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Tropospheric water vapor is among the most important trace gases of the Earth's climate system and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the non-linear relation of air temperature and saturated vapor pressure, it is highly variable which hampers the development of high resolution and three-dimensional maps of regional extent. As a complement to the sparsely distributed radio sounding observation network, GNSS meteorology and interferometric radar satellite remote sensing can assist with their complementary high temporal or spatial resolution. In addition, data fusion with collocation and tomography methods enables the construction of detailed maps in either two or three dimensions. By assimilation of these observation based datasets into regional dynamic atmospheric models the optimal state of the tropospheric water vapor conditions can be guessed. In the following, a collection of basic and processed datasets, obtained with the above listed methods, is presented that describes the state and course of atmospheric water vapor within the range of the GNSS Upper Rhine Graben Network (GURN) region. [ABSTRACT FROM AUTHOR]

Seasonal forecasts have the potential to substantially improve water management particularly in water scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface re-run of the latest ECMWF reanalysis ERA5. By that, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1°. This is exemplary performed over 4 predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco (Brazil), the Tekeze-Atbara and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo-Chira (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts. Furthermore, the lead-dependent drift effects are remarkably reduced in the BCSD-forecasts. However, our analysis also showed that computing monthly averages from daily bias-corrected forecasts can lead to statistical inconsistencies particularly during periods and seasons with strong temporal climate gradients or heteroscedasticity. During such periods, particularly the lowest- and highest-lead forecasts can show remaining biases. Our dataset covers the whole (re-)forecast period from 1981 to 2019, for which we provide bias-corrected and spatially disaggregated daily ensemble forecasts for precipitation, average, minimum and maximum temperature as well as for shortwave radiation from the initial date to the coming 214 days. This sums up to more than 100,000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the 5 analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM%5FD01%5FSEAS5%5FBCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM%5FD02%5FSEAS5%5FBCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM%5FD03%5FSEAS5%5FBCSD (Domain D03: Tekeze-Atbara and Blue Nile Basins (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM%5FD04%5FSEAS5%5FBCSD (Domain D04: Catamayo-Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test-bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while well established sensors typically suffer from a small spatial support. With a sensor footprint up to several hectares, Cosmic-Ray Neutron Sensing (CRNS) is an emerging technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km²: the pre-alpine Rott headwater catchment in Southern Germany which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, that network was designed to study root zone soil moisture dynamics at the catchment-scale. The observations of the dense CRNS network were complemented by extensive measurements that allow to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from Unmanned Areal Systems (UAS), permanent and temporary wireless sensor networks, profile probes as well as comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive dataset to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatio-temporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land - atmosphere exchange processes. The data set is available through EUDAT, splitted into the two subsets https://doi.org/10.23728/b2share.85fe0f9dac0f48df9215c17e65d1f1e1 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.93ed99e486904d48a8a6a68083066198 (Fersch et al., 2020b). [ABSTRACT FROM AUTHOR]

Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007-2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar%5FVCS%5Fv1. [ABSTRACT FROM AUTHOR]

Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region are provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations as well as detailed information on the experiment design, and provides information on the format and dissemination of the available data. All data is made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512). Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research & Forecasting Model (WRF). The simulations cover the validation period 1980-2010 and the two future periods 2020-2050 and 2070-2100. A brief comparison to observations and two climate change scenarios from the CORDEX initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 scenario, the results suggest an increase in temperature by 1.5 °C at the Coast of Guinea and by up to 3 °C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the Coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the North, and almost no change in precipitation in the Sahel. This stands in contrast to existing regional climate projections, which predict increasingly drier conditions. The high spatial and temporal resolution of the data, the extensive list of output variables, the large computational domain and the long time periods covered make this data set a unique resource for follow-up analyses and impact modelling studies over the greater West African region. The comprehensive documentation and standardisation of the data facilitate and encourage its use within and outside of the WASCAL community. [ABSTRACT FROM AUTHOR]