand how differences in the decisions impact flood simulations. For this purpose, the capability of SUMMA model is made used as it allows a straightforward comparison of different model structure choices and can easily be reconfigured for different spatial organizations. Based on the various choices of model decisions, 36 unique model configurations are constructed and each of them are separately calibrated by the performance metrics. The impact and relative importance of modeling decisions are quantified by Analysis of Variance (ANOVA) and effect size respectively. As a test case, flood peaks that occurred in the Netravathi basin are accurately simulated (expressed as deviation in peak flow, deviation in peak time and relative volume error) to study the impact of the model decisions. For floods, the choice of spatial discretization of the modeling domain is the most impacting decision, followed by the choice of objective function during calibration, model structure and spatial representation of forcing, respectively, for the catchment. The results provide key insights regarding the ideal option a modeler must choose for each modeling decision for simulating floods in Netravathi. More generally, this study shows that model configuration decisions that are anecdotally often made based on convenience or habit can strongly impact simulation accuracy for specific modeling purposes. Alongside the need to quantify more traditional uncertainty sources, such as data and parameter uncertainty, there is a need to quantify the impact of these model configuration decisions.
Parameter Estimation using Convolutional Neural Network-Guided Dynamically Dimensioned Search Approach
In hydrological modeling, parameter estimation is inevitable due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modellers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. In this study, a novel approach called hydroCNN+DDS is introduced. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the
general patterns and relationships between discharge time series and parameters without compromising the underlying physics. HydroCNN+DDS is used to estimate parameters in the highly parameterized hydrological model, SUMMA using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, the proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in this approach. The methodology is demonstrated for the CAMELS (catchment Attributes and Meteorology for Large-sample Studies) basins of CONUS (Contiguous United States).
Page updated
Google Sites
Report abuse