Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks

Abstract

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and World-Clim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the rootmean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.

Predicting probabilities of streamflow intermittency across a temperate mesoscale catchment

“…We assume for the selection of predictor variables in this study that climatic heterogeneity plays a minor role in our catchment, which is supported by the small differences in annual precipitation (Pfister et al, 2005;Wrede et al, 2014;Fig. S3).…”

“…S2). Mean annual precipitation varies slightly from 1000 mm a −1 in the north-west to 800 mm a −1 in the southeast (Pfister et al, 2017), with a mean for the whole catchment of about 850 mm a −1 for the years 1971-2000(Pfister et al, 2005. Seasonal changes in soil moisture and surface hydrology are induced by seasonal fluctuations of mean monthly temperatures (min.…”

“…70 mm in August-September, max. 100 mm in December-February; Pfister et al, 2005;Wrede et al, 2014). Pfister et al (2017) showed the strong impact of bedrock geology on the storage, mixing and release of water in the Attert catchment, which determine the strong differences of seasonal flow regimes in areas of predominantly low permeable bedrock (slate and marls) compared to permeable sandstone bedrock or diverse geology.…”

“…Due to the small dataset, which does not allow for a split validation approach, a leave-oneout cross validation (LOOCV) approach (e.g. Akbar et al, 2019;Ossa-Moreno et al, 2019) was chosen to validate the model based on the original dataset. Thus 185 models were calibrated, each leaving out one of the data points.…”

Evaluation of IMERG V05B 30-Min Rainfall Estimates over the High-Elevation Tropical Andes Mountains

“…Importantly, it would also further the understanding of IMERG performance over different mountain terrains and climatic regimes, supporting larger-scale regionalization of results. To address these issues, future work should capitalize on new nontraditional sources of climate data such as those collected by mining companies (e.g., Ossa-Moreno et al 2019) and/or community participatory monitoring programs (e.g., Ochoa-Tocachi et al 2018). These datasets are often valuable and complement national monitoring networks due to the high density of gauges and/or monitoring in otherwise data-sparse regions.…”