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  • 1
    Publication Date: 2021-10-14
    Description: This study provides a comprehensive evaluation of a great variety of state-of-the-art precipitation datasets against gauge observations over the Karun basin in southwestern Iran. In particular, we consider (a) gauge-interpolated datasets (GPCCv8, CRU TS4.01, PREC/L, and CPC-Unified), (b) multi-source products (PERSIANN-CDR, CHIRPS2.0, MSWEP V2, HydroGFD2.0, and SM2RAIN-CCI), and (c) reanalyses (ERA-Interim, ERA5, CFSR, and JRA-55). The spatiotemporal performance of each product is evaluated against monthly precipitation observations from 155 gauges distributed across the basin during the period 2000–2015. This way, we find that overall the GPCCv8 dataset agrees best with the measurements. Most datasets show significant underestimations, which are largest for the interpolated datasets. These underestimations are usually smallest at low altitudes and increase towards more mountainous areas, although there is large spread across the products. Interestingly, no overall performance difference can be found between precipitation datasets for which gauge observations from Karun basin were used, versus products that were derived without these measurements, except in the case of GPCCv8. In general, our findings highlight remarkable differences between state-of-the-art precipitation products over regions with comparatively sparse gauge density, such as Iran. Revealing the best-performing datasets and their remaining weaknesses, we provide guidance for monitoring and modelling applications which rely on high-quality precipitation input.
    Keywords: 551.6 ; evaluation ; interpolated dataset ; Karun basin ; precipitation datasets ; reanalysis dataset ; satellite rainfall estimate
    Language: English
    Type: map
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  • 2
    Publication Date: 2021-09-29
    Description: The productivity of terrestrial vegetation is determined by multiple land surface and atmospheric drivers. Water availability is critical for vegetation productivity, but the role of vertical variability of soil moisture (SM) is largely unknown. Here, we analyze dominant controls of global vegetation productivity represented by sun‐induced fluorescence and spectral vegetation indices at the half‐monthly time scale. We apply random forests to predict vegetation productivity from several hydrometeorological variables including multi‐layer SM and quantify the variable importance. Dominant hydrometeorological controls generally vary with latitudes: temperature in higher latitudes, solar radiation in lower latitudes, and root‐zone SM in between. We find that including vertically resolved SM allows a better understanding of vegetation productivity and reveals extended water‐related control. The deep(er) SM control for semi‐arid grasses and shrubs illustrates the potential of deep(er) rooting systems to adapt to water limitation. This study highlights the potential to infer sub‐surface processes from remote sensing observations.
    Description: Key Points: Vertically resolved soil moisture (SM) improves the understanding of large‐scale vegetation productivity and yields extended water‐related controls. Data‐driven evidence for the meaningfulness of the long‐standing modeling paradigm of vertical soil layer discretization. Comparatively deep SM is most relevant in semi‐arid areas and for grasses and shrubs.
    Description: German Research Foundation
    Description: ESA Living Planet Fellowship
    Keywords: 577.22 ; vegetation productivity ; hydrometeorological controls
    Type: map
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  • 3
    Publication Date: 2020-07-03
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Springer Nature
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  • 4
    Publication Date: 2017-03-06
    Description: Both sea surface temperatures (SSTs) and soil moisture (SM) can influence climate over land. This paper presents a comprehensive comparison of SM versus SST impacts on land climate in the warm season. The authors perform fully coupled ensemble experiments with the Community Earth System Model in which they prescribe SM or SSTs to the long-term median seasonal cycles. It is found that SM variability overall impacts warm-season land climate to a similar extent as SST variability, in the midlatitudes, tropics, and subtropics. Removing SM or SST variability impacts land climate means and reduces land climate variability at different time scales by 10%–50% (temperature) and 0%–10% (precipitation). Both SM- and SST-induced changes are strongest for hot temperatures (up to 50%) and for extreme precipitation (up to 20%). These results are qualitatively similar for the present day and the end of the twenty-first century. Removed SM variability affects surface climate through corresponding variations in surface energy fluxes, and this is controlled to first order by the land–atmosphere coupling strength and the natural SM variability. SST-related changes are partly controlled by the relation of local temperature or precipitation with the El Niño–Southern Oscillation. In addition, in specific regions SST-induced SM changes alter the “direct” SST-induced climate changes; on the other hand, SM variability is found to slightly affect SSTs in some regions. Nevertheless a large level of independence is found between SM–climate and SST–climate coupling. This highlights the fact that SM conditions can influence land climate variables independently of any SST effects and that (initial) soil moisture anomalies can provide valuable information in (sub)seasonal weather forecasts.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 5
