Journal of Hydrometeorology

doi: 10.1175/jhm-273coverpmid: N/A

Dollan, Ishrat J.; Reed, Kevin A.; Wehner, Michael F.; Devineni, Naresh

doi: 10.1175/jhm-d-25-0088.1pmid: N/A

AbstractGaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events.Significance StatementDaily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Osman, Mahmoud; Zaitchik, Benjamin; Lawston-Parker, Patricia; Santanello, Joseph; Anderson, Martha

doi: 10.1175/jhm-d-25-0074.1pmid: N/A

AbstractFlash droughts, known for their rapid onset and intensification, pose a significant threat to agriculture and water resources. The 2011 Texas flash drought, with its widespread agricultural losses exceeding $7.6 billion (U.S. dollars) and severe ecological consequences, was a stark demonstration of their devastating impacts. This study investigates the crucial role of vegetation in numerical modeling of flash droughts, focusing on the 2011 Texas event. Utilizing the NASA Unified Weather Research and Forecasting (NU-WRF) and NASA Land Information System (LIS) modeling frameworks and the Noah land surface model (LSM) with multiparameterization options (Noah-MP) land surface model, we examine the influence of vegetation dynamics on simulating drought characteristics. By integrating satellite-derived vegetation observations and conducting controlled numerical experiments, we evaluate the model’s ability to reproduce observed features of the 2011 drought. Our findings underscore the importance of vegetation representation in capturing the complex land–atmosphere feedbacks that drive the evolution of flash droughts. The incorporation of observed vegetation anomalies into the model leads to improved simulations of surface energy fluxes, atmospheric warming, and evapotranspiration patterns, particularly during the crucial onset and intensification phases of the drought. This points to the potential importance of representing vegetation variability in dynamically based forecasts of flash drought.

Kisembe, Jesse; Wen, Yixin; Wainwright, Caroline M.; Funk, Chris; Odongo, Ronald I.; Qian, Weikang

doi: 10.1175/jhm-d-25-0080.1pmid: N/A

AbstractInterannual rainfall variability presents critical challenges across eastern Africa. However, studies often focus on trends in seasonal rainfall totals, overlooking the intraseasonal characteristics that directly affect societal livelihoods. Our analysis of daily rainfall from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset (1982–2023) reveals significant and contrasting shifts across the region. In northern East Africa, the rainy season has lengthened by over a month and become wetter due to a higher frequency of rainy days. Similarly, the season in southern East Africa has lengthened by 3 weeks and become wetter, primarily due to rising rainfall intensity. Over the bimodal eastern Horn of Africa, the trends diverge sharply: The boreal spring “long rains” have shortened by up to a month and become drier with fewer rainy days, with intensity increasing in the southeast but decreasing in the northeast. Conversely, the boreal fall “short rains” have become longer and wetter in the northeast while shortening and becoming drier in the southeast. Crucially, for both the long and short rains, changes in the number of rainy days are the primary factors determining seasonal totals; even where intensity has increased, it often fails to offset the decline caused by fewer rain events. These findings underscore the need for climate adaptation strategies to specifically account for subregional shifts in rainfall frequency and intensity. Longer dry periods between rainfall events may produce stress not represented by seasonal totals, while more intense rainfall may offer opportunities for water storage and infiltration.

Kansara, Prakrut; Tran, Vinh Ngoc; Le, Manh-Hung; Bolten, John D.; Lakshmi, Venkataraman

doi: 10.1175/jhm-d-25-0009.1pmid: N/A

AbstractThis study evaluates the utility of gridded precipitation products (GPPs) in hydrological applications across major central and southern Indian river basins (IRBs). The basins were selected to capture diverse climatic regimes from 2001 to 2020 and assesses the value of each product for driving streamflow simulation using the Soil and Water Assessment Tool (SWAT). The GPP datasets include Indian Meteorological Department (IMD), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–climate data record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). Validation against rain gauge observations revealed varying magnitudes of agreement among GPPs, though no systematic errors were identified. At the grid cell, we observed significant disagreement between GPPs in western regions. At the subbasin level, MSWEP exhibited the greatest heterogeneity in precipitation estimates. For daily streamflow simulation performance, MSWEP demonstrated superior capability in driving hydrological model simulations, followed by IMD and IMERG. However, in the studied tropical monsoon basins, annual peak-flow analysis revealed that none of the GPPs adequately captured peak-flow values. We used Sobol’ indices for global sensitivity analysis, identifying curve number (CN), base-flow alpha factor (ALPHA_BF), and saturated hydraulic conductivity (SOL_K) as key parameters. In conclusion, this study demonstrates that GPPs not only influence the accuracy of streamflow simulations but also play a significant role in impacting the sensitivity of SWAT model parameters, emphasizing the critical importance of careful precipitation data selection in hydrological modeling applications for optimal application and interpretation.

