The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.

This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.

This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.

Soil can be eroded by rainfall, and this is a major threat to agricultural sustainability. Estimating the erosivity of rainfall is essential as a first step to determine how much soil might be lost. Until recently, satellite data have not been used to estimate rainfall erosivity, but the data quality is now sufficient to do so. In this study, I test several methods to calculate rainfall erosivity using satellite rainfall data and contrast this with ground-based estimates.

Citizen scientists often submit high-quality data, but a robust method for assessing data quality is needed. This study develops a semi-automated program that characterizes the mistakes made by citizen scientists by grouping them into communities of citizen scientists with similar mistake tendencies and flags potentially erroneous data for further review. This work may help citizen science programs assess the quality of their data and can inform training practices.

1. Blue/red and green/red Rrs(λ) are sensitive to lake TSI. 2. Machine learning algorithms reveal optimum performance of TSI retrieval. 3. An accurate TSI model was achieved by MSI imagery data and XGBoost. 4. Trophic status in five limnetic regions was qualified. 5. The 10m TSI products were first produced in 555 typical lakes in China.

A generalized approach that requires limited field data and well-established models is tested for assessing groundwater recharge in the southern Lake Chad basin. E and T coefficients are estimated with the FAO-dual K_c concept at six locations. Measured soil water content and chloride concentrations along vertical soil profiles together with different scenarios for E and T partitioning and a Bayesian calibration approach are used to simulate water flow and chloride transport using Hydrus-1D.

We present an open-source platform in response to the NASA Open-Source Science Initiative for accessing and presenting quantitative remote-sensing earth observation,and climate data. With our platform scientists, stakeholders and concerned citizens can engage in the exploration, modeling, and understanding of data. We envisioned this platform as lowering the technical barriers and simplifying the process of accessing and leveraging additional modeling frameworks for data.