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Ecosystems provide vital services to humans, which are regulated by the underlying ecological processes. Because of rapid population growth and urbanization, land‐use and land‐cover (LULC) changes ultimately impact the services an ecosystem can offer. The LULC of the Kathmandu Valley, Nepal have been altered due to urbanization, which is further expected to change in the future. However, a quantitative assessment of LULC impact on ecosystem services has not been undertaken. The purpose of this study is to assess the impacts affecting the ecosystem services values (ESV) in the Kathmandu Valley due to historical and projected LULC changes. We used satellite‐based datasets to assess LULC changes in the past, and Dyna‐CLUE to model future LULC changes. The benefit transfer approach was used to estimate ESV and their changes across time. Results confirmed the increase in urban area of 3.4% per year. This led to declining ESV from 1990 (US$ 231 million) to 2010 (US$ 205 million), which is predicted to decline further (US$ 157 million in 2050); on average US$ 12.3–13.2 million every decade u 1990 2050. This loss in ESV was dominated by the decline in agricultural land (40%, US$ 46.9 million), followed by deforestation (11%, US$ 11.1 million). Development (like urbanization) often overlooks the values from ecosystem services (ES), but ES quantification serves as a tool to make appropriate decisions during those changes. As shown in this study, ESV can be effectively quantified via application of a parsimonious approach by utilizing globally available datasets. This is especially more important for the data scarce regions of developing countries that are undergoing rapid changes. [ABSTRACT FROM AUTHOR]

The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the Fractions Skill Score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA are found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75 % quantile (90 % quantile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important. [ABSTRACT FROM AUTHOR]

An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, modified Kling-Gupta efficiency), categorical metrics, and other statistics (wet day frequency, transition probabilities and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both point and grid scale of 5 km. BARRA also consistently reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias and modified Kling-Gupta efficiency (KGE') indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms AWAP in terms of KGE' score and the components of the KGE' score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions. [ABSTRACT FROM AUTHOR]