Modanesi, Sara; Busschaert, Louise; De Lannoy, Gabriëlle; De Santis, Domenico; Natali, Martina; Dari, Jacopo; Quintana-Seguí, Pere; Castelli, Mariapina; Grasso, Fabio Massimo; Massari, Christian

doi: 10.1175/jhm-d-25-0057.1pmid: N/A

AbstractQuantifying irrigation is a key challenge in hydrology due to its impact on water and carbon cycles. This is compounded by the scarcity of benchmark irrigation data and limitations of land surface models (LSMs), which hinder accurate gridscale irrigation estimation. In this study, we optimize a sprinkler irrigation scheme within the Noah-MP LSM, as part of the NASA Land Information System, using Sentinel-1-based irrigation estimates and a genetic algorithm. Experiments in an intensively irrigated region of northeastern Spain (0.01° resolution) compare two calibration approaches: one adjusting the root-zone soil moisture threshold (Thirr) which triggers irrigation and another introducing a scale irrigation coefficient (SIC) parameter to account for spatial heterogeneity in irrigation practices. The Thirr calibration shows limitation in the system’s flexibility, causing sparse irrigation applications with excessive water amounts that optimization cannot correct. In contrast, SIC calibration improves irrigation dynamics, reduces model errors, and better represents interannual surface soil moisture anomalies, outperforming the default scheme against in situ data. Results highlight that assuming full irrigation at resolutions equal or beyond 1 km is unrealistic due to two reasons: First, farmers cannot irrigate all fields within a grid cell simultaneously; second, the heterogeneous field mosaic further complicates uniform irrigation. Comparisons with satellite-based evapotranspiration (ET) and gross primary production (GPP) datasets highlight inconsistencies between model estimates and satellite ET products, revealing persistent vegetation dynamics issues. Future efforts could leverage the calibrated scheme with satellite data assimilation to improve soil moisture and vegetation conditions, capturing complex interactions between irrigation and the water–carbon cycles.Significance StatementOptimizing irrigation schemes in land surface models (LSMs) is crucial for improving hydrological predictions. This study addresses the scarcity of in situ irrigation data by leveraging satellite-based irrigation estimates to calibrate a sprinkler irrigation scheme coupled to the Noah-MP LSM. Findings reveal that default irrigation schemes, running at 1-km spatial resolution or coarser, rely on unrealistic assumptions. By calibrating a scale irrigation coefficient accounting for spatial heterogeneity in irrigation practices, the model better captures irrigation dynamics and reduces errors compared to conventional approaches. Nonetheless, results underscore the need for refining vegetation dynamics representation to enhance our understanding of the irrigation’s role in the water–carbon cycle.

Yi, Lu; He, Liangtao; Wang, Yafei; Zhou, Yuxin; Zheng, Ziyan; Chen, Junxu; Yong, Bin; Li, Ling

doi: 10.1175/jhm-d-24-0029.1pmid: N/A

AbstractAs global warming intensifies, accurate prediction of increasing extreme rainfall appears to be particularly important for local water management and flood-related policymaking. To investigate the sensitivity of the microphysics parameterization (MP) and cumulus parameterization (CP) in forecasting extreme rainfall, we applied the advanced model of the Weather Research and Forecasting (WRF) Model and predicted two typical extreme rainfall events over the Yangtze River delta (YRD) region based on twelve different scheme settings. The mei-yu (MY) and typhoon (TP) rainfall events were predicted in a convection-allowing resolution of 1 km. Compared with the daily rain gauge and hourly satellite-observed precipitation, the WRF simulation for the TP event generally outperformed that for the MY event at the hourly scale, with a lower comprehensive index of Chen, Chen, Hu, and Zhou (CCHZ)–distance between indices of simulation and observation (DISO) which indicates a lower bias between the WRF simulation and the observation data. As for the MY event, the WRF prediction was more sensitive to the MP scheme than the CP scheme, while it was sensitive to both schemes for the TP event. Among the 12 different scheme settings, the MP of the New Thompson scheme showed the best suitability for the two typical extreme rainfall events. Its combination with the Kain–Fritsch (KF) or Grell–Freitas (GF) scheme showed outstanding performance for both the MY and TP events since the KF/GF scheme takes explicit mass flux calculations of the cloud bottom and advanced convective triggering mechanisms, and the Thompson scheme does better in the ice-phase particle dynamics, hydrometeor distribution, and their transformation mechanisms. Our investigation can offer a scientific and valuable reference for the numerical prediction of extreme rainfall events over similar subtropical coastal regions.

