In this study, we corrected the bias in the Princeton forcing dataset, i.e., precipitation, maximum and minimum temperatures, and wind speed, by adjusting its long-term mean monthly climatology to match observations for the period 1988–2012 using the delta-ratio method. To this end, we collected meteorological data from 97 stations covering the domain of Iran. We divided Iran into three climatic zones based on the De Martonne classification, i.e., Arid, Humid, and Per-Humid zones, and then applied the delta-ratio method for each climatic zone separately to adjust the bias. After adjustment, the new datasets were compared to the observations in 1958–1987. Results based on four skill scores, including the Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), root-mean-square error (RMSE), and R², indicate that the adjustment greatly improved the quality of the gridded dataset, specifically, precipitation, maximum temperature, and wind speed. For example, NSE for annual precipitation during the validation time period increased from −0.03 to 0.72, PBIAS reduced from 29.2% to 6.6%, RMSE decreased by 182.44 mm, and R² increased from 0.06 to 0.75. Assessing the results in different climatic zones of Iran reveals that precipitation improved more significantly in the Per-Humid zone followed by the Humid zone, while maximum temperature improved better in the Arid areas. For wind speed, the values improved comparably in the three climate zones. However, the delta values for monthly minimum temperature calculated during the adjustment time period cannot be applied in the validation time period, due to the fact that the Princeton climate data cannot follow the behavior of minimum temperature during the validation phase. In short, we showed that a simple bias adjustment approach, along with minimum observed station data, can significantly improve the performance of global gridded datasets.

Hydrologic models have been applied to predict land surface water and energy budgets in mountainous watersheds that are characterized by complex geological features and climatic variability. A common practice is to calibrate the models and achieve the best performing parameter set according to historical observations, and then the calibrated model was used to do future projections. One drawback is that the influence of parameter uncertainty on model projections is not well discussed. In this study, we applied multiple objective functions to choose a group of best performing parameter sets to the Boulder Creek Watershed, USA to investigate how parameter uncertainties can propagate to future projections. We used 16 parameter sets that have similar performance in simulating streamflow amount and regime historically, and applied the same parameter sets to predict hydrologic variables including streamflow, evapotranspiration, and soil moisture in two future phases (Phase 1 is 2040-2069 and Phase 2 is 2070-2099). The results show that variability due to parameter uncertainty was up to 10 % annually and 26 % monthly under future climate change scenarios, and the uncertainties are especially prominent during May to September. The different parameter sets can result to annual streamflow changes in opposite directions. The results indicate that a single parameter set may yield biased representation of hydrologic variability. It is necessary to consider multiple optimal parameter sets in applying hydrologic models for hydrological projections and water resources decision making.

Understanding historical patterns of changes in drought is essential for drought adaptation and mitigation.While the negative impacts of drought over east Africa have attracted increasing attention,a comprehensive and long-term spatiotemporal assessment of drought is still lacking. Here, we provideda comprehensive spatiotemporal drought pattern analysis during the period of 1964–2015 overthe GHA. The Standardised Precipitation-Evapotranspiration Index (SPEI) at various timescales (1 month(SPEI-01), 3 month (SPEI-03), 6 month (SPEI-06), and 12 month (SPEI-12)) was used to investigate droughtpatterns on a monthly, seasonal, and interannual basis. The results showed that despite regional differences,an overall increasing tendency of the drought was observed across the GHA over the past 52 yr, with trends ofchange of -0.0017 yr-1, -0.0036 yr-1, -0.0031 yr-1, and -0.0023 yr-1 for SPEI-01, SPEI-03, SPEI-06, andSPEI-12, respectively. Droughts were more frequent, persistent, and intense in Sudan and Tanzania, whilemore severe droughts were found in Somalia, Ethiopia, and Kenya. Droughts occurred frequently beforethe 1990 s, and then became intermittent with large-scale impacts occurred during 1973–1974, 1984–1985, and 2010–2011. A turning point was also detected in 1989, with the SPEI showing a statistically significantdownward trend during 1964–1989 and a non-statistically significant downward trend from 1990to 2015. Seasonally, droughts exhibited an increasing trend in winter, spring, and summer, but a decreasingtrend in autumn. The research findings have significant implications for drought adaptation and mitigationstrategies through identifying the hotspot regions over east Africa at various timescales. Area-specificefforts are required to alleviate environmental and societal vulnerabilities to drought events.