[1] Pan-evaporation (E pan ) as the indicator of atmospheric evaporative demand has decreased worldwide with climate change in the last decades, which is called "Pan Evaporation Paradox". This study investigates the recent changes in E pan dynamics in China using the observed E pan records for the period 1960-2007. The records show that E pan decreased in China from 1960 to 1991 by −5.4 mm yr −2 . The attribution results show that the significant decreases (P < 0.001) in wind speed and solar radiation offset the effect of increasing air temperature and led to the decrease in E pan . However, the observed E pan has increased since 1992 by 7.9 mm yr −2 . From 1992 to 2007, the amplitude of increase in air temperature rose seriously, while the amplitude of decrease in wind speed declined and solar radiation even increased insignificantly (P > 0.1). The results show that increasing air temperature dominated the change in E pan , which offset the effect of wind speed and led to the increase in E pan . Citation:

[1] In many places around the world, panevaporation has been detected to decrease with the increase in temperature, which is known as the ''panevaporation paradox.'' An example of the paradox was found in the Haihe River Basin from 1957 to 2001. To explain the mechanism of the paradox, an approach to quantify the contributions of climate factors to the panevaporation trend has been proposed, in which the individual contribution was defined as the product of the partial derivative and slope of the trend for the concerned variables. Four variables, including temperature, wind speed, solar radiation, and vapor pressure, were selected based on the Penman-Monteith method to assess their individual contribution to the panevaporation trend. The results showed that an increase in temperature resulted in the increase of panevaporation, but this effect had been offset by an increase in vapor pressure and decrease in wind speed and solar radiation. Wind speed was the dominant factor contributing to panevaporation decreases in the Haihe River Basin.

Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast‐informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month‐ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

.[1] The Danjiangkou Reservoir is the headwater source of the central route of China's South to North Water Diversion Project (SNWDP). Average annual streamflow into the Reservoir was 40.97 km 3 from 1951 to 1989, while it was 31.64 km 3 from 1990 to 2006. Between the two periods, the average annual streamflow was reduced by 9.33 km 3 , accounting for 71.8% of the proposed amount of water diversion of the central route (13 km 3 per year). The sharply decreasing streamflow would inevitably have negative impacts on the implementation of the SNWDP. The reasons for the decrease in streamflow should be investigated before developing any adaption strategies. In this study, the impacts of climatic variation and human activities on streamflow were evaluated by a climate elasticity method. The results show that the impact of climatic variation (indicated by precipitation and potential evapotranspiration) was responsible for 84.1-90.1% of the streamflow reduction, while human activities or other indentified uncertainties contributed 9.9-15.9% of the streamflow reduction. The observed 69.89 mm decrease in average annual precipitation contributed 81.6-87.3% of the decrease in streamflow. According to the observed data during the study period, the planned water diversion could lead to an ecological disaster of the downstream area of the Danjiangkou Reservoir in certain years. We suggest that the water diversion from the Danjiangkou Reservoir should be conducted in an adaptive manner to avoid such an adverse consequence, instead of the current plan of a fixed annual amount of water.Citation: Liu, X., C. Liu, Y. Luo, M. Zhang, and J. Xia (2012), Dramatic decrease in streamflow from the headwater source in the central route of China's water diversion project: Climatic variation or human influence?,

Despite the observed increase in global temperature, observed pan evaporation in many regions has been decreasing over the past 50 years, which is known as the "pan evaporation paradox". The "pan evaporation paradox" also exists in the Tibetan Plateau, where pan evaporation has decreased by 3.06 mm a −2 (millimeter per annum). It is necessary to explain the mechanisms behind the observed decline in pan evaporation because the Tibetan Plateau strongly influences climatic and environmental changes in China, Asia and even in the Northern Hemisphere. In this paper, a derivation based approach has been used to quantitatively assess the contribution rate of climate factors to the observed pan evaporation trend across the Tibetan Plateau. The results showed that, provided the other factors remain constant, the increasing temperature should have led to a 2.73 mm a −2 increase in pan evaporation annually, while change in wind speed, vapor pressure and solar radiation should have led to a decrease in pan evaporation by 2.81 mm a −2 , 1.96 mm a −2 and 1.11 mm a −2 respectively from 1970 to 2005. The combined effects of the four climate variables have resulted in a 3.15 mm a −2 decrease in pan evaporation, which is close to the observed pan evaporation trend with a relative error of 2.94%. A decrease in wind speed was the dominant factor for the decreasing pan evaporation, followed by an increasing vapor pressure and decreasing solar radiation, all of which offset the effect of increasing temperature across the Tibetan Plateau.

Abstract. On the Tibetan Plateau, the limited groundbased rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellitebased rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks -Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.

The performance of a nonlinear formulation of the complementary principle for evaporation estimation was investigated in 241 catchments with different climate conditions in the eastern monsoon region of China. Evaporation ( Ea) calculated by the water balance equation was used as the reference. Ea estimated by the calibrated nonlinear formulation was generally in good agreement with the water balance results, especially in relatively dry catchments. The single parameter in the nonlinear formulation, namely αe as a weak analog of the alpha parameter of Priestley and Taylor (), tended to exhibit larger values in warmer and humid near‐coastal areas, but smaller values in colder, drier environments inland, with a significant dependency on the aridity index (AI). The nonlinear formulation combined with the equation relating the one parameter and AI provides a promising method to estimate regional Ea with standard and routinely measured meteorological data.

Abstract. The Budyko hypothesis (BH) is an effective approach to investigating long-term water balance at large basin scale under steady state. The assumption of steady state prevents applications of the BH to basins, which is unclosed, or with significant variations in root zone water storage, i.e., under unsteady state, such as in extremely arid regions. In this study, we choose the Heihe River basin (HRB) in China, an extremely arid inland basin, as the study area. We firstly use a calibrated and then validated monthly water balance model, i.e., the abcd model, to quantitatively determine annual and monthly variations of water balance for the sub-basins and the whole catchment of the HRB, and find that the roles of root zone water storage change and that of inflow from upper sub-basins in monthly water balance are significant. With the recognition of the inflow water from other regions and the root zone water storage change as additional possible water sources to evapotranspiration in unclosed basins, we further define the equivalent precipitation (P e ) to include local precipitation, inflow water and root zone water storage change as the water supply in the Budyko framework. With the newly defined water supply, the Budyko curve can successfully describe the relationship between the evapotranspiration ratio and the aridity index at both annual and monthly timescales, whilst it fails when only the local precipitation being considered. Adding to that, we develop a new Fu-type Budyko equation with two non-dimensional parameters (ω and λ) based on the deviation of Fu's equation. Over the annual timescale, the new Fu-type Budyko equation developed here has more or less identical performance to Fu's original equation for the sub-basins and the whole catchment. However, over the monthly timescale, due to large seasonality of root zone water storage and inflow water, the new Fu-type Budyko equation generally performs better than Fu's original equation. The new Fu-type Budyko equation (ω and λ) developed here enables one to apply the BH to interpret regional water balance over extremely dry environments under unsteady state (e.g., unclosed basins or sub-annual timescales).