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Climate change increases the frequency, duration, and intensity of extreme heatday (HD) and heatwave (HW) events. Accurate forecasting of HD and HW is crucial for disaster risk reduction. This study aimed to forecast HD and HW in 51 major cities across China by using the Global Ensemble Forecasting System (GEFS) v2 data and employing statistical downscaling. The results showed that extreme gradient boosting (XGBoost) and equidistant cumulative distribution function matching (EDCDFm) significantly enhanced the accuracy of GEFS-v2 data. However, the forecast performance declined with lead time, with a slowdown observed after 8 days. EDCDFm demonstrated the best performance in forecasting HD (1–16 days) with average values of probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) at 49.2%, 44.3%, and 35.4%, respectively, reaching an average POD as high as 70.12% in some regions. For HW (number (HWN), the longest duration (HWD), frequency (HWF), and EDCDFm achieved the most accurate forecasts (1–16 days), with average POD, FAR, and CSI values of 53.9%, 20.9%, and 45.4% for HWN; 50.6%, 22.8%, and 41.4% for HWD; and 55.6%, 18.6%, and 48.5% for HWF, respectively. Some regions achieved a minimum POD of 81.2% and a minimum CSI of 67.9%. Finally, EDCDFm exhibited even more pronounced improvements in forecasting HD and HW in the later period (9–16 days). Therefore, the use of EDCDFm enabled the effective prediction of HD and HW. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is a key parameter in lake energy budget and is highly vulnerable to climate change. However, the long‐term trends in LSWT across China and their driving factors remain uncertain. Here, we used a calibrated lake model to simulate LSWT over 1979–2018 for 91 large lakes (>100 km2) across China. Simulations reveal an overall LSWT warming trend (0.040°C yr−1, p < 0.05), but with large spatial variations. The majority of these lakes show significant warming trends (84%, 0.053°C yr−1), while a significant cooling trend is found in the seven lakes in the northwestern Tibetan Plateau (−0.064°C yr−1). LSWT of approximately 42% of the lakes increases more rapidly than the corresponding ambient air temperature. Regionally, the warming trend is highest for lakes in the Eastern Plain (0.049°C yr−1) and the lowest in the Yunnan‐Guizhou Plateau (0.016°C yr−1). The increases in simulated LSWT also vary across seasons, with a higher rate in winter and spring than in summer and autumn. Changes in air temperature, downward longwave radiation, and wind speed are the most important climatic drivers for LSWT changes. Lake surface warming could be more rapid under future global warming, necessitating greater attention to lake‐atmosphere interactions. Plain Language Summary: Lake surface water temperature (LSWT) is an essential factor in regulating heat and water exchange between lake surface and air. Previous studies have relied on satellite or in situ data to study changes in LSWT at various scales. However, the spatiotemporal variations in LSWT at a national scale and their driving factors are still poorly understood for large countries such as China. Here, we used a one‐dimensional lake model (FLake) to simulate the spatiotemporal variability of LSWT for 91 large lakes (>100 km2) across China from 1979 to 2018. Our simulations reveal a significant warming trend for most lakes, although a few lakes in the northwestern Tibetan Plateau show a cooling trend. Interestingly, 42% (38 of 91) of these lakes showed faster warming rates than nearby air temperatures. LSWT increased faster in spring and winter than in summer and autumn. Lake surface warming is mainly explained by locally rising air temperature, increasing downward longwave radiation, and weakened wind speed. Under the projected warmer climate in the future, lake surface warming is expected to continue, with important implications for lake management and climate change mitigation. Key Points: Lake model revealed a significant annual increase of 0.040°C (p < 0.05) in the surface water temperature of large lakes across China from 1979 to 2018The simulated surface water temperature warmed faster than the local surface air temperature in approximately 42% of the studied lakesChanges in air temperature, downward longwave radiation, and wind speed are the primary contributors to the trends in lake surface water temperature [ABSTRACT FROM AUTHOR]

