Water scientists and managers currently face the question of whether trends in climate variables that affect water supplies and hazards can be anticipated. We investigate to what extent climate model simulations may provide accurate forecasts of future hydrologic nonstationarity in the form of changes in precipitation amount. We compare gridded station observations (GPCC Full Data Product, 1901 and climate model outputs (CMIP5 Historical and RCP8.5 simulations, 1901-2100) in real and synthetic-data hindcast experiments. The hindcast experiments show that imputing precipitation trends based on the climate model mean reduced the root mean square error of precipitation trend estimates for 1961-2010 by 9% compared to making the assumption (implied by hydrologic stationarity) of no trend in precipitation. Given the accelerating pace of climate change, the benefits of incorporating climate model assessments of precipitation trends in water resource planning are projected to increase for future decades. The distribution of climate models' simulated precipitation trends shows substantial spatially coherent biases, suggesting that there may be room for further improvement in how climate models are parametrized and used for precipitation estimation. Linear extrapolation of observed trends in long precipitation records may also be useful, particularly for lead times shorter than about 25 years. Overall, our findings suggest that simulations by current global climate models, combined with the continued maintenance of in situ hydrologic observations, can provide useful information on future changes in the hydrologic cycle.
High quality and long-term precipitation data are required to study the variability and trends of rainfall and the impact of climate change. In developing countries like Morocco, the quality of climate data collected from various weather stations faces numerous obstacles. This paper presents methods for collecting, correcting, reconstructing, and homogenizing precipitation series of Morocco’s Fez-Meknes region from 1961 to 2019. Data collected from national specialized agencies based on 83 rain gauge stations was processed through an algorithm specially designed for the homogenization of climatic data (Climatol). We applied the Mann-Kendall test and Sen’s slope estimator to raw and homogenized data to calculate rainfall trend magnitudes and significance. The homogenization process allows for the detection of a larger number of stations with statistically significant negative trends with 95% and 90% confidence levels, particularly in the mountain ranges, that threatens the main sources of water in the largest watershed in the country. The regionalization of our rain gauge stations is highlighted and compared to previous studies. The monthly and annual means of raw and homogenized data show minor differences over the three main climate zones of the region.
Numerous studies have focused on the local and regional climate effects of irrigated agriculture and other land cover and land use change (LCLUC) phenomena, but there are few studies on the role of oceanatmosphere interaction in modulating irrigation climate impacts. Here, we compare simulations with and without interactive sea surface temperatures of the equilibrium effect on climate of contemporary (year 2000) irrigation geographic extent and intensity. We find that ocean-atmosphere interaction does impact the magnitude of globalmean and spatially varying climate impacts, greatly increasing their global reach. Local climate effects in the irrigated regions remain broadly similar, while non-local effects, particularly over the oceans, tend to be larger. The interaction amplifies irrigation-driven standing wave patterns in the tropics and midlatitudes in our simulations, approximately doubling the global-mean amplitude of surface temperature changes due to irrigation. The fractions of global area experiencing significant annual-mean surface air temperature and precipitation change also approximately double with ocean-atmosphere interaction. Subject to confirmation with other models, these findings imply that LCLUC is an important contributor to climate change even in remote areas such as the Southern Ocean, and that attribution studies should include interactive oceans and need to consider LCLUC, including irrigation, as a truly global forcing that affects climate and the water cycle over ocean as well as land areas.
Water resources have always been a major concern, particularly in arid and semiarid parts of the world. Low precipitation and its uneven distribution in Algeria, along with fast population and agriculture activity increase and, particularly, recent droughts, have made water availability one of the country’s most pressing issues. The objectives of the studies reported in this article are to investigate and forecast the meteorological and hydrological drought in Wadi Ouahrane basin (270 km2) using linear stochastic models known as Autoregressive Integrated Moving Average (ARIMA) and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA). In particular, data from 6 precipitation stations and 1 hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) for 12 months. Then, the multiplicative ARIMA model was applied to forecasting drought based on SPI and SRI. As a result, the ARIMA model (1,0,1)(0,0,1)12 for SPI and (1,0,1)(1,0,1)12 for SRI were shown to be the best models for drought forecast. In fact, both models exhibited high quality for SPI and SRI of 0.97 and 0.51 for 1-month and 12-month lead time, respectively, based on validation R2. In general, prediction skill decreases with increase in lead time. The models can be used with reasonable accuracy to forecast droughts with up to 12 months of lead time.
A persistent precipitation deficiency (meteorological drought) could spread to surface water bodies and produce a hydrological drought. Meteorological and hydrological droughts are thus closely related, even though they are separated by a time lag. For this reason, it is paramount for water resource planning and for drought risk analysis to study the connection between these two types of drought. With this aim, in this study, both meteorological and hydrological drought were analyzed in the Wadi Ouahrane Basin (Northwest Algeria). In particular, data from six rainfall stations and one hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) at multiple timescales (1, 2, 3, 4, …, 12 months). By means of a copula function, the conditional return period for both types of drought was evaluated. Results evidenced that runoff is characterized by high level of temporal correlation in comparison to rainfall. Moreover, the composite index JDHMI (Joint Deficit Hydro-meteorological Index) was evaluated. This index is able to reflect the simultaneous hydrological and meteorological behavior at different timescales of 1–12 months well and can present the probability of a common hydrological and meteorological deficit situation more accurately and realistically compared to precipitation or runoff-based indicators. It was found that, over the analyzed basin, the average severity of combined hydro-meteorological drought (JDHMI) was 10.19, with a duration of 9 months and a magnitude of 0.93.
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