Search

A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for "average conditions," little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions' growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid. [ABSTRACT FROM AUTHOR]

This paper describes how the articles in this special issue support the Global Energy and Water Cycle Experiment (GEWEX) priorities with a specific focus on the advancement of hydrometeorological sciences. It explores how hydrometeorological research has been used to improve process understanding and forecast models, provide datasets for model validation, and support water resource applications. In particular, in this collection of papers, the water balance is considered at both global and watershed scales. In this process the limitations of reanalysis products and inputs to hydrologic models are identified. Some of these limitations arise from the lack of understanding of orographic processes and the best way to incorporate them into models. Several modeling studies reported in this special issue address different aspects of the role of topography in land–atmosphere interaction over mountain systems including the mountains in Asia and North America. Other land processes are considered as well including soil and vegetation processes. A limitation in these modeling studies arises from issues related to model initialization and validation data. One precipitation paper in this collection considers the information on extreme precipitation events that can be extracted from these data while another reports on a new algorithm for observing light rain and drizzle events. As phase II of GEWEX progresses, more emphasis will be placed on the use of GEWEX products to explore climate science questions related to the global energy and water cycle and its applications. Some areas of opportunity for future GEWEX activities include the development of high-resolution integrated products, flux estimates from satellites, and open processes (or test beds) for product improvement. [ABSTRACT FROM AUTHOR]

Precipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M- B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954-2013 to investigate regional trends in intermittency due to climate variability and global warming. No evidence is found of a global shift in intermittency but a weak trend toward burstier precipitation patterns and longer dry spells in the south of Europe (i.e., Portugal, Spain, and Italy) and an opposite trend toward steadier and more correlated precipitation patterns in Norway, Sweden, and Finland is observed. [ABSTRACT FROM AUTHOR]

Extreme precipitation associated with extratropical cyclones can lead to flooding if cyclones track over land. However, the dynamical mechanisms by which moist air is transported into cyclones is poorly understood. In this paper we analyze airflows within a climatology of cyclones in order to understand how cyclones redistribute moisture stored in the atmosphere. This analysis shows that within a cyclone's warm sector the cyclone-relative airflow is rearwards relative to the cyclone propagation direction. This low-level airflow (termed the feeder airstream) slows down when it reaches the cold front, resulting in moisture flux convergence and the formation of a band of high moisture content. One branch of the feeder airstream turns toward the cyclone center, supplying moisture to the base of the warm conveyor belt where it ascends and precipitation forms. The other branch turns away from the cyclone center exporting moisture from the cyclone. As the cyclone travels, this export results in a filament of high moisture content marking the track of the cyclone (often used to identify atmospheric rivers). We find that both cyclone precipitation and water vapor transport increase when moisture in the feeder airstream increases, thus explaining the link between atmospheric rivers and the precipitation associated with warm conveyor belt ascent. Atmospheric moisture budgets calculated as cyclones pass over fixed domains relative to the cyclone tracks show that continuous evaporation of moisture in the precyclone environment moistens the feeder airstream. Evaporation behind the cold front acts to moisten the atmosphere in the wake of the cyclone passage, potentially preconditioning the environment for subsequent cyclone development. [ABSTRACT FROM AUTHOR]

How mountain hydrology at different elevations will respond to climate change is a challenging question of great importance to assessing changing water resources. Here, three North American Cordilleran snow-dominated basins—Wolf Creek, Yukon; Marmot Creek, Alberta; and Reynolds Mountain East, Idaho—each with good meteorological and hydrological records, were modeled using the physically based, spatially distributed Cold Regions Hydrological Model. Model performance was verified using field observations and found adequate for diagnostic analysis. To diagnose the effects of future climate, the monthly temperature and precipitation changes projected for the future by 11 regional climate models for the mid-twenty-first century were added to the observed meteorological time series. The modeled future was warmer and wetter, increasing the rainfall fraction of precipitation and shifting all three basins toward rainfall–runoff hydrology. This shift was largest at lower elevations and in the relatively warmer Reynolds Mountain East. In the warmer future, there was decreased blowing snow transport, snow interception and sublimation, peak snow accumulation, and melt rates, and increased evapotranspiration and the duration of the snow-free season. Annual runoff in these basins did not change despite precipitation increases, warming, and an increased prominence of rainfall over snowfall. Reduced snow sublimation offset reduced snowfall amounts, and increased evapotranspiration offset increased rainfall amounts. The hydrological uncertainty due to variation among climate models was greater than the predicted hydrological changes. While the results of this study can be used to assess the vulnerability and resiliency of water resources that are dependent on mountain snow, stakeholders and water managers must make decisions under considerable uncertainty, which this paper illustrates. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

This paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the "east plus west" sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983–94, when station density was relatively high, and 1998–2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983–94 in the east plus west sector is on the order of 20 mm month−1 for GPCC V7 and 20–30 mm month−1 for the other products. The compares with 40–60 mm month−1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30–40 mm month−1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998–2010), when the station network is sparse, than during the period 1983–94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30–40 mm month−1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites. [ABSTRACT FROM AUTHOR]

In the past few decades, there have been more extreme climate events occurring worldwide, including Canada, which has also suffered from many extreme precipitation events. In this paper, trend analysis, probability distribution functions, principal component analysis, and wavelet analysis were used to investigate the spatial and temporal patterns of extreme precipitation events of Canada. Ten extreme precipitation indices were calculated using long-term daily precipitation data (1950–2012) from 164 Canadian gauging stations. Several large-scale climate patterns such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Pacific–North American (PNA), and North Atlantic Oscillation (NAO) were selected to analyze the relationships between extreme precipitation and climate indices. Convective available potential energy (CAPE), specific humidity, and surface temperature were employed to investigate potential causes of trends in extreme precipitation. The results reveal statistically significant positive trends for most extreme precipitation indices, which means that extreme precipitation of Canada has generally become more severe since the mid-twentieth century. The majority of indices display more increasing trends along the southern border of Canada while decreasing trends dominated the central Canadian Prairies. In addition, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Furthermore, complex interactions of climate patterns with synoptic atmospheric circulations can also affect precipitation variability, and changes to the summer and winter extreme precipitation could be explained more by the thermodynamic impact and the combined thermodynamic and dynamic effects, respectively. The seasonal CAPE, specific humidity, and temperature are correlated to Canadian extreme precipitation, but the correlations are season dependent, which could be positive or negative. [ABSTRACT FROM AUTHOR]

This paper studies the atmospheric moisture residence times over China for the period 1980–2009 using the dynamic recycling model (DRM). We define both the residence times for atmospheric moisture of precipitation (backward tracking) and evaporation (forward tracking) and show that each has significant spatial and seasonal variations. The area-averaged precipitation-moisture residence time is approximately 8.3 days, while the evaporation residence time is approximately 6.3 days. In addition, we investigate the concept of “tracking time” or time selected for moisture tracking in numerical source–sink studies. The area-averaged backward and forward tracking times at the 90% threshold (i.e., when 90% of initial moisture is attributed for tracking) are approximately 22 and 15 days, respectively. Finally, we theoretically deduced the explicit expressions for residence and tracking times for idealized cases and found the analytical proportional relationship between these times. In this way, the analytical link between residence time and e-folding time was reestablished. This proportional relationship was further verified against the DRM-derived values. In the DRM results, the proportional relation generally fluctuates along the trajectory, which leads to the differences between the theoretical and the DRM-derived values. These results can enhance our understanding of water cycling, and they are likely to help choose tracking times in relevant studies. [ABSTRACT FROM AUTHOR]

In the last several decades, extended dry periods have affected the Mediterranean area with dramatic impacts on water resources. Climate models are predicting further warming, with negative effects on water availability. The authors analyze the hydroclimatic tendencies of a typical Mediterranean basin, the Flumendosa basin located in Sardinia, an island in the center of the Mediterranean Sea, where in the last 30 years a sequence of dry periods has seriously impacted the water management system. Interestingly, in the historic record the annual runoff reductions have been more pronounced than the annual precipitation reductions. This paper performs an analysis that links this runoff decrease to changes in the total annual precipitation and its seasonal structure. The seasonality is a key determinant of the surface runoff process, as it reflects the degree to which rainfall is concentrated during the winter. The observed reductions in winter precipitation are shown here to be well correlated (Pearson correlation coefficient of 20.5) with the North Atlantic Oscillation (NAO) index. Considering the predictability of the winter NAO, there is by extension an opportunity to predict future winter precipitation and runoff tendencies. The recent hydroclimatic trends are shown to impact hydrologic design criteria for water resources planning. The authors demonstrate that there is a dangerous increase of the drought severity viewed from the perspective of water resources planning. [ABSTRACT FROM AUTHOR]

Droughts are high-impact events that have substantial implications for both human and natural systems. As such, improved understanding of the hydroclimatological processes involved in drought development is a major scientific imperative of direct practical relevance. To address this research need, this paper investigates the chain of processes linking antecedent ocean-atmosphere variation to summer streamflow drought in Great Britain. Analyses are structured around four distinct drought regions (defined using hierarchical cluster analysis) for the period 1964-2001. Droughts were identified using a novel regional drought area index. Composite analysis of monthly sea surface temperature (SST) prior to drought onset reveals a horseshoe- or tripole-shaped pattern of North Atlantic SST anomalies that is similar to patterns of SST anomalies associated with the North Atlantic Oscillation (NAO). Patterns in geopotential height, wind, moisture vapor flux, and precipitation prior to drought onset support the influence of the NAO but also demonstrate that the atmospheric bridge linking North Atlantic SST to drought development is too complex to be described solely by indices of the NAO. In revealing new information on the chain of processes leading to the development of hydrological drought in Great Britain, this paper has the potential to inform drought-forecasting research and so improve drought preparedness and management. [ABSTRACT FROM AUTHOR]

This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (>200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively. [ABSTRACT FROM AUTHOR]

Introduces a series of articles on hydrometeorology, climatology and other related topics.

