Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their ...livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures.
Urban stormwater runoff is a critical source of degradation to stream ecosystems globally. Despite broad appreciation by stream ecologists of negative effects of stormwater runoff, stormwater ...management objectives still typically center on flood and pollution mitigation without an explicit focus on altered hydrology. Resulting management approaches are unlikely to protect the ecological structure and function of streams adequately. We present critical elements of stormwater management necessary for protecting stream ecosystems through 5 principles intended to be broadly applicable to all urban landscapes that drain to a receiving stream: 1) the ecosystems to be protected and a target ecological state should be explicitly identified; 2) the postdevelopment balance of evapotranspiration, stream flow, and infiltration should mimic the predevelopment balance, which typically requires keeping significant runoff volume from reaching the stream; 3) stormwater control measures (SCMs) should deliver flow regimes that mimic the predevelopment regime in quality and quantity; 4) SCMs should have capacity to store rain events for all storms that would not have produced widespread surface runoff in a predevelopment state, thereby avoiding increased frequency of disturbance to biota; and 5) SCMs should be applied to all impervious surfaces in the catchment of the target stream. These principles present a range of technical and social challenges. Existing infrastructural, institutional, or governance contexts often prevent application of the principles to the degree necessary to achieve effective protection or restoration, but significant potential exists for multiple co-benefits from SCM technologies (e.g., water supply and climate-change adaptation) that may remove barriers to implementation. Our set of ideal principles for stream protection is intended as a guide for innovators who seek to develop new approaches to stormwater management rather than accept seemingly insurmountable historical constraints, which guarantee future, ongoing degradation.
Rainfall‐runoff models are used to project the impact of climate change on water resources. Often, models are parameterized over historical climate prior to simulating runoff under changed climates. ...An increasing body of literature indicates that this assumption of model parameter invariance to climate can introduce significant simulation biases as projected climate deviates from historical climate. One way to consider the effect of climate on model parameterization is to regionalize model parameters using historical data and use that information to project parameters’ probable distribution under a changed climate. These approaches, termed trading space‐for‐time (TSFT), have shown promising results when simulating mean annual runoff, but their ability to reproduce time varying daily runoff response remains unexplored. Here, we design experiments to identify possible ways to account for the influence of climate on model parameters by building upon a recently developed Whittaker‐biome‐based TSFT framework. We perform 35 experiments, each representing different hypotheses regarding how model parameters may be altered with climate. We test these hypotheses for seven watersheds across the conterminous United States that undergo significant climate change between 1980–1990 and 1999–2009. Our results show that updated parameters for the best experiment increased median Nash–Sutcliffe Efficiency for the two most climate‐impacted watersheds in our analyses from 0.37 to 0.35 to 0.64 and 0.49, respectively. Improvements in simulation performance for watersheds transitioning from the boreal forest and the woodland/shrubland biomes to the temperate seasonal forest biome were attributed to altered snow and routing parameters, respectively.
Key Points
Develop and test hypotheses to adapt hydrologic models to climate change using the Whittaker‐biome‐based trading space‐for‐time framework
Climate‐adapted parameters attain median Nash–Sutcliffe Efficiency of 0.64 compared to 0.37 from calibrated parameters for the most climate‐impacted watershed
Hypotheses that adapt snow parameters are found to be particularly effective in reducing runoff timing errors
Demand for quantitative assessments of likely climate change impact on runoff is increasing and conceptual rainfall‐runoff models are essential tools for this task. However, the capacity of these ...models to extrapolate under changing climatic conditions is questionable. A number of studies have found that model predictive skill decreases with changed climatic conditions, especially when predicting drier climates. We found that model skill only declines under certain circumstances, in particular, when a catchment's rainfall‐runoff processes change due to changed climatic drivers. In catchments where the rainfall‐runoff relationship changed significantly in response to prolonged dry conditions, runoff was consistently overestimated. In contrast, modeled runoff was unbiased in catchments where the rainfall‐runoff relationship remained unchanged during the dry period. These conclusions were not model dependent. Our results suggest that current projections of runoff under climate change may provide overly optimistic assessments of future water availability in some regions expecting rainfall reductions.
