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Anne Felsberg, Gabriëlle J. M. De Lannoy, Manuela Girotto, Jean Poesen, Rolf H. Reichle, and Thomas Stanley

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.

Open access
ZONGJIAN KE, XINGWEN JIANG, JINMING FENG, and ZUNYA WANG

Abstract

In the last two decades, southwestern China (SWC) has experienced severe droughts, which are always accompanied by severe deficiencies in precipitation. In this study, we found that the interannual variability in boreal winter precipitation in SWC is modulated by the Philippine Sea anomalous anticyclone (PSAC). The interannual relationship between the PSAC and SWC precipitation experienced an interdecadal change around the early 1980s. The correlation between them was enhanced in the period from 1981 to 2001 (P2) compared to the period from 1961 to 1980 (P1). In P1, the moisture transported by the PSAC mainly affected eastern China, as the PSAC was located over the northern Philippine Sea, and the moisture budget of SWC was dominated by moisture transport at the western boundary. The PSAC, however, strengthened and shifted southwestward in P2, accompanied by a deepened India-Burma trough. As such, the PSAC transported moist air from the western North Pacific and the Indian Ocean into SWC through its southern boundary. Meanwhile, the stronger PSAC in P2 was accompanied by an upper-level convergence from the western North Pacific to the Bay of Bengal, which induced an upper-level divergence and ascending motion over SWC. Thus, the PSAC caused a significant increase in precipitation in P2. Stronger air-sea interactions in the western North Pacific induced by the El Niño–Southern Oscillation may be responsible for the enhancement and southwestward shift of the PSAC in P2 compared to that in P1.

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Maheshwari Neelam, Rajat Bindlish, Peggy O’Neill, George J. Huffman, Rolf Reichle, Steven Chan, and Andreas Colliander

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).

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Catherine E. Finkenbiner, Stephen P. Good, Scott T. Allen, Richard P. Fiorella, and Gabriel J. Bowen

Abstract

Sampling intervals of precipitation geochemistry measurements are often coarser than those required by fine-scale hydrometeorological models. This study presents a statistical method to temporally downscale geochemical tracer signals in precipitation so that they can be used in high-resolution, tracer-enabled applications. In this method, we separated the deterministic component of the time series and the remaining daily stochastic component, which was approximated by a conditional multivariate Gaussian distribution. Specifically, statistics of the stochastic component could be explained from coarser data using a newly identified power-law decay function, which relates data aggregation intervals to changes in tracer concentration variance and correlations with precipitation amounts. These statistics were used within a copula framework to generate synthetic tracer values from the deterministic and stochastic time series components based on daily precipitation amounts. The method was evaluated at 27 sites located worldwide using daily precipitation isotope ratios, which were aggregated in time to provide low resolution testing datasets with known daily values. At each site, the downscaling method was applied on weekly, biweekly and monthly aggregated series to yield an ensemble of daily tracer realizations. Daily tracer concentrations downscaled from a biweekly series had average (+/- standard deviation) absolute errors of 1.69‰ (1.61‰) for δ2H and 0.23‰ (0.24‰) for δ18O relative to observations. The results suggest coarsely sampled precipitation tracers can be accurately downscaled to daily values. This method may be extended to other geochemical tracers in order to generate downscaled datasets needed to drive complex, fine-scale models of hydrometeorological processes.

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Abedeh Abdolghafoorian and Paul A. Dirmeyer

Abstract

The interactions between land and atmosphere (with terrestrial and atmospheric coupling segments) play a significant role in weather and climate. A predominant segment of land-atmosphere (L-A) feedbacks is the coupling between soil moisture (SM) and surface heat fluxes, the terrestrial coupling leg. The lack of high-quality long-term globally distributed observations, however, has hindered a robust, realistic identification of the terrestrial leg strength on a global scale. This exploratory study provides insight into how SM signals are translated into surface flux signals through the construction of a global depiction of the terrestrial leg from several recently developed global, gridded, observationally- and satellite-based data sets. The feasibility of producing global gridded estimates of L-A coupling metrics is explored. Five weather and climate models used for subseasonal to seasonal forecasting are confronted with the observational estimates to discern discrepancies that may affect their ability to predict phenomena related to L-A feedbacks, such as drought or heat waves. The terrestrial feedback leg from observations corroborates the “hot spots” of L-A coupling found in modeling studies, but the variances in daily time series of surface fluxes differ markedly. Better agreement and generally higher confidence are seen in metrics using latent heat flux than sensible heat flux. Observational metrics allow for clear stratification of model fidelity that is consistent across seasons, despite observational uncertainty. The results highlight the impact of SM on partitioning available surface energy and illustrate the potential of global observationally-based data sets for the assessment of such relationships in weather and climate models.

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Jessica C. A. Baker, Dayana Castilho de Souza, Paulo Y. Kubota, Wolfgang Buermann, Caio A. S. Coelho, Martin B. Andrews, Manuel Gloor, Luis Garcia-Carreras, Silvio N. Figueroa, and Dominick V. Spracklen

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. Focusing on the satellite-era period of 2003–14, we assess land–atmosphere interactions on annual to seasonal time scales 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 U.K. 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 modeled 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.

Open access
Prabhakar Shrestha

Abstract

A 10-yr simulation of shallow groundwater table (GWT) depth over a temperate region in northwestern Europe, using a physics-based integrated hydrological model at kilometer scale, exhibits a strong seasonal cycle. This is also well captured in terms of near-surface soil moisture anomalies, terrestrial water storage anomalies, and shallow GWT depth anomalies from observations over the region. The modeled monthly anomaly of GWT depth exhibits a statistically significant (p < 0.05) moderate positive/negative correlation with non-rain- and rain-affected monthly anomalies of incoming solar radiation. The vegetation cover also produces a strong local control on the variability of shallow GWT depth. Thus, much of the variability in the simulated seasonal cycle of shallow GWT depth could be linked to the distribution of clouds and vegetation. The spatiotemporal distribution of clouds, partly influenced by the Rhine Massif, modulates the seasonal variability of incoming solar radiation and precipitation over the region. Particularly, the southwestern and northern part of the Rhine Massif divided by the Rhine Valley exhibits a dipole behavior with relatively high (low) shallow GWT depth fluctuations, associated with positive (negative) anomaly of incoming solar radiation and negative (positive) anomaly of precipitation.

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Shanshui Yuan, Laiyin Zhu, and Steven M. Quiring

Abstract

Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.

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David A MacLeod, Rutger Dankers, Richard Graham, Kiswendsida Guigma, Luke Jenkins, Martin C. Todd, Augustine Kiptum, Mary Kilavi, Andrew Njogu, and Emmah Mwangi

Abstract

Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive 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 2 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.

Open access
Shan Lin, Genxu Wang, Zhaoyong Hu, Kewei Huang, Xiangyang Sun, Juying Sun, Meng Luo, and Xiao Xiao

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.

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