Abstract. One of the major obstacles for designing solutions against the imminent climate crisis is the scarcity of high spatio-temporal resolution model projections for variables such as precipitation. This kind of information is crucial for impact studies in fields like hydrology, agronomy, ecology, and risk management. The currently highest spatial resolution datasets on a daily scale for projected conditions fail to represent complex local variability. We used deep-learning-based statistical downscaling methods to obtain daily 1 km resolution gridded data for precipitation in the Eastern Ore Mountains in Saxony, Germany. We built upon the well-established climate4R framework, while adding modifications to its base-code, and introducing skip connections-based deep learning architectures, such as U-Net and U-Net++. We also aimed to address the known general reproducibility issues by creating a containerized environment with multi-GPU (graphic processing unit) and TensorFlow's deterministic operations support. The perfect prognosis approach was applied using the ERA5 reanalysis and the ReKIS (Regional Climate Information System for Saxony, Saxony-Anhalt, and Thuringia) dataset. The results were validated with the robust VALUE framework. The introduced architectures show a clear performance improvement when compared to previous statistical downscaling benchmarks. The best performing architecture had a small increase in total number of parameters, in contrast with the benchmark, and a training time of less than 6 min with one NVIDIA A-100 GPU. Characteristics of the deep learning models configurations that promote their suitability for this specific task were identified, tested, and argued. Full model repeatability was achieved employing the same physical GPU, which is key to build trust in deep learning applications. The EURO-CORDEX dataset is meant to be coupled with the trained models to generate a high-resolution ensemble, which can serve as input to multi-purpose impact models.

Despite intense research on climate change (CC), regional studies for Central America, which is considered a CC hot spot, remain scarce. The information provided by general circulation models (GCMs) is too coarse to accurately reproduce local-scale climatic features, which are needed for impact assessment. Thus, downscaling techniques are employed to address this scale mismatch. Costa Rica is the present case study, for which suitable predictors were tailored for downscaling related to regional climatic characteristics, such as the Inter-Tropical Convergence Zone, El Niño Southern Oscillation, the Caribbean Low-Level Jet, and the MidSummer Drought. Statistical downscaling models were calibrated for precipitation, maximum and minimum temperature, using the perfect prognosis methodology by means of station data, ERA-INTERIM reanalysis and artificial neural networks, yielding satisfactory results. As found in several studies, the temperature models replicated more accurately the statistics of the observed datasets. However, here, through the implemented approach and the tailored predictors, the precipitation models conveyed an improvement compared to standard methods. Projected daily climate was obtained employing CORDEX data under the RCP8.5 scenario for the central region of the country. Overall, the changes in climate estimated by the end of the 21st century agree with coarser-scale projections. Finally, projected climate extremes indices were calculated and rendered further details on the intensity of future CC by the end of the century.

In a field experiment with white cabbage (Brassica oleracea L. var. capitata (L.) alef.) in Germany, three irrigation scheduling approaches were tested: (i) three sprinkler irrigation schedules based on soil water balance calculations using different development-dependent crop coefficients; (ii) automatic drip irrigation based on soil water tension thresholds; (iii) irrigation scheduling by real-time application of a partially calibrated mechanistic crop growth model. Multi-objective calibration was applied to derive a fully calibrated model as a diagnostic tool to identify the water loss terms of the individual irrigation strategies.The results of the experiment showed that: (i) high yields can be achieved with relatively low amounts of irrigation water (~100 mm), provided the optimal irrigation strategy and technique are applied; (ii) automated tension threshold-based drip irrigation outperformed soil water balance or crop growth model-based strategies; (iii) the soil water balance calculation approach relying on recommended K c factors led to an enormous overirrigation; (iv) the application of a partially calibrated crop growth model led to an underestimation of the crop water requirements in conjunction with an incorrect timing of irrigation events and therefore resulted in the lowest yields. The diagnostic model identified percolation, for the wet sprinkler irrigation treatments, and canopy evaporation, for the dry and model-based treatments, as major water loss sources; only minimal additional water was lost in the tension-controlled treatment. Copyright © 2016 John Wiley & Sons, Ltd. RESUMÉDans une expérience de terrain avec du chou blanc (Brassica oleracea convar capitata var. alba) en Allemagne, trois approches de calendriers d'irrigation ont été testées: (i) trois programmes d'irrigation par aspersion, fondés sur des calculs du bilan hydrique du sol basés sur des coefficients culturaux; (ii) une irrigation au goutte à goutte automatique basée sur des seuils de pression d'eau du sol; (iii) une irrigation par l'application en temps réel d'un modèle mécaniste de croissance des cultures partiellement calibré. Des calibrations multiples objectives ont été conduites pour dériver un modèle entièrement calibré, qui servira d'outil de diagnostic et d'identification des termes de perte d'eau résultant des stratégies individuelles d'irrigation.Les résultats de l'expérience ont montré que: (i) des rendements élevés peuvent être atteints avec des quantités relativement faibles d'eau d'irrigation (~100 mm), à condition que les calendriers optimaux et que les techniques adaptées soient appliquées; (ii) l'irrigation au goutte à goutte à seuil de tension automatique a surperformé les stratégies fondées bilan hydrique du ‹‹sol bas » sur le modèle de croissance des cultures; (iii) les méthodes basées sur le calcul de bilan hydrique du sol à partir des coefficients culturaux K c ont conduit à une énorme surirrigation; (iv) l'application d'un modèle de croissance des cultures partiellement calibrée a conduit à une ...

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As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near‐surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large‐scale predictors to the target site is a reasonable alternative to predictors derived from grid‐boxes. The validation metrics, measures (i.e. bias, root‐mean‐square error, and Pearson's correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near‐surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single‐site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse‐resolution projections. Its performance is higher than a quantile‐mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near‐surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.

In this study, recurrent networks to downscale meteorological fields of the ERA-40 re-analysis dataset with focus on the meso-scale water balance were investigated. Therefore two types of recurrent neural networks were used. The first approach is a coupling between a recurrent neural network and a distributed watershed model and the second a nonlinear autoregressive with exogenous inputs (NARX) network, which directly predicted the component of the water balance. The approaches were deployed for a meso-scale catchment area in the Free State of Saxony, Germany. The results show that the coupled approach did not perform as well as the NARX network. But the meteorological output of the coupled approach already reaches an adequate quality. However the coupled model generates as input for the watershed model insufficient daily precipitation sums and not enough wet days were predicted. Hence the long-term annual cycle of the water balance could not be preserved with acceptable quality in contrary to the NARX approach. The residual storage change term indicates physical restrictions of the plausibility of the neural networks, whereas the physically based correlations among the components of the water balance were preserved more accurately by the coupled approach.