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In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety "FENOUYA" was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L−1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L−1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L−1), and three levels of nitrogen rates, namely, N1 (120 kg ha−1), N2 (180 kg ha−1) and N3 (240 kg ha−1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM−0.658), A2 (Nc = 101.116DM−0.455), A3 (Nc = 119.527DM−0.535), N1 (Nc = 33.819DM−0.153), N2 (Nc = 127.759DM−0.555) and N3 (Nc = 209.696DM−0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha−1 was the optimal nitrogen application rate, and 15 mg L−1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is a key parameter in understanding the variability of lake thermal conditions and evaporation. The MODIS-derived LSWT is widely used as a reference for lake model validations and process studies in data-scarce regions. In this study, the accuracy of the MODIS LSWT was examined on the Tibetan Plateau (TP). In-situ subsurface temperatures were collected at five large lakes. Although the observation period covers from summer to winter, only the observations during the lake turnover period (from October to freeze-up), when the lakes are well mixed, can be used as ground truth. The MODIS LSWT agrees well with the selected in-situ data for the five large lakes, with root mean square error (RMSE) < 1 °C at nighttime and <2 °C in the daytime, indicating a high accuracy of the MODIS LSWT data. Before the turnover period, the water is thermally stratified and the surface water is warmer than the subsurface water, and thus the in-situ subsurface water temperature data and the MODIS LSWT have different representativeness. In this case, if the observations are used as a validation basis, the MODIS errors could be much magnified. This in turn indicates the importance of period selection for the validation. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal. [ABSTRACT FROM AUTHOR]