  • 6
    Publication Date: 2016-04-01
    Description: The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, the authors construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 7
    Publication Date: 2020-05-15
    Description: Soil moisture droughts have comprehensive implications for terrestrial ecosystems. Here we study time-accumulated impacts of the strongest observed droughts on vegetation. The results show that drought duration, the time during which surface soil moisture is below seasonal average, is a key diagnostic variable for predicting drought-integrated changes in (i) gross primary productivity, (ii) evapotranspiration, (iii) vegetation greenness, and (iv) crop yields. Drought-integrated anomalies in these vegetation-related variables scale linearly with drought duration with a slope depending on climate. In arid regions, the slope is steep such that vegetation drought response intensifies with drought duration, whereas in humid regions, it is small such that drought impacts on vegetation are weak even for long droughts. These emergent large-scale linearities are not well captured by state-of-the-art hydrological, land surface, and vegetation models. Overall, the linear relationship of drought duration versus vegetation response and crop yield reductions can serve as a model benchmark and support drought impact interpretation and prediction.
    Print ISSN: 1726-4170
    Electronic ISSN: 1726-4189
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
    Publication Date: 2020-07-23
    Description: Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over poorly gauged regions. This study examines the usefulness of current state-of-the-art precipitation data sets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation data sets in 〉200 European catchments to obtain runoff and evapotranspiration. We consider a wide range of precipitation products, which are generated via (1) the interpolation of gauge measurements (E-OBS and Global Precipitation Climatology Centre (GPCC) V.2018), (2)  data assimilation into reanalysis models (ERA-Interim, ERA5, and Climate Forecast System Reanalysis – CFSR), and (3) a combination of multiple sources (Multi-Source Weighted-Ensemble Precipitation; MSWEP V2). Evaluation is done at the daily and monthly timescales during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs; in contrast, simulated evapotranspiration is generally much less influenced in our comparatively wet study region. We also find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation data sets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the particularly strong agreement, while ERA5, GPCC V.2018, and MSWEP V2 show good performances. We further reveal climate-dependent performance variations of the considered data sets, which can be used to guide their future development. The overall best agreement is achieved when using an ensemble mean generated from all the individual products. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions, such as central Europe, there are increasing implications for evapotranspiration in drier regions.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 9
    Publication Date: 2020-08-25
    Description: Future climate projections require Earth system models to simulate conditions outside their calibration range. It is therefore crucial to understand the applicability of such models and their modules under transient conditions. This study assesses the robustness of different types of models in terms of rainfall–runoff modeling under changing conditions. In particular, two process-based models and one data-driven model are considered: 1) the physically based land surface model of the European Centre for Medium-Range Weather Forecasts, 2) the conceptual Simple Water Balance Model, and 3) the Long Short-Term Memory-Based Runoff model. Using streamflow data from 161 catchments across Europe, a differential split-sample test is performed, i.e., models are calibrated within a reference period (e.g., wet years) and then evaluated during a climatically contrasting period (e.g., drier years). Models show overall performance loss, which generally increases the more conditions deviate from the reference climate. Further analysis reveals that the models have difficulties in capturing temporal shifts in the hydroclimate of the catchments, e.g., between energy- and water-limited conditions. Overall, relatively high robustness is demonstrated by the physically based model. This suggests that improvements of physics-based parameterizations can be a promising avenue toward reliable climate change simulations. Further, our study illustrates that comparison across process-based and data-driven models is challenging due to their different nature. While we find rather low robustness of the data-driven model in our particular split-sample setup, this must not apply generally; by contrast, such model schemes have great potential as they can learn diverse conditions from observed spatial and temporal variability both at the same time to yield robust performance.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 10
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