Mohamed, Issam; Najafi, Mohammad Reza; Joe, Paul; Mo, Ruping

doi: 10.1175/jhm-d-25-0105.1pmid: N/A

AbstractHailstorms pose serious risks to agriculture, infrastructure, and urban systems, yet their interactions with drought remain poorly understood. Drought can influence convective environments by altering surface heating, moisture availability, and atmospheric stability, potentially modulating hail frequency and intensity. This study investigates these relationships in Alberta using radar-derived vertically integrated liquid water content (VIL) as a proxy for hail size and ERA5 reanalysis data. Drought conditions are characterized using the standardized precipitation evapotranspiration index at short-, intermediate-, and long-term scales. Composite analyses suggest that short- and intermediate-term droughts enhance early season (May–June) hail intensity at lower VIL quantiles, consistent with increased surface heating and atmospheric instability. In contrast, prolonged droughts suppress late-season (August–September) hail activity as soil moisture depletion limits convective development. Convective available potential energy (CAPE) analysis further indicates that instability is generally lower in dry years, reducing convective potential and hailstorm severity, while nondry years exhibit a wider CAPE distribution, supporting more intense convective storms. These results highlight the role of antecedent drought conditions in modulating storm dynamics and suggest that different drought durations exert distinct influences on hail severity. The findings provide new insights into compound drought–hail interactions, with implications for seasonal forecasting and climate adaptation in hail-prone regions.

Woody, Jonathan; Prochnow, Penelope; Kong, JiaJie; Dyer, Jamie

doi: 10.1175/jhm-d-25-0061.1pmid: N/A

AbstractThis study presents a statistical analysis of snow-cover trends in the Northern Hemisphere using the Rutgers Northern Hemisphere 24 km Weekly Snow Cover Extent, September 1980 Onward, Version 1, data which observe either snow presence or absence at each cell in binary format. A quality control procedure is applied prior to trend assessment to identify problematic cells, which are excluded from further analysis. Of the cells studied over the Northern Hemisphere that meet the quality control requirements, 9.4% indicate a statistically significant positive trend, while 23.8% report a statistically significant negative trend, indicating a roughly 2.5:1 ratio of area with snow-cover declines relative to snow-cover increases. On a seasonal scale, there is a general positive trend in snow-covered area across the Northern Hemisphere beginning in August and peaking in early November, followed by negative trends beginning in March, reflecting a general shift toward earlier snowpack generation and melt. Regional analyses show this seasonal pattern is most prevalent across Europe and central Asia, which are also characterized by general negative trends in annual snow presence. Increasing trends are noted through central Canada and the northern Great Plains in the United States, although snow trends in all regions exhibit a clear local-scale influence from topographical patterns. Additionally, the southern border of the seasonal snow-cover extent shows significant decreasing trends in snow presence in most locations across the Northern Hemisphere.Significance StatementSnow presence is a key element of Earth’s radiation budget due to its high albedo and insulating effects, thereby playing a crucial role in large-scale atmospheric processes. Snow cover enhances Arctic climatic stability, and reduced snow-cover presence could facilitate large-scale releases of carbon and methane gas due to subsequent increases in temperature and exposure of permafrost. Quantification of trends in snow presence is, therefore, a crucial aspect of understanding global climatic change. This paper presents an analysis of snow-cover trends in the Northern Hemisphere by applying a statistical analysis capable of assessing trends in binary snow presence data to a recently released high-resolution data product. Weekly snow-cover presence over the 43-yr study period is analyzed.