Journal of Hydrology最新文献

{"title":"Improving daily precipitation estimation using a double triple collocation-based (DTC) merging framework","authors":"Jingjing Gu ,&nbsp;Yuntao Ye ,&nbsp;Yunzhong Jiang ,&nbsp;Haozhe Guan ,&nbsp;Jianxiong Huang ,&nbsp;Yin Cao","doi":"10.1016/j.jhydrol.2024.132422","DOIUrl":"10.1016/j.jhydrol.2024.132422","url":null,"abstract":"<div><div>The availability of accurate precipitation data is crucial for water resources management, disaster prevention, and related research. While gridded products offer precipitation information at high spatial resolution, they still exhibit significant errors in precipitation estimation. The merging of multi-source gridded products has become a mainstream approach for improving precipitation estimation. However, many existing frameworks rely on gauge observations to estimate the merging weights, which limits their applicability in data-scarce regions. Moreover, these frameworks predominantly focus on enhancing precipitation estimation rather than on precipitation events. This study proposes a novel Double Triple Collocation-based (DTC) merging framework, which combines time–space TC (TC_2D)-based precipitation rate merging with categorical triple collocation (CTC)-based rain/no-rain merging. The objective is to minimize errors in precipitation estimation and enhance the detection capability for precipitation events without relying on rain gauge observations. Given that the TC_2D-based method is initially applied to precipitation merging, its effectiveness must be verified by comparing it with classic TC-based merging approaches (TC_Space and TC_Time). Taking the Jiulong River Basin (JRB) as a case study, the performance of the DTC and its comparative objects was evaluated with three triplets composed of independent precipitation products. The results indicated that all merged precipitation products outperform their parent products. Furthermore, the merged precipitation datasets, after being corrected using rain/no-rain time series generated by CTC-based merging, showed enhanced capability in detecting precipitation events. The performance of merged precipitation products was found to be highly dependent on the quality of the satellite precipitation products (SPPs) within the triplets. This study provides a promising approach for generating high-quality precipitation datasets, particularly in regions with limited observation data availability.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132422"},"PeriodicalIF":5.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variations of cropland phosphorus runoff loss in China","authors":"Zheqi Pan ,&nbsp;Yufu Zhang ,&nbsp;Longdan Ma ,&nbsp;Jia Zhou ,&nbsp;Yucang Wang ,&nbsp;Kaibin Wu ,&nbsp;Qian Zhang ,&nbsp;Dingjiang Chen","doi":"10.1016/j.jhydrol.2024.132419","DOIUrl":"10.1016/j.jhydrol.2024.132419","url":null,"abstract":"<div><div>Quantitative assessment of cropland phosphorus (P) loss via surface runoff is essential for developing effective pollution mitigation strategies. In this study, we compiled 812 datasets from 114 peer-reviewed papers for cropland P loss across China. We then developed machine learning (ML) approaches to estimate temporal and spatial variations in P runoff loss across China from 1990 to 2020. Four prevalent ML models were considered, namely, multiple linear regression (MLR), random forest (RF), classification and regression trees (CART), and boosted regression trees (BRT). Among these four models, RF exhibited the highest predictive accuracy for both uplands (calibration: R² = 0.86, n = 293; validation: R² = 0.61, n = 96) and paddy fields (calibration: R² = 0.88, n = 137; validation: R² = 0.60, n = 44). According to RF, China’s croplands are estimated to have lost an average of 148 ± 27 Gg P yr⁻¹ from 1990 to 2020, with uplands and paddy fields contributing 114 ± 26 Gg P yr⁻¹ and 34 ± 4 Gg P yr⁻¹, respectively. There was a significant increase in upland TP runoff loss over the study period (p &lt; 0.001), whereas paddy field TP loss remained relatively constant. Regions in southern, eastern, and southwestern China, notably in Hainan, Guangxi, and Fujian provinces, were identified as hotspots of cropland TP runoff loss. Improved cropland management scenarios were predicted to reduce TP runoff loss by 1.4–11.8 %, with the best results obtained by minimizing runoff depth. To effectively mitigate TP runoff loss, an integrated management approach involving water, soil, and fertilizer is recommended. This study enhances quantitative understanding of cropland TP runoff loss in China, providing crucial insights for efficient cropland P management, which is key to managing nonpoint source pollution on a national level.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132419"},"PeriodicalIF":5.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence","authors":"Entong Ke ,&nbsp;Juchao Zhao ,&nbsp;Yaolong Zhao","doi":"10.1016/j.jhydrol.2024.132398","DOIUrl":"10.1016/j.jhydrol.2024.132398","url":null,"abstract":"<div><div>Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132398"},"PeriodicalIF":5.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal dynamics of water-use strategies and response to precipitation in different habitats of Nitraria L.","authors":"Huli Gu ,&nbsp;Guopeng Chen ,&nbsp;Heng Ren ,&nbsp;Bing Liu ,&nbsp;Qiyue Yang ,&nbsp;Xiangyan Feng ,&nbsp;Mingyan Fan ,&nbsp;Hai Zhou","doi":"10.1016/j.jhydrol.2024.132388","DOIUrl":"10.1016/j.jhydrol.2024.132388","url":null,"abstract":"<div><div><em>Nitraria L.</em> is a dominant shrub in arid areas and its survival is hampered by low and unpredictable precipitation and uncertain future water conditions. However, little is known about the shrub’s water requirements or its response to precipitation. We determined the water utilization strategies of <em>Nitraria L.