Comprehensive assessment of hydro-climatic variations and change trends is essential for understanding, mitigating, and adapting to key water resource changes in different parts of the world. We performed such an assessment on Iran, as representative of an arid/semi-arid and geopolitically important world region. We acquired and calculated data time series of surface temperature (T), precipitation (P), runoff (R), evapotranspiration (ET), and water storage change (DS), to determine their status and changes in and among the 30 main hydrological basins in Iran over the period 1986–2016. From 1986–2000 to 2001–2016, the country warmed, P mostly decreased and R even more so, while water storage was depleted (DS < 0) and ET increased in some basins. Overall, the extra water provided from primarily groundwater depletion has fed and kept ET at levels beyond those sustained by the annually renewable water input from P. This indicates unsustainable use of water for maintaining and expanding human activities, such as irrigated agriculture, in this part of the world.

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and the overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961–2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into periods 1961–1995 (normal lake level) and 1996–2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ET_wb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ET_ERA5 provides higher base average values and lower maximum annual average values than ET_GLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ET_ERA5 has a higher correlation with available energy than available water, while ET_GLEAM has higher correlation with available water, and ET_GLDAS does not correlate with none of these drivers. Based on the comparison of ET_ERA5 and ET_GLEAM with ET_wb, both have similar errors in spatial distribution, while ET_GLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ET_ERA5 and ET_GLEAM). All three datasets provide better ET estimates (values closer to ET_WB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran.

Water bodies provide essential ecosystem services linked to morphometric features that might differ between natural lakes and reservoirs. We use the HydroLAKES global dataset to quantitatively compare large (area > 1 km²) reservoirs and natural lakes in terms of scaling exponents between morphometric measures (volume, area, shore length). These exponents are further compared to those expected from geometrical assumptions and constraints. Lakes cover a larger range of volumes for the same range of surface areas than reservoirs, and have a larger volume-area scaling exponent. The volume-area scaling exponent for reservoirs (but not natural lakes) and the area-shore length exponent for all water bodies follow the predictions for self-affine surfaces. Land cover and terrain influence the scaling relations more for lakes than for reservoirs. These morphometric differences may be used to model the impact of reservoirs and lakes on hydrological processes and associated ecosystem services at regional to global scales.