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ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model. WATER 2022. [DOI: 10.3390/w14121950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The reproducibility of computational hydrology is gaining attention among hydrologists. Reproducibility requires open and reusable code and data, allowing users to verify results and process new datasets. The creation of input files for global hydrological models (GHMs) requires complex high-resolution gridded dataset processing, limiting the model’s reproducibility to groups with advanced programming skills. GlobWat is one of these GHMs, which was developed by the Food and Agriculture Organization (FAO) to assess irrigation water use. Although the GlobWat code and sample input data are available, the methods for pre-processing model inputs are not available. Here, we present a set of open-source Python and YAML scripts within the Earth System Model Evaluation Tool (ESMValTool) that provide a formalized technique for developing and processing GlobWat model weather inputs. We demonstrate the use of these scripts with the ERA5 and ERA-Interim datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). To demonstrate the advantage of using these scripts, we ran the GlobWat model for 30 years for the entire world. The focus of the evaluation was on the Urmia Lake Basin in Iran. The validation of the model against the observed discharge in this basin showed that the combination of ERA5 and the De Bruin reference evaporation method yields the best GlobWat performance. Moreover, the scripts allowed us to examine the causes behind the differences in model outcomes.
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Abstract
The type of modelling of gully erosion for the projects of land management depend on the targets and degree of details of these projects, as well as on the availability of input data. The set of four models cover a broad range of possible applications. The most detailed information about predicted gullies, change of their depth, width, and volume throughout the gully lifetime is obtained with the gully erosion and thermoerosion dynamic model. The calculation requires the time series of surface runoff, catchment relief, and lithology and the complex of coefficients and parameters, some of which can be estimated only by model calibration on the measurements. The difficulty in obtaining some of these coefficients makes it necessary to use less complicated models. The stable gully model predicts final gully depths and widths and is useful for projects where only stable gully geometry is used. The modified area–slope approach is used in the two simplest models, where the position on the slopes of possible gullies is calculated without details of the gully geometry. One of these models calculates total erosion potential, taking into account all water runoff transforming a gully. The second calculates gully erosion risk, using the information about slope inclination, contributing area and maximum surface runoff.
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Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau. WATER 2021. [DOI: 10.3390/w13030307] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.
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