Dry Catchments Research Articles

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI

AbstractExplaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments.

Simple hydrological models for assessing the impact of forest cover change on streamflow for large catchments with a dry tropical climate

ABSTRACT Hydrological models are widely used to assess the impact of climate variability and land use change on streamflow. However, it is uncertain whether these models are capable of simulating the impact given parameter interactions and inter-annual climate variability for large dry catchments. Two such catchments with extensive land clearing were selected to test three models – AWBM, Sacramento and SimHyd – in terms of their capacity to simulate streamflow during periods of vastly different levels of forest cover. AWBM outperformed others, and thus was further simplified as AWBM_C a with minimum performance deterioration to have only one transferable parameter: the average storage capacity, C a. A reduction of 21–28% in the C a value for the post-clearing period indicates a decrease in the amount of water stored for transpiration and consequently an increase in runoff-generation capacity. AWBM_C a can be used to predict the impact of forest cover change on streamflow with different C a values.

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.

Connected headwaters: Indelible field evidence of dispersal by a diverse caddisfly assemblage up stream valleys to dry catchment boundaries

Abstract1. Integral to many ecological models is the notion that dispersal connects populations and communities in disparate locations. For stream insects, however, there is very little empirical information about which species and what proportion of the species in a community are likely to disperse, in what direction and how far they travel, and whether dispersal is successful in connecting populations and communities. This study examines whether species of caddisfly can disperse between catchments and potentially connect communities.2. We used a novel sampling design comprising five pairs of streams. Each stream in a pair flowed from the same boundary, but in opposite directions and into isolated catchments. We surveyed abundances of adult caddisflies on the catchment boundary (boundary species) where there was no water (Up location), and on two locations per stream (Middle, Down locations) where there was permanently flowing water (resident species). If caddisflies fly along the topographic features of stream valleys, then we expected to find adults on the ridge between catchments and, if dispersal is successful, stream communities in adjacent catchments should have similar species composition.3. Over 130 species of caddisfly (from 18 families) were collected. Approximately half of them occurred on at least one catchment boundary, and Up locations averaged almost 20 species. Boundary species included both the smallest‐ and largest‐bodied taxa and they were a near perfect subset of species in resident assemblages. However, boundary species were not simply the most common in resident assemblages. There was no evidence of sex‐biased dispersal. Unsurprisingly, assemblage composition varied across the landscape, within and among catchments. However, resident assemblages within stream pairs were significantly more similar to one another than unpaired streams in either the same or in different catchments.4. Our results suggest that a surprisingly diverse set of caddisflies disperse up to catchment boundaries and potentially connect streams on either side of catchment divides. Nevertheless, many other caddisfly species were never recorded at Up sites and hence may never or only rarely reach catchment boundaries.5. These outcomes suggest, firstly, that some caddisflies are capable of trans‐catchment dispersal but it is incorrect to assume that all caddisflies are good dispersers, as is often stated in the literature. Secondly, caddisfly assemblages in headwater streams may be less isolated than is often considered and the dendritic structure of stream networks may be less important for their distribution patterns than the proximity of tributaries in neighbouring catchments, in many landscapes. Trans‐catchment dispersal could ensure community connectivity and facilitate recovery after catchment‐wide extinctions.

A decadal record of soil moisture space–time variability over a south‐east Australian catchment

AbstractSemi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate unique hydro‐climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non‐linear relationship between these factors and soil moisture, and the lack of long‐term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south‐east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in‐situ dataset. This unique in‐situ soil moisture monitoring network is established over a semi‐arid to temperate catchment representing typical south‐east Australian agricultural landscape and the data record has captured some major climatic events. Time stability of catchment‐scale soil moisture and the potential of predicting catchment mean soil moisture content using one representative station were also examined using a linear regression model. Soil texture was found as the dominating factor driving the spatial variability of soil moisture in the area. The temporal patterns of soil moisture showed a positive agreement with vegetation dynamics and rainfall at topsoil layers (0–5 cm and 0–30 cm). A higher spatial variability of soil moisture was observed during dry catchment conditions compared to wet catchment conditions. The deeper soil layers (30–60 cm and 60–90 cm) showed highly stable soil moisture values, which might be the driving force of the agriculture in the area. A linear regression based prediction model demonstrated a good potential in estimating spatial mean soil moisture content from one representative station. The results are useful in parameterization of soil moisture variability in land surface, climatic and hydrologic models, agricultural applications and in remote sensing of soil moisture.

