A retrospective on hydrological modelling based on half a century with the HBV model

Hydrological models are important tools that are commonly used as the basis for water resource management planning. In the 1970s the development of several relatively simple models started and a number of so-called conceptual (or bucket-type) models were suggested. In these models, the complex and heterogeneous hydrological processes in a catchment are represented by a limited number of storage elements and fluxes between these. While a major motivation for such relatively simple models in the early days were computational limitations, today some of these models are still used frequently despite vastly increased computational opportunities. The HBV model, which was first applied about 50 years ago in Sweden, is a typical example of a conceptual catchment model and has gained large popularity over the past 50 years. During several model intercomparisons, the HBV model performed well despite (or because of) its relatively simple model structure. Here, the history of model development from thoughtful considerations of different model structures to modelling studies using hundreds of catchments and cloud computing facilities, is described. Furthermore, the wide range of model applications is discussed. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability, which will face hydrologists also in the coming decades.

Climate Change Impact On Lake Tana Water Storage, Upper Blue Nile Basin, Ethiopia

Temperature and precipitation trend fluctuations influence the components of the hydrological cycle and the availability of water supplies and their resulting shifts in the balance of lake water (lake level). Quantile mapping was applied to correct temperature biases, and power transformation was applied for rainfall correction. The performance of the HBV model was evaluated through calibration and validation using objective functions (RVE, NSE) and provide RVE of 3.7%, -1.27%,1.05%, -0.72%,8.9% and -0.68 during calibration and RVE of -1.5%, 6.93%, -3.04%,8.796%, -5.89% and 8.5 % during validation for Gumara, Kiltie, Koga, Gilgel Abay, Megech and Rib respectively, While the model provided NS of 0.79,0.63,0.72,0.803,0.68 and 0.797 during calibration and NSE of 0.8,0.64,0.7,0.82,0.801 and 0.82 during validation for Gumara, Kiltie, Koga, Gilgel Abay, Megech, and Rib respectively. The simulated Lake level showed adequate agreement to the observed with NS and RVE of 0.7 and 6.44 % respectively. The result confirmed that over lake evaporation and rainfall increase for all future scenarios. The ungauged surface inflow is also increased shortly scenarios while gauged surface inflow increased for RCP4.5 (the 2070s) and RCP8.5 (2040s) and decreased for RCP4.5 (2040s) and RCP8.5 (2070s). The decreased in gauged surface water inflow is due to a decrease in inflow for Gilgel Abay, Koga and Gumara gauged catchments. Lake storage results showed a decrease in all future scenarios of all-time horizons.

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.

Improving the Pareto Frontier in multi-dataset calibration of hydrological models using metaheuristics

Hydrological models are crucial tools in water and environmental resource management but they require careful calibration based on observed data. Model calibration remains a challenging task, especially if a multi-objective or multi-dataset calibration is necessary to generate realistic simulations of multiple flow components under consideration. In this study, we explore the value of three metaheuristics, i.e. (i) Monte Carlo (MC), (ii) Simulated Annealing (SA), and (iii) Genetic 5 Algorithm (GA), for a multi-data set calibration to simultaneously simulate streamflow, snow cover and glacier mass balances using the conceptual HBV model. Based on the results from a small glaciated catchment of the Rhone River in Switzerland, we show that all three metaheuristics can generate parameter sets that result in realistic simulations of all three variables. Detailed comparison of model simulations with these three metaheuristics reveals however that GA provides the most accurate simulations (with lowest confidence intervals) for all three variables when using both the 100 and the 10 best parameter sets for 10 each method. However, when considering the 100 best parameter sets per method, GA yields also some worst solutions from the pool of all methods’ solutions. The findings are supported by a reduction of the parameter equifinality and an improvement of the Pareto frontier for GA in comparison to both other metaheuristic methods. Based on our results, we conclude that GA-based multi-dataset calibration leads to the most reproducible and consistent hydrological simulations with multiple variables considered.