Showing papers in "Journal of Hydrometeorology in 2023"

Tropical Cyclone Rainfall Climatology, Extremes and Flooding Potential over the Continental U.S.

Abstract: Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain-gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental US across a set of widely-used QPE products. We examine five datasets over an 18-year span (2002-2019). The products include three satellite-based products, CPC MORPHing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission - Multisatellite Precipitation Analysis (TRMM-TMPA), the ground-radar and rain-gauge-based NCEP Stage IV, and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans and Houston metropolitan areas. Along the East Coast, TC can contribute up to 20% of the warm-season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that the Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM and ERA5. As a result, satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty is further reflected in the TC flooding potential measured by the Extreme Rain Multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced compared to the NCEP Stage IV.

A Generalized Density-Based Algorithm for the Spatio-Temporal Tracking of Drought Events

Abstract: Drought events evolve simultaneously in space and time; hence, a proper characterization of an event requires the tracking of its full spatio-temporal evolution. Here we present a generalized algorithm for the tracking of drought events based on a three-dimensional application of the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering approach. The need of a generalized and flexible algorithm is dictated by the absence of a unanimous consensus on the definition of a drought event, which often depends on the target of the study. The proposed methodology introduces a set of six parameters that control both the spatial and the temporal connectivity between cells under drought conditions, also accounting for the local intensity of the drought itself. The capability of the algorithm to adapt to different drought definitions is tested successfully over a study case in Australia in the period 2017-2020 using a set of standardized precipitation index (SPI) data derived from the ERA5 precipitation reanalysis. Insights on the possible range of variability of the model parameters, as well as on their effects on the delineation of drought events, are provided for the case of meteorological droughts in order to incentivize further applications of the methodology.

Comparison of three radar-based precipitation nowcasts for the extreme July 2021 flooding event in Germany

Abstract: Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts especially for lead times up to 12 hours, but their evaluation depends on a variety of factors, namely the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140-1670 km2). Two deterministic (advection-based and S-PROG) and one probabilistic (STEPS) QPN with maximum lead time of 3 h were used as input to two hydrological models: a physically-based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared to hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results: (1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared to adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7-12 h depending on the benchmark. (2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. (3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.

The Nonstationary Flood Hydrology of an Urbanizing Arid Watershed

Abstract: The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.

How land surface characteristics influence the development of flash drought through the drivers of soil moisture and vapor pressure deficit

Abstract: As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the US that are crucial for agriculture, forestry, and natural ecology. A pressing need is to better understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. In order to better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land-surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the US. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from MODIS to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the Standardized Evaporative Stress Ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture co-evolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the US. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region-specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events.

A 440-Year Reconstruction of Heavy Precipitation in California from Blue Oak Tree Rings*

Abstract: The variability of water year precipitation and selected blue oak tree-ring chronologies in California are both dominated by heavy precipitation delivered during just a few days each year. These heavy precipitation events can spell the difference between surplus or deficit water supply, and elevated flood risk. Some blue oak chronologies are highly correlated with water year precipitation (r = 0.84) but are equally well correlated (r = 0.82) with heavy precipitation totals ≥25.4 mm (one inch, ≈95th percentile of daily totals, 1949-2004). The blue oak correlation with non-heavy daily totals is much weaker (<25.4 mm; r = 0.55). Consequently, some blue oak chronologies represent selective proxies for the temporal and spatial variability of heavy precipitation totals and are used to reconstruct the amount and number of days with heavy precipitation in northern California from 1582-2021. Instrumental and reconstructed heavy precipitation totals are strongly correlated with gridded atmospheric river related precipitation over the western United States, especially in central California. Spectral analysis indicates that instrumental heavy precipitation totals may be dominated by high frequency variability and the non-heavy totals by low frequency variance. The reconstruction of heavy precipitation is coherent with instrumental heavy totals across the frequency domain and include concentrations of variance at ENSO and biennial frequencies. Return period analyses calculated using instrumental heavy precipitation totals are representative of the return periods in the blue oak reconstruction despite the large differences in series length. Decadal surges in the amount, frequency, and inter-annual volatility of heavy precipitation totals are reconstructed, likely reflecting episodes of elevated atmospheric river activity in the past.