Diffuse solar radiation (Rd) provides basic data for designing and optimizing solar energy systems. Owing to the notable unavailability in many regions of the world, Rd is traditionally estimated by models through other easily available meteorological factors. However, in the absence of ground weather station data, such models often need to be supplemented according to satellite remote sensing data. The performance of Himawari-7 satellite inversion of Rd was evaluated in the study, and hybrid models were established (XGBoost_DE, XGBoost_FPA, XGBoost_GOA, and XGBoost_GWO), so as to improve the satellite data and achieve a better utilization effect. The meteorological data of 14 Rd stations in mainland China from 2011 to 2015 were used. Four input combinations (L1–L4) and eight input combinations (S1–S8) of meteorological factors corresponding to satellite remote sensing data were used for model simulation, while two optimal combinations (S7 and S8) were selected for cross-station application. The results revealed that the accuracy of Himawari-7 satellite Rd data was low, with RMSE, R2, MAE, and MBE values of 2.498 MJ·m−2·d−1, 0.617, 1.799 MJ·m−2·d−1, and 0.323 MJ·m−2·d−1, respectively. The performance of these coupled models based on satellite data was significantly improved. The RMSE and MAE values increased by 15.5% and 9.4%, respectively, while the R2 value decreased by 10.9 %. Compared with others based on satellite data, the XGBoost_GOA model exhibited optimal performance. The mean values of RMSE, R2, and MAE were 1.63 MJ·m−2·d−1, 0.76 and 1.21 MJ·m−2·d−1, respectively. The XGBoost_GWO model exhibited optimal performance in the cross-station application, and the average RMSE value was reduced by 2.3–10.5% compared with the other models. The meteorological factors input by the models exhibited different levels of significance in different scenarios. Rd_s was the main meteorological parameter that affected the model based on satellite data, while RH exhibited a significant improvement in the XGBoost_FPA and XGBoost_GWO models based on ground weather stations data. Accordingly, the present authors believe that the XGBoost_GOA model has excellent ability for simulating Rd, while the XGBoost_GWO model allows for cross-station simulation of Rd from satellite data. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil temperature (Ts) at a national scale under different climatic conditions is important for soil–plant–atmosphere interactions. This study estimated daily Ts at the 0 cm depth for 689 meteorological stations in seven different climate zones of China for the period 1966–2015 with the M5P model tree (M5P), random forests (RF), and the extreme gradient boosting (XGBoost). The results showed that the XGBoost model (averaged coefficient of determination (R2) = 0.964 and root mean square error (RMSE) = 2.066 °C) overall performed better than the RF (averaged R2 = 0.959 and RMSE = 2.130 °C) and M5P (averaged R2 = 0.954 and RMSE = 2.280 °C) models for estimating Ts with higher computational efficiency. With the combination of mean air temperature (Tmean) and global solar radiation (Rs) as inputs, the estimating accuracy of the models was considerably high (averaged R2 = 0.96–0.97 and RMSE = 1.73–1.99 °C). On the basis of Tmean, adding Rs to the model input had a greater degree of influence on model estimating accuracy than adding other climatic factors to the input. Principal component analysis indicated that soil organic matter, soil water content, Tmean, relative humidity (RH), Rs, and wind speed (U2) are the main factors that cause errors in estimating Ts, and the total error interpretation rate was 97.9%. Overall, XGBoost would be a suitable algorithm for estimating Ts in different climate zones of China, and the combination of Tmean and Rs as model inputs would be more practical than other input combinations. [ABSTRACT FROM AUTHOR]

Evaporation is important basic information for irrigation decision making in water resources management. Developing countries usually use a small pan to observe surface evaporation, but the relationship between evaporation in different small pans is not sufficiently clear. In this paper, we use an extreme learning machine (ELM) model to predict and convert E20 (diameter 0.20 m) and E601 (diameter 0.62 m) pan data for 38 meteorological stations in southern China. Firstly, we obtained the best combination of meteorological parameters for forecasting E20 and E601, respectively, and we also found that the accuracy of the model can be significantly improved by adding pan data. Secondly, we found that during the conversion between E20 and E601, the model performance when using E601 data to predict the E20 evaporation is better than that when using E20 data to predict the E601 evaporation. Finally, the geographical factors were analysed, and the model performance was found to be relatively poor in the coastal area and the North–South junction. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05 ∘ , monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d'Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: - 0.057, - 0.063 and - 0.027 m3m-3 ; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at 10.5281/zenodo.4738556 (Meng et al., 2021a). [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002-2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: -0.024, -0.030 and -0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = -0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020). [ABSTRACT FROM AUTHOR]

