This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of th...

Worldwide, billions of people rely on fresh groundwater reserves for their domestic, agricultural and industrial water use. Extreme droughts excessive pumping put pressure authorities in maintaining sustainable usage. High-resolution integrated models are valuable assets supporting them. The Netherlands Hydrological Instrument (NHI) provides the Dutch with open source modeling software data. Ho...

Surface and ground waters can be considered the main sources of water supply for agricultural, municipal, industrial consumers. Over centuries, combination both naturally occurring conditions humanity’s actions has placed increasing pressure on these resources. As an example, climate change natural variability in distribution occurrence are among driving forces that complicate sustainable devel...

gpuPOM: a GPU-based Princeton Ocean Model S. Xu, X. Huang, Y. Zhang, H. Fu, L.-Y. Oey, F. Xu, and G. Yang Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, 100084, and Joint Center for Global Change Studies, Beijing, 100875, China Institute of Hydrological & Oceanic Sciences, National Central University, Jhongli, Taiwan Program...

High-resolution hydrological modeling is crucial for detecting extreme events and understanding fundamental terrestrial processes. However, spatial resolutions in current studies have been mostly constrained to relatively coarse resolution (~10–100 km), they therefore a difficult time addressing flooding or drought issues with fine resolutions. In this study, continental-scale high-resolution f...

successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. on the other hand in training phase of any artificial intelligence (ai) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole pop...