Using regionally downscaled and adjusted outputs of three global climate models (GCMs), meteorological drought analysis was accomplished across Ankara, the capital city of Turkey. To this end, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were projected under (representative concentration pathway) RCP4.5 and RCP8.5 greenhouse gas scenarios. In general, our results show that Ankara experienced six severe and two extreme drought events during the reference period, 1971–2000. However, the projections indicate fewer drought events for the near future period of 2016–2040, with no potential extreme drought events. While the RCP4.5 scenario showed that dry spells will be dominant in the second half of the near future period, the RCP8.5 scenario projected that dry spells will be evenly distributed during the entire near future period.

Climate change, one of the major environmental challenges facing mankind, has caused intermittent droughts in many regions resulting in reduced water resources. This study investigated the impact of climate change on the characteristics (occurrence, duration, and severity) of meteorological drought across Ankara, Turkey. To this end, the observed monthly rainfall series from five meteorology stations scattered across Ankara Province as well as dynamically downscaled outputs of three global climate models that run under RCP 4.5 and RCP 8.5 scenarios was used to attain the well-known SPI series during the reference period of 1986–2018 and the future period of 2018–2050, respectively. Analyzing drought features in two time periods generally indicated the higher probability of occurrence of drought in the future period. The results showed that the duration of mild droughts may increase, and extreme droughts will occur with longer durations and larger severities. Moreover, joint return period analysis through different copula functions revealed that the return period of mild droughts will remain the same in the near future, while it declines by 12% over extreme droughts in the near future.

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.

In this study, commonly used copula functions belonging to Archimedean and Elliptical families are fitted to the univariate cumulative distribution functions (CDF) of the drought characteristics duration ( $\text{LD}$ ), average severity ( $\overline{\mathrm{S}}$ ), and average areal extent ( $\overline{\mathrm{A}}$ ) of droughts obtained using standardized precipitation index (SPI) between 1960 and 2013 over Ankara, Turkey. Probabilistic modeling of drought characteristics with seven different fitted copula functions and their comparisons with independently estimated empirical joint distributions show normal copula links drought characteristics better than other copula functions. On average, droughts occur with an average $\text{LD}$ of 6.9 months, $\overline{\mathrm{S}}$ of 0.94, and $\overline{\mathrm{A}}$ of 73%, while such a drought event happens on average once in every 6.65 years. Results also show a very strong and statistically significant relation between $\overline{\mathrm{S}}$ and $\overline{\mathrm{A}}$ , and drought return periods are more sensitive to the unconditioned drought characteristic, while return periods decrease by adding additional variables to the analysis (i.e., trivariate drought analysis compared to bivariate).