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The analysis of hydroclimate extremes is gaining more attention due to the devastating effects of intense floods, droughts, etc. This study aims to analyze the stationary (S) and non-stationary (NS) behavior of the annual maximum temperatures (AMT) for two different climatic zones of Iran including the arid and excessively humid provinces of Kerman and West Azerbaijan, respectively. The research datasets included maximum temperature (from CRU TS) and soil moisture (from ERA5) on a monthly time scale (spanning 1901–2019 and 1979–2019). Trend, homogeneity, and stationarity tests were applied to define the basic characterization of the AMTs. The frequency analyses of the AMTs were carried out using generalized extreme value (GEV) under two assumptions of S-GEV and NS-GEV. Moreover, the fitted distribution parameters were estimated using a maximum likelihood estimator. In addition to time-varying NS-GEV investigations, the soil moisture during summer (SM-June, July, and August) was also employed as the covariate to quantify the relationship between drought and AMTs in these climatic zones. The research findings revealed that the Akaike information criterion in S-GEV and NS-GEV estimations decreased from 309 to 223 and 329 to 254 for arid and excessively humid climatic zones, respectively. Therefore, the NS-GEV frequency analyses has increasing effects on return levels of the AMTs than the S-GEV. In the following, the spatial NS-GEV investigations in all 12 and 15 stations of both provinces, showed that NS-GEV with SM as a covariate has better performance in excessively humid climatic zones. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

Considering the periodical changes in stream flow, it is essential to use rule curves for the optimal operation of reservoirs. This study aims to investigate the performance of Zarrineh Rud reservoir by implementing strategies for adaptation to climate change. Daily meteorological and hydrometric data were collected from selected stations upstream of the dam over a 26-year period (1990–2016). Using sequent peak algorithm (SPA) and with respect to the drinking and agricultural water demand, the active storage and its rule curve were simulated. Then, the optimal rule curve was procured through GA-SPA, aiming to minimize the downstream water shortage. The future climate data were downscaled using SDSM based on CanEsm2 model and under RCP2.6 and RCP8.5 scenarios for near (2020–2038), middle (2039–2058), and far (2059–2076) future periods. Then, the rainfall-runoff of HBV-light model was employed to calculate reservoir inflow for the mentioned periods. Finally, in view of environmental demand, reservoir performance indices were calculated for both non-adaptive and adaptive (static and dynamic hedging rules) policies. Results showed a significant decrease in the annual reservoir inflow compared to the baseline for all future periods. The least decrease was observed in RCP2.6 (nearly 23%) for the near future, whereas the largest decrease was in RCP8 (39%) for the middle period. Simulation with the static hedging rules managed to significantly reduce the average vulnerability index (by 60%) compared to no hedging, while the dynamic hedging rules outperformed static hedging rules only by 9%. Therefore, considering the insignificant improvement in reservoir performance using dynamic rules and their complexity, static hedging rules are recommended as the better option for adaptation during climate change. [ABSTRACT FROM AUTHOR]

As the most constraining environmental factor of vegetation development in arid areas, soil moisture is mainly supplied by precipitation and groundwater resources. Considering the varying sensitivity of different plant communities to precipitation and groundwater‐induced water shortages, the communities' management requires the study of the effects of both meteorological and hydrogeological droughts on vegetation. Therefore, this study sought to model the effects of meteorological and hydrogeological droughts on vegetation indices obtained from MODIS satellite images in the Sirjan plain from 2000 to 2019. To this end, vegetation communities were first identified and separated based on extensive field operations, taking into account the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) at the plant communities' scale. Then, meteorological drought was calculated using Standardized Precipitation Index (SPI) and the hydrogeological drought was also measured by Groundwater Resource Index (GRI) via the Kriging technique. Finally, the relationship was modelled using Multivariate Linear Regression (MLR). The results revealed that SPI at a 6‐month time scale (as an important index) played a significant role in elaborating the changes in VCI in eight communities out of 18 ones at the 6‐month scale, acting as a strong and reliable estimator. Moreover, 61.6% of VCI changes in Artemisia sieberi‐Seidlitzia rosmarinus community were explained with GRI and SPI at a 6‐month time scale, indicating the dependence on groundwater and precipitation. Nonetheless, some communities (i.e. six cases) were unresponsive. These findings draw our attention to the importance of considering the special reaction of each plant community in the management. [ABSTRACT FROM AUTHOR]

Streamflow forecast models are essential ingredients for water resources management. Due to nonlinear nature of streamflow generation, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbour (K-NN) have turned into popular forecast models. The main objective of this paper was to study the effect of various non-commonly used data, including spatially distributed climatic data, as well as Southern Oscillation Index (SOI), on the improvement of one- to three-month ahead flow forecasts in the Karun basin, Iran. Temperature maps were produced via elevation-temperature relationships, while inverse distance-weighted interpolation was applied to generate precipitation maps. In addition, three different input architectures were constructed for ANNs and ANFIS models using cross-correlation and principal component analysis techniques. Next, the most accurate models with input point data were identified based on the root mean squared error and mean absolute error. Finally, the effect of using spatial climatic data and SOI on the performance of the most accurate models was investigated. Results showed that distributed precipitation data improved the performance of ANN and ANFIS models, while K-NN model accuracy was only marginally affected. Adding SOI as an additional input improved the forecast performance slightly in all lead times. [ABSTRACT FROM AUTHOR]