The hydrological processes within the catchment are generally influenced by both climate change (CC) and land use/land cover (LULC) change. However, most of the studies are focused on their individual impact on the catchment’s hydrology, while their combined effects have received little attention. This study employs the physically based distributed hydrological model, MIKE SHE, to study the separate and combined effect of climate and LULC change on the hydrology of a mesoscale catchment in the near future (2050s). An Artificial Neural Network–Cellular Automata (ANN-CA) based prediction model was trained to simulate the future LULC map. The future meteorological data under four CC scenarios was obtained from the Royal Netherlands Meteorological Institute (KNMI). The model results showed that the combined effects of CC with LULC changes did not significantly differ from the individual impact of CC on the catchment scale. However, on the local scale, the changes in LULC can significantly influence the variations in groundwater table, soil moisture, and actual evapotranspiration ranging from approximately–6–15%,–9–27%, and–30–10% respectively, depending on the specific change in LULC class and season. In summary, this research provides valuable insights into the complex interactions between LULC changes, CC, and hydrology.

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Study region
Gatun Lake, Panama Canal, Republic of Panama.

Study focus
The Panama Canal expansion, which was completed in June 2016, included the construction of new locks, known as Neo-Panamax, which are 3.3 times larger in volume than the old locks, known as Panamax. Water quality measurements of Gatun Lake, the main lake of the Panama Canal, are available, at different temporal scales, for the periods before and after the expansion. However, a statistical analysis of the salinity data has not been made available to the scientific community. This study quantifies spatiotemporal variations in salinity concentrations of Gatun Lake before and after the expansion of the Panama Canal, and examines their interaction with lake water levels and El Niño Southern Oscillation (ENSO) cycles. To achieve this, summary statistics, trend analyses and interpolation methods were applied to the available salinity and water level data for Gatun Lake.

New hydrological insights for the region
Before the expansion of the Panama Canal, average salinity in Gatun Lake was < 0.05 Practical Salinity Units (PSU). After the expansion, average salinity is 0.21 PSU, which represents an increase of over four times. In Gatun Lake salinity has been observed to be the highest near the Neo-Panamax locks, averaging 0.6 and 0.5 PSU after the expansion near Agua Clara locks in the Atlantic and Cocolí locks in the Pacific, respectively. After the expansion, salinity in Culebra Cut, the narrowest part of the Panama Canal, is about 0.1 PSU. This study concludes that average salinity in Gatun Lake is weakly anti-correlated to its water level and it responds to changes in water level with a delay of one to two months. In June 2020, at the end of a strong El Niño period, average salinity in Gatun Lake reached its peak of 0.39 ± 0.19 PSU, only one month after the lake’s water level reached its second lowest level in the past decade (24.5 m). During El Niño events, salinity showcases a statistically significant increasing trend whereas during La Niña events no significant trend could be identified.@en

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation.@en

Flood protection infrastructures are crucial for enhancing the resilience of societies exposed to natural hazards. Newly designed infrastructures are evaluated for sustainability using a coherent and internationally recognized method defined by the International Hydropower Association (IHA). However, in operation, old structures require a different assessment approach. Different work proposes a modified IHA protocol, mHSAP, which identifies opportunities for improvement and develops a sustainability evaluation framework for existing infrastructures. This paper applies the modified protocol to evaluate the sustainability of two types of flood protection structures: a unique canal system for flood–drought protection of an urban area and a flood protection dike. The time of operation of these structures is over 250 years and over 50 years, respectively. The application of the modified framework demonstrates its advantages in identifying areas for improving flood protection structure operation while maintaining the structure’s sustainability. It also illustrates how Romanian water boards can use such tools to facilitate collaboration between structure owners and stakeholders, allowing them to assess the risks and effects of flooding on society. Through these two examples from Romania, we also show that the mHSAP framework has the potential to actively support the fulfillment of the United Nations Agenda 2030 Sustainable Development Goals (SDGs). The results presented here show that this method can be further utilized by water board authorities to account for climate change effects, address related challenges in a coordinated and efficient manner, develop resilient flood management strategies, inform infrastructure investment decisions, and enhance collaboration among water management authorities.@en

Gatun Lake, located in the Panama Canal Watershed (PCW), is the main source of freshwater for the Canal's operations and it provides drinking water for nearly 600,000 people, which represents roughly 15% of the country's population. Since its creation at the beginning of the 20th century, Gatun Lake has slowly been transitioning from a swamp environment to a more saline-governed ecosystem. However, since the completion of the Canal's expansion project and inauguration of the Neo-Panamax locks in 2016, salinity in the lake has been increasing at a faster pace. The progressive salinization of this water body is not only a concern from the perspective of drinking water supply and human health, but for the lake's biodiversity as well. In order to understand the magnitude of this issue, evaluate the impact of climate change and design effective mitigation and management strategies, robust modelling tools are required. Nevertheless, these tools often require high volumes of high-quality data that are not always readily available. This paper illustrates the characterization of Gatun Lake's hydrodynamic behaviour and water quality condition using a numerical model built with Delft3D and publicly available open data, which includes bathymetry from GEBCO and hydrodynamic data from ACP's AQUARIUS web portal. Although further refinement of the model is still required, overall, it was demonstrated that reasonably good results can be obtained through a model built using publicly available open data.@en