Key Points
Hydrological model performance often degrades during prolonged shifts in climate
Climate shifts sometimes lead to changes in internal catchment functioning
Models perform poorly and become strongly biased where such changes occur, but not otherwise
Scale dependence of Hortonian rainfall‐runoff processes has received much attention in the literature but has not been fully resolved. To further explore this issue, a recently developed model was ...applied to simulate rainfall‐infiltration‐runoff processes at multiple spatial scales. The model consists of the coupling between a two‐dimensional runoff routing module and a two‐layer infiltration module, thus accounting for spatial variability in soil properties, soil surface sealing, topography, and partial vegetation cover. A 76 m2 semiarid experimental plot with sparse cover of vegetation patches and a sealed soil surface in inter‐patch bare areas was used as a representative elementary area (REA). A series of four larger artificial plots of different areas was created based on this REA to examine the scale dependence of rainfall‐runoff relationships in the case of stationary heterogeneity. Results show that runoff depth (or runoff coefficient) decreases with increasing scale. This trend is more prominent at scales less than 10 times the REA length. Power law relationships can quantitatively describe the scaling law. The major mechanism of the scale effect is run‐on infiltration. However, rainfall intensity and soil properties can both affect the scaling trend through their interaction with run‐on. Higher intensity and less temporal variability of rainfall can both reduce the scale effect. Temporally intermittent rainfall may produce spatially oscillating infiltration rates at large scales. Vegetation patterns are another factor that may affect the scaling. Random‐vegetation patterns, compared with regular patterns with similar statistical properties, change the spatial distributions, but do not significantly change either the total amount and statistical properties of infiltration and runoff or the scale dependence of the rainfall‐runoff process.
Key Points
Runoff decreases with scales and the scaling follows power law relationships
Rainfall intensity and temporal distribution significantly affect the scaling law
Random‐vegetation patterns slightly strengthen the scale effect compared to regular patterns
While the majority of hydrological prediction methods assume that observed interannual variability explores the full range of catchment response dynamics, recent cases of prolonged climate drying ...suggest otherwise. During the ∼decade‐long Millennium drought in south‐eastern Australia significant shifts in hydrologic behavior were reported. Catchment rainfall‐runoff partitioning changed from what was previously encountered during shorter droughts, with significantly less runoff than expected occurring in many catchments. In this article, we investigate the variability in the magnitude of shift in rainfall‐runoff partitioning observed during the Millennium drought. We re‐evaluate a large range of factors suggested to be responsible for the additional runoff reductions. Our results suggest that the shifts were mostly influenced by catchment characteristics related to predrought climate (aridity index and rainfall seasonality) and soil and groundwater storage dynamics (predrought interannual variability of groundwater storage and mean solum thickness). The shifts were amplified by seasonal rainfall changes during the drought (spring rainfall deficits). We discuss the physical mechanisms that are likely to be associated with these factors. Our results confirm that shifts in the annual rainfall‐runoff relationship represent changes in internal catchment functioning, and emphasize the importance of cumulative multiyear changes in the catchment storage for runoff generation. Prolonged drying in some regions can be expected in the future, and our results provide an indication of which catchments characteristics are associated with catchments more susceptible to a shift in their runoff response behavior.
Key Points
We explain the variability in the magnitudes of shifts in the rainfall‐runoff partitioning observed during the decadal Millennium drought
During decade‐long dry periods, the severity of hydrological drought is strongly influenced by the catchment biophysical structure
Catchments susceptibility to shifts in hydrologic response was mostly related to predrought climate and catchment storage characteristics
Conceptual rainfall‐runoff models are commonly used to estimate potential changes in runoff due to climate change. The development of these models has generally focused on reproducing runoff ...characteristics, with less scrutiny on other important processes such as the conversion from potential evapotranspiration (PET) to actual evapotranspiration (AET). This study uses three conceptual rainfall‐runoff models (GR4J, AWBM, and IHACRES_CMD) and five catchments in climatologically different regions of Australia to explore the role of ET process representation on the sensitivity of runoff to plausible future changes in PET. The changes in PET were simulated using the Penman‐Monteith model and by perturbing each of the driving variables (temperature, solar radiation, humidity, and wind) separately. Surprisingly, the results showed the potential of a more than sevenfold difference in runoff sensitivity per unit change in annual average PET, depending on both the rainfall‐runoff model and the climate variable used to perturb PET. These differences were largely due to different ways used to convert PET to AET in the conceptual rainfall‐runoff models, with particular dependencies on the daily wet/dry status, as well as the seasonal variations in store levels. By comparing the temporal patterns in simulated AET with eddy‐covariance‐based observations at two of the study locations, we highlighted some unrealistic behavior in the simulated AET from AWBM. Such process‐based evaluations are useful for scrutinizing the representation of physical processes in alternative conceptual rainfall‐runoff models, which can be particularly useful for selecting models for projecting runoff under a changing climate.
