Hourly Relationship between Reference Evapotranspiration and Shoot Shrinkage in Walnut Trees and Pomegranate under Deficit Irrigation

Diameter fluctuations of branches, shoots, or fruits are related to plant transpiration and water potential. In the past, several models have related dendrometric variables and evapotranspiration on a daily scale. However, trunk–branch shrinkage occurs only between dawn and midday, while evapotranspiration occurs most of the day from sunrise to sunset. Previous models have failed to incorporate this key fact. The objective of the present study was to assess the relationship of hourly daily shrinkage (HDS) between dawn and the next 4 h to the hourly reference evapotranspiration (EToh) of the same period in walnut trees and pomegranate plants under different irrigation regimes. Our data show that the relationship between EToh and HDS is much better than several previous models that included maximum daily shrinkage (MDS) and reference evapotranspiration (ETo). The novel slope analysis of the relationship between HDS versus time used here corresponds to the velocity at which the HDS occurs, which depends on the ETo intensity at that moment. This new method of analyzing this type of data calls for a revision of these models and sets a new baseline for future analysis.

Improving GSMaP V06 Precipitation Products Over the Upper Tocantins River Basin in the Brazilian Cerrado, Based on Local Rain-Gauge Network

This study aimed to evaluate the performance of GSMaP (Global Satellite Mapping of Precipitation) in estimating rainfall in central Brazil, using the Upper Tocantins River sub-basin as a specific area of ​​analysis. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1~1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October-April) than in the dry season (May-September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area.

Evaluation of Seven Near-Real-Time Satellite-Based Precipitation Products for Wet Seasons in the Nierji Basin, China

This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle–high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.

Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers

Snow over sea ice controls energy budgets and affects sea ice growth/melting, and thus has essential effects on the climate. Passive microwave radiometers can be used for basin-scale snow depth estimation at a daily scale; however, previously published methods applied to Antarctica clearly underestimated snow depth, limiting their further application. Here, we estimated snow depth using microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2 since 2002. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements showed that the gradient ratio (GR) calculated using brightness temperatures in vertically polarized 37 and 19 GHz, i.e., GR(37/7), was optimal for deriving Antarctic snow depth, with a correlation coefficient of −0.64. We hence derive new coefficients based on GR(37/7) to improve the current snow depth estimation from passive microwave radiometers. Comparing the new retrieval with in situ measurements from the Australian Antarctic Data Centre showed that this method outperformed the previously available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, compared to values of −14.47 cm and 19.49 cm, respectively. A comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicated that in thin ice regions, the proposed method performed slightly better than the previous method (with RMSDs of 16.85 cm and 17.61 cm, respectively). Comparable performances during the growth and melting seasons suggest that the proposed method can still be used during the melting season. Gaussian error propagation found an average snow depth uncertainty of 3.81 cm, which accounted for 12 % of the estimated mean snow depth. We generated a complete snow depth product over Antarctic sea ice from 2002 to 2020 on a daily scale, and negative trends could be found in all sea sectors and seasons. This dataset (including both snow depth and snow depth uncertainty) can be downloaded from National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at http://data.tpdc.ac.cn/en/disallow/61ea8177-7177-4507-aeeb-0c7b653d6fc3/ (Shen and Ke, 2021, DOI: 10.11888/Snow.tpdc.271653).

Warm Island Effect in the Lake Region of the Tengger Desert Based on MODIS and Meteorological Station Data

The northeastern part of the Tengger Desert accommodates several lakes. The effect of these lakes on local temperatures is unclear. In this study, the effects of the lakes were investigated using land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer) data from 2003 to 2018 and air temperatures from meteorological stations in 2017. LST and air temperatures are compared between the lake-group region and an area without lakes to the north using statistical methods. Our results show that the lake-group region is found to exhibit a warm island effect in winter on an annual scale and at night on a daily scale. The warm island effect is caused by the differing properties of the land and other surfaces. Groundwater may also be an important heat source. The results of this study will help in understanding the causative factors of warm island effects and other properties of lakes.

Testing a maximum evaporation theory over saturated land: Implications for potential evaporation estimation

State-of-the-art evaporation models usually assume the net radiation (Rn) and surface temperature (Ts; or near-surface air temperature) to be independent forcings on evaporation. However, Rn depends directly on Ts via outgoing longwave radiation and this creates a physical coupling between Rn and Ts that extends to evaporation. In this study, we test a maximum evaporation theory originally developed for global ocean over saturated land surfaces, which explicitly acknowledges the interactions between radiation, Ts and evaporation. Similar to the ocean surface, we find a maximum evaporation (LEmax) emerges over saturated land that represents a generic trade-off between a lower Rn and a higher evaporation fraction as Ts increases. Compared with flux site observations at the daily scale, we show that LEmax corresponds well to observed evaporation under non-water-limited conditions and that the Ts at which LEmax occurs also corresponds with the observed Ts. Our results suggest that saturated land surfaces behave essentially the same as ocean surfaces at time scales longer than a day and further imply that the maximum evaporation concept is a natural attribute of saturated land surfaces, which can be the basis of a new approach to estimating evaporation.