Seasonal streamflow forecasting for fresh water reservoir management in the Netherlands: an assessment of multiple prediction systems

Abstract: For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed seasonal forecast data sets derived from EFAS, E-HYPE and HTESSEL, which differ in their underlying hydrological formulation, but are all forced by meteorological forecasts from ECMWF SEAS5. We post-processed the streamflowforecasts using quantile mapping (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Averaged over the reforecast period, forecasts were skillful for up to four months in spring, and early summer. Later in summer the skillful period deteriorated to 1-2 months. When investigating specific years with either low or high flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skillful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to three months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management.

A Deep Learning Data Fusion Model using Sentinel-1/2, SoilGrids, SMAP-USDA, and GLDAS for Soil Moisture Retrieval

Abstract: We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), and Sentinel-2 (multispectral imagery) as well as geophysical variables from SoilGrids and modelled soil moisture fields from SMAP-USDA and GLDAS. The model was trained and evaluated on data from ~1000 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m3/m3, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.

Turkana Low-Level Jet Influence on Southwest Ethiopia Climate

Abstract: The Turkana Jet in northern Kenya is shown to modulate the climate of southwest Ethiopia’s Omo River Valley using in situ hydrometeorological data, satellite measurements, and atmospheric reanalyses from decadal to diurnal time scales. Temporal statistics from lowland (2.5°–5°N, 35°–38°E) and highland (6°–9°N, 35°–38°E) areas show that 850-hPa westward airflow over Lake Turkana is stronger in March and October but is weakened when western Indian Ocean sea temperatures become warmer than usual at intervals of 2–7 years. A case study on 24 March 2019 reveals how a stronger Turkana Jet induces warming and drying of the Omo Valley. A second case study on 27 September 2018 reveals Hadley cell subsidence over the southern flank of the Turkana Jet. We demonstrate how nocturnal airflow draining off the mountains joins the channelized jet. Satellite and atmospheric reanalyses exhibit realistic diurnal cycles in the east Omo mountains, but some products have incorrect phase and warm bias. Omo Valley soil moisture and runoff exhibit little trend in historical records and model projections; however, unpredictable multiyear wet and dry spells and a growing demand for water are ongoing concerns.

Sub-seasonal ensemble prediction of flash droughts over China

Abstract: Flash droughts have been occurring 13 frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the sub-seasonal forecast, but there are limited knowledges about their forecast skill due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to three weeks by using hindcast datasets from the sub-seasonal-to-seasonal prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1-week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead-1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1-week. In terms of probabilistic forecast, ECMWF also has higher brier skill score than NCEP especially over Eastern China, which is consistent with higher temperature and precipitation forecast skill. And the multi-model ensemble also has the highest brier skill score. This study suggests the importance of multi-model ensemble flash drought forecasting.

Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland

Abstract: Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.

Land–Atmosphere Feedbacks Weaken the Cooling Effect of Soil Organic Matter Property toward Deep Soil on the Eastern Tibetan Plateau

Abstract: Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands. The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

Impact of design factors for ESA CCI satellite soil moisture data assimilation over Europe

Abstract: In this study, soil moisture retrievals of the combined active-passive ESA CCI soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-year study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. (1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. (2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parametrization if the observations are rescaled monthly. (3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Developing a methodology for model intercomparison and its application to improve simulated streamflow by land surface models

Abstract: Runoff generated by land surface models (LSMs) is extensively used to predict future river discharge under global warming. However, the structural bias of LSM, the precipitation bias of the climate model, and other factors could cause the runoff to be biased. A model intercomparison study can help understand the LSM behavior. Traditional model intercomparison can discover output variation and evaluate performance, but explaining the reason for the difference is challenging. This study developed a novel method to identify the reasons for disparities and suggest improvements. Consequently, we investigated the impacts of model settings, by adopting the settings of another model in one model until it can mimic similar features in its output. Hence, we developed a process called the ”emulation model”. We employed two LSMs (Simple Biosphere with Urban Canopy (SiBUC) and Meteorological Research Institute–Simple Biosphere model (MRI–SiB)) in the Thai River basin. SiBUC produced a higher surface runoff than MRI–SiB, and the development of the MRI–SiB emulation revealed the cause of this variation. The differences in runoff characteristics affected streamflow estimation. For instance, the SiBUC peak discharge was faster and higher than observed in the dry year. Conversely, there was a tendency to underestimate the flow estimated by MRI–SiB runoff during the transition from dry to wet seasons. Incorporating another model settings can alleviate the shortcomings of each model. Overall, the proposed method can identify the strengths and weaknesses of a model and enhance the reproducibility of the hydrological characteristics of the observed discharge in the basin.