This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13-km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation. In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables. [ABSTRACT FROM AUTHOR]

The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River. [ABSTRACT FROM AUTHOR]

This paper describes changes in the climatology, structure, and seasonality of cool-season atmospheric rivers influencing the U.S. West Coast by examining the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that are forced by the representative concentration pathway (RCP) 8.5 scenario. There are only slight changes in atmospheric river (AR) frequency and seasonality between historical (1970-99) and future (2070-99) periods considering the most extreme days (99th percentile) in integrated water vapor transport (IVT) along the U.S. West Coast. Changes in the 99th percentile of precipitation are only significant over the southern portion of the coast. In contrast, using the number of future days exceeding the historical 99th percentile IVT threshold produces statistically significant increases in the frequency of extreme IVT events for all winter months. The peak in future AR days appears to occur approximately one month earlier. The 10-model mean historical and end-of-century composites of extreme IVT days reflect canonical AR conditions, with a plume of high IVT extending from the coast to the southwest. The similar structure and evolution associated with ARs in the historical and future periods suggest little change in large-scale structure of such events during the upcoming century. Increases in extreme IVT intensity are primarily associated with integrated water vapor increases accompanying a warming climate. Along the southern portion of the U.S. West Coast there is less model agreement regarding the structure and intensity of ARs than along the northern portions of the coast. [ABSTRACT FROM AUTHOR]

This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climate with opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud-climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land-cloud-atmosphere system in both forecast and climate models. [ABSTRACT FROM AUTHOR]

A thorough understanding of the peak flows under urbanization and climate change-with the associated uncertainties-is indispensable for mitigating the negative social, economic, and environmental impacts from flooding. In this paper, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River basin (SARB), Texas. Historical and future land-cover maps were assembled to represent the urbanization process. Future climate and its uncertainties were represented by a series of designed scenarios using the Change Factor (CF) method. The factors were calculated by comparing the model ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with baseline historical climatology during two future periods (2020-49, period 1; 2070-99, period 2). It was found that with urban impervious areas increasing alone, annual peak flows may increase from 601 (period 1) to 885 m3 s−1 (period 2). With regard to climate change, annual peak flows driven by forcings from maximum, median, and minimum CFs under four representative concentration pathways (RCPs) were analyzed. While the median values of future annual peak flows-forced by the median CF values-are very similar to the baseline under all RCPs, in each case the uncertainty range (calculated as the difference between annual peak flows driven by the maximum and minimum CFs) is very large. When urbanization and climate change coevolve, these averaged annual peak flows from the four RCPs will increase from 447 (period 1) to 707 m3 s−1 (period 2), with the uncertainties associated with climate change more than 3 times greater than those from urbanization. [ABSTRACT FROM AUTHOR]

This study aims to identify the impact of vertical support on the information content of soil moisture (SM) for latent heat flux estimation. This objective is achieved via calculation of the mutual information (MI) content between multiple soil moisture variables (with different vertical supports) and current/future evaporative fraction (EF) using ground-based soil moisture and latent/sensible heat flux observations acquired from the AmeriFlux network within the contiguous United States. Through the intercomparison of MI results from different SM-EF pairs, the general value (for latent heat flux estimation) of superficial soil moisture observations , vertically integrated soil moisture observations , and vertically extrapolated soil moisture time series [soil wetness index (SWI) from a simple low-pass transformation of ] are examined. Results suggest that, contrary to expectations, 2-day averages of and have comparable mutual information with regards to EF. That is, there is no clear evidence that the information content for flux estimation is enhanced via deepening the vertical support of superficial soil moisture observations. In addition, the utility of SWI in monitoring and forecasting EF is partially dependent on the adopted parameterization of time-scale parameter T in the exponential filter. Similar results are obtained when analyses are conducted at the monthly time scale, only with larger error bars. The contrast between the results of this paper and past work focusing on utilizing soil moisture to predict vegetation condition demonstrates that the particular application should be considered when characterizing the information content of soil moisture time series measurements. [ABSTRACT FROM AUTHOR]

This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System-based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the 'average' soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results. [ABSTRACT FROM AUTHOR]

