Journal of Hydrometeorology

doi: 10.1175/jhm-266coverpmid: N/A

Yu, Yang; Chen, Shu-Hua; Huang, Chu-Chun; Paw U, Kyaw Tha; Schmitt, Cameron; Zhao, Zhan; Suvočarev, Kosana; Kisekka, Isaya; Pyles, Rex D.; Avise, Jeremy; Cai, Chenxia; Chang, Kuang-Yu; Lo, Min-Hui

doi: 10.1175/jhm-d-24-0129.1pmid: N/A

AbstractCalifornia’s Central Valley (CV) is one of the most productive agricultural regions in the world, relying significantly on irrigation. This study explores the impacts of agricultural irrigation on soil moisture and near-surface meteorology in the CV. Employing the Weather Research and Forecasting (WRF) Model with a modified irrigation module, the WRF-irrigation simulation (WIR3D) reproduces observed humid surface soil moisture in the CV, as identified by satellite data. Without irrigation effects (WoIR), the model presents a dry surface soil moisture bias, with an average value below 0.05 m3 m−3 over the CV. A comparison with surface-station observations reveals that the mean bias of the simulated 2-m dewpoint temperature is −1.62°C in WoIR but only 0.05°C in WIR3D. The 2-m temperature change by irrigation could be cooling or warming. The complexity of the temperature change arises from the combination of evapotranspirative cooling, soil heat flux, and radiative feedback, with differences between daytime evapotranspirative cooling and nighttime greenhouse gas effects. Compared with derived planetary boundary layer height (PBLH) from ceilometer observations, irrigation reduces simulated maximum positive PBLH bias by approximately 580 m (53%), resulting in a daily wind speed decrease of 0.5 m s−1. In addition, this study examines the irrigation spatial heterogeneity effect, and the results show the variations of approximately 30% and 40% differences in PBLH and other near-surface variables between incorporating a county-level versus uniform irrigation rate over the CV. These findings underscore the importance of better integrating irrigation practices to improve weather forecasting in the CV.

Kelsey, Eric P.; Green, Mark B.; Evans, Daniel M.

doi: 10.1175/jhm-d-24-0099.1pmid: N/A

AbstractSynoptic-scale weather patterns affect local meteorological variables, such as vapor pressure deficit (VPD), temperature, and insolation, that are known to influence evapotranspiration (ET) and net CO2 flux (FC). However, little research exists that links synoptic-scale patterns to ET and FC. In this study, we seek to understand how synoptic-scale patterns influence ET and FC for the temperate mixed-hardwood forest at Hubbard Brook Experimental Forest (HBEF) in New Hampshire, United States. We use self-organizing maps to identify the most common synoptic pattern types impacting HBEF during the 2016–21 growing seasons and determine how ET and FC vary with these synoptic pattern types. Our analysis reveals that high ET and most negative FC days occur for the weather pattern phases starting after the departure of a low pressure system and through the approach of a high pressure system. ET and the magnitude of FC remain high if the latitude of the high is south of HBEF but moderate (especially for ET) if the high is to the north and causes east winds to advect a humid maritime air mass over the region. ET is lowest when HBEF is located between high pressure to the east and low pressure to the west, which causes humid southerly flow to decrease VPD and insolation. Meanwhile, FC magnitude may remain high when this pattern occurs in June–July when photosynthetic capacity is at its highest. Our results suggest that future changes in the frequency of passing low pressure systems and pathways of high pressure systems could impact the fluxes of water and CO2 from this forest.Significance StatementFor decades, we have understood that local meteorological variables, such as insolation, temperature, and relative humidity, have a strong influence on a forest ecosystem’s use of water and carbon dioxide, two important greenhouse gases. We also understand that large-scale weather patterns and their interactions with forests shape these local meteorological conditions. This research advances knowledge of the relationship between various large-scale weather patterns and their impacts on forest’s use of water and carbon dioxide via local meteorological variables for a mixed-hardwood forest in the Northeastern United States. Connecting these results to the frequency of these various large-scale weather pattern types projected by global climate models will help us predict how forest ecosystems will influence water vapor and carbon dioxide concentrations and thus impact global climate.