Multiscale Processes Leading to Heavy Precipitation in the Eastern Nepal Himalayas

Abstract: The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well-known despite its importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 reanalysis and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on July 8, 2019, at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 19–23 local time (LT). The synoptic-scale environment is characterized by a monsoon low-pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. Numerical simulation showed surface heat fluxes prevailed under the middle tropospheric (~ 500hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 19 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 20–22 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface.

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

Abstract: The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally-distributed single-catchment locations for a 10-year experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF systematically increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results also demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Developing Impacts-Based Drought Thresholds for Ohio

Abstract: Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the United States Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the State of Ohio: Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer’s Z-Index and Palmer Hydrological Drought Index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring.

Meteorological analysis on extremely depleted 18O rainfall events during the summer in Adelaide, Australia

Abstract: The transport of atmospheric water vapor plays a crucial role in the production of precipitation and the variation of precipitation isotopic composition (δ18Op). This study investigates three precipitation events with extremely depleted precipitation isotopes in the summer rainfall of the Adelaide, Australia. Using fundamental water vapor diagnostic and moisture calculation methods, this research analyzes the impact of rainout levels along moisture transport paths, atmospheric circulation patterns, water vapor sources, and moisture transport on the extreme depletion of precipitation isotopes in the study area. The purpose of this study is to reveal the direct cause of generating extremely depleted δ18Op at hourly time scale, and to understand the influence of water vapor transport on δ18Op. The results show the diversity and complexity of δ18Op variation in summer precipitation events in Adelaide. The rainout caused by local and upstream large precipitation may be the main reason for the steep drop to an extremely low value of δ18Op. The phenomenon of sub-cloud secondary evaporation, which is driven by the interaction between relatively low humidity and high temperature at near-surface levels, plays a pivotal role in the entire precipitation process. This mechanism is particularly pronounced during the onset or cessation of precipitation events, thereby resulting in the observed enrichment of δ18Op values. The oxygen stable isotopic composition of water vapor (δ18Oa) would usually become higher, when the air mass mixes with new moisture with relatively high δ18Oa suppressing the influence of the previous rainout. The evapotranspiration(ET) from the underlying surface along water vapor transport pathways modulates the isotopic composition of atmospheric water vapor . When the δ18O in ET exceeds that in precipitation, δ18Oa gradually becomes enriched.

The Isotopic Composition of Rainfall on a Subtropical Mountainous Island

Abstract: Abstract Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. The data show considerable differences in isotopic ratios produced by different weather systems, with Kona lows and upper-level lows having the lowest δ 2 H and δ 18 O values, and trade-wind showers the highest. The data also show significant spatial variability, with some sites being characterized by higher isotope ratios than others. The amount effect is not observed consistently at all sites. Deuterium excess shows a marked seasonal cycle, which is attributed to the different origin and history of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line and a comparison of this dataset with a long-term historical record illustrate strong interannual variability and the need to establish a long-term precipitation isotope monitoring network for Hawai‘i. Significance Statement The isotopic composition of water is often used in the study of island water resources, but the scarcity of high-frequency datasets makes the interpretation of data difficult. The purpose of this study is to investigate the isotopic composition of rainfall on a mountainous island in the subtropics. Based on weekly data collection on O‘ahu, Hawai‘i, the results improve our understanding of the isotopic composition of rainfall due to different weather systems, like trade-wind showers or cold fronts, as well as its spatial and temporal variability. These results could inform the interpretation of data from other mountainous islands in similar climate zones.

Optimal Design of a Surface Precipitation Network in Canada

Abstract: The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie provinces (Alberta, Saskatchewan and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions are in the greatest needs for higher accuracy measurements.

Characterizing Spatial Heterogeneity in Reservoir Evaporation within the Rio Grande Basin using a Coupled Version of the Weather, Research, and Forecasting Model

Abstract: Increasing evaporative demand from storage reservoirs is aggravating water scarcity issues across the American West. In the Rio Grande Basin, open water evaporation estimates represent approximately one-fifth of all water losses from the Basin. However, most estimates of reservoir evaporation rely on outdated methods, point measurements, or simplistic models. Warming temperatures and increasing atmospheric evaporative demand are stressing over-allocated resources, increasing the need for improved evaporation estimates. In response to this need, we develop open water evaporation estimates at Elephant Butte Reservoir (EBR), New Mexico, using three evaporation models and field measurements. Few studies quantify spatial heterogeneity in evaporation rates across large reservoirs; we therefore focus our efforts on using the Weather, Research, and Forecasting model coupled to an energy budget lake model, WRF-Lake, to simulate evaporation across EBR over the course of two years. We compare results from WRF-Lake, which simulates lake heat storage, to results from the Complementary Relationship Lake Evaporation (CRLE) model and the Global Lake Evaporation Volume dataset (GLEV). Results indicate that monthly and annual evaporation totals from WRF-Lake and GLEV are similar, while CRLE overestimates annual evaporation totals, with monthly peak evaporation offset compared to WRF-Lake and GLEV. While WRF-Lake and GLEV appear to capture monthly and annual evaporation totals, only WRF-Lake simulates differences in evaporation totals across the reservoir surface. Average annual evaporation at EBR was approximately 1487 mm, yet annual totals differed by up to 545 mm depending on location. This study improves understanding of open water evaporation and elucidates limitations of extrapolating point in-situ or bulk evaporation estimates across large reservoirs.

