Abstract. The PEACH project (Projet en Electricité Atmosphérique pour la Campagne HyMeX -the Atmospheric Electricity Project of the HyMeX Program) is the atmospheric electricity component of the Hydrology cycle in the Mediterranean Experiment (HyMeX) experiment and is dedicated to the observation of both lightning activity and electrical state of continental and maritime thunderstorms in the area of the Mediterranean Sea. During the HyMeX SOP1 (Special Observation Period) from 5 September to 6 November 2012, four European operational lightning locating systems (ATDnet, EUCLID, LINET, ZEUS) and the HyMeX lightning mapping array network (HyLMA) were used to locate and characterize the lightning activity over the northwestern Mediterranean at flash, storm and regional scales. Additional research instruments like slow antennas, video cameras, microbarometer and microphone arrays were also operated. All these observations in conjunction with operational/research ground-based and airborne radars, rain gauges and in situ microphysical records are aimed at characterizing and understanding electrically active and highly precipitating events over southeastern France that often lead to severe flash floods. Simulations performed with cloud resolving models like Meso-NH and Weather Research and Forecasting are used to interpret the results and to investigate further the links between dynamics, microphysics, electrification and lightning occurrence. Herein we present an overview of the PEACH project and its different instruments. Examples are discussed to illustrate the comprehensive and Published by Copernicus Publications on behalf of the European Geosciences Union. E. Defer et al.: Atmospheric electricity observations during HyMeX SOP1unique lightning data set, from radio frequency to acoustics, collected during the SOP1 for lightning phenomenology understanding, instrumentation validation, storm characterization and modeling.

Abstract. The PEACH (Projet en Electricité Atmosphérique pour la Campagne HyMeX – the Atmospheric Electricity Project of HyMeX Program) project is the Atmospheric Electricity component of the HyMeX (Hydrology cycle in the Mediterranean Experiment) experiment and is dedicated to the observation of both lightning activity and electrical state of continental and maritime thunderstorms in the area of the Mediterranean Sea. During the HyMeX SOP1 (Special Observation Period; 5 September–6 November 2012), four European Operational Lightning Locating Systems (OLLSs) (ATDNET, EUCLID, LINET, ZEUS) and the HyMeX Lightning Mapping Array network (HyLMA) were used to locate and characterize the lightning activity over the Southeastern Mediterranean at flash, storm and regional scales. Additional research instruments like slow antennas, video cameras, micro-barometer and microphone arrays were also operated. All these observations in conjunction with operational/research ground-based and airborne radars, rain gauges and in situ microphysical records aimed at characterizing and understanding electrically active and highly precipitating events over Southeastern France that often lead to severe flash floods. Simulations performed with Cloud Resolving Models like Meso-NH and WRF are used to interpret the results and to investigate further the links between dynamics, microphysics, electrification and lightning occurrence. A description of the different instruments deployed during the field campaign as well as the available datasets is given first. Examples of concurrent observations from radio frequency to acoustic for regular and atypical lightning flashes are then presented showing a rather comprehensive description of lightning flashes available from the SOP1 records. Then examples of storms recorded during HyMeX SOP1 over Southeastern France are briefly described to highlight the unique and rich dataset collected. Finally the next steps of the work required for the delivery of reliable lightning-derived products to the HyMeX community are discussed.

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world’s growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires good insight in land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1, to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcseconds (~1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database, version 1.2 (HWSDv1.2), and generic crop information. Daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM) and 0.52 (CGLS-SSM), respectively. The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the interannual variability, with a mean anomaly correlation of 0.46 for daily biomass, and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model proves to be useful in assessing crop production and soil moisture at various scales and could serve as a bridge between point-based and global models.