Casado-Rodríguez, Jesús; Carton de Wiart, Corentin; Grimaldi, Stefania; Zsoter, Ervin; Baugh, Calum; Bosshard, Nina; Mikuličková, Michaela; Pechlivanidis, Ilias; Prudhomme, Christel; Salamon, Peter

doi: 10.1175/jhm-d-24-0054.1pmid: N/A

AbstractFlood early warning systems (FEWSs) rely on hydrological simulations driven by numerical weather prediction (NWP) models, both of which are inherently uncertain. Ensemble prediction systems (EPSs) address these uncertainties by generating multiple future scenarios. Discharge forecasts from EPS are converted into flood warnings by applying a set of criteria whose definition is crucial for the skill of the system. While meteorological and hydrological models are under continuous development, these warning criteria lack continuous evaluation. In this paper, we use discharge simulations from the European Flood Awareness System (EFAS) to assess the skill of four NWPs—probabilistic and deterministic—and explore methods to create a grand ensemble that enhances overall skill. We examine the effects of the current warning criteria—probability threshold and persistence—optimize their values, and evaluate their effectiveness in smaller catchments. Our results indicate that probabilistic NWPs outperform deterministic models in flood warning skill and demonstrate that removing the persistence criterion from EPS enhances skill. Optimized warning criteria for a single probabilistic NWP boost EFAS skill by 6.6% in terms of f score, and a grand ensemble can further increase it by 4.1%. We identify two effective grand ensemble methods—member based and skill based—and discuss their advantages and drawbacks. The improved criteria demonstrate comparable skill in catchments half the size of those currently used. In conclusion, our study presents a methodology for evaluating and refining the skill of multimodal FEWS, stressing the critical role of tailoring the flood warning criteria to the end users.Significance StatementThis study analyzes methodologies to improve the skill of the notifications issued by a state-of-the-art flood warning system. We have analyzed the skill of four meteorological models and the effects of several warning criteria. We have identified an optimal blend of meteorological models and warning criteria that boosts the system skill up to 10%. The methods developed in this research are adaptable to other flood warning systems, and the refined criteria have been implemented operationally.

Oghbaei, Bahram; Arsenault, Richard; Brissette, François; Matte, Simon

doi: 10.1175/jhm-d-24-0097.1pmid: N/A

AbstractMember-by-member postprocessing (MBMP), a nonparametric method that undertakes bias and dispersion correction on individual ensemble members, has emerged as a promising approach. Traditionally, MBMP variants have relied on regression for bias correction, a technique that does not take into account type-1 conditional bias, i.e., reliability. This study introduces novel approaches to implement MBMP that seek to improve forecast quality by focusing on ensemble reliability rather than accuracy during the bias-correction process. A new evaluation metric is proposed, and an innovative multiobjective combination of metrics is implemented during coefficient estimation. This is tested on daily air temperature forecasts with lead times of 2, 5, and 9 days over 44 watersheds in Quebec, Canada. Results demonstrate that higher ensemble forecast reliability is achieved when it is emphasized during the bias-correction step compared to other MBMP variants.

Waterman, Tyler; Dirmeyer, Paul; Chaney, Nathaniel

doi: 10.1175/jhm-d-24-0098.1pmid: N/A

AbstractIn large-scale Earth system models (ESMs) used to study climate processes, surface heterogeneity that is subgrid to the larger atmospheric grid is often represented by a number of land tiles, effectively providing a higher-resolution land surface to a coarser resolution overlying atmosphere. ESMs, however, average the surface fluxes and other surface characteristics before they are communicated to the atmosphere, ignoring the effect that this variability can have on the atmosphere. In this study, we examine the impact of this flux averaging through 257 two-day summer WRF simulations over the contiguous United States (CONUS) at 3-km resolution, including runs where the surface fluxes and temperatures are homogenized at 60 km prior to communication to the overlying atmosphere. Results show large increases (200 mm and higher) in precipitation in moisture-limited regions of CONUS, a persistent increase in precipitation bias when compared to observations, and a near universal increase in evaporative fraction. Changes are most significant where moist areas (i.e., water bodies) are averaged with dry areas as the feedback between atmospheric moisture concentrations and the land are weakened when that moisture flux is more spatially distributed through homogenization. Results also show a significant decline in mesoscale flow activity within the atmospheric boundary layer, which in energy-limited regions may cause the simulated decreases in precipitation due to less frequent convective initiation. Overall, results indicate that flux averaging applied in large-scale models can have unintended consequences by neglecting the heterogeneous imprint of the surface on the atmosphere.Significance StatementThis work examines what happens when higher-resolution information from the land surface is averaged and homogenized before being communicated to the atmosphere in coarse-resolution models such as climate models. Results show that this homogenization can yield significant changes (up to 100% increase) in precipitation in some regions. The most dramatic changes tend to appear when wet areas, including lakes and coastlines, are averaged with the land. Many climate models average ocean and land in atmospheric exchange, which may lead to errors in the water cycle and long-term climate prediction. Significant changes to small mesoscale (3–60 km) atmospheric flows are also observed which may impact convective initiation; however, more work is encouraged to assess this aspect of atmospheric impact.