Key Points
Contrasting ET process representations can have substantial impact on runoff projections under a changing climate
Conceptual rainfall‐runoff models can interact with potentially complex changes to PET, causing contrasting runoff projections
Comparing AET simulations with observations provides useful insights to evaluate the process representation within rainfall‐runoff models
It has been widely shown that rainfall‐runoff models often provide poor and biased simulations after a change in climate, but evidence suggests existing models may be capable of better simulations if ...calibration strategies are improved. Common practice is to use “least squares”‐type objective functions, which focus on hydrological behavior during high flows. However, simulation of a drying climate may require a more balanced consideration of other parts of the flow regime, including mid‐low flows and drier years in the calibration period, as a closer analogue of future conditions. Here we systematically test eight objective functions over 86 catchments and five conceptual model structures in southern and eastern Australia. We focus on performance when evaluated over multiyear droughts. The results show significant improvements are possible compared to least squares calibration. In particular, the Refined Index of Agreement (based on sum of absolute error, not sum of squared error) and a new objective function called the Split KGE (which gives equal weight to each year in the calibration series) give significantly better split‐sample results than least squares approaches. This improvement held for all five model structures, regardless of basin characteristics such as slope, vegetation, and across a range of climatic conditions (e.g., mean precipitation between 500 and 1,500 mm/yr). We recommend future studies to avoid least squares approaches (e.g., optimizing NSE or KGE with no prior transformation on streamflow) and adopt these alternative methods, wherever simulations in a drying climate are required.
Plain Language Summary
Rainfall‐runoff models are useful tools in water resource planning under climate change. They are commonly used to quantify the impact of changes in climatic variables, such as rainfall, on water availability for human consumption or environmental needs. Many parts of the world are projected to be substantially drier, possibly with threatened water resources. Given the importance of water, reliable tools for understanding future water availability are vital for society. However, literature would suggest that the current generation of rainfall‐runoff models is not reliable when applied in changing climatic conditions, underestimating the sensitivity of runoff to a given change in precipitation. Many hydrologists have assumed deficiencies in the underlying model equations are to blame. However, this paper demonstrates significant improvement without changing model equations, by using a different “objective function.” The objective function defines how the model is “tuned” to observations of river discharge, and this article identifies objective functions that tend to make model simulations more robust when applied in a drying climate. Using these objective functions can improve the accuracy and plausibility of future water availability estimates made for climate change impact studies.
Key Points
“Least squares” approaches should not be used to calibrate models for a drying climate
Sum‐of‐absolute‐error calibration approaches tend to select more robust parameter sets
Equally weighting each year in the calibration data tends to make calibration more robust
Rainfall‐runoff models are often deficient under changing climatic conditions, yet almost no recent studies propose new or improved model structures, instead focusing on model intercomparison, input ...sensitivity, and/or quantification of uncertainty. This paucity of progress in model development is (in part) due to the difficulty of distinguishing which cases of model failure are truly caused by structural inadequacy. Here we propose a new framework to diagnose the salient cause of poor model performance in changing climate conditions, be it structural inadequacy, poor parameterization, or data errors. The framework can be applied to a single catchment, although larger samples of catchments are helpful to generalize and/or cross‐check results. To generate a diagnosis, multiple historic periods with contrasting climate are defined, and the limits of model robustness and flexibility are explored over each period separately and for all periods together. Numerous data‐based checks also supplement the results. Using a case study catchment from Australia, improved inference of structural failure and clearer evaluation of model structural improvements are demonstrated. This framework enables future studies to (i) identify cases where poor simulations are due to poor calibration methods or data errors, remediating these cases without recourse to structural changes; and (ii) use the remaining cases to gain greater clarity into what structural changes are needed to improve model performance in changing climate.
Plain Language Summary
Rainfall runoff models are tools used by hydrologists in climate change assessments to estimate how future streamflow might change in response to a given (often hypothetical) climate scenario. For example, suppose we can assume that rainfall in a particular location is going to reduce by 20% in the future. Does this mean that streamflow will also reduce by 20%? Or will it be 10% less or 40% less? Although rainfall runoff models are among the best tools available, they are often not very good at answering this question. When tested on historical multiyear droughts, they often perform poorly, and we are unsure why. One problem is that when a model fails in this task, it is difficult to know what went wrong. Perhaps there was a problem with the data, since environmental monitoring is often subject to large errors. Perhaps the problem lay not with the model itself but with the way it was trained, or calibrated, to the data. Lastly, perhaps the model itself—its mathematical equations—need to be changed. To improve our estimates, we need a method to test which cause is behind the model failure; otherwise, we might make changes where none are warranted. This paper proposes such a method, in the form of a multistep framework that can isolate the causes of model failures. By ensuring that our attention is focused in the correct direction, this framework will help us to understand and make better estimates of how river flow will be altered by a changing climate.
Key Points
Diagnosing model deficiency is difficult as there are many possible causes of poor simulations
Split sample failure does not mean model structural inadequacy—further tests are required
This framework diagnoses the cause of poor performance and prioritizes remedial action
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