The entropy theory has been widely applied in hydrology for probability inference based on incomplete information and the principle of maximum entropy. Meanwhile, copulas have been extensively used for multivariate analysis and modeling the dependence structure between hydrologic and climatic variables. The underlying assumption of the principle of maximum entropy is that the entropy variables are mutually independent from each other. The principle of maximum entropy can be combined with the copula concept for describing the probability distribution function of multiple dependent variables and their dependence structure. Recently, efforts have been made to integrate the entropy and copula concepts (hereafter, entropy-copula) in various forms to take advantage of the strengths of both methods. Combining the two concepts provides new insight into the probability inference; however, limited studies have utilized the entropy-copula methods in hydrology and climatology. In this paper, the currently available entropy-copula models are reviewed and categorized into three main groups based on their model structures. Then, a simple numerical example is used to illustrate the formulation and implementation of each type of the entropy-copula model. The potential applications of entropy-copula models in hydrology and climatology are discussed. Finally, an example application to flood frequency analysis is presented. [ABSTRACT FROM AUTHOR]

Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial--temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB. [ABSTRACT FROM AUTHOR]

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 United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002-19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and 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, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs 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 Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysisbased QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV. [ABSTRACT FROM AUTHOR]

The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude-longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed. [ABSTRACT FROM AUTHOR]

Winter orographic precipitation over the Andes between 30° and 37°S is examined using precipitation gauges in the mountains and adjacent lowlands. Because of the limited number of precipitation gauges, this paper focuses on the large-scale variation in cross-barrier precipitation and does not take into account the fine ridge-valley scale. The maximum amount of precipitation was observed on the windward slope of the mountain range below the crest, which was twice that observed on the low-windward side between 32.5° and 34°S. Toward the east of the crest, precipitation amounts drop sharply, generating a strong cross-barrier gradient. The rain shadow effect is greater in the north (32°-34.5°S) than in the south (35°-36.5°S) of the low-lee side, which is probably due to more baroclinic activity in southernmost latitudes and a southward decrease in the height of the Andes enabling more spillover precipitation. The effect of the Andes on winter precipitation is so marked that it modifies the precipitation regimes in the adjacent windward and leeward lowlands north of 35°S. Based on the fact that ~75%% of the wintertime precipitation accumulated in the fourth quartile, through four or five heavy events on average, the synoptic-scale patterns of the heavy (into fourth quartile) orographic precipitation events were identified. Heavy events are strongly related to strong water vapor transport from the Pacific Ocean in the pre-cold-front environment of extratropical cyclones, which would have the form of atmospheric rivers as depicted in the reanalysis and rawinsonde data. The composite fields revealed a marked difference between two subgroups of heavy precipitation events. The extreme (100th-95th percentiles) events are associated with deeper cyclones than those for intense (95th-75th percentiles) events. These deeper cyclones lead to much stronger plumes of water vapor content and cross-barrier moisture flux against the high Andes, resulting in heavier orographic precipitation for extreme events. In addition, regional airflow characteristics suggest that the low-level flow is typically blocked and diverted poleward in the form of an along-barrier jet. On the lee side, downslope flow dominates during heavy events, producing prominent rain shadow effects as denoted by the domain of downslope winds extending to low-leeward side (i.e., zonda wind). [ABSTRACT FROM AUTHOR]

This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km--the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961--90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors'' main area of interest, is mostly less than 20%%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.9 for temperature and 0.6 for precipitation. Comparison with two Hungarian station time series shows that the model performance does not degrade when going to the 10-km gridpoint scale. Finally, the model reproduces the spatial distribution of dry and wet spells over the region. Overall, it is assessed that this high-resolution version of RegCM3 is of sufficiently good quality to perform climate change experiments over the Carpathian region--and, in particular, the Hungarian territory--for application to impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Overlying the challenge of managing within natural hydroclimatic variability is the likely modification of runoff variability along with average runoff due to anthropogenic enhancement of greenhouse gas concentrations. In this paper analytical models are developed in which runoff mean and variability, the latter defined by the variance (or standard deviation) of annual runoff, are related to the variances and the covariance of annual precipitation and potential evapotranspiration, and the aridity index (mean annual potential evapotranspiration divided by mean annual precipitation). The method was validated using observed runoff data for 699 worldwide catchments. It was concluded that combining the Schreiber function, which relates the ratio of annual actual evapotranspiration to annual precipitation, with the analytical models provided satisfactory estimates of observed annual runoff mean and interannual variability. It was also concluded that estimates of annual runoff variability based on the simplified model of Koster and Suarez were unsatisfactory. By way of illustrating the new methodology, the approach was applied to projected annual values of precipitation from the Hadley Centre Global Environment Model version 1 (HadGEM) and it showed that considerable changes in reservoir yield are likely to occur if climate change projections of precipitation from HadGEM are realistic. Finally, further simplifications of the equations, based on the Schreiber function, are developed to estimate the mean and standard deviation of annual runoff that allow climate analysts to estimate the impact of potential climate changes on annual runoff characteristics and reservoir yield performance without having to resort to the calibration and application of a rainfall-runoff model or rely on the runoff output from general circulation models to examine such characteristics. [ABSTRACT FROM AUTHOR]

Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time--space scales over the whole domain of the dataset. Here, ''uncertainty'' refers to the general concept of the ''deviation'' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20%% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer. [ABSTRACT FROM AUTHOR]

Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings. [ABSTRACT FROM AUTHOR]

This paper tests whether seasonal mean precipitation is predictable using a new method that estimates and analyzes joint probabilities. The new estimation method is to partition the globe into boxes, pool all data within the box to estimate a single joint probability of precipitation for two consecutive seasons, and then apply the resulting joint probability to individual pixels in the box. Pooling data in this way allows joint probabilities to be estimated in relatively small sample sizes; however, the new method assumes that the transition probabilities of pixels in a box are homogeneous and stationary. Joint probabilities are estimated from the Global Precipitation Climatology Project dataset in 21 land boxes and 5 ocean boxes during the period 1979–2008. The state of precipitation is specified by dry, wet, or normal terciles of the local climatological distribution. Predictability is quantified by mutual information, which is a fundamental measure of predictability that allows for nonlinear dependencies, and is tested using bootstrap methods. Predictability was verified by constructing probabilistic and quantitative forecasts directly from the transition probabilities and showing that they have superior cross-validated skills than forecasts based on climatology, persistence, or random selection. Spring was found to be the most predictable season, whereas summer was the least predictable season. Analysis of joint probabilities reveals that although the probabilities are close to climatology, the predictability of precipitation arises from a slight tendency of the state to persist from one season to the next, or if a transition occurs, then it is more often from one extreme to normal than from one extreme to the other. [ABSTRACT FROM AUTHOR]

In this paper, a diagnostic metric—termed the local-convergence ratio—is used to analyze the contribution of evaporation and atmospheric moisture-flux convergence to model-based estimates of climatological precipitation over the North American continent. Generally, the fractional evaporative contribution is largest during spring and summer when evaporation is largest and decreases as evaporation decreases. However, there appears to be at least three regions with distinct spatiotemporal seasonal evolutions of this ratio. Over both the northern and western portions of the continent, the fractional evaporative contribution peaks in spring and early summer and decreases during fall and into winter. Over the northern portion, this fall decrease is related to an increase in atmospheric moisture-flux convergence associated with enhanced meridional moisture fluxes into the region; over the western coastal regions, the fall decrease in evaporative contribution is associated with a decrease in evaporation and an increase in total moisture-flux convergence, most likely associated with increased storm activity. In contrast, over the central portions of the continent, the fractional evaporative contribution to precipitation remains relatively low in spring—when enhanced low-level jet activity increases the low-level atmospheric moisture flux convergence into the region—and instead peaks in summer and fall—when the moisture-flux convergence associated with the low-level jet decreases and precipitation is balanced predominantly by local evaporation. Finally, over the southwestern United States and northwestern Mexico, the fractional evaporative contribution to precipitation is found to contain a wintertime minimum as well as a secondary minimum during summer. This latter feature is due to a substantial increase in low-level atmospheric moisture-flux convergence associated with the large-scale monsoon circulation that influences this region during this time. [ABSTRACT FROM AUTHOR]

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach to stochastically generating rainfall sequences that can take into account natural climate phenomena, such as the El Niño–Southern Oscillation and the interdecadal Pacific oscillation. The approach is also amenable to modeling projected affects of anthropogenic climate change. The method uses a relatively new technique, empirical mode decomposition (EMD), to decompose a historical rainfall series into several independent time series that have different average periods and amplitudes. These time series are then recombined to form an intradecadal time series and an interdecadal time series. After separate stochastic generation of these two series, because they are independent, they can be recombined by summation to form a replicate equivalent to the historical data. The approach was applied to generate 6-monthly rainfall totals for six rainfall stations located near Canberra, Australia. The cross correlations were preserved by carrying out the stochastic analysis using the Matalas multisite model. The results were compared with those obtained using a traditional autoregressive lag-one [AR(1)], and it was found that the new EMD stochastic model performed satisfactorily. The new approach is able to realistically reproduce multiyear–multidecadal dry and wet epochs that are characteristic of Australia’s climate and are not satisfactorily modeled using traditional stochastic rainfall generation methods. The method has two advantages over the traditional AR(1) approach, namely, that it can simulate nonstationarity characteristics in the historical time series, and it is easy to alter the decomposed time series components to examine the impact of anthropogenic climate change. [ABSTRACT FROM AUTHOR]