Characterizing the groundwater storage–discharge relationship of small catchments in China

Abstract Baseflow recession is an essential part of the hydrological cycle, as it transfers unconfined aquifer storage to runoff. This study derived the parameterization of the baseflow recession from recession curves extracted from 382 catchments in China. The recession parameters of recession curves in power-law form, which control the shape of the curves and reflect the net effects of hillslope on the runoff decline process, are estimated by the baseflow recession analysis. The results show that the ranges of recession coefficient α and recession exponent β across China are 0–0.70 and 0.57–3, respectively. Most of the α values range between 0 and 0.20, and most of the β values range between 1 and 2. Generally, the α values of relatively dry catchments are higher than that of the wet catchments, and the distribution pattern of β values is opposite to that of α values. Statistical analysis is used to construct parametric equations for the recession parameters, indicating that catchment area, field capacity, etc., are essential for predicting α and β. In addition, the transplantation results of parametric equations show that equations can be applied to the estimation of α and β in other catchments. The results provide data support for storage estimation of data-scarce catchments.

Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters

This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.

A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves

AbstractThere has been significant progress made towards understanding the process controls that govern the shapes of flow duration curves (FDC). However, the transferability of the knowledge gained to large spatial scales is still lacking, partly due to the fixed representation of the model structures representing the unobserved underground layer. In the context of improving large scale applicability/transferability of FDC studies: (1) we propose a novel approach to transform/restructure the representation of the subsurface component in hydrological models to be flexible and adaptable in any environment and (2) provide a thorough interpretation of the dominant processes that impact the shape of FDCs. The flexible structure is designed from a series of multiple connected linear reservoirs that are developed from a unique multiple hydrograph separation procedure, to estimate the number of distinct hydrological processes at play. The main results are: (1) wet catchments have more complex model structures (more reservoirs) than arid catchments; indicating that subsurface processes are potentially more present in wet catchments than in drier ones. Dry catchments have more near surface occurring processes, thus have less complex model structures. However, more complexity is potentially required within the internal reservoirs of dry catchments. (2) The strength of interference in the intermediate reservoirs in controlling the dominant processes towards the bottom determined the shape of FDCs, and this increased with aridity. The novel finding is that a model structure can be determined by the catchment's ability to generate flows as well as the strength of low flow persistence. Hence, we propose a conceptual framework for the development of flexible model structures based on the shapes of FDCs. The transformation of models to be flexible in this research, should contribute to studies that seek the transferability of models over large spatial scales.

Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments

AbstractDaily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall‐runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (IA) and baseflow index (IB). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (IA < 0.9) with low baseflow (IB < 0.4) were the easiest to model, with a single‐reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall‐runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (IB < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (IA < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.

From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia\u2019s southern Murray-Darling Basin

The Murray-Darling Basin (MDB) is Australia’s major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April–September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April–September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992–2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April–May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April–September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April–May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability.

Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models

Rainfall–runoff models are not perfect, and the suitability of a model structure depends on catchment characteristics and data. It is important to investigate the pros and cons of a rainfall–runoff model to improve both its high- and low-flow simulation. The production and routing components of the GR4J and IHACRES models were combined to create two new models. Specifically, the GR_IH model is the combination of the production store of the GR4J model and the routing store of the IHACRES model (vice versa in the IH_GR model). The performances of the new models were compared to those of the GR4J and IHACRES models to determine components improving the performance of the two original models. The suitability of the parameters was investigated with sensitivity analysis using 40 years’ worth of spatiotemporally different data for five catchments in Australia. These five catchments consist of two wet catchments, one intermediate catchment, and two dry catchments. As a result, the effective rainfall production and routing components of the IHACRES model were most suitable for high-flow simulation of wet catchments, and the routing component improved the low-flow simulation of intermediate and one dry catchments. Both effective rainfall production and routing components of the GR4J model were suitable for low-flow simulation of one dry catchment. The routing component of the GR4J model improved the low- and high-flow simulation of wet and dry catchments, respectively, and the effective rainfall production component improved both the high- and low-flow simulations of the intermediate catchment relative to the IHACRES model. This study provides useful information for the improvement of the two models.

Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis

The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runoff extremes. We calibrate parsimonious intensity-duration-frequency scaling models to data from 314 rain gauges and 428 stream gauges in Austria, and analyze the spatial patterns of the resulting distributions and model parameters. Results indicate that rainfall extremes tend to be more variable in the dry lowland catchments dominated by convective rainfall than in the mountainous catchments where annual rainfall is higher and rainfall mechanisms are mainly orographic. Flood frequency curves are always steeper than the corresponding rainfall frequency curves with the exception of glaciated catchments. Based on the proposed approach of combined intensity-duration-frequency statistics we analyze elasticities as the percent change of flood discharge for a 1% change in extreme rainfall through comparing rainfall and flood quantiles. In wet catchments, the elasticities tend to unity, i.e. rainfall and flood frequency curves have similar steepness, due to persistently high soil moisture levels. In dry catchments, elasticities are much higher, implying steeper frequency curves of floods than those of rainfall, which is interpreted in terms of more skewed distributions of event runoff coefficients. While regional differences in the elasticities can be attributed to both dominating regional rainfall mechanisms and regional catchment characteristics, our results suggest that catchment characteristics are the dominating controls. With increasing return period, elasticities tend towards unity, which is consistent with various runoff generation concepts. Our findings may be useful for process-based flood frequency extrapolation and climate impact studies, and further studies are encouraged to explore the tail behavior of elasticities.

