A warming-induced reduction in snow fraction amplifies rainfall extremes.

The intensity of extreme precipitation events is projected to increase in a warmer climate1-5, posing a great challenge to water sustainability in natural and built environments. Of particular importance are rainfall (liquid precipitation) extremes owing to their instantaneous triggering of runoff and association with floods6, landslides7-9 and soil erosion10,11. However, so far, the body of literature on intensification of precipitation extremes has not examined the extremes of precipitation phase separately, namely liquid versus solid precipitation. Here we show that the increase in rainfall extremes in high-elevation regions of the Northern Hemisphere is amplified, averaging 15 per cent per degree Celsius of warming-double the rate expected from increases in atmospheric water vapour. We utilize both a climate reanalysis dataset and future model projections to show that the amplified increase is due to a warming-induced shift from snow to rain. Furthermore, we demonstrate that intermodel uncertainty in projections of rainfall extremes can be appreciably explained by changes in snow-rain partitioning (coefficient of determination 0.47). Our findings pinpoint high-altitude regions as 'hotspots' that are vulnerable to future risk of extreme-rainfall-related hazards, thereby requiring robust climate adaptation plans to alleviate potential risk. Moreover, our results offer a pathway towards reducing model uncertainty in projections of rainfall extremes.

Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the contiguous United States.

Salinity is an important water quality parameter that affects ecosystem health and the use of freshwaters for industrial, agricultural, and other beneficial purposes. Although a number of studies have investigated the variability and trends of salinity in rivers and streams, the effects of floods on salinity across a wide range of watersheds have not been determined. Here, we examine this question by utilizing long-term observational records of daily streamflow and specific conductance (SC; a proxy for salinity) in addition to catchment characteristics for 259 United States Geological Survey (USGS) monitoring sites in the contiguous United States spanning a wide range of climatic, geologic and hydrologic conditions. We used a combination of statistical methods, random forest machine learning models, and information-theoretic causal inference algorithms to determine the response of SC to floods and the factors that impact salinity changes within sites (intra-site variability) and across sites (inter-site variability). Our results show that changes to SC during flood events exhibited substantial variability ranging from a 100% decrease to 34% increase relative to the long-term mean. We found that dilution is the prevailing mechanism that decreases SC levels during floods for most sites, but other mechanisms caused an increase of SC for 6.1% (n = 5521) of flood events. Our analysis revealed that antecedent conditions of SC in the few days preceding the flood are the most important factor in explaining intra-site variability. The response of salinity to floods also varied considerably across sites with different characteristics, with a notable effect of urbanization in temperate climates resulting in increased dilution of SC, and mining in arid climates, which adversely increases SC levels. Overall, we find that the combined effect of aridity and anthropogenic factors is of primary importance in determining how salinity responds to floods, and it bears strongly on water quality conditions in a future world - one in which floods are expected to increase in frequency and intensity, concurrent with shifting aridity patterns and increasing urbanization.

PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset.

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15-60 min). It is intended to supersede the PERSIANN-Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm's fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017-18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

A cloud-free MODIS snow cover dataset for the contiguous United States from 2000 to 2017.

This article presents a cloud-free snow cover dataset with a daily temporal resolution and 0.05° spatial resolution from March 2000 to February 2017 over the contiguous United States (CONUS). The dataset was developed by completely removing clouds from the original NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Area product (MOD10C1) through a series of spatiotemporal filters followed by the Variational Interpolation (VI) algorithm; the filters and VI algorithm were evaluated using bootstrapping test. The dataset was validated over the period with the Landsat 7 ETM+ snow cover maps in the Seattle, Minneapolis, Rocky Mountains, and Sierra Nevada regions. The resulting cloud-free snow cover captured accurately dynamic changes of snow throughout the period in terms of Probability of Detection (POD) and False Alarm Ratio (FAR) with average values of 0.955 and 0.179 for POD and FAR, respectively. The dataset provides continuous inputs of snow cover area for hydrologic studies for almost two decades. The VI algorithm can be applied in other regions given that a proper validation can be performed.

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data.

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.