This paper presents an intercomparison of precipitation observations for the western United States. Using nine datasets, the authors provide a comparative climatology and season- and location-specific evaluations of precipitation uncertainty for the western United States and for five subregions that have distinct precipitation climates. All data are shown to represent the general climate features but with high bias among datasets. Interannual variability is similar among datasets with respect to the timing of precipitation excesses and deficits, but important differences occur in the spatial distribution of specific anomalous events. Dataset distribution differences, as represented by their cumulative density functions (CDFs), are statistically significant for 80% of data combinations stratified by subregion and season. The CDFs of anomaly fields are more similar but uncertainty remains, as data differences are significant for 40% of dataset comparisons. Observational uncertainty is low for persistence studies because the data are found to be similar with respect to (i) grid cell estimates of a characteristic persistence time scale and (ii) distributions of anomaly length scales. Spatially, the greatest uncertainty in magnitude differences occurs along the Rocky Mountains in winter, spring, and fall, and along the California coastline in summer. In linear (phase) association, the greatest differences occur in northern Mexico during all seasons; along the Rocky Mountains in winter, spring, and fall; and in California, Nevada, and the intermountain region in summer. Overall, data similarity is lowest in summer as a result of a reduction in phase association and an increase in amplitude differences. [ABSTRACT FROM AUTHOR]

This paper presents the results of a regionalization study of the precipitation climate of the western United States using principal component analysis. Past eigen-based regionalization studies have relied on rain gauge networks, which is restrictive because rain gauge coverage is sparse, especially over complex terrain that exists in the western United States. Here, the use of alternate data products is examined by conducting a comparative regionalization using nine precipitation datasets used in hydrometeorological research. Five unique precipitation climates are identified within the western United States, which have centers and boundaries that are physically reasonable and that highlight the relationship between the precipitation climatology and local topography. Using the congruence coefficient as the measure of similarity between principal component solutions, the method is found to be generally stable across datasets. The exception is the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis 2, which frequently demonstrates only borderline agreement with the other datasets. The loading pattern differences among datasets are shown to be primarily a result of data differences in the representation of (i) precipitation over the Rocky Mountains, (ii) the eastward wet-to-dry precipitation gradient that occurs during the cold season, (iii) the magnitude and spatial extent of the North American monsoon signal, and (iv) precipitation in the desert southwest during spring and summer. Sensitivity tests were conducted to determine whether the spatial resolution and temporal domain of the input data would dramatically affect the solution, and these results show the methodology to be stable to differences in spatial/temporal data features. The results suggest that alternate data products can be used in regionalization studies, which has applications for rain gauge installation and planning, climate research, and numerical modeling experiments. [ABSTRACT FROM AUTHOR]

A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta) or land surface temperature (Ts) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn, enhanced vegetation index (EVI), daily maximum Ts, and DTsR is about 0.92 for all the sites, the bias is -1.9 W m-2, and the root-mean-square error (RMSE) is 28.6 W m-2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m-2, the RMSE is 19.8 W m-2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET. [ABSTRACT FROM AUTHOR]

Because of the lack of field measurements, models are often used to monitor soil moisture conditions. Therefore, it is important to find a model that can accurately simulate soil moisture under a variety of land surface conditions. In this paper, three models of varying complexities [the Variable Infiltration Capacity (VIC), Decision Support System for Agrotechnology Transfer (DSSAT), and Climatic Water Budget (CWB) models] that are commonly used for simulating soil moisture were evaluated and compared using soil moisture data (1997–2005) from three Soil Climate Analysis Network (SCAN) sites (Bushland, Texas; Prairie View, Texas; Powder Mill, Maryland). Results demonstrated that DSSAT and VIC simulated soil moisture more accurately than CWB at the three SCAN sites. DSSAT and VIC both accurately simulated the annual cycle of soil moisture and the wetting and drying in response to weather conditions, as evidenced by the relatively strong correlations, but could not accurately simulate the actual soil water content in the upper soil layers (the mean coefficients of efficiency E for all DSSAT and VIC simulations were -0.8 and -2.6, respectively). CWB could not accurately simulate soil moisture at any of the SCAN sites. Model performance varied significantly not only from model to model but also from year to year and from location to location. Model sensitivity analysis using the factorial approach suggests that DSSAT is more sensitive than VIC and that model sensitivity varies by locations, indicating that parameter sensitivity is more strongly controlled by climatic gradients than by changes in soil properties. [ABSTRACT FROM AUTHOR]

