Depending on the purpose of the study, aggregated hydrological models are preferred over distributed models because they provide acceptable results in terms of precision and are easy to run, especially in data scarcity scenarios. To obtain acceptable results in terms of hydrological process representativeness, it is necessary to understand and assess the models. In this study, the relative importance of the parameters of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model is analyzed using sensitivity analysis to detect if the simulated processes represent the predominant hydrological processes at watershed scale. As a case study, four watersheds with different hydrological regimes (glacial and pluvial) and therefore different dominant processes are analyzed. The results show that in the case of the rivers with a glacial regime, the model performance depends highly on the snow module parameters, while in the case of the rivers with a pluvial regime, the model is sensitive to the soil and evapotranspiration modules. The results are directly related to the hydrological regime, which indicates that the HBV model, complemented by sensitivity analysis, is capable of both detecting and representing hydrological processes at watershed scale.

Data on historical extreme events provides information not only for water resources planning and management but also for the design of disaster-prevention measures. However, most basins around the globe lack long-term hydro-meteorological information to derive the trend of hydrological extremes. This study aims to investigate a method to estimate maximum and minimum flow trends in basins with limited streamflow records. To carry out this study, data from the Allipén River watershed (Chile), the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model at a daily time step, and an uncertainty analysis were used. Through a calibration using only five years of records, 21-year mean daily flow series were generated and the extreme values derived. To analyze the effect of the length of data availability, 2, 5, and 10 years of flows were eliminated from the analyses. The results show that in the case of 11 years of simulated flows, the annual maximum and minimum flow trends present greater uncertainty than in the cases of 16 and 19 years of simulated flows. Simulating 16 years, however, proved to properly simulate the observed long-term trends. Therefore, in data-scarce areas, the use of a hydrological model to simulate extreme mean daily flows and estimate long-term trends with at least 16 years of meteorological data could be a valid option.

Due to population growth and expansion in the agricultural and industrial sectors, the demand for water has increased. However, water availability in some regions has decreased due to climate change trends and variability, necessitating innovative strategies and adaptation in water allocation to avoid conflicts among users in a hydrological system. This paper presents a resilience analysis and a conceptual hydrological modeling approach to evaluate the resilience capacity of a new water allocation rule in the Laja Lake basin in southern Chile. Resilience assessments included absorptive and adaptive capacities with four system states: resilient, susceptible, resistant, and vulnerable. A modeling approach was used considering the climate variability uncertainty and climate change trends of the Laja system. Characterization of adaptive and absorptive capacities showed that the Laja Lake basin moved from resistant to vulnerable. Hydrological modeling analyses showed that after a new water allocation agreement, the Laja Lake system is moving from vulnerable to susceptible, since the new rule has more adaptive alternatives to face climate variability. The new rule diminishes the possibilities of conflicts among users, ensuring the fulfillment of water needs for uses such as farming and ecosystem services such as landscaping, and allows for increased water allocation for energy in wet hydrological years.

Time-varying sensitivity analysis (TVSA) allows sensitivity in a moving window to be estimated and the time periods in which the specific components of a model can affect its performance to be identified. However, one of the disadvantages of TVSA is its high computational cost, as it estimates sensitivity in a moving window within an analyzed series, performing a series of repetitive calculations. In this article a function to implement a simple TVSA with a low computational cost using regional sensitivity analysis is presented. As an example of its application, an analysis of hydrological model results in daily, monthly, and annual time windows is carried out. The results show that the model allows the time sensitivity of a model with respect to its parameters to be detected, making it a suitable tool for the assessment of temporal variability of processes in models that include time series analysis. In addition, it is observed that the size of the moving window can influence the estimated sensitivity; therefore, analysis of different time windows is recommended.

Understanding the groundwater storage and release (S-Q) process and its contribution to river flows is essential for different hydrological applications, especially in periods of water scarcity. The S-Q process can be characterized based on recession parameter b, which is the slope of the power–law relationship −dQ/dt = aQ^b of the recession flow analysis, where recession parameter b represents the linearity of the S-Q process. In various studies, it has been found that this parameter can present high variability, which has been associated with the approach or spatial variability of basin characteristics. However, the variability of parameter b and its relationship with geology and the behavior of groundwater storage over time (evolution over time) have not been sufficiently studied. The objective of this study is to analyze the variability of recession parameter b and its relationship with geological and morphological characteristics and climate variability at different time scales. To this end, 72 drainage basins located in south central Chile were examined via recession flow analysis, considering five different time scales (5 years, 10 years, 15 years, 20 years, and 25 years). In addition, to analyze spatial variability patterns and generate groups of basins with similar characteristics, a cluster analysis was carried out. Clusters were obtained using the principal component analysis (PCA) and K-means methods. The results show that in wet periods, the slope of recession parameter b tends to increase (fast drainage process), while in dry periods, the recession slope tends to decrease (slow drainage processes). In general, the results suggest that the variability of recession coefficient b indicates changes in S-Q behavior; therefore, it could be used as an indicator of the sensitivity of a basin to climate variability.

The use of unmanned aerial vehicles (UAVs) has been steadily increasing due to their ability to acquire high-precision ground elevation information at a low cost. However, these devices have limitations in estimating elevations of the water surface and submerged terrain (i.e., channel bathymetry). Therefore, the creation of a digital terrain model (DTM) using UAVs in low-water periods means a greater dry channel surface area and thus reduces the lack of information on the wet area not appropriately measured by the UAV. Under such scenarios, UAV-DTM-derived data present an opportunity for practical engineering in estimating floods; however, the accuracy of estimations against current methods of flood estimations and design needs to be measured. The objective of this study is therefore to develop an exploratory analysis for the creation of hydraulic models of river floods using only UAV-derived topographic information. Hydraulic models were constructed based on DTMs created in (i) the traditional manner, considering the bathymetry measured with RTK-GPS and topography, and via (ii) remote sensing, which involves topography measurement with a UAV and assumes a flat bed in the part of the channel covered by water. The 1D steady-state HEC-RAS model v.5.0.3 was used to simulate floods at different return periods. The applied methodology allows a slightly conservative, efficient, economical, and safe approach for the estimation of floods in rivers, with an RMSE of 6.1, 11.8 and 12.6 cm for the Nicodahue, Bellavista and Curanilahue rivers. The approach has important implications for flood studies, as larger areas can be surveyed, and cost- and time-efficient flood estimations can be performed using affordable UAVs. Further research on this topic is necessary to estimate the limitations and precision in rivers with different morphologies and under different geographical contexts.