Lunar exploration has attracted considerable attention, with the lunar poles emerging as the next exploration hot spot for the cold trapping of volatiles in the permanently shadowed regions (PSRs) at these poles. Remote sensing via the satellite's optical load is one of the most important ways to get the scientific data of PSRs. However, the illumination conditions at the lunar poles are quite different from the low latitude areas and how to get appropriate optical signal remains challenging. Thus, simulation of the optical remote sensing process, which provides reference for the choice of satellites' imaging parameters to ensure the implementation of lunar exploration project, is of great value. In this article, an optical imaging chain modeling for the PSRs at the lunar south pole, which includes lunar 3-D topography, observing satellite's orbit, instrument's parameters, and other environmental parameters, has been built. To demonstrate the physical accuracy, some PSRs' observations acquired by narrow angle cameras (NACs) equipped on the lunar reconnaissance orbiter (LRO) are compared with the corresponding images simulated by the proposed imaging chain model. The digital value's difference between the simulated images and real captured images is generally less than 50 for 12-bit images ranging from 0 to 4095, indicating a good fit considering the uncertainty of soil's absolute reflectance and the noise in the real captured images. In addition, the impact of the imaging chain's parameters is revealed with the proposed algorithm. The simulation method will provide reference and assist future optical imaging of PSRs.

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub-daily and small-scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high-intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data-scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world. Flash floods have become increasingly common worldwide, with catastrophic damages to both human life and the economy. While the extent of global warming and climate change impacting these events is still under much debate, it is almost certain now that we need to be better equipped to understand and model these extremes to prevent and mitigate the possible risk to human life and infrastructure in a warming climate. To test, if we can use first principles derived from thermodynamic conservation laws and process based hydrological models for the same, we modeled flash flood response in four headwater catchments over Southern Germany using the concept of representative hillslope. Since the regions considered in our work are poorly gauged, we made use of global climate reanalysis products and parameter transfer from past experiments. The encouraging results obtained in predicting the flood magnitude and volume speak to the overall applicability of our approach. We are able to get decent uncalibrated predictions in three out of the four catchments considered with minimum computational effort. Understanding and managing the adverse impacts of such extreme hydroclimatic events remains one of the crucial hurdles facing humanity toward the sustainable development goals (SDG17) in this decade. Physically based representative hillslope models can be used for flash flood predictions in small data-scarce and rural catchments Climate reanalysis data enable the initialization of a process-based model, helping to reduce the uncertainties in estimating antecedent soil conditions Transfer of model parameters within the same hydrological landscape is feasible