Urdiales-Flores, Diego; Célleri, Rolando; Mariéthoz, Grégoire; Bendix, Jörg; Peleg, Nadav

doi: 10.1175/jhm-d-24-0105.1pmid: N/A

AbstractEcuador’s southern tropical Andes are heavily impacted by intense tropical moisture, leading to flash floods and landslides. Here, we provide insights into the characteristics of the heavy rainfall triggering high streamflow in this area. High spatiotemporal resolution (500 m and 5 min) estimates from a high-elevation X-band weather radar (6 years, recording 1632 rainfall events) were analyzed for the small-scale rainfall properties, and a Lagrangian approach was employed to detect the moisture trajectories and sources. Employing a statistical model to analyze the rainfall space–time–intensity structures, rainfall was classified and characterized into three intense spatially clustered (convective) and one spatially homogenous (stratiform) rain types, which differ in their advection properties, intensities, and spatial structure. Tracking the rainfall trajectories, we found a predominant pathway for air masses to reach the study area, with the majority originating from the eastern flank of the Andes through the north Amazon basin (63.5%), followed by the high Andes Mountains (29%) and coastal plains (3.9%), whereas only a small proportion stems from the Pacific Ocean (3.6%). The major focal area from which air masses originate is only 250 km east of the study region. Analyzing high streamflow in the Tomebamba catchment, we estimated that convective rainfall types with a minimum duration of 2 h and intensity of 23 mm h−1 originating mostly from the Andes and the north Amazon basin can potentially trigger high runoff events, with peaks ranging between 100 and 200 m3 s−1. Rainfall characteristics and moisture sources are crucial for nearby catchments in tropical–temperate climates, aiding in weather forecasting and short-term flood warnings.Significance StatementThe populations residing in the tropical Andes face significant challenges due to intense precipitation, driven by tropical moisture. The complex terrain initiates and enhances precipitation systems, making these regions vulnerable to flash floods and other precipitation-related natural disasters. We aim to better understand the complex space–time characteristics of heavy rainfall in the southern Ecuadorian Andes using an X-band weather radar and a Lagrangian approach to tracking moisture trajectories. Based on our findings, we have identified rainfall types and their sources that can potentially cause high streamflow. This knowledge is the basis for developing forecasting systems to provide near-real-time early warnings of possible flooding in the future.

Webb, Mariana J.; Albano, Christine M.; Harpold, Adrian A.; Wagner, Daniel M.; Wilson, Anna M.

doi: 10.1175/jhm-d-24-0078.1pmid: N/A

AbstractAtmospheric rivers (ARs) drive most riverine floods on the U.S. West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust nonlinear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, 2–4.5 times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.Significance StatementAtmospheric river (AR) storm strength and duration are useful indicators of potential flooding, but because the on-the-ground conditions when a storm arrives can influence the timing and amount of precipitation reaching river channels, prestorm soil wetness should be considered. In this study, we identify important watershed-specific values of prestorm soil moisture wetness, above which, high streamflows are more likely. We find that with wet prestorm conditions, streamflow is typically 2–4.5 times larger than streamflow with dry prestorm conditions. We also establish the types of regions where prestorm soil wetness conditions have the largest influence on streamflow and suggest that flood prediction in these regions could be enhanced by considering more detailed information on the prestorm hydrologic states.

