Understanding the land use/cover change (LUCC) in watersheds is essential for sustainable development. The CA-Markov model has been proven to be an effective method for land use modeling because of its simplicity and potential for evolution. However, it is difficult to apply this method to meet the requirement of land use planning and management since it fails to consider the driving forces of LUCC. To evaluate the factors that influence LUCC comprehensively, we developed and implemented a machine learning-based CA-Markov model to understand the dynamics of LUCC in a coastal watershed in Southeast China, the Minjiang River Watershed (MRW). The proposed method performed well for each land use category, with average AUC values of 0.999 and 0.916 for the training and testing periods, respectively, for suitable images. The overall accuracy for LUCC was 0.971. The urbanization process in the MRW was speeding up recently. Urban area increased by 2.22% of the total area during 2015–2020, and most of that was from conversion of woodland and agricultural land. Additionally, the proposed method provided a much deeper understating of the forces driving the LUCC on a regional scale. Population and gross domestic product (GDP) were the major factors influencing the distribution of urbanized land in the MRW. In contrast, woodland distribution was highly related to topographic factors in the MRW. Scenario analysis was also employed to identify patterns of LUCC under different scenarios. The results showed that the process of urbanization may become more complex with increasing population and GDP and that land use evolution may be more sustainable with scientific spatial plans which consider facilities for people and ecological protection. The proposed method quantifies the LUCC in changing environmental settings and can serve as a helpful tool for sustainable watershed management.

The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.

Pesticides are detected in surface water and groundwater, endangering the environment. In lowland regions with subsurface drainage systems, drained depressions become hotspots for transport of pesticides and their transformation products (TPs). This study focuses on detailed modelling of the degradation and transport of pesticides with different physico-chemical properties. The objective is to analyse complex hydrological transport processes, to understand the temporal and spatial dynamics of the degradation and transport of pesticides. The ecohydrological model SWAT+ simulates hydrological processes as well as agricultural management and pesticide degradation, and can therefore be used to develop pesticide loss reduction strategies. This study focuses on modelling of three pesticides (pendimethalin, diflufenican, and flufenacet), and two TPs, flufenacet-oxalic acid (FOA) and flufenacet sulfonic acid (FESA). The study area is a 100-hectare farmland in the northern German lowlands of Schleswig-Holstein that is characterized by an spacious drainage network of 6.3 km and managed according to common conventional agricultural practice. SWAT+ modelled streamflow with very good agreement between observed and simulated data during calibration and validation. Regarding pesticides, the model performance for highly mobile substances is better than for non-mobile pesticides. While the transport of the moderately to very mobile substances via tile drains played an important role in both wet and dry conditions, no transport via tile drains was modelled for the highly sorptive and non-mobile pendimethalin. In conclusion, the model can reliably represent the degradation of moderately to very mobile pesticides in small-scale tile drainage-dominated catchments, as well as surface runoff-induced peak loads. However, it has weaknesses in accounting for the subsurface transport of non-mobile substances, which can lead to an underestimation of the subsequent delivery after precipitation events and thus underestimates the total load.