Showing papers in "Journal of Hydrometeorology in 2021"

Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

Abstract: Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; (2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and (3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 Simulations

Abstract: Climate change will result in more dry days and longer dry spells, however, the resulting impacts on crop growth depend on the timing of these longer dry spells in the annual cycle. Using an ensemble of Coupled Model Intercomparison Project Phase 5 and Phase 6 (CMIP5 and CMIP6) simulations, and a range of emission scenarios, here we examine changes in wet and dry spell characteristics under future climate change across the extended tropics in wet and dry seasons separately. Delays in the wet seasons by up to two weeks are projected by 2070-2099 across South America, Southern Africa, West Africa and the Sahel. An increase in both mean and maximum dry spell length during the dry season is found across Central and South America, Southern Africa and Australia, with a reduction in dry season rainfall also found in these regions. Mean dry season dry spell lengths increase by 5-10 days over north-east South America and south-west Africa. However, changes in dry spell length during the wet season are much smaller across the tropics with limited model consensus. Mean dry season maximum temperature increases are found to be up to 3C higher than mean wet season maximum temperature increases over South America, Southern Africa and parts of Asia. Longer dry spells, fewer wet days, and higher temperatures during the dry season may lead to increasing dry season aridity, and have detrimental consequences for perennial crops.

Streamflow Simulation in Data-Scarce Basins Using Bayesian and Physics-Informed Machine Learning Models

Abstract: Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long Short-TermMemory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most of LSTM applications focused on well-monitored catchments with abundant and high quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that: (1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash-Sutcliffe efficiency above 0.8, even with only two years of calibration data. (2) When the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy. (3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting. (4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. These insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

Abstract: Soil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 soil moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with 1/4°, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, 1/2°, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the instrumental variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10–0.11 compared to an increase of 0.02–0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous United States reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

An assessment of land-atmosphere interactions over South America using satellites, reanalysis and two global climate models

Abstract: In South America, land-atmosphere interactions have an important impact on climate, particularly the regional hydrological cycle, but detailed evaluation of these processes in global climate models has been limited. Focussing on the satellite-era period of 2003–2014, we assess land-atmosphere interactions on annual to seasonal timescales over South America in satellite products, a novel reanalysis (ERA5-Land) and two global climate models: the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) and the UK Hadley Centre Global Environment Model version 3 (HadGEM3). We identify key features of South American land-atmosphere interactions represented in satellite and model datasets, including seasonal variation in coupling strength, large-scale spatial variation in the sensitivity of evapotranspiration to surface moisture, and a dipole in evaporative regime across the continent. Differences between products are also identified, with ERA5-Land, HadGEM3 and BAM-1.2 showing opposite interactions to satellites over parts of the Amazon and the Cerrado, and stronger land-atmosphere coupling along the North Atlantic coast. Where models and satellites disagree on the strength and direction of land-atmosphere interactions, precipitation biases and misrepresentation of processes controlling surface soil moisture are implicated as likely drivers. These results show where improvement of model processes could reduce uncertainty in the modelled climate response to land-use change, and highlight where model biases could unrealistically amplify drying or wetting trends in future climate projections. Finally, HadGEM3 and BAM-1.2 are consistent with the median response of an ensemble of nine CMIP6 models, showing they are broadly representative of the latest generation of climate models.

Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications

Abstract: Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5th Generation Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India

Abstract: The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bias correct precipitation forecast. The bias correction of precipitation forecast resulted in significant improvement in the precipitation forecast skill. Runoff and root zone soil moisture forecast was also significantly improved due to bias correction of precipitation forecast where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecast did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecast performs better than the streamflow forecast simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecast and post-processing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).

Assimilation of vegetation conditions improves the representation of drought over agricultural areas

Abstract: This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.

