TY  - JOUR
A1  - McMillan, Hilary K.
A1  - Gnann, Sebastian J.
A1  - Araki, Ryoko
T1  - Large scale evaluation of relationships between hydrologic signatures and processes
JF  - Water resources research
N2  - Dominant processes in a watershed are those that most strongly control hydrologic function and response. Estimating dominant processes enables hydrologists to design physically realistic streamflow generation models, design management interventions, and understand how climate and landscape features control hydrologic function. A recent approach to estimating dominant processes is through their link to hydrologic signatures, which are metrics that characterize the streamflow timeseries. Previous authors have used results from experimental watersheds to link signature values to underlying processes, but these links have not been tested on large scales. This paper fills that gap by testing signatures in large sample data sets from the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds. We found that most inter-signature correlations are consistent with process interpretations, that is, signatures that are supposed to represent the same process are correlated, and most signature values are consistent with process knowledge in CZO watersheds. Some exceptions occurred, such as infiltration and saturation excess processes that were often misidentified by signatures. Signature distributions vary by country, emphasizing the importance of regional context in understanding signature-process links and in classifying signature values as "high" or "low." Not all signatures were easily transferable from single, small watersheds to large sample studies, showing that visual or process-based assessment of signatures is important before large-scale use. We provide a summary table with information on the reliability of each signature for process identification. Overall, our results provide a reference for future studies that seek to use signatures to identify hydrological processes.
KW  - signatures
KW  - processes
KW  - CAMELS
KW  - CZO
KW  - metrics
KW  - large scale
Y1  - 2022
U6  - https://doi.org/10.1029/2021WR031751
SN  - 0043-1397
SN  - 1944-7973
VL  - 58
IS  - 6
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Haarmann, Stephan
A1  - Holfter, Adrian
A1  - Pufahl, Luise
A1  - Weske, Mathias
T1  - Formal framework for checking compliance of data-driven case management
JF  - Journal on data semantics : JoDS
N2  - Business processes are often specified in descriptive or normative models. Both types of models should adhere to internal and external regulations, such as company guidelines or laws. Employing compliance checking techniques, it is possible to verify process models against rules. While traditionally compliance checking focuses on well-structured processes, we address case management scenarios. In case management, knowledge workers drive multi-variant and adaptive processes. Our contribution is based on the fragment-based case management approach, which splits a process into a set of fragments. The fragments are synchronized through shared data but can, otherwise, be dynamically instantiated and executed. We formalize case models using Petri nets. We demonstrate the formalization for design-time and run-time compliance checking and present a proof-of-concept implementation. The application of the implemented compliance checking approach to a use case exemplifies its effectiveness while designing a case model. The empirical evaluation on a set of case models for measuring the performance of the approach shows that rules can often be checked in less than a second.
KW  - Compliance checking
KW  - Case management
KW  - Model verification
KW  - Data-centric
KW  - processes
Y1  - 2021
U6  - https://doi.org/10.1007/s13740-021-00120-3
SN  - 1861-2032
SN  - 1861-2040
VL  - 10
IS  - 1-2
SP  - 143
EP  - 163
PB  - Springer
CY  - Heidelberg
ER  -