Accurate particle size distribution (PSD) measurements of suspended particulate matter composed of flocs and aggregates are important to improve understanding of ecological and geomorphological processes, and for environmental engineering applications. PSD can be measured in situ (in the field) using a submersible sensor, or ex situ (in the laboratory) using samples. The methodological choice is often guided by logistical factors, and the differences in PSDs acquired by in situ and ex situ measurements are not acknowledged. In this study, a laser-diffraction instrument (LISST-200X) was used to compare in situ and ex situ PSD measurements. Samples measured ex situ were stored for three consecutive weeks and measured each week in a laboratory using different stirrer speeds. We observed that ex situ measurements display a higher D50 (median particle size) than in situ measurements of the same sample (up to 613% larger, 112% on average). Our experiments show that the difference between in situ and ex situ measurements can be explained by flocculation of the riverine sediments during the first week of storage. During the subsequent ex situ measurements, the stirring results in a significantly lower D50. Ex situ measurements are therefore unsuitable for flocculated suspended particulate matter. This study provides recommendations for optimizing PSD measurements by calculating the measurement times required to obtain robust PSD measurements (exceeding three minutes per sample), which are larger for field samples with coarser particles and wider PSDs.

Human disturbance has substantially altered real-time flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensively streamflow changes, permafrost degradation and ecological deterioration under a warming climate. However, the damming of the Yellow River complicates examining the relations between hydroclimatic variables and streamflow dynamics. In this study, monthly streamflow of the Yellow River (YR) at the Huanghe’yan Hydrological Station is reconstructed for 1955-2019 dusing the double mass curve (DMC) method and then forecasted for the next 20 years (2020-2040) using Elman Neural Network (ENN) time-series method. Construction of dam (1998-2000) has caused a reduction of 53.5%-68.4% in annual streamflow and a reduction of 71.8 %-94.4% in annual streamflow of dry years (2003-2005) in the HAYR and recent dam removal (September 2018) has boosted annual streamflow by 123% -210% (2018-2019). Post-correction trends of annual maximum (QMax) and minimum (QMin) streamflows and the ratio of the QMax/QMin of the YR in the HAYR (0.18 and 0.03 m3s1yr1 and -0.04 yr1) compared to those of pre-correction values (-0.25, -0.004 m3s1yr1 and 0.001 yr1) have revealed hydrological impacts of degrading permafrost. Based on the ENN model predictions, over the next 20 years, the increasing trend of the YR flow in the HAYR would generally be accelerated at a rate of 0.42 m3s1yr1. Boosting rates of spring (0.57 m3s1yr1) and autumn (0.18 m3s1yr1) YR flow would see an advance of snow-melt season and delayed arrival of winter. This suggests an elongating growing season, which indicates ameliorating phonological and soil nutrient and hydrothermal environments for vegetation in the HAYR. These hydrological and ecological change trends in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream YR basins.