Drivers and Subseasonal Predictability of Heavy Rainfall in Equatorial East Africa and Relationship with Flood Risk

Abstract: Equatorial East Africa (EEA) suffers from significant flood risks These can be mitigated with pre-emptive action, however currently available early warnings are limited to a few days lead time Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity However before these forecasts can be used, the basis of their skill and relevance for flood risk must be established Here we demonstrate that subseasonal forecasts are particularly skillful over EEA Forecasts can skillfully anticipate weekly upper quintile rainfall within a season, at lead times of two weeks and beyond We demonstrate the link between the Madden-Julian Oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia river in Western Kenya Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper quintile weekly rainfall shown to skillfully discriminate flood events We additionally evaluate GloFAS global flood forecasts for the Nzoia basin Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed

Attributing Terrestrial Water Storage Variations across China to Changes in Groundwater and Human Water Use

Abstract: This study investigated the attribution of terrestrial water storage (TWS) variations across China to changes in groundwater and human water use. As one vital storage component, the groundwater storage (GWS) derived from the Jet Propulsion Laboratory’s GRACE (Gravity Recovery and Climate Experiment) mass concentration solution compared reasonably well with the in situ groundwater table depth, with the correlation coefficients ranging from −0.83 to −0.18, all of which were statistically significant at the 95% confidence level. About 71% of the trends in derived GWS had the same sign as those of observations, without systematic deviation, across China. The GWS variation contributed a large portion of the TWS trend in most regions of China, and the majority of contribution values reached 50%–150% in the Hai River basin, the Loess Plateau, and the middle portion of the Yangtze River basin. The dominant role of GWS is closely related to the detected long-term “memories” in both TWS and GWS. The increase of irrigation consumption accelerated the TWS depletion trend by 13.4% in the Huai River basin, while the decrease of consumptive agricultural water use alleviated the TWS decline rate by 4.1% in the Hai River basin. Importantly, the correlation coefficients reached 0.74–0.95 between the TWS change and the residual of precipitation, evapotranspiration, flow into the sea, and irrigation consumption in the four river basins of particular interest. The findings of this study are helpful for understanding regional water cycles in China.

Combining Radar Attenuation and Partial Beam Blockage Corrections for Improved Quantitative Application

Abstract: Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).

Hydrometeorological Observations and Modeling of an Extreme Rainfall Event Using WRF and WRF-Hydro during the RELAMPAGO Field Campaign in Argentina

Abstract: Some of the most intense convective storms on Earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November–December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were 1) to perform hydrological streamflow and meteorological observations in previously ungauged basins and 2) to build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage–discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5 and 6 h from the peak of the rain event. The satellite-observed rainfall product IMERG-Final showed a better representation of rain gauge–estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-h simulation of an extreme hydrometeorological event that occurred on 27 November 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast, and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region.

Variability and Transitions in Precipitation Extremes in the Midwest United States

Abstract: Monthly to seasonal precipitation extremes, both flood and drought, are important components of regional climates worldwide, and are the subjects of numerous investigations. However, variability in and transition between precipitation extremes, and associated impacts are the subject of far fewer studies. Recent such events in the Midwest region of the United States, such as the 2011–12 flood to drought transition in the upper Mississippi River basin and the flood to drought transition experienced in parts of Kentucky, Ohio, Indiana, and Illinois in 2019, have sparked concerns of increased variability and rapid transitions between precipitation extremes and compounded economic and environmental impacts. In response to these concerns, this study focuses on characterizing variability and change in Midwest precipitation extremes and transitions between extremes over the last 70 years. Overall we find that the Midwest as a region has gotten wetter over the last seven decades, and that in general the annual maximum and median wetness, defined using the standardized precipitation index (SPI), have increased at a larger magnitude than the annual minimum. We find large areas of the southern Midwest have experienced a significant increase in the annual SPI range and associated magnitude of transition between annual maximum and minimum SPI. We additionally find wet to dry transitions between extremes have largely increased in speed (i.e., less time between extremes), while long-term changes in transition frequency are more regional within the Midwest.

