The digital elevation models (DEMs) are the primary and most important spatial inputs for a wide range of hydrological applications. However, their availability from multiple sources and at various spatial resolutions poses a challenge in watershed modeling as they influence hydrological feature delineation and model simulations. In this study, we evaluated the effect of DEM choice on stream and catchment delineation and streamflow simulation using the SWAT model in four distinct geographic regions with diverse terrain surfaces. Performance evaluation metrics, including Willmott's index of agreement, and nRMSE combined with visual comparisons were employed to assess each DEM's performance. Our results revealed that the choice of DEM has a significant impact on the accuracy of stream and catchment delineation, while its influence on streamflow simulation within the same catchment was relatively minor. Among the evaluated DEMs, AW3D30 and COP30 performed the best, closely followed by MERIT, whereas TanDEM-X and HydroSHEDS exhibited poorer performance. All DEMs displayed better accuracy in mountainous and larger catchments compared to smaller and flatter catchments. Forest cover also played a role in accuracy, mainly due to its association with steep slopes. Our findings provide valuable insights for making informed data selection decisions in watershed modeling, considering the specific characteristics of the catchment and the desired level of accuracy.

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time-series prediction across diverse catchments and compares its results against SWAT predictions. We found strong evidence of RF’s better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas SWAT generally yielded unsatisfactory results (NSE < 0.5) and tended to overestimate daily streamflow by up to 27% (PBIAS). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.

Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better model performance. In this study, we investigate the effect of input data on the sensitivity and uncertainty of the SWAT model in the Porijõgi catchment in Estonia. We created four model setups using global/regional level data (HWSD soil and CORINE) and local high-resolution spatial data, including the Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). We employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. The results illustrated that models with high-resolution local soil data performed lower than models with global soil data, but in contrast, in the case of land use datasets, the local high-resolution ETAK dataset improved performance over the CORINE data.

The distylous plant Primula veris has long served as a model species for studying heterostyly, that is the occurrence of multiple floral morphs within a population to ensure outcrossing. Habitat loss, reduced plant population sizes, and climate change have raised concerns about the impact of these factors on morph ratios and the related consequences on fitness of heterostylous species. We studied the deviation of floral morphs of P. veris from isoplethy (i.e. equal frequency) in response to plant population size, landscape context and climatic factors, based on a pan‐European citizen science campaign involving observations from 28 countries. In addition, we examined the relative frequency of morphs to determine whether landscape and climatic factors disrupt morph frequencies or whether a specific morph has an advantage over the other. Theory predicts equal frequencies of short‐styled S‐morphs and long‐styled L‐morphs in populations at equilibrium. However, data from >3000 populations showed a substantial morph deviation from isoplethy and a significant excess of S‐morphs (9% higher compared to L‐morphs). Deviation of morph frequency from equilibrium was substantially stronger in smaller populations and was not affected by morph identity. Higher summer precipitation and land use intensity were associated with an increased prevalence of S‐morphs. Five populations containing individuals exhibiting short homostyle phenotypes (with the style and anthers in low positions) were found. Genotyping of the individuals at CYP734A50 gene of the S locus, which determines the length of the style and the position of anthers of P. veris , revealed no mutations in this region. Our results based on an unprecedented geographic sampling suggest that changes in land use and climate may be responsible for non‐equilibrium morph frequencies. This large‐scale citizen science initiative sets foundations for future studies to clarify whether the unexpected excess of S‐morphs is due to partial intra‐morph compatibility, disruption of heterostyly or survival advantage of S‐morphs. Synthesis . Human‐induced environmental change may affect biodiversity indirectly through altering reproductive traits, which can also lead to reduced fitness and genetic diversity. Further research should consider the possible role of pollinators in mediating the ecological and evolutionary consequences of recent landscape and climatic shifts on plant reproductive traits.