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This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

Climate change is projected to impact multiple components of the flow regime. However, changes in some ecologically important aspects of flow seasonality and variability are not well‐represented by global climate models. We used a stress testing method and global sensitivity analysis to investigate whether interactions between five different, but plausible, change "dimensions" (hydroclimatic variables or relationships) led to worse ecological outcomes than individual changes. The five dimensions include changes in long‐term average rainfall and temperature, low‐frequency variability of rainfall, seasonality of rainfall, and the rainfall‐runoff relationship. Our case study involved regulated and unregulated sections of the Goulburn River, Australia. We found that four different modeled ecological outcomes (condition of small bodied fish, large bodied fish, in‐channel vegetation, and floodplain vegetation) are most sensitive to changes in long‐term average rainfall. Sensitivity to changes in rainfall seasonality depends on river characteristics and appears to be heavily dampened by regulation and actively managed environmental water. Changes to the rainfall‐runoff relationship (which may be triggered by long‐term drying) were found to greatly influence ecological outcomes, but remain poorly understood. However, when considering the worst outcomes that are likely to present severe threats to ecological survival, all five dimensions were significant. These worst outcomes only manifest under certain combinations of changes with interactive effects. These joint interactions have implications for climate risk assessments that do not consider multiple dimensions of change, particularly those aimed at evaluating and mitigating severe threats or extinction probability. Plain Language Summary: Different kinds of climate changes, such as lower rainfall overall, increased temperatures, or changes to the time of year when the most rain falls can all threaten aquatic plants and animals. By manually changing the inputs to our water resource models, we can answer questions like "what happens if rainfall decreases by 20% and temperatures go up by two degrees?" We can do this many times, and build a picture of how rivers respond to a range of changes. We used this concept to understand how river ecology responds to up to five different changes at once and asked whether the outcomes are worse when these changes are combined versus when they occur separately. Ecological outcomes were most sensitive to changes in overall rainfall. The second biggest impact was changes in how much rainfall makes it into the river, which sometimes happens during long droughts. The other changes were less significant overall. Importantly, the most severe threats which could even lead to extinction only come about under combinations of the five changes. This means that in planning to avoid the worst outcomes under climate change, we might need to look at a wider range of combined changes. Key Points: We assessed sensitivity of four modeled freshwater ecosystem outcomes to five different hydroclimatic changes using a stress testVariation in ecological outcomes were most sensitive to changes in long‐term annual rainfall and rainfall‐runoff relationshipsHowever, in differentiating the worst ecological outcomes, a larger number of hydroclimatic changes were significant [ABSTRACT FROM AUTHOR]