Land-use/land-cover (LULC) change is considered a key human factor influencing groundwater recharge in floodplains. Without accurate estimations, the impact of LULC change on water balance components may be either significantly understated or exaggerated. This paper assesses the impacts of LULC changes from 1990 to 2018 on water balance components and groundwater levels of the Drava floodplain, Hungary, where human interference has led to a critical environmental situation. In this study, a spatially-distributed water balance model (WetSpass-M), and a groundwater flow model (MODFLOW-NWT) were integrated to assess the impacts of LULC changes. The moderate expansion of built-up areas increased surface runoff, while the afforestation of arable land and meadows and the overgrowth of bare mudflats with willow shrubs increased evapotranspiration. As a consequence, total annual groundwater recharge decreased by 5.3 × 107 m3 in the floodplain with an average of 335 mm year−1 and 317 mm year−1 in 2012 and 2018, respectively. Moreover, an average groundwater level decline by 0.1 m is observed in the same period. Declined groundwater recharge, increased runoff, and evapotranspiration exerted a negative effect on water resources in the Drava basin. The approach tested in this paper allows temporal and spatial estimation of hydrological components under the changes of LULC, providing quantitative information for decision-makers and stakeholders to implement efficient and sustainable management of water resources in the Drava floodplain. The provided integrated model is also applicable to regionally.

The increasing intensity and frequency of extreme storms pose a growing challenge to stormwater management in highly urbanized areas. Without an adequate and appropriate stormwater system, the storms and associated floods will continue to cause significant damage to infrastructure and loss of life. Low Impact Development (LID) has become an emerging alternative to the traditional stormwater system for stormwater management. This study evaluates and optimizes applications of different combinations of LIDs to minimize flows from a catchment under past and future storm conditions. The Storm Water Management Model (SWMM), forced by observed and downscaled precipitation from Coupled Model Intercomparison Project phase 6 (CMIP6), was used to simulate the runoff and apply the LIDs in the Renton City, WA. The final results show that the performance of LIDs in reducing total runoff volume varies with the types and combinations of LIDs utilized. A 30% to 75% runoff reduction was achieved for the past and future 50 year and 100 year storms. The study demonstrates the effectiveness of LID combinations with conventional stormwater systems to manage the future runoff in the study area, which is expected to increase by 26.3% in 2050.

Low-impact development (LID) is increasingly used to reduce stormwater’s quality and quantity impacts associated with climate change and increased urbanization. However, due to the significant variations in their efficiencies and site-specific requirements, an optimal combination of different LIDs is required to benefit from their full potential. In this article, the multi-objective genetic algorithm (MOGA) was coupled with the stormwater management model (SWMM) to identify both hydrological and cost-effective LIDs combinations within a large urban watershed. MOGA iteratively optimizes the types, sizes, and locations of different LIDs using a combined cost- and runoff-related objective function under both past and future stormwater conditions. The infiltration trench (IT), rain barrel (RB), rain gardens (RG), bioretention (BR), and permeable pavement were used as potential LIDs since they are common in our study area—the city of Renton, WA, USA. The city is currently adapting different LIDs to mitigate the recent increase in stormwater system failures and flooding. The results from our study showed that the optimum combination of LIDs in the city could reduce the peak flow and total runoff volume by up to 62.25% and 80% for past storms and by13% and 29% for future storms, respectively. The findings and methodologies presented in this study are expected to contribute to the ongoing efforts to improve the performance of large-scale implementations of LIDs.