The deep drainage of irrigated cropland, which is one of the main causes of water losses in irrigation, plays an important role in water resource management. However, its magnitudes and variations remain unclear at regional scales. In this study, we established a remote sensing deep drainage model for irrigated cropland (ICDD_RS model) based on satellite observations and a deep neural network (DNN). First, deep drainage for natural vegetation was calculated by the water balance equation. Then, the ICDD_RS model was established by linking the calculated deep drainage and remote sensing data for natural vegetation based on a DNN. Finally, the model was used to predict the deep drainage of irrigated cropland through remote sensing data. The model was tested in the Heihe River Basin of China and the results showed the following: (1) The ICDD_RS model can well describe the relationship between deep drainage and selected influencing factors (0.83 < R2 < 0.86). At the site scale, it is highly consistent with in-situ observations with R2 values of 0.86, 0.74, and 0.71 for the Daman, Yingke, and Pingchuan stations, respectively. At the catchment scale, a low bias of 12% is obtained. (2) Deep drainage is large, accounting for 6–17% of the irrigation water. This model should be valuable in providing more detailed and effective information on agricultural water resource management. • Development of ICDD_RS model to simulate the deep drainage for irrigated cropland. • ICDD_RS captures the dynamic of deep drainage at both site and watershed scales. • Deep drainage accounts for 6–17% of irrigation in the Heihe River Basin. [ABSTRACT FROM AUTHOR]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change. [ABSTRACT FROM AUTHOR]

Drip irrigation (DI) has been widely utilized for crops and its water-saving effect has been confirmed by numerous studies. However, whether this technology can save so much water under the field scale during practical application is still uncertain. In order to answer this question, evapotranspiration (ET), soil water content, transpiration and evaporation over the DI and border irrigation (BI) in an arid area of NW China were continuously measured by two eddy covariance systems, micro-lysimeters, the packaged stem sap flow gauges and CS616 sensors during 2014–2018 growing seasons. The results showed that the DI averagely increased crop water use efficiency (CWUE) by 11% per year against BI. The deep drainage under DI treatment was lower than BI by 8% averagely for the five-year period. While for the ET, the DI averagely decreased ET by 7% and 40mm per year against the traditional BI. The decrease in ET was mainly due to the significant reduction in soil evaporation instead of transpiration. Oppositely, we found that DI may increase maize (Zea mays L.) transpiration in some year for the better preponderant growth of crop. Thus, the accelerating effect on transpiration of DI and its reducing effect on soil evaporation should be considered simultaneously. In our experiment, DI only improved CWUE and WUE (water use efficiency) by 11% and 15% on average in a large farmland scale, unable to always be more than a 20% improvement, as concluded by many other field experiments. Consequently, the water-saving effect of DI should not be overestimated in water resource evaluation. [ABSTRACT FROM AUTHOR]

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change. [ABSTRACT FROM AUTHOR]

Satellite retrievals of thermal infrared data from the Earth's surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle. [ABSTRACT FROM AUTHOR]

• The inter-annual evapotranspiration (ET) increased significantly. • The annual mean ET was 521.68 mm and the water demand pressure was severe. • The dominant factors of the trend and fluctuation of annual ET were different. • The mean annual total potential water saving amount was 2.2 billion m3 in Northwest China. To alleviate the contradiction between crop evapotranspiration (ET) and available water resources, and to develop agricultural production, mulched irrigation, as a water saving technology, is popularized in arid areas of China. However, the observations of ET in long-term mulched farmlands are scarce, so the inter-annual variation and controlling factors of ET after mulching remain uncertain, and the overall water saving effect of mulching in arid areas has not been quantified. Therefore, we conducted the observation experiments (2007–2021) on a mulched maize field, applied the modified Shuttleworth-Wallace model and obtained multi-source data statistics to fill in the gaps in the previous research. The results showed that the mean ET in the past 15 years was 521.68 mm, and the arid areas faced more severe water demand pressure. The inter-annual ET trend of the mulched maize field increased significantly, mainly caused by the reference evapotranspiration (ET 0), while the inter-annual fluctuation was mainly attributed to the crop coefficient (K c). The contribution of plant transpiration (T r) to ET was always greater than that of soil evaporation (E s), and the contribution of solar radiation (Rs) to ET was the largest among environmental factors. In addition, based on observation data and model simulation, it was revealed that the water saving amount and ratio of the long-term mulched maize field were estimated to be 83.8 mm and 13.3 %, respectively. Furthermore, the statistical results from 49 sites in five provinces (regions) in Northwest China showed that compared with the agricultural water use in the recent 10 years, the mean annual total potential water saving amount and ratio of the mulched maize field were 2.2 billion m3 and 2.5 %, respectively. Our study will provide important guidance for understanding the variation of crop ET and water resources allocation. [ABSTRACT FROM AUTHOR]