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Allan Frei, Rajith Mukundan, Jie Chen, Rakesh K. Gelda, Emmet M. Owens, Jordan Gass, and Arun Ravindranath

Abstract

The use of global climate model (GCM) precipitation simulations typically requires corrections for precipitation biases at subgrid spatial scales, typically at daily or monthly time scales. However, over many regions GCMs underestimate the magnitudes of multiyear precipitation extremes in the observed climate, resulting in a likely underestimation of the magnitudes of multiyear precipitation extremes in future scenarios. The objective of this study is to propose a method to extract from GCMs more realistic scenarios of multiyear precipitation extremes over time horizons of decades to one century. This proposed correction method is analogous to widely used bias correction methods, except that it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). A case study of precipitation over a basin from the New York City water supply system demonstrates the potential magnitude of the underestimation of multiyear precipitation using uncorrected GCM scenarios, and the potential impact of the correction on multiyear hydrological extremes. Overall, it is a practical, conceptually simple approach meant for water supply system impact studies, but can be used for any impact studies that require more realistic multiyear extreme precipitation extreme scenarios.

Significance Statement

The purpose of this study is to present a practical method to address a particular difficulty that in some regions arises in climate change impact studies: global climate models tend to underestimate the multiyear variability of precipitation over some regions, resulting in an underestimation of the magnitudes and/or intensities of prolonged droughts as well as prolonged wet periods. The method is analogous to widely used bias correction methods, except it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). It is designed to provide more realistic estimates of extreme hydrological scenarios during the twenty-first century. Our particular interest is for managers of water supply systems, but the method may be of interest to others for whom multiyear precipitation extremes are critical.

Open access
Eli J. Dennis and E. Hugo Berbery

Abstract

Soil hydrophysical properties are necessary components in weather and climate simulation, yet the parameter inaccuracies may introduce considerable uncertainty in the representation of surface water and energy fluxes. This study uses seasonal coupled simulations to examine the uncertainties in the North American atmospheric water cycle that result from the use of different soil datasets. Two soil datasets are considered: the State Soil Geographic dataset (STATSGO) from the U.S. Department of Agriculture and the Global Soil Dataset for Earth System Modeling (GSDE) from Beijing Normal University. Two simulations are conducted from 1 June to 31 August 2016–18 using the Weather Research and Forecasting (WRF) Model coupled with the Community Land Model (CLM) version 4 and applying each soil dataset. It is found that changes in soil texture lead to statistically significant differences in daily mean surface water and energy fluxes. The boundary layer thermodynamic structure responds to these changes in surface fluxes resulting in differences in mean CAPE and CIN, leading to conditions that are less conducive for precipitation. The soil-texture-related surface fluxes instigate dynamic responses as well. Low-level wind fields are altered, resulting in differences in the associated vertically integrated moisture fluxes and in vertically integrated moisture flux convergence in the same regions. Through land–atmosphere interactions, it is shown that soil parameters can affect each component of the atmospheric water budget.

Open access
Ellen Eckert, David Hudak, Éva Mekis, Peter Rodriguez, Bo Zhao, Zen Mariani, Stella Melo, Kimberly Strong, and Kaley A. Walker

Abstract

To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multisatellite Retrievals for GPM (IMERG), namely, V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with 25 precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends to agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurements suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that passive microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75–0.8 during summer and fall are very encouraging for potential future applications.

Open access
Vesta Afzali Gorooh, Ata Akbari Asanjan, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

Abstract

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

Open access
Christine M. Albano, John T. Abatzoglou, Daniel J. McEvoy, Justin L. Huntington, Charles G. Morton, Michael D. Dettinger, and Thomas J. Ott

