Abstract. Droughts continue to affect ecosystems, communities and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rainfed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rainfed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a socioecological-system (SES) perspective, using socioecological susceptibility and lack of coping-capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rainfed and irrigated agricultural systems displays a heterogeneous pattern at the global level, with higher risk for southeastern Europe as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.

The regular drought episodes in South Africa highlight the need to reduce drought risk by both policy and local community actions. Environmental and socioeconomic factors in South Africa's agricultural system have been affected by drought in the past, creating cascading pressures on the nation's agro-economic and water supply systems. Therefore, understanding the key drivers of all risk components through a comprehensive risk assessment must be undertaken in order to inform proactive drought risk management. This paper presents, for the first time, a national drought risk assessment for irrigated and rainfed systems, that takes into account the complex interaction between different risk components. We use modeling and remote sensing approaches and involve national experts in selecting vulnerability indicators and providing information on human and natural drivers. Our results show that all municipalities have been affected by drought in the last 30 years. The years 1981The years -1982The years , 1992The years , 2016The years and 2018 were marked as the driest years during the study period compared to the reference period . In general, the irrigated systems are remarkably less often affected by drought than rainfed systems; however, most farmers on irrigated land are smallholders for whom drought impacts can be significant. The drought risk of rainfed agricultural systems is exceptionally high in the north,

Abstract. Identifying and quantifying drought in retrospective is a necessity for better understanding drought conditions and the propagation of drought through the hydrological cycle and eventually for developing forecast systems. Hydrological droughts refer to water deficits in surface and subsurface storage, and since these are difficult to monitor at larger scales, several studies have suggested exploiting total water storage data from the GRACE (Gravity Recovery and Climate Experiment) satellite gravity mission to analyze them. This has led to the development of GRACE-based drought indicators. However, it is unclear how the ubiquitous presence of climate-related or anthropogenic water storage trends found within GRACE analyses masks drought signals. Thus, this study aims to better understand how drought signals propagate through GRACE drought indicators in the presence of linear trends, constant accelerations, and GRACE-specific spatial noise. Synthetic data are constructed and existing indicators are modified to possibly improve drought detection. Our results indicate that while the choice of the indicator should be application-dependent, large differences in robustness can be observed. We found a modified, temporally accumulated version of the Zhao et al. (2017) indicator particularly robust under realistic simulations. We show that linear trends and constant accelerations seen in GRACE data tend to mask drought signals in indicators and that different spatial averaging methods required to suppress the spatially correlated GRACE noise affect the outcome. Finally, we identify and analyze two droughts in South Africa using real GRACE data and the modified indicators.

Abstract. Droughts continue to affect ecosystems, communities, and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rain-fed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rain-fed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a social-ecological systems perspective, using social-ecological susceptibility and lack of coping capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rain-fed and irrigated agricultural systems displays heterogeneous pattern at the global level with higher risk for southeastern Europe, as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure, and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.

We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5$$^\circ $$ ∘ , covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.Highlights We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5$$^\circ $$ ∘ , covering the period 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account. Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.