How does the choice of DEMs affect catchment hydrological modeling?

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.

Random forest-based modeling of stream nutrients at national level in a data-scarce region.

Nutrient runoff from agricultural production is one of the main causes of water quality deterioration in river systems and coastal waters. Water quality modeling can be used for gaining insight into water quality issues in order to implement effective mitigation efforts. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Recently, ML approaches have shown to achieve an accuracy comparable to the process-based models and even outperform them when describing nonlinear relationships. We used observations from 242 Estonian catchments, amounting to 469 yearly TN and 470 TP measurements covering the period 2016-2020 to train random forest (RF) models for predicting annual N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate and topography parameters and applied a feature selection strategy to reduce the number of dependent features in the models. The SHAP method was used for deriving the most relevant predictors. The performance of our models is comparable to previous process-based models used in the Baltic region with the TN and TP model having an R2 score of 0.83 and 0.52, respectively. However, as input data used in our models is easier to obtain, the models offer superior applicability in areas, where data availability is insufficient for process-based approaches. Therefore, the models enable to give a robust estimation for nutrient losses at national level and allows to capture the spatial variability of the nutrient runoff which in turn enables to provide decision-making support for regional water management plans.

Low water level drives high nitrous oxide emissions from treatment wetland.

Wetlands that are restored for carbon sequestration or created for water treatment are an important sources of greenhouse gases, especially methane. The emission of nitrous oxide (N2O) from these systems is often considered negligible due to the inundation and anerobic conditions that support complete denitrification. We used closed chamber method to analyze N2O fluxes over a long-term period across heterogeneous wetland ecosystem constructed for treating nitrate-rich agricultural runoff. Our results showed that the water depth and temperature were most important factors affecting high N2O emissions. The shallow areas where water depth was less than 9 cm created N2O hot spots that emitted 48.8% of the total wetlands annual emission while only covering 6% of the total area. The annual emission from shallow-water hot spots with dense helophytic vegetation was 4.85 ± 0.5 g N2O-N m-2 y-1 while it was only 0.37 ± 0.01 g N2O-N m-2 y-1 in deeper zones. While the water depth was the main factor for high N2O emissions, the temperatures increased the magnitude of the flux and therefore summer droughts and water drawdown created even larger hot spots. These results also suggest that IPCC benchmarks could underestimate N2O emission from shallow waterbodies. Thus, it is important that the shallow zones and water level drawdown in the created or restored wetlands is avoided to minimize the N2O flux.

Predicting lake dissolved organic carbon at a global scale.

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km2. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L-1. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.

Increasing fragmentation of forest cover in Brazil's Legal Amazon from 2001 to 2017.

Persistent forest loss in the Brazilian Legal Amazon (BLA) is responsible for carbon emission, reduction of ecosystem services, and loss of biodiversity. Combining spatial data analysis with high spatial resolution data for forest cover and forest loss, we quantified the spatial and temporal patterns of forest dynamics in the BLA. We identified an alarming trend of increasing deforestation, with especially high rates in 2016 and 2017. Moreover, the creation of forest cover fragments is faster than ever due to decreasing size and dispersion of forest loss patches. From 2001 to 2017, the number of large forest loss patches decreased significantly, accompanied by a reduction in the size of these patches. Enforcement of field inspections and of initiatives to promote forest conservation will be required to stop this trend.

Relationships between landscape pattern, wetland characteristics, and water quality in agricultural catchments.

Water quality in streams is dependent on landscape metrics at catchment and wetland scales. A study was undertaken to evaluate the correlation between landscape metrics, namely patch density and area, shape, heterogeneity, aggregation, connectivity, land-use ratio, and water quality variables (salinity, nutrients, sediments, alkalinity, other potential pollutants and pH) in the agricultural areas of a semiarid Mediterranean region dominated by irrigated farmlands (NE Spain). The study also aims to develop wetland construction criteria in agricultural catchments. The percentage of arable land and landscape homogeneity (low value of Simpson index) are significantly correlated with salinity (r(2) = 0.72) and NO(3)-N variables (r(2) = 0.49) at catchment scale. The number of stock farms was correlated (Spearman's corr. = 0.60; p < 0.01) with TP concentration in stream water. The relative abundance of wetlands and the aggregation of its patches influence salinity variables at wetland scale (r(2) = 0.59 for Na(+) and K(+) concentrations). The number and aggregation of wetland patches are closely correlated to the landscape complexity of catchments, measured as patch density (r(2) = 0.69), patch size (r(2) = 0.53), and landscape heterogeneity (r(2) = 0.62). These results suggest that more effective results in water quality improvement would be achieved if we acted at both catchment and wetland scales, especially reducing landscape homogeneity and creating numerous wetlands scattered throughout the catchment. A set of guidelines for planners and decision makers is provided for future agricultural developments or to improve existing ones.