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Climate change and anthropogenic activities alter the ecosystem which affects the ecosystem services (ES) associated with it. Therefore, the objective in this study is to quantify the impact of climate change on different regulation and provisioning ecosystem services. For this, we propose a modelling framework to simulate the impact of climate change on streamflow, nitrate loads, erosion, and crop yield in terms of ES indices for two agricultural catchments (Schwesnitz and Schwabach) located in Bavaria, Germany. The agro-hydrologic model Soil and Water Assessment Tool (SWAT) is used to simulate the considered ES in past (1990-2019), near future (2030-2059) and far future (2070-2099) climatic conditions. Three different bias-corrected (Representative Concentration Pathway, RCP 2.6, 4.5, and 8.5) climate projections from five different climate models retrieved from the Bavarian State Office for Environment (∼5 km) are used in this research to simulate the impact of climate change on ES. The developed SWAT models were calibrated for the major crops (1995 to 2018) present in the respective watersheds as well as for daily streamflow (1995 to 2008), which gave promising results with good PBIAS and Kling-Gupta Efficiency. The impact of climate change on erosion regulation, food and feed provisioning, and water quantity and water quality regulation were quantified in terms of indices. When using the ensemble of the five climate models, no significant impact on ES was seen due to climate change. Furthermore, the impact of climate change on different ES services from the two catchment is different. The findings of this study will be valuable for devising suitable management practices for sustainable water management at the catchment level to cope with climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Identification of critical erosion-prone areas and selection of best management practices (BMPs) for watersheds are necessary to control their degradation by reducing sediment yields. The current research assesses and proposes a combination of potential BMPs for the Baitarani catchment in India using the Soil and Water Assessment Tool (SWAT). After the successful calibration and validation of the SWAT model developed for this catchment, the model was applied to evaluate the efficacy of eight agricultural and structural management practices and their combinations (three scenarios) for controlling sediment yields at watershed and sub-watershed levels as well as to assess the impacts of combined BMPs on water balance components. A combination of BMPs was found more effective in reducing sediment yields than individual BMPs. Comparative evaluation revealed that structural BMPs (0.66-70%) are better than agricultural BMPs (2-7%) in minimizing sediment yields at watershed level. The costly measures like grade and streambank stabilization structures can reduce the sediment yield up to 70% at the watershed level. The modeling results of the impacts of different combinations of BMPs (three scenarios) indicated that if all the eight BMPs are implemented, the reduction of sediment yields is increased by 76% and 80% at sub-watershed and watershed levels, respectively compared to the Base Scenario. Based on funds availability, a suitable combination of BMPs can be adopted by the concerned decision-makers to effectively reduce sediment yields in the study area. Further, the simulation results of BMPs impacts on water balance components revealed that the annual average surface runoff reduces by 4-14% in the three scenarios, while aquifer recharge (6.8-8.7%), baseflow (8-10.5%), and percolation (1.2-3.9%) increase due to implementation of BMPs. Overall, the findings of this study are very useful for ensuring sustainable management of land and other resources at a catchment scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data., (Copyright © 2018 Elsevier B.V. All rights reserved.)