Comparative evaluation of precipitation-temperature based drought indices (DIs): A case study of Moroccan Lower Sebou basin

Due to the lack of studies on drought in the Lower Sebou basin (LSB), the complexity of drought event and the difference in climate conditions. The identification of the most appropriate drought indices (DIs) to assess drought conditions has become a priority. Therefore, assessing the performance of different drought indices was considered in order to identify the universal drought indices that are well adapted to the LSB. Based on data availability, five DIs were used: Standardized Precipitation Index (SPI), Standardized Precipitation and Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), self-calibrated Palmer Drought Severity Index (sc-PDSI) and Streamflow Drought Index (SDI). The DIs were calculated on an annual scale using monthly time series of precipitation, temperature and river flow from 1984-2016. Thornthwaite's method was used to calculate potential evapotranspiration (PET). Pearson's correlation (r) were analyzed. Furthermore, five decision criteria namely robustness, traceability, transparency, sophistication and scalability were used to evaluate the performance of these indices. The results proved the fact that SPI is suitable to detect the drought duration and intensity compared to other indices with high correlation coefficients especially in sub humid regions, knowing that it tends to give more results that are humid in stations with semi-arid climates. SPI, SPEI and RDI follow the same trend during the period studied. However, sc-PDSI appears to be the most sensitive to temperature and precipitation by overestimating the drought conditions. Eventually, the results of the performance evaluation criteria revealed that SPEI classified first (total score = 137) among other meteorological drought indices, followed by SPI, RDI and sc-PDSI.

Application of meteorological element combination-driven SWAT model based on meteorological datasets in alpine basin

Thus far, reanalysis-based meteorological products have drawn little attention to the influence of meteorological elements of products on hydrological modeling. This study aims to evaluate the hydrological application potential of the precipitation, temperature, and solar radiation of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR) in an alpine basin. The precipitation, temperature, and solar radiation of the gauge-observed meteorological dataset (GD), CFSR, and CMADS were cross-combined, and 20 scenarios were constructed to drive the SWAT model. From the comprehensive comparisons of all scenarios, we drew the following conclusions: (1) among the three meteorological elements, precipitation has the greatest impact on the simulation results, and using GD precipitation from sparse stations yielded better performance than CMADS and CFSR; (2) although the SWAT modeling driven by CMADS and CFSR performed poorly, with CMADS underestimation and CFSR overestimation, the temperature and solar radiation of CMADS and CFSR can be an alternative data source for streamflow simulation; (3) models using GD precipitation, CFSR temperature, and CFSR solar radiation as input yielded the best performance in streamflow simulation, suggesting that these data sources can be applied to this data-scarce alpine region.

Spatiotemporal and cross-scale interactions in hydroclimate variability: a case-study in France

Understanding how water resources vary in response to climate at different temporal and spatial scales is crucial to inform long-term management. Climate change impacts and induced trends may indeed be substantially modulated by low-frequency (multi-year) variations, whose strength varies in time and space, with large consequences for risk forecasting systems. In this study, we present a spatial classification of precipitation, temperature, and discharge variability in France, based on a fuzzy clustering and wavelet spectra of 152 near-natural watersheds between 1958 and 2008. We also explore phase–phase and phase–amplitude causal interactions between timescales of each homogeneous region. A total of three significant timescales of variability are found in precipitation, temperature, and discharge, i.e., 1, 2–4, and 5–8 years. The magnitude of these timescales of variability is, however, not constant over the different regions. For instance, southern regions are markedly different from other regions, with much lower (5–8 years) variability and much larger (2–4 years) variability. Several temporal changes in precipitation, temperature, and discharge variability are identified during the 1980s and 1990s. Notably, in the southern regions of France, we note a decrease in annual temperature variability in the mid 1990s. Investigating cross-scale interactions, our study reveals causal and bi-directional relationships between higher- and lower-frequency variability, which may feature interactions within the coupled land–ocean–atmosphere systems. Interestingly, however, even though time frequency patterns (occurrence and timing of timescales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate (precipitation and temperature) to hydrological variability (discharge). Phase–amplitude interactions are indeed absent in discharge variability, although significant phase–amplitude interactions are found in precipitation and temperature. This suggests that watershed characteristics cancel the negative feedback systems found in precipitation and temperature. This study allows for a multi-timescale representation of hydroclimate variability in France and provides unique insight into the complex nonlinear dynamics of this variability and its predictability.

Eliminating the “hook” in Precipitation-Temperature Scaling

Observational studies of extreme daily and subdaily precipitation-temperature sensitivities (apparent scaling) aim to provide evidence and improved understanding of how extreme precipitation will respond to a warming climate. However, interpretation of apparent scaling results is hindered by large variations in derived scaling rates and divergence from theoretical and modelled projections of systematic increases in extreme precipitation intensities (climate scaling). In warmer climatic regions, rainfall intensity has been reported to increase with temperature to a maximum before decreasing, creating a second order discontinuity or “hook” like structure. Here we investigate spatial and temporal discrepancies in apparent scaling results by isolating rainfall events and conditioning event precipitation on duration. We find that previously reported negative apparent scaling at higher temperatures which creates the hook structure, is the result of a decrease in the duration of the precipitation event, and not to the decrease in precipitation rate. We introduce standardized pooling using long records of Australian station data across climate zones, to show average precipitation intensities and 1-h peak precipitation intensities increase with temperature across all event durations and locations investigated. For shorter duration events (< 6-h), average precipitation intensity scaling is in line with the expected Clausius- Clapeyron (CC) relation at ~7 %/°C, and this decreases with increasing duration, down to 2 %/°C at 24-h duration. Consistent with climate scaling derived from model projections, 1-h peak precipitation intensities are found to increase with temperature at elevated rates compared to average precipitation intensities, with super-CC scaling (10 – 14 %/°C) found for short-duration events in tropical climates.

Radiative cooling by clouds affects the precipitation - temperature scaling derived from observations

&lt;p&gt;One direct effect of climate warming on hydrology is the increase in moisture holding capacity of atmosphere at the rate of 7%/&amp;#176;C as suggested by the Clausius Clapeyron equation. Extreme precipitation largely depends on the amount of precipitable water in the atmospheric column and is thus expected to scale with temperature at the same rate. Observations, however, show significant variability in precipitation - temperature scaling rates, with negative scaling dominating in the tropical regions. These scaling relationships assume a one way causality, i.e. temperature is independent of precipitation. However, we show here that temperatures strongly co-vary with precipitation through the effect that clouds have on surface radiation. The presence of clouds associated with precipitation events result in lower solar isolation at the surface, further leading to reduced temperatures. This induces a two-way causality and thus temperature is no longer independent of precipitation. To remove this cooling effect of clouds, we used a surface energy balance model with a thermodynamic constraint to derive clear sky temperatures during precipitation events. We then show using observations from India, that extreme precipitation scaled with clear sky temperatures shows an increase consistent with the CC rate. On contrary, the negative scaling obtained using observed temperatures misrepresent the precipitation response to warming as a result of the co-variation with the cloud radiative effect. Our findings reveal that scaling relationships not only show how precipitation changes with temperature but also how atmospheric conditions associated with precipitation affect temperature. Thus, this covariation needs to be taken into account when using observations to derive scaling relationships that are then used to infer the extreme precipitation response to climate change.&lt;/p&gt;