Methods for Estimating Surface Water Storage Changes and Their Evaluations

Abstract: Developing effective methods for estimating regional-scale surface water storage change (ΔSW) has become increasingly important for water resources studies and environmental impact assessment. Three methods for estimating monthly ΔSW are proposed in this study, of which one is based on land surface runoff and two use waterbody water budgets. Water areas observed by Landsat satellites for Canada’s entire landmass are used for evaluation of the results. The surface runoff method achieved the least satisfactory results, with large errors in cold season or dry regions. The two water-budget methods demonstrated significant improvements, particularly when water area dynamics is considered in the estimation of waterbody water budget. The three methods performed consistently across different climate regions in the country, and showed better correlations with observations over wet climate regions than over dry regions with poorly connected hydrological system. The results also showed impact of glacier and permanent snow melts over the Rocky Mountains on basin-scale surface water dynamics. The methods and outputs from this study can be used for calibrating and validating hydrological and climate models, assessing climate change and human disturbance impacts on regional water resources, and filling the ΔSW data gaps in GRACE-based total water storage decompositions studies.

Variations in the Diurnal Cycle of Precipitation and its Changes with Distance from Shore Over Two Contrasting Regions as Observed by IMERG, ERA5, and Spaceborne Ku Radar

Abstract: The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing, highly localized effects. The diurnal cycle in regions such as the Amazon and the Maritime Continent are of particular interest, due to the complex coastal and terrain effects. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset, is used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-year (2000 – 2018) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on half-hourly averaged precipitation at 0.1°×0.1°. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broad scale patterns of diurnal variability but does not capture all the fine scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

An increase in precipitation driven by irrigation over the North China Plain based on RegCM and WRF simulations

Abstract: Anthropogenic land-use change, irrigation, is considered to strongly modulate the hydroclimate at the regional scale by directly triggering evaporative cooling as the preliminary local effect. However, subsequent interactions with the background climate are highly nonlinear, which introduces diverse and unexpected consequences. The North China Plain (NCP) is one of the regions where irrigation has expanded most rapidly since the 20th century. The scarce rainfall in this region makes it necessary for irrigation to supplement the level of soil water for agricultural production. In this study, we quantify the effect of irrigation on the regional climate in China. Two regional climate models, WRF and RegCM, are used to mimic the large-scale practice of irrigation on the NCP. The results of our experiments show consistent cooling and moistening effects centered over the NCP across all experiments. Although the moisture budget and wind field pattern demonstrate that the vertical downdraft and low-level divergence could inhibit rainfall, the humidification dominates the climatic response in the dry April-May-June and increases the amount of precipitation significantly and consistently in the NCP region and the surrounding area in northern China. The enhanced CAPE increase sharply on some ‘calm days’ when the vertical moisture advection is small, especially during afternoon, triggering frequent light rains convectively by destabilizing the atmosphere. The consistent response to irrigation in two different models that employ structurally different land surface schemes could enhance the robustness of the physical mechanism behind the precipitation increase in the heavily irrigated region of NCP.

Percentile-Based Relationship between Daily Precipitation and Surface Air Temperature over the Qinghai–Tibet Plateau

Abstract: Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C. This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature.

Future Increases in North American Extreme Precipitation in CMIP6 downscaled with LOCA

Abstract: A new set of CMIP6 data downscaled using the Localized Constructed Analogs (LOCA) statistical method has been produced, covering central Mexico through Southern Canada at 6 km resolution. Output from 27 CMIP6 Earth System Models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training data set that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over Southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (~25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (~15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. 1-in-100 yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30-40 years in the southeastern U.S. and Pacific Northwest by end of century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to LOCA-downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.

Evaluating Seasonal Climate Forecasts from Dynamical Models over South America

Abstract: Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.