{'fraction_non_alphanumeric': 0.01463907117617365, 'fraction_numerical': 0.0, 'mean_word_length': 5.787671232876712, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A conceptual model for soil moisture estimation and downscaling based on topographic attributes

Soil moisture is a key state variable in land surface-atmospheric interactions, strongly influencing radiation and precipitation partitioning and thus many components of the hydrologic cycle. However, physical and economic constraints make measurement of soil moisture patterns with adequate spatial resolutions and spatial extents impractical. In particular, available remote- sensing methods produce soil moisture patterns that have relatively coarse spatial resolutions. This research presents a conceptual model that is based on representations of the processes that control soil moisture and is designed to downscale coarse-resolution soil moisture patterns using topographic data. The model assumes that soil moisture at any given location is in equilibrium with the local climate, which is represented by the available spatial-average soil moisture and a potential evapotranspiration rate. The model includes infiltration, lateral unsaturated flow, groundwater recharge, and evapotranspiration. The spatial variations in soil moisture that are produced by these processes are ultimately represented by a collection of indices derived from topographic attributes. The final model includes six parameters that are related to soil and vegetation characteristics and require calibration. In this work, the model is calibrated using soil moisture data from multiple catchments in different climatic settings. We explore the differences in parameter values from different calibration schemes as well as the model performance at each catchment. The model performance is also compared to other available downscaling methods. The results indicate that the current model's performance is comparable to other methods. However, the model's flexibility means that it is more easily calibrated with limited data, and it can more readily accept additional sources of information if available.