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

Plants versus streams: Their groundwater‐mediated competition at “El Morro,” a developing catchment in the dry plains of Argentina

AbstractOur understanding of how groundwater mediates evapotranspiration/streamflow partitioning is still fragmented and catchment studies under changing vegetation conditions can provide a useful frame for integration. We explored this partition in a flat sedimentary dry catchment in central Argentina in which the replacement of native vegetation with rainfed crops was accompanied by the abrupt formation of groundwater‐fed streams by subsurface erosion (i.e., sapping) episodes. Historical records indicated widespread water table rises (~0.3 m y−1 on average). Groundwater level and stream baseflow fluctuated seasonally with minima in the warm rainy season, indicating that evaporative discharge rather than rainfall shapes saturated flows. Diurnal groundwater level fluctuations showed that plant uptake was widespread where water tables are shallow (<3 m) but restricted to deep‐rooted Prosopis forests where they are deep (7–10 m). MODIS and LANDSAT NDVI revealed a long‐term greening for native vegetation, new wetlands included, but not for croplands, suggesting more limited evapotranspiration‐groundwater level regulation under agriculture. Close to the deepest (20 m) and most active incisions, groundwater level and greenness declined and stream baseflow showed no seasonal fluctuations, hinting decoupling from evapotranspiration. Intense ecological and geomorphological transformations in this catchment exposed the interplay of five mechanisms governing evapotranspiration/streamflow partition including (a) unsaturated uptake and both (b) riparian and (c) distributed uptake from the saturated zone by plants, as well as (d) deepening incisions and (e) sediment deposits over riparian zones by streams. Acknowledging the complex interplay of these mechanisms with groundwater is crucial to predict and manage future hydrological changes in the dry plains of South America.

Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: A comparison of LSTM-based models with four lumped conceptual models

Long short-term memory models (LSTMs) are recurrent neural networks from the emerging field of Deep Learning (DL), which have shown recent promise when predicting time-series especially when data are abundant. Rainfall-runoff modelling presents a challenge, yet accurate hydrological models are vital for flood forecasting, hazard impact assessment, and to assess the potential effects of climate change on floods and water resources. In this study, we compare the performance of two DL-based models, a LSTM and an Entity Aware LSTM (EA LSTM). The DL models were trained using a newly published data set, CAMELS-GB, for a sample of 518 catchments across Great Britain. To identify spatial and seasonal patterns in model performance, we compare the DL models against benchmark outputs from four lumped conceptual models recently configured for rainfall-runoff modelling in Great Britain. Our findings show that the LSTM models simulate discharge with consistently high model performance scores, including in catchments typically considered difficult to model. The LSTM achieves a mean catchment NSE of 0.88 (0.86 for the EALSTM), which represents a performance improvement of 10 %–16 % compared with the benchmark conceptual models. Seasonal and spatial patterns indicate that the largest performance improvement relative to the benchmark is in the drier summer months and in drier catchments in the South East of England. By comparing LSTMs with conceptual models, we diagnose possible reasons for their different performance. We suggest that LSTMs offer useful predictive capability for rainfall-runoff modelling in Great Britain and elsewhere and note their value to support process understanding in locations where processes are less well understood.

Impact of climate on landscape form, sediment transfer and the sedimentary record

AbstractThe relationship between climate, landscape connectivity and sediment export from mountain ranges is key to understanding the propagation of erosion signals downstream into sedimentary basins. We explore the role of connectivity in modulating the composition of sediment exported from the Frontal Cordillera of the south‐central Argentine Andes by comparing three adjacent and apparently similar semi‐glaciated catchment‐fan systems within the context of an along‐strike precipitation gradient. We first identify that the bedrock exposed in the upper, previously glaciated reaches of the cordillera is under‐represented in the lithological composition of gravels on each of three alluvial fans. There is little evidence for abrasion or preferential weathering of sediment sourced from the upper cordillera, suggesting that the observed bias can only be explained by sediment storage in these glacially widened and flattened valleys of the upper cordillera (as revealed by channel steepness mapping). A detailed analysis of the morphology of sedimentary deposits within the catchments reveals catchment‐wide trends in either main valley incision or aggradation, linked to differences in hillslope–channel connectivity and precipitation. We observe that drier catchments have poor hillslope–channel connectivity and that gravels exported from dry catchments have a lithological composition depleted in clasts sourced from the upper cordillera. Conversely, the catchment with the highest maximum precipitation rate exhibits a high degree of connectivity between its sediment sources and the main river network, leading to the export of a greater proportion of upper cordillera gravel as well as a greater volume of sand.Finally, given a clear spatial correlation between the resistance of bedrock to erosion, mountain range elevation and its covariant, precipitation, we highlight how connectivity in these semi‐glaciated landscapes can be preconditioned by the spatial distribution of bedrock lithology.These findings give insight into the extent to which sedimentary archives record source erosion patterns through time.

Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale

The dynamics of flood event characteristics, such as the runoff coefficient and the recession time constant, differ in time and space, due to differences in climate, geology, and runoff generation mechanisms. This study examines the variability of event runoff characteristics and relates them to climatic and hydro-geological characteristics available at the regional scale. The main focus is to examine the role of rainfall patterns (i.e., event precipitation volume, precipitation intensity, and antecedent precipitation) and runoff regime (i.e., initial flow before runoff event and event duration) characteristics on the seasonal dynamics of runoff response. The analysis is performed in four small Austrian catchments representing different hydro-geological settings obtained by field mapping. The results are based on an analysis of 982 runoff events identified from hourly measurements of streamflow and precipitation in the period 2002 to 2013. The results show that larger event runoff coefficients and flow peaks are estimated in catchments with high mean annual precipitation than in drier catchments. In contrast to some previous studies, the results show only poor relation between antecedent precipitation (as an index of catchment wetness) and event runoff response. The initial flow is found to be the main factor influencing the magnitude of runoff coefficient and event peaks in all analyzed catchments and geological settings. The recession time constant tends to be inversely related to the maximum event precipitation intensity, with an exception for one catchment (Wimitzbach), which is characterized by the largest proportion of deep interflow contribution to runoff. The analysis of the runoff response by different event types indicates that runoff coefficients and recession time constants are the largest for snowmelt runoff events.

Flash-Flood Potential Assessment by Integrating the Remote Sensing Data and GIS with Reference to Adam Area, Western Saudi Arabia

Heavy rainstorms are common occurrences in the Western mountainous region of Saudi Arabia that results in hazardous floods damaging the infrastructure and development plans. Severe rainstorms and heavy showers cause instant flash floods that result in major damage of properties and loss of human lives. Therefore, it becomes crucial during the development planning that floods are accurately analyzed. For the calculation and spatial mapping of flood features, an integrated remote sensing and GIS methodology has been formed. This new methodology makes use of various landscape, metrological, geological, and land use datasets in a GIS environment by employing the technique of Curve Number (CN) of flood modeling for unrestricted dry catchments. The prediction of rainfall depths for 50 and 100-years are 73.6 and 82.3 mm respectively. 4.3679 and 8.0605 million cubic meters are the flood volumes for 50- and 100-year return periods. Moreover, the flood’s statistical data like the depth and volume of runoff is added in GIS layers’ attribute tables so that all results are collected in the same environment. The application of advanced methodology aids in providing exact estimations and digital results. Moreover, it is economical and can be re-operated in different circumstances as well.

Investigation of the relationship between precipitation extremes and sediment discharge production under extensive land cover change in the Chinese Loess Plateau

To quantify the effects of rainfall and catchment characteristics on sediment yield is of essential importance for sustainable land management. Precipitation extremes are the external cause of the majority of serious soil erosion and suspended sediment yield (SSY) events in the Chinese Loess Plateau (CLP), which has undergone extensive land use/cover change (LUCC). The investigation of the linkage between climate variables and SSY production is crucial to the precise implementation of ecological restoration strategies, thereby reducing soil loss in the CLP. In this study, a partial least squares regression (PLSR) approach is used to investigate this issue using 1961–2015 data from 16 main catchments in the CLP. This period includes the reference period (1961–1979, Period-I), integrated soil conservation project period (1980–1999, Period-II), and the “Grain for Green” program period (2000–2015, Period-III). The results indicate that the dominant climate factors driving SSY are precipitation extremes including maximum precipitation events, heavy precipitation (daily precipitation ≥25 mm), consecutive wet days, erosive precipitation (daily precipitation ≥12 mm), and annual total precipitation. The effects of the precipitation extremes on sediment yield generation are stronger in drier catchments. The influence of the precipitation extremes on sediment generation can be strengthened by cultivation and weakened by revegetation (especially reforestation). The climate-driven sediment yield decreased by 951 t·km−2·yr−1 during Period-II and 505 t·km−2·yr−1 during Period-III in comparison with Period-I. The dominant cause of the sediment yield reduction was LUCC, with the contribution increasing from 73% in Period-II to 92% in Period-III. This study provides a greater understanding of the effects of climate variation and LUCC on sediment yield, and is beneficial for better catchment management.