Search

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 rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1 ∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (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 as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. 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 five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at 10.26050/WDCC/SaWaM_D01_SEAS5_BCSD Domain D01, Karun Basin (Iran), , 10.26050/WDCC/SaWaM_D02_SEAS5_BCSD Domain D02: São Francisco Basin (Brazil), , 10.26050/WDCC/SaWaM_D03_SEAS5_BCSD Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), , and 10.26050/WDCC/SaWaM_D04_SEAS5_BCSD Domain D04: Catamayo–Chira Basin (Ecuador, Peru),. 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]

While weather forecasts are used for the warning of flood events and climate projections for long-term climate adaptation measures, it is the knowledge of the coming months that is crucial for the management and control of water reservoirs, e.g. for power generation or for irrigation. This is particularly relevant in regions characterized by dry seasons, i.e. semi-arid areas. In semi-arid regions, unlike in arid regions, much can be achieved with sustainable and science-based water resources management. Our study addresses the regionalization of globally available seasonal forecasts to support local water resources management and decision-makers in semi-arid regions. We investigate the performance of the global seasonal predictions of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) with forecast horizons up to 7 months in advance for selected semi-arid target regions. These include the catchments of the Rio Sao Francisco in northeastern Brazil, the Rio Chira in Ecuador / Peru and the Karun in Iran. The uncertainties in the global seasonal forecasting system are covered by ensemble predictions. As a measure of the quality of the seasonal ensemble predictions of precipitation and temperature, we analyze accuracy (mean absolute error skill score), overall performance (continuous ranked probability skill score), sharpness (interquantile range skill score) and reliability. The ensemble forecasts are bias corrected with a quantile-quantile transformation and compared with global gridded satellite data (GPM) and station observation data. Results generally show higher performance measures for the retrospective precipitation forecasts of the Rio Sao Francisco area than for the Rio Chira or the Karun area. For the Rio Sao Francisco area, the seasonal predictions of the rainfall over the four main months of the rainy season are even more efficient than statistical reference forecasts up to two months ahead.One reason for the limited performance of the global seasonal forecasts in the catchment areas of the Rio Chira and the Karun can be the complex topography and the inadequate horizontal resolution of 35 km of the global forecasting system for regional applications. We therefore use the Weather Research and Forecasting (WRF) model to dynamically downscale the global forecasts. Our dynamical downscaling is progressively nesting from 35 km to 9 km and finally to 3 km. Finally, these refined seasonal predictions will not only serve for hydrometeorological drought forecasts in the catchment areas, but also serve as driving data in further subsequent impact modelling for hydrological-, sediment- and ecosystem modeling within the international framework of this study. [ABSTRACT FROM AUTHOR]

It is expected that global climate change will increase the probability of extreme hydrometeorological events like droughts and floods. In order to adapt to this changing climate and to maintain basic water-, power-, and food supply, especially water-scarse regions require reliable knowledge about the available freshwater resources and potential extreme events during the next season. However, the headwaters of semi-arid catchments are often characterized by complex terrain and strong topographic gradients. To predict seasonal extreme events in such regions, hydrological models require high-resolution forecasts of precipitation and temperature. A 35 km horizontal resolution of the latest global seasonal forecast products is not suited for regional hydrological applications in such catchments. Therefore, it is mandatory to improve the spatial resolution of the hydrometeorological information to better represent the regional conditions in river basins with complex terrain. In particular, if such information is used as driving data in a one-way coupled model chain with subsequent hydrological models, a high-resolution and high-quality meteorological input is a key factor.In this study, we demonstrate the concept of achieving this improved and regionalized meteorological input. For eight river basins in semi-arid regions like Iran, Brazil, Sudan, and West-Africa, we perform a dynamical downscaling of the global atmospheric fields with the use of the Weather Research and Forecasting (WRF) model. The best possible regionalization of the global fields is identified by testing up to 32 parameterization combinations of the WRF model for different representations of subgrid-scale convection, cloud microphysics, radiation physics and boundary layer physics for each individual region. For these configuration runs we use ERA-Interim re-analyses which also serve as the initialization for the latest seasonal predictions of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) re-forecasts. Five consecutive years for each of the 32 parameterization combinations are simulated and their performance is analyzed with the use of local station data for identifying an optimized model setup for each region. Preliminary results already indicate that this identified configuration does not only depend on the performance of the modeled meteorological information, but also on the required computation time that strongly differs between the individual configurations. With this optimized WRF setup for each region, we finally downscale the global SEAS5 forecasts and thereby provide the regionalized input for the subsequent hydrological model systems. Our study is the first step towards an one-way coupled model chain that finally allows to provide high-resolution hydrometeorological and hydrological forecasts for the next season. In the end, this derived information has the potential to significantly improve the regional water management, especially in semi-arid regions and in the context of extreme events like e.g. droughts. [ABSTRACT FROM AUTHOR]