Demonstration of an online web services tool incorporating automatic retrieval and comparison of precipitation data

Abstract

Input data acquisition and preprocessing is time-consuming and difficult to handle and can have major implications on environmental modeling results. US EPA’s Hydrological Micro Services Precipitation Comparison and Analysis Tool (HMS-PCAT) provides a publicly available tool to accomplish this critical task. We present HMS-PCAT’s software design and its use in gathering, preprocessing, and evaluating precipitation data through web services. This tool simplifies catchment and point-based data retrieval by automating temporal and spatial aggregations. In a demonstration of the tool, four gridded precipitation datasets (NLDAS, GLDAS, DAYMET, PRISM) and one set of gauge data (NCEI) were retrieved for 17 regions in the United States and evaluated on 1) how well each dataset captured extreme events and 2) how datasets varied by region. HMS-PCAT facilitates data visualizations, comparisons, and statistics by showing the variability between datasets and allows users to explore the data when selecting precipitation datasets for an environmental modeling application.

Addressing data challenges in riverine nutrient load modeling of an intensively managed agro‐industrial watershed

“…This inadequate simulation of flow seasonality was likely due to poor representation of temporal variability in precipitation caused by sporadically missing observations in the precipitation time series. As SWAT uses stochastic weather generators (Schuol & Abbaspour, 2007) to fill in the missing precipitation, the outputs from these generators are often skewed toward their historical inputs and do not accurately estimate wet or dry spells (Sitterson et al, 2017). Thus, instead of relying on stochastic estimates, a secondary dataset like PRISM (Daly et al, 2008) was preferred to fill the missing NCDC precipitation observations.…”

Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa

“…Although high rainfall is the primary driver of floods in headwater streams, the extent and magnitude of floods can be influenced by temperature, evapotranspiration, topography, landscape gradient, type of soil, soil infiltration rate, soil saturation level, land drainage, land use, landcover, and river channel alterations including water retention and flood control structures (Cronshey, 1986;Sitterson et al, 2018;Ball and Babister, 2019;Davie and Quinn, 2019). Small floods and flow variability are essential for the maintenance of channel geomorphology and resilience of biota to withstand the scouring flows of large floods (King et al, 2003).…”

Rainfall-induced hydroplaning risk over road infrastructure of the continental USA

“…Precipitation and air temperature data from the North American Land Data Assimilation System (NLDAS) project [ 16 ] are used. These data are available at 0.125° spatial and an hourly temporal resolution, and have been widely used in a range of disciplines such as hydrology [ 19 ], public health [ 20 ], and agro-meteorology [ 21 ]. Since the focus of this study is on hydroplaning induced by intense rainfall, rainfall events are filtered out from the precipitation data which may also include snow events or events with a mix of snow and rain.…”