Abstract: Despite the attention currently given to the potential environmental benefits of large-scale forest planting, there is a shortage of clear observational evidence regarding the effects on river flows, and what there is has often been contradictory or inconclusive. This paper presents three independently conducted paired-catchment forestry studies covering 66 station-years of flow measurements in the UK and Ireland. In each case coniferous evergreen trees were removed from one catchment with minimal soil disturbance while the adjoining control catchment was left unchanged. Trees were removed from 20% – 90% of the three experimental basins. Following woodland removal there was an increase in dry weather baseflow at all sites. Baseflows increased by about 8% after tree removal from a quarter of the Hore basin and by 41% for the near-total cut at Howan. But the changes were more complex for peak flows. Tree harvesting increased the smallest and most frequent peak storm flows, indicating that afforestation would lead to the suppression of such events. This was however restricted to events well below the mean annual flood, indicating that the impact of forests upon the largest and most damaging floods is likely to be limited. Whilst a forest cover can be effective in mitigating small and frequent stormflows it should never be assumed to provide protection against major flood events.

Abstract: CONTEXT Ancient irrigation systems (AISs) have been providing a multitude of ecosystem services to rural farming and urban communities in Asia and Africa, especially in arid and semi-arid climatic areas with low rainfall. Many AISs, however have now been degraded. A systematic analysis of AISs on their typologies, causes of degradation, and their ecosystem services is lacking. OBJECTIVE The objective of this review was to synthesize the knowledge on AISs on their typologies, status and causes of degradation, ecosystem services and functions, and identify gaps in research in Asia and Africa. METHOD A critical review of peer-reviewed journal papers, conference and workshop proceedings, book chapters, grey literature, and country reports was conducted. Qualitative and quantitative information from journal papers were used to conceptualize the typologies and analyze the status and causes of degradation, and ecosystems services and functions provided by the AISs. RESULTS AND CONCLUSION Based on the review, we classified AISs into three groups by source of irrigation water: Rainwater harvesting system (RHS) with small reservoirs, ground water based system, and floodwater based system. The RHSs, which used to receive reliable rainfall and managed by well cohesive social organizations for their maintenance and functioning in past, have now been silting due to extreme rainfall pattern and breakdown of the cohesive organizations in recent decades. In ground water based systems, indiscriminate development of deep tube wells causing siltation of channels has been a major challenge. In floodwater irrigation systems, irregular rainfall in the highlands and the breakage of irrigation structures by destructive floods were the main causes of degradation. Lack of maintenance and increased soil erosion, inadequate skilled manpower, and declining support from the government for repair and maintenance were the main causes of degradation of all AISs. The main ecosystem service provided by all AISs is water for agriculture. In tank- and pond-based systems, fish farming is also practiced. Tank irrigation systems provide various types of provisioning, regulatory, cultural and supporting services, especially in India and Sri Lanka. Ground water based systems provide water for domestic purposes and various cultural services. Floodwater based systems provide water for power generation and wildlife habitat maintenance and help in flood control. SIGNIFICANCE The knowledge generated through the review provide evidence-based information, and help aware governments, private sectors and development agencies for improved policy planning and decision making, and prioritizing the restoration, rehabilitation, and management of various AISs.

Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.