The role of vegetation in flash drought occurrence: A sensitivity study using community earth system model, version 2

Abstract: Flash droughts are noted by their unusually rapid rate of onset or intensification, which makes it difficult to anticipate and prepare for them, thus resulting in severe impacts. Although the development of flash drought can be associated with certain atmospheric conditions, vegetation also plays a role in propagating flash drought. This study examines the climatology of warm season (March–September) flash drought occurrence in the United States between 1979 and 2014, and quantifies the possible impacts of vegetation on flash drought based on a set of sensitivity experiments using the Community Earth System Model, version 2 (CESM2). With atmospheric nudging, CESM2 well captures historical flash drought. Compared with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and National Climate Assessment–Land Data Assimilation System (NCA-LDAS), CESM2 shows agreement on the high flash drought frequency in the Great Plains and southeastern United States, but overestimates flash drought occurrence in the Midwest. The vegetation sensitivity experiments suggest that vegetation greening can significantly increase the flash drought frequency in the Great Plains and the western United States during the warm seasons through enhanced evapotranspiration. However, flash drought occurrence is not significantly affected by vegetation phenology in the eastern United States and Midwest due to weak land–atmosphere coupling. In response to vegetation greening, the extent of flash drought also increases, but the duration of flash drought is not sensitive to greening. This study highlights the importance of vegetation in flash drought development, and provides insights for improving flash drought monitoring and early warning.

Drought Variability over the Conterminous United States for the Past Century

Abstract: Abstract: We examine the drought variability over the Conterminous United States (CONUS) for 1915-2018 using the Noah-MP land-surface model. We examine different model options on drought reconstruction including optional representation of groundwater and dynamic vegetation phenology. Over our 104-year reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS over the past century in all configurations. Groundwater tends to increase drought duration for great droughts, primarily by leading to earlier drought onset (associated with short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, dynamic vegetation tends to shorten drought duration, primarily by earlier drought demise (water loss from ET is reduced during droughts due to closed stoma or dead vegetation). We also examine drought characteristics in six subregions. We find that the U.S. Southwest (Southeast) has the longest (shortest) drought durations. Dry area coverage in all subregions except the Southwest showed decreasing trends over our study period. The effects of groundwater and dynamic vegetation vary in subregions due to differences in groundwater depths (hence connectivity with the surface) and vegetation types. We examine the moisture deficits in different soil layers and find dynamic vegetation has a greater influence on soil moisture in root zones where soil and vegetation most actively interact.

Are Storm Characteristics the Same When Viewed Using Merged Surface Radars or a Merged Satellite Product

Abstract: High-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane’s lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes.

Evaluation of IMERG Satellite Precipitation over the Land–Coast–Ocean Continuum. Part I: Detection

Abstract: As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land-coast-ocean continuum in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B. It is examined over three coastal regions of the U.S., i.e. the West Coast, the Gulf of Mexico, and the East Coast, each of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range of [25-39]%), followed by morphing ([20-34]%), morphing+IR ([17-27]%) and IR ([11-16]%) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10-53]%). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land-coast-ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.

Enhanced Large-Scale Validation of Satellite-Based Land Rainfall Products

Abstract: Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.

An Assessment of Concurrency in Evapotranspiration Trends across Multiple Global Datasets

Abstract: While broad consensus exists that temperatures are increasing, there is uncertainty surrounding the direction of change manifested in actual evapotranspiration (ET) worldwide. This study assessed trends in ET across the land surface using 11 widely used global datasets for a 32-yr study period. To demonstrate the agreement and disagreement of trends, the spatial distribution, concurrence, correlation, and similitude were estimated. The results showed that while the global average trend in ET is −0.072 mm month−1 yr−1, the trends from individual datasets show a wide range of differences in magnitudes and directions. The considerable differences in the trends in each dataset were found to be weakly correlated with each other and highly divergent in their distribution and direction. No single dataset was sufficiently similar to another to offer a fair representation of trends. In a dynamic trend analysis using a 10-yr moving window over the study period, high concurrence in the significant trends throughout the datasets was found to be rare for each time period. In general, the global data concurrence became negative by 1997 but rebounded to positive toward the end of the study period. In terms of spatial tendency, some regions were more prone to change the direction of their significant trends within the study period. This result shows a high inconsistency in the location and direction of significant ET trends, implying that selection of an ET dataset should consider its spatiotemporal uncertainty before use for any water balance study aiming to infer hydrological change over time.

