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Xing Wang
,
Shuaiyi Shi
,
Litao Zhu
,
Yunfeng Nie
, and
Guojun Lai

Abstract

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowdsourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors.

Significance Statement

New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems.

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Free access
Jian Li
,
Rucong Yu
,
Xiaoyuan Yue
,
Mingming Zhang
, and
Nina Li

Abstract

Spatial unevenness, especially local unevenness, is a key characteristic of precipitation and has the potential to be a metric to gauge the performance of high-resolution models. In this paper, a local unevenness index (LUI) is proposed to quantify the heterogeneity of precipitation in central and eastern China. The local unevenness of precipitation is dominantly influenced by local topography, and high LUIs spatially correspond to high local terrain relief. Along 30°N, the correlation coefficient between the LUI and local relief reaches 0.893. Stations with large enhancement and steep gradients in precipitation are identified as local maximum (LM) stations. According to the distinct impacts of various scales of terrain, all 59 LM stations are categorized into three groups: the high-elevation group, the edge group, and the eastern isolated-mountain group. The three groups present different distributions of precipitation with altitude: a double-peak pattern in the high-elevation group, a low-altitude peak in the edge group, and a high-altitude peak in the eastern isolated-mountain group. The seasonal variations in all groups are characterized by relatively more precipitation occurring at higher (lower) elevations in the warm (cold) season. The high-elevation group shows a general increasing (decreasing) frequency tendency with altitude in the warm (cold) season. The low-altitude (high-altitude) frequency peak in the edge (eastern isolated-mountain) group is more prominent in the cold (warm) season. The LUI and LM can be used as straightforward and quantified metrics to measure the performance of high-resolution models in reproducing the local-scale features of precipitation and their seasonal variations.

Significance Statement

The purpose of this study is to promote the knowledge of precipitation unevenness on a local scale. This is important to hydrological processes and water resource management. Our results provide LUI and LM to quantify the spatial unevenness of precipitation on a local scale and further analyze the climatic characteristics and the seasonal variations of precipitation unevenness over central and eastern China. These metrics can be used as quantitative criteria to evaluate the performance of high-resolution models.

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Alison Cobb
,
Daniel Steinhoff
,
Rachel Weihs
,
Luca Delle Monache
,
Laurel DeHaan
,
David Reynolds
,
Forest Cannon
,
Brian Kawzenuk
,
Caroline Papadopolous
, and
F. M. Ralph

Abstract

This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) Model, tailored for the prediction of extreme hydrometeorological events over the western United States (West-WRF) spanning 34 cool seasons (1 December–31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering western North America and the eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast relative to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin scale, the reforecast can improve MAP relative to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coast Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high-resolution (<25 km) regional models. The applications of this high-resolution multiyear reforecast include process-based studies, assessment of model performance, and machine learning applications.

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Lois I. Tang
and
Kaighin A. McColl

Abstract

The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century.

Significance Statement

Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.

Open access
Guo Yu
,
Benjamin J. Hatchett
,
Julianne J. Miller
,
Markus Berli
,
Daniel B. Wright
, and
John F. Mejia

Abstract

In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996–2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.

Open access
Shiyuan Liu
,
Wentao Li
, and
Qingyun Duan

Abstract

Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables.

Significance Statement

The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.

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Junho Ho
,
Guifu Zhang
,
Petar Bukovcic
,
David B. Parsons
,
Feng Xu
,
Jidong Gao
,
Jacob T. Carlin
, and
Jeffrey C. Snyder

Abstract

Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.

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Jacob Mardian
,
Catherine Champagne
,
Barrie Bonsal
, and
Aaron Berg

Abstract

Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.

Significance Statement

This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.

Open access
Hong Wang
,
Fubao Sun
,
Tingting Wang
,
Yao Feng
,
Fa Liu
, and
Wenbin Liu

Abstract

Pan evaporation (E pan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly E pan was calculated over China during 1951–2021, resulting in an average R 2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of E pan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of E pan trends, the E pan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of E pan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted E pan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in E pan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in E pan. These results show the complex and dynamic nature of E pan and underscore the need for continued monitoring and in-depth analysis of its drivers.

Significance Statement

The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.

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