The water cycle over a given region is governed by many complex multiscale interactions and feedbacks, and their representation in climate models can vary in complexity. To understand which of the key processes require better representation, evaluation and validation of all components of the simulated water cycle are required. Adequate assessing of the simulated hydrological cycle over a given region is not trivial because observations for various water cycle components are seldom available at the regional scale. In this paper, a comprehensive validation method of the water budget components over a river basin is presented. In addition, the sensitivity of the hydrological cycle in the Canadian Regional Climate Model (CRCM) to a more realistic representation of the land surface processes, as well as radiation, cloud cover, and atmospheric boundary layer mixing is investigated. The changes to the physical parameterizations are assessed by evaluating the CRCM hydrological cycle over the Mississippi River basin. The first part of the evaluation looks at the basin annual means. The second part consists of the analysis and validation of the annual cycle of all water budget components. Finally, the third part is directed toward the spatial distribution of the annual mean precipitation, evapotranspiration, and runoff. Results indicate a strong response of the CRCM evapotranspiration and precipitation biases to the physical parameterization changes. Noticeable improvement was obtained in the simulated annual cycles of precipitation, evapotranspiration, moisture flux convergence, and terrestrial water storage tendency when more sophisticated physical parameterizations were used. Some improvements are also observed for the simulated spatial distribution of precipitation and evapotranspiration. The simulated runoff is less sensitive to changes in the CRCM physical parameterizations. [ABSTRACT FROM AUTHOR]

The state-of-the-art AOGCM simulations have recently (late 2004–early 2005) been completed for the Intergovernmental Panel on Climate Change (IPCC) in order to provide input to the IPCC’s Fourth Assessment Report (AR4). The present paper synthesizes the new simulations of both the twentieth- and twenty-first-century arctic freshwater budget components for use in the IPCC AR4, and attempts to determine whether demonstrable progress has been achieved since the late 1990s. Precipitation and its difference with evapotranspiration are addressed over the Arctic Ocean and its terrestrial watersheds, including the basins of the four major rivers draining into the Arctic Ocean: the Ob, the Yenisey, the Lena, and the Mackenzie. Compared to the previous [IPCC Third Assessment Report (TAR)] generation of AOGCMs, there are some indications that the models as a class have improved in simulations of the Arctic precipitation. In spite of observational uncertainties, the models still appear to oversimulate area-averaged precipitation over the major river basins. The model-mean precipitation biases in the Arctic and sub-Arctic have retained their major geographical patterns, which are at least partly attributable to the insufficiently resolved local orography, as well as to biases in large-scale atmospheric circulation and sea ice distribution. The river discharge into the Arctic Ocean is also slightly oversimulated. The simulated annual cycle of precipitation over the Arctic Ocean is in qualitative agreement between the models as well as with observational and reanalysis data. This is also generally the case for the seasonality of precipitation over the Arctic Ocean’s terrestrial watersheds, with a few exceptions. Some agreement is demonstrated by the models in reproducing positive twentieth-century trends of precipitation in the Arctic, as well as positive area-averaged P–E late-twentieth-century trends over the entire terrestrial watershed of the Arctic Ocean. For the twenty-first century, three scenarios are considered: A2, A1B, and B1. Precipitation over the Arctic Ocean and its watersheds increases through the twenty-first century, showing much faster percentage increases than the global mean precipitation. The arctic precipitation changes have a pronounced seasonality, with the strongest relative increase in winter and fall, and the weakest in summer. The river discharge into the Arctic Ocean increases for all scenarios from all major river basins considered, and is generally about twice as large as the increase of freshwater from precipitation over the Arctic Ocean (70°–90°N) itself. The across-model scatter of the precipitation increase for each scenario is significant, but smaller than the scatter between the climates of the different models in the baseline period. [ABSTRACT FROM AUTHOR]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

This paper describes a flexible method to generate ensemble gridded fields of precipitation in complex terrain. The method is based on locally weighted regression, in which spatial attributes from station locations are used as explanatory variables to predict spatial variability in precipitation. For each time step, regression models are used to estimate the conditional cumulative distribution function (cdf) of precipitation at each grid cell (conditional on daily precipitation totals from a sparse station network), and ensembles are generated by using realizations from correlated random fields to extract values from the gridded precipitation cdfs. Daily high-resolution precipitation ensembles are generated for a 300 km × 300 km section of western Colorado (dx = 2 km) for the period 1980–2003. The ensemble precipitation grids reproduce the climatological precipitation gradients and observed spatial correlation structure. Probabilistic verification shows that the precipitation estimates are reliable, in the sense that there is close agreement between the frequency of occurrence of specific precipitation events in different probability categories and the probability that is estimated from the ensemble. The probabilistic estimates have good discrimination in the sense that the estimated probabilities differ significantly between cases when specific precipitation events occur and when they do not. The method may be improved by merging the gauge-based precipitation ensembles with remotely sensed precipitation estimates from ground-based radar and satellites, or with precipitation and wind fields from numerical weather prediction models. The stochastic modeling framework developed in this study is flexible and can easily accommodate additional modifications and improvements. [ABSTRACT FROM AUTHOR]

Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations. [ABSTRACT FROM AUTHOR]

This paper presents the results of bias corrections of Chinese standard precipitation gauge (CSPG) measurements for wind-induced undercatch, a trace amount of precipitation, and wetting loss. Long-term daily data of precipitation, temperature, and wind speed during 1951–98 at 710 meteorological stations in China were used for this analysis. It is found that wind-induced gauge undercatch is the greatest error in most regions, and wetting loss and a trace amount of precipitation are important in the low-precipitation regions in northwest China. Monthly correction factors ratio of corrected amount to measured amount of precipitation differ by location and by type of precipitation. Considerable interannual variation of the corrections exists in China due to the fluctuations of wind speed and frequency of precipitation. More importantly, annual precipitation has been increased by 8 to 740 mm with an overall mean of 130 mm at the 710 stations over China because of the bias corrections for the study period. This corresponds to 6%–62% increases (overall mean of 19% at the 710 stations over China) in gauge-measured yearly total precipitation over China. This important finding clearly suggests that annual precipitation in China is much higher than previously reported. The results of this study will be useful to hydrological and climatic studies in China. [ABSTRACT FROM AUTHOR]

The 4-month drift of Ice Station Weddell (ISW) produced over 2000 h of nearly continuous measurements in the atmospheric surface layer and in the snow and sea ice in the western Weddell Sea. This paper reports simulations, based on these data, of processes in the air, snow, and sea ice at ISW using SNTHERM, a one-dimensional mass and energy balance model. An earlier version of SNTHERM had to be adapted, however, to treat the flooding that often occurs on sea ice in the western Weddell Sea. To treat this layer of slush and brine, SNTHERM holds the brine salinity constant at its initial value of 31.5 psu until 80% of this slush layer freezes. The current version of SNTHERM also incorporates a new parameterization for the roughness length for wind speed, z0, derived from analyses of ISW eddy-covariance data. SNTHERM's simulations are validated with temperature measurements within the ice and snow and with eddy-covariance measurements of the surface momentum and sensible and latent heat fluxes. The simulated turbulent fluxes agree fairly well with the measured fluxes, except the simulated sensible heat flux is biased low by 4–5 W m-2 for both stable and unstable stratification. The simulated temperature profiles in the snow and ice also agree well with the measured temperatures. In particular, allowing seawater to flush the slush layer until it is 80% frozen delays the freezing of this layer such that its behavior mirrors the data. [ABSTRACT FROM AUTHOR]

This study describes flash drought (FD) inferred from the evaporative stress index (ESI) over Australia and its relationship to vegetation. During 1975–2020, FD occurrence ranges from less than 1 per decade in the central arid regions to 10 per decade toward the coasts. Although FD can occur in any season, its occurrence is more frequent in summer in the north, winter in the southern interior and southwest, and across a range of months in the far southeast and Tasmania. With a view toward real-time monitoring, FD "declaration" is defined as the date when the ESI declines to at least −1, i.e., drought conditions, after at least 2 weeks of rapid decline. Composite analysis shows that evaporative demand begins to increase about 5–6 weeks before declaration with an increase in solar radiation, while evapotranspiration initially increases with evaporative demand but then decreases in response to the soil moisture depletion. Solar radiation increases simultaneously with precipitation deficit, both reaching their peak around declaration. FD intensity peaks with soil moisture depletion, 2–3 weeks after declaration. The composite wind speed only shows a modest increase around declaration. The composite FD ends 4 weeks after rapid decreases in solar radiation and increases in precipitation. Satellite-derived vegetation health composites show pronounced decline in the nonforested regions, peaking about 4–8 weeks after FD declaration, followed by a recovery period lasting about 12 weeks after flash drought ends. The forest-dominated regions, however, are little impacted. Modeled pasture growth data show reduced values for up to 3 months after the declaration month covering the main agricultural areas of Australia. Significance Statement: Flash drought describes a fast intensification or rapid development of drought conditions with potential severe impacts on agriculture and ecosystems. This study describes the climatology and typical evolution of flash drought over Australia for the period 1975–2020. An objective definition of flash drought, using high-resolution observational-based datasets, is proposed and its spatiotemporal variability is provided, as well as its relationship with vegetation health and pasture growth. This constitutes a guideline for understanding flash drought in Australia and its impacts on vegetation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15, P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an "adaptive sampling" approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning. Significance Statement: Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an "adaptive sampling" application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts. [ABSTRACT FROM AUTHOR]

Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA's strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends. [ABSTRACT FROM AUTHOR]

This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979-2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2-5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1-3 stations concurrently. The majority of extreme days (75%-100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days. [ABSTRACT FROM AUTHOR]