Dynamics of Evapotranspiration and Variations in Different Land-Cover Regions over the Tibetan Plateau during 1961–2014

Abstract: In this study, the spatiotemporal changes and driving factors of evapotranspiration (ET) over the Tibetan Plateau (TP) are assessed from 1961 to 2014, based on a revised generalized nonlinear complementary (nonlinear-CR) model. The average annual ET on the TP was 328 mm. The highest ET value (711 mm) was found in the forest region in the southeastern part of the TP, and the lowest value (151 mm) was found in the desert region in the northwestern part of the TP. In terms of the contribution of different subregions to the total amount of ET for the whole plateau, the meadow and steppe regions contributed the most to the total amount of ET of TP, accounting for 30% and 18.5%, respectively. The interannual ET presented a significant increasing trend with a value of 0.26 mm yr−1 from 1961 to 2014, and a significant positive ET trend was found over 35% of the region, mainly in the southeastern part of the plateau. The increasing trend of ET in swamp areas was the largest, while that in the desert areas was the smallest. In terms of the seasonality, the ET over the plateau and different land-cover regions increased the most in summer, followed by spring, while the change in ET in winter was not obvious. The energy factors dominated the long-term change in the annual ET over the plateau. In addition, the available energy is the controlling factor for ET changes in humid areas such as forests and shrublands. Energy and water factors together dominate the ET changes in arid areas.

Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations

Abstract: The rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) “drydowns,” i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale τ, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider τ calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare τ from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using τ observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based τ estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, τ-calibration parameters were able to improve drydowns for 73% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization.

The sequence of heavy precipitation and flash flooding of 12 and 13 September 2019 in eastern Spain. Part I: Mesoscale diagnostic and sensitivity analysis of precipitation

Abstract: The Mediterranean region is frequently affected by heavy precipitation episodes and subsequent flash flooding. An exemplary case is the heavy precipitation episode that occurred in the regions of València, Murcia, and Almería (eastern Spain) on 12 and 13 September 2019. Observed rainfall amounts were close to 500 mm in 48 h, causing seven fatalities and estimated economical losses above EUR 425 million. This case exemplifies the challenging aspects of convective-scale forecasting in the Mediterranean region, with kilometer-resolution meteorological fields required over long forecast spans. Understanding the key mesoscale factors acting on the triggering, location, and intensity of the convective systems responsible for extreme accumulations is essential to gain insight into these episodes and contribute toward their accurate hydrometeorological forecasting. Mesoscale diagnosis suggests that local and distant orography, together with air–sea fluxes, were instrumental in developing convection and intensifying precipitation rate. Sensitivity experiments confirm the role of orography in organizing the cyclonic flow over the southeast part of the western Mediterranean, and also acting as a convection-triggering mechanism. Furthermore, results highlight the role of latent heat flux from the Mediterranean Sea in enhancing convective instability at lower levels and moistening the environment. These moist feeding flows substantially contribute to increasing precipitation rates. Such high sensitivity to environmental moisture distribution naturally propagates to the sea surface temperature, which, by means of sensible and latent heat flux exchanges, dominated the evolution of convective activity for the 12–13 September 2019 episode.

Vegetation Restoration Projects Intensify Intraregional Water Recycling Processes in the Agro-Pastoral Ecotone of Northern China

Abstract: From 1998 to the present, the Chinese government has implemented numerous large-scale ecological programs to restore ecosystems and improve environmental protection in the agro-pastoral ecotone of Northern China (APENC). However, it remains unclear how vegetation restoration modulates intraregional moisture cycles and changes regional water balance. To fill this gap, we first investigated the variation in precipitation (P) from the China Meteorological Forcing Dataset and evapotranspiration (ET) estimated using the Priestly-Taylor Jet Propulsion Laboratory model under two scenarios: dynamic vegetation (DV) and no dynamic vegetation (no-DV). We then used the dynamic recycling model to analyze the changes in precipitation recycling ratio (PRR). Finally, we examined how vegetation restoration modulates intraregional moisture recycling to change the regional water cycle in APENC. Results indicate P increased at an average rate of 4.42 mm yr-2 from 1995 to 2015. ET with DV exhibited a significant increase at a rate of 1.57, 3.58, 1.53, and 1.84 mm yr-2 in the four subregions, respectively, compared with no-DV, and the annual mean PRR values were 10.15%, 9.30%, 11.01%, and 12.76% in the four subregions, and significant increasing trends were found in the APENC during 1995-2015. Further analysis of regional moisture recycling shows that vegetation restoration does not increase local P directly, but has an indirect effect by enhancing moisture recycling process to produce more P by increasing PRR. Our findings show that large-scale ecological restoration programs have a positive effect on local moisture cycle and precipitation.