de Waal, Jan; Maranan, Marlon; Fink, Andreas H.; Watson, Andrew; Bennett, Ashwin; Helmschrot, Jörg

doi: 10.1175/jhm-d-24-0123.1pmid: N/A

AbstractPrecipitation gauging networks have been declining in many developing countries such as South Africa. Satellite-based precipitation products (SPPs) and reanalysis data have shown vast improvements over the last 5 years and are postulated to mitigate the lack of ground-based observations. The Western Cape of South Africa has recently experienced a devastating drought (2015–18) and consecutive local floods (2023/24) but lacks sufficient precipitation observations to fully conceptualize the impacts of these climatic extremes. Using a variety of statistical metrics, we analyze the performance of six SPPs and reanalysis-driven datasets, namely, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS V2), ERA5, Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.8), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) (Early and Final Runs of V6B, Final Run of V7) compared with historical records collected from local weather bureaus from 2012 to 2021. On a daily scale, reanalysis products such as ERA5 and MSWEP outperform other SPPs concerning a dichotomous wet-/dry-day distinction, temporal rainfall pattern, and Heidke skill score. All products performed best in the wet parts of the year (JJA), and performance is influenced by climate zone and topography. All products performed poorly for extreme values (i.e., >95th percentile) and are therefore of less use for evaluating potential flood peaks. SPP performance was more related to climate type as opposed to altitude, and future improvements in SPPs should focus on local microclimatic variability to capture headwater precipitation patterns. While in the African tropics, infrared (IR)/passive microwave (PMW)-based products perform best, in the Mediterranean climate region of the Western Cape, reanalysis-driven products are superior.Significance StatementThe use of satellite-based precipitation products (SPPs) and reanalysis data can mitigate the decline in observation stations in many countries, particularly in the Global South. While the performance of SPPs in tropical and subtropical climates in Africa has been previously investigated, this paper is among the first research into the efficacy of SPPs in the Mediterranean climate zone of southern Africa. Results from the research can potentially be extrapolated to other Mediterranean climates globally.

Chen, Bin; Zhao, Ruiyu; Zhang, Wei; Yang, Shuai; Xu, Xiangde; Wang, Chunzhu; Huo, Juan

doi: 10.1175/jhm-d-24-0066.1pmid: N/A

AbstractThe southeastern edge of the Tibetan Plateau (SETP) is prone to severe extreme precipitation events (EPEs), causing huge damages by triggering water-related hazards during summer season. Given the complexity of weather systems and the intricate nature of terrain, the formation mechanisms of the EPEs and related water cycle processes remain unclear. Here, based on 38-yr Lagrangian simulations and moisture source diagnosis, we show that the anomalous moisture uptakes for the daily EPEs exhibit an analogous “tripole pattern,” with Southeast Asia acting as the dominant contributor (fractional contribution, 74.5%), followed by the Arabian Sea (9.7%), the Bay of Bengal (9.5%), and the Tibetan Plateau (6.3%). Under different weather types (WTs), the moisture uptakes exhibit dramatic contrasts, which are largely shaped by the shift and intensity of the west Pacific subtropical high, the surface thermodynamic conditions over Southeast Asia, and the cyclonic anomaly over the tropical ocean. The thermodynamic characteristics of the underlying surface over those anomalous moisture supply “hotspots” identified here, together with the corresponding favorable atmospheric circulation pattern, thereby could potentially serve as the precursors for EPEs over the SETP.Significance StatementThe southeastern edge of the Tibetan Plateau (SETP) is subject to frequent extreme precipitation events (EPEs) that cause huge damages by triggering water-related hazards during summer season. In spite of its significance, anomalous moisture uptakes for the EPEs over SETP are understudied. Here, an ensemble Lagrangian numerical modeling and moisture source diagnostics are adopted to quantify the anomalous moisture sources of the EPEs associated with weather types. The results provide insights into the formation mechanisms of EPEs by studying many cases in historical data. Furthermore, the valuable information on the anomalous moisture uptakes and associated synoptic circulation patterns serves as a supplemental precursor and thereby could potentially enhance the predictive skill of EPEs, which is undoubtedly beneficial to disaster prevention and mitigation.