Abstract

Increased atmospheric evaporative demand has important implications for humans and ecosystems in water-scarce lands. While temperature plays a significant role in driving evaporative demand and its trend, other climate variables are also influential and their contributions to recent trends in evaporative demand are unknown. We address this gap with an assessment of recent (1980–2020) trends in annual reference evapotranspiration (ETo) and its drivers across the continental United States based on five gridded datasets. In doing so, we characterize the structural uncertainty of ETo trends and decompose the relative influences of temperature, wind speed, solar radiation, and humidity. Results highlight large and robust changes in ETo across much of the western United States, centered on the Rio Grande region where ETo increased 135–235 mm during 1980–2020. The largest uncertainties in ETo trends are in the central and eastern United States and surrounding the Upper Colorado River. Trend decomposition highlights the strong and widespread influence of temperature, which contributes to 57% of observed ETo trends, on average. ETo increases are mitigated by increases in specific humidity in non-water-limited regions, while small decreases in specific humidity and increases in wind speed and solar radiation magnify ETo increases across the West. Our results show increases in ETo across the West that are already emerging outside the range of variability observed 20–40 years ago. Our results suggest that twenty-first-century land and water managers need to plan for an already increasing influence of evaporative demand on water availability and wildfire risks.

Significance Statement

Increased atmospheric thirst due to climate warming has the potential to decrease water availability and increase wildfire risks in water-scarce regions. Here, we identified how much atmospheric thirst has changed across the continental United States over the past 40 years, what climate variables are driving the change, and how consistent these changes are among five data sources. We found that atmospheric thirst is consistently emerging outside the range experienced in the late twentieth century in some western regions with 57% of the change driven by temperature. Importantly, we demonstrate that increased atmospheric thirst has already become a persistent forcing of western landscapes and water supplies toward drought and will be an essential consideration for land and water management planning going forward.

Open access
Lu Li, Yongjiu Dai, Wei Shangguan, Nan Wei, Zhongwang Wei, and Surya Gupta

Abstract

Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.

Open access
Carlos Antonio Fernandez-Palomino, Fred F. Hattermann, Valentina Krysanova, Anastasia Lobanova, Fiorella Vega-Jácome, Waldo Lavado, William Santini, Cesar Aybar, and Axel Bronstert

Abstract

A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (Rain for Peru and Ecuador), at 0.1° spatial resolution for the period 1981–2015 covering Peru and Ecuador. It is based on the application of 1) the random forest method to merge multisource precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and 2) observed and modeled streamflow data to first detect biases and second further adjust gridded precipitation by inversely applying the simulated results of the ecohydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Pacific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low, high, and peak flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.

Significance Statement

We developed a novel precipitation dataset RAIN4PE for Peru and Ecuador by merging multisource precipitation data (satellite, reanalysis, and ground-based precipitation) with terrain elevation using the random forest method. Furthermore, RAIN4PE was hydrologically corrected using streamflow data in watersheds with precipitation underestimation through reverse hydrology. The results of a comprehensive hydrological evaluation showed that RAIN4PE outperformed state-of-the-art precipitation datasets such as CHIRP, ERA5, CHIRPS, MSWEP, and PISCO in terms of daily and monthly streamflow simulations, including extremely low and high flows in almost all Peruvian and Ecuadorian catchments. This underlines the suitability of RAIN4PE for hydrometeorological applications in this region. Furthermore, our approach for the generation of RAIN4PE can be used in other data-scarce regions.

Open access
Ren Wang, Pierre Gentine, Longhui Li, Jianyao Chen, Liang Ning, Linwang Yuan, and Guonian Lü

Abstract

Land–atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land–atmosphere coupling. One of the linkages in land–atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes, which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, northeast Asia, and southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hot spot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidence for the extreme climate change accelerated by surface energy partitioning.

Open access
M. A. Stern, L. E. Flint, A. L. Flint, R. M. Boynton, J. A. E. Stewart, J. W. Wright, and J. H. Thorne

Abstract

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.

Significance Statement: Gridded historical climate datasets are vital inputs for hydrological and other models used to quantify current water supply, drought risk, and other ecosystem processes. They are also compared to future climate projections to assess the future climate change risk. Numerous interpolated climate datasets are available with varying resolution and methods, yet all are based on climate station data and represent the same historical record at and in between each station. We found significant disagreement between historical gridded datasets, including the one used to bias correct the current climate change projections used by the state of California. Some datasets had large biases compared to station data, especially in snow-dominated regions, leading to large disagreements in modeled monthly streamflow.

Open access
Mahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jason Otkin, Yafang Zhong, David Lorenz, Martha Anderson, Trevor F. Keenan, David L. Miller, Christopher Hain, and Thomas Holmes

Abstract

Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.

Open access