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

Abstract: This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Quantitative Analysis of the Performance of Spatial Interpolation Methods for Rainfall Estimation Using Commercial Microwave Links

Abstract: Using signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semisynthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the German Weather Service serves as ground truth for generating semisynthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225 × 245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods—inverse distance weighting and ordinary kriging—in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48-h rain event. The results reconfirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.

Intense Precipitation Events during the Monsoon Season in Bangladesh as Captured by Satellite-Based Products

Abstract: Extreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain with knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN–Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC morphing technique (CMORPH). Five indices of intense rainfall were considered for the period 2000–19 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country.While randomerrors are higher over the wetter and higher-elevation northeastern and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR, and IMERG perform similar for capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERGandCMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20mmday; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential uses and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.

The Numerical Implementation of Land Models: Problem Formulation and Laugh Tests

Abstract: The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are two-fold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral sub-surface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as “laugh tests” for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives model (SUMMA) implemented using the SUite of Nonlinear and DIfferential/Algebraic equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against (1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature; (2) analytical solutions; and (3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models.

Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration

Abstract: The NOAA National Water Model (NWM), maintained and executed by the NOAA National Weather Service (NWS) Office of Water Prediction, provides operational hydrological guidance throughout the Contiguous United States. Based on the WRF-Hydro model architecture developed by the National Center for Atmospheric Research (NCAR), the NWM was recently modified for semi-arid domains, by permitting it to explicitly resolve infiltration from ephemeral channels into the underlying channel bed as an added model sink term. To analyze the added value of channel infiltration in semi-arid environments, we calibrated NWM v2.1 (with the channel infiltration function) to 56 independent basins in the western CONUS, following identical calibration methods as the pre-operational NWM v2.1 (not including channel infiltration). Calibration of the model consists of two parts, including 1) calibration of channel infiltration only with other parameters set to the calibrated parameters used for pre-operational NWM v2.1 and 2) calibration of all parameters including channel infiltration with settings otherwise equivalent to the calibration of NWM v2.1. The calibrated channel-infiltration enhanced NWM improves predictive skill compared to the control NWM in 85% of evaluated basins, for the calibration period. The current NWM settings for physical processes and the biases of the calibration scheme limit model performance in semi-arid environments. To explore whether channel infiltration paired with an alternative calibration scheme could address these limitations, NWM v2.1 was calibrated with a new objective function in selected basins. We found that this updated objective function could ameliorate model biases in some semi-arid environments.

Influence of Organic Matter on Soil Hydrothermal Processes in the Tibetan Plateau: Observation and Parameterization

Abstract: In the central-eastern Tibetan Plateau (TP) there is abundant organic matter in topsoils, which plays a crucial role in determining soil hydraulic properties that need to be properly described in land surface models. Limited soil parameterizations consider the impacts of soil organic matter (SOM), but they still show poor performance in the TP. A dedicated field campaign is therefore conducted by taking undisturbed soil samples in the central TP to obtain in-situ soil hydraulic parameters and to advance SOM parameterizations. The observed findings are twofold. 1) The SOM pore-size distribution parameter, derived from measured soil water retention curves, has been demonstrated to be much underestimated in previous studies. 2) SOM saturated hydraulic conductivity is overestimated. Accordingly, a new soil hydraulic parameterization is established by modifying a commonly used one based on observations, which is then evaluated by incorporating it into Noah-MP. Compared with the original ones, the new parameterization significantly improves surface soil liquid water simulations at stations with high surface SOM content, especially in the warm season. A further application with the revised Noah-MP indicates that SOM can enhance sensible heat flux but decrease evaporation and subsurface soil temperature in the warm season, and tends to have a much weak effect in the cold season. This study provides insights into the role of SOM in modulating soil state and surface energy budget. Note that, however, there are many other factors at play and the new parameterization is not necessarily applicable beyond the TP.