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  • 1
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    Transboundary Rainfall Estimation Using Commercial Microwave Links (2023)
    Details
    Publication Date: 2023-12-05
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation.〈/p〉
    Description: Plain Language Summary: Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉German and Czech data sets of CMLs differ significantly with respect to network characteristics〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Quality control is important for heterogeneous data of CMLs〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: German Research Foundation
    Description: Czech Science Foundation
    Description: https://doi.org/10.5281/zenodo.4810169
    Description: https://doi.org/10.5281/zenodo.7973736
    Description: https://opendata.dwd.de/climate_environment/CDC
    Keywords: ddc:551.6 ; transboundary rainfall maps ; commercial microwave links ; quantitative precipitation estimation ; data quality control
    Language: English
    Type: doc-type:article
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  • 2
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    Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach (2022)
    Details
    Publication Date: 2024-02-28
    Description: Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random‐Mixing‐Whittaker‐Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure‐, Amplitude‐, and Location‐error. Precipitation estimates obtained are in good agreement with the gauge‐adjusted weather radar product RADOLAN‐RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error.
    Description: Plain Language Summary: Rainfall is commonly measured by dedicated sensors such as rain gauges or weather radars. Commercial microwave links (CMLs), which have the primary purpose of signal forwarding within cellular networks, can be used for rainfall measurements too. The signal, which is transmitted from one antenna to another, is being attenuated if it rains along the path. From the amount of attenuation an average rain rate can be retrieved. For many hydrological applications, it is of major interest to estimate area‐wide rainfall (i.e., rainfall maps) while observations provide only scattered information. In this study, we used the local information from almost 1,000 rain gauges and the information along the paths of 3,900 CMLs distributed over Germany to reconstruct rainfall maps. We did this by applying a method of stochastic simulation (called Random Mixing) which we compared to a more common method of estimation (Ordinary Kriging). To evaluate the quality of the obtained maps, we compared them to rainfall information from weather radars. We found that the general agreement is high, and that maps reconstructed by Random Mixing have particular advantages in representing the spatial structure, that is, the shape of rainfall cells.
    Description: Key Points: Geostatistical Random Mixing simulation now capable of countrywide spatial rainfall interpolation. Variability assessment via commercial microwave link path consideration and ensemble estimation. Realistic rainfall pattern representation quantified by ensemble Structure‐, Amplitude‐, and Location‐error metrics.
    Description: German Research Foundation
    Description: Federal Ministry of Education and Research
    Description: https://doi.org/10.5281/zenodo.4810169
    Description: https://opendata.dwd.de/climate_environment/CDC
    Description: https://maps.dwd.de/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.SRSDescriptionPage?10 26code=EPSG:1000001
    Description: https://doi.org/10.5281/zenodo.5380342
    Description: https://doi.org/10.5281/zenodo.7048941
    Description: https://doi.org/10.5281/zenodo.7049826
    Description: https://doi.org/10.5281/zenodo.7049846
    Keywords: ddc:551.5 ; precipitation estimation ; geostatistical simulation ; spatial pattern analysis ; commercial microwave links ; rain gauges ; random mixing
    Language: English
    Type: doc-type:article
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  • 3
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    Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain (2023)
    John Wiley & Sons, Ltd. | Chichester, UK
    Details
    Publication Date: 2024-01-24
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.〈/p〉
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview
    Description: https://cdc.dwd.de/portal/
    Keywords: ddc:551.5 ; bias correction ; circulation patterns ; ERA5 ; extreme events ; heavy precipitation ; simulated annealing ; statistical downscaling
    Language: English
    Type: doc-type:article
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  • 4
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    Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes (2023)
    Dong, Ningpeng ; Yang, Mingxiang ; Wei, Jianhui ; [et al.]
    Details
    Publication Date: 2024-03-25
    Description: Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in‐situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model‐simulated reservoir inflow with conceptual reservoir operation schemes within a land surface‐hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography.
    Description: Key Points: Satellite remote sensing can improve the representation of ungauged reservoirs and streamflow simulations in hydrologic models. A reservoir operation scheme for ungauged reservoirs is extended and tailored to the use of remotely sensed reservoir operation data. Reservoir operation schemes with storage‐based model structures can be more reliable in reservoir simulations under a changing flow regime.
    Description: National Key Research and Development Program of China http://dx.doi.org/10.13039/501100012166
    Description: Belt and Road Special Foundation of the State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering
    Description: German Research Foundation
    Description: German Federal Ministry of Science of Education
    Description: https://doi.org/10.5281/zenodo.7190469
    Description: https://global-surface-water.appspot.com/download
    Description: https://doi.org/10.18738/T8/DF80WG
    Description: https://aviso-data-center.cnes.fr/
    Keywords: ddc:551.48 ; reservoir operation schemes ; remote sensing ; satellite altimetry ; SWOT ; hydrologic prediction ; hydrologic simulation
    Language: English
    Type: doc-type:article
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  • 5
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    Bias correction of daily precipitation for ungauged locations using geostatistical approaches: A case study for the CORDEX‐Africa ensemble (2022)
    John Wiley & Sons, Ltd. | Chichester, UK
    Details
    Publication Date: 2023-01-14
    Description: Climate model simulations typically exhibit a bias, which can be corrected using statistical approaches. In this study, a geostatistical approach for bias correction of daily precipitation at ungauged locations is presented. The method utilizes a double quantile mapping with dry day correction for future periods. The transfer function of the bias correction for the ungauged locations is established using distribution functions estimated by ordinary kriging with anisotropic variograms. The methodology was applied to the daily precipitation simulations of the entire CORDEX‐Africa ensemble for a study region located in the West African Sudanian Savanna. This ensemble consists of 23 regional climate models (RCM) that were run for three different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The evaluation of the approach for a historical 50‐year period (1950–2005) showed that the method can reduce the inherent strong precipitation bias of RCM simulations, thereby reproducing the main climatological features of the observed data. Moreover, the bias correction technique preserves the climate change signal of the uncorrected RCM simulations. However, the ensemble spread is increased due to an overestimation of the rainfall probability of uncorrected RCM simulations. The application of the bias correction method to the future period (2006–2100) revealed that annual precipitation increases for most models in the near (2020–2049) and far future (2070–2099) with a mean increase of up to 165mm⋅a−1 (18%). An analysis of the monthly and daily time series showed a slightly delayed onset and intensification of the rainy season.
    Description: Adapting water management strategies to future precipitation projected by climate models is associated with high uncertainty in sparsely gauged catchments. Kriging was utilized to estimate distribution parameters for ungauged locations in a West African region to perform a bias correction of the CORDEX‐Africa ensemble. The application of the bias correction method revealed higher annual precipitation amounts and an intensifaction of the rainy season but only little change to the onset of the rainy season.
    Description: German Federal Ministry of Education and Research, Bonn (BMBF), West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)
    Keywords: ddc:551.6 ; bias correction ; climate change ; CORDEX‐Africa ; geostatistical approaches ; precipitation ; quantile mapping ; West Africa
    Language: English
    Type: doc-type:article
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