</em> species in habitats with different soil textures using the isotopic composition of xylem water and those of potential water sources (groundwater and vadose zone soil water). We used the MixSIAR model to quantify the relative contribution of potential water sources to shrub water in different soil textures. Our results showed that (1) The dynamic characteristics of water use of <em>Nitraria L.</em> species differed in different soil textures. In gravel soils, water in all soil layers was recharged by precipitation, and the shrub water source was controlled by precipitation with significant seasonal changes. In sandy and clay soils, shallow soil water was recharged by precipitation infiltration, but deep soil water was recharged by capillary rise. Nonetheless, the shrub water sources exhibited considerable seasonal fluctuations. During wet seasons, the shrub’s primary water sources were in shallow and mid-soil depths. However, during the dry season, the shrubs relied on groundwater, with more than half of their water originating in deep soil layers. (2) <em>Nitraria L.</em> species generally responded significantly to precipitation events, and those that survived in three soil textures were able to rapidly switch water sources to varying degrees. In particular, in sandy and gravelly soils, the proportion of deep soil water (24.3 %) and groundwater (16.2 %) used by <em>Nitraria L.</em> plants decreased significantly after a large precipitation event (e.g., 18.8 and 11.9 mm), shifting to a predominantly transient use of shallow soil water (48.5 %). <em>Nitraria L.</em> could shift water-absorbing soil layers according to the availability of potential water sources in different soil textures. This flexibility allows them to access the most readily available water more rapidly. Such optimal ecological adaptation can ensure that the plants will have an advantage in future predicted water shortage conditions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132388"},"PeriodicalIF":5.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying hydrologic fluxes in an irrigated region characterized by groundwater return flows","authors":"Ryan T. Bailey","doi":"10.1016/j.jhydrol.2024.132402","DOIUrl":"10.1016/j.jhydrol.2024.132402","url":null,"abstract":"<div><div>In flood irrigation systems in which water is diverted from a river system, the return of recharge water to the river via groundwater discharge can play a key role in sustaining streamflow during irrigation and post-irrigation months. In this study, we use a combination of field data analysis and numerical hydrologic modeling to quantify the spatio-temporal hydrologic fluxes in a flood irrigated canal-field-aquifer-river system. To accomplish this objective, we develop a new irrigation package for MODFLOW that includes all major hydrologic features and fluxes: precipitation; canal diversions; irrigation type (sprinkler, drip, flood); runoff capture by downgradient canals; seepage from irrigation canals; and a soil water balance for each field, soil unit, and natural area that simulates crop ET and recharge. The model is applied to the White River Valley in the Meeker, Colorado (USA) region (180 km²), noted for extensive flood irrigation practices. From results, we conclude that of the water diverted from the White River for irrigation, approximately 75 % returns to the river. The 25 % irrigation efficiency is extremely low but, through extensive groundwater recharge, creates conditions conducive to groundwater return flow to the White River. The aquifer therefore acts as a slow-release reservoir of diverted river water to maintain streamflow and its ecosystem function during post-irrigation months. A holistic, basin-scale approach should be taken when considering conversion from flood irrigation to sprinkler irrigation, as benefits in conserving water at the farm scale likely will result in a decrease in groundwater return flows and therefore late season streamflow.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132402"},"PeriodicalIF":5.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive assessment of reservoir scheduling to hydrometeorological comprehensive dry and wet condition evolution in a multi-reservoir region of southeastern China","authors":"Hao Chen ,&nbsp;Bingjiao Xu ,&nbsp;He Qiu ,&nbsp;Saihua Huang ,&nbsp;Ramesh S.V. Teegavarapu ,&nbsp;Yue-Ping Xu ,&nbsp;Yuxue Guo ,&nbsp;Hui Nie ,&nbsp;Huawei Xie","doi":"10.1016/j.jhydrol.2024.132392","DOIUrl":"10.1016/j.jhydrol.2024.132392","url":null,"abstract":"<div><div>The role of reservoirs in water resource management is becoming crucial for flood control and drought mitigation in any basin because of the frequent occurrence of extreme weather events attributed to global climate change and human activities. Therefore, evaluating the relationship between reservoir storage (discharge) and wet (dry) evolution is crucial. This study explores the time-delay effect and spatial heterogeneity of reservoir discharge and storage on dry and wet conditions in several basins of Lin’an District (LAD) in southeastern China. An integrated methodology is developed in this study to assess the relationship by a monthly streamflow simulation model, the meteorological and hydrological comprehensive drought index (CDI) using a Frank Copula function, and an eXtreme Gradient Boosting (XGBoost) model and Shapley Additive exPlanations (SHAP) framework were used to develop a model to forecast dry and wet conditions and to evaluate the key factors affecting their changes. Results from the study indicate that the monthly water balance model can simulate the monthly hydrological processes with relatively high accuracy in the LAD region. The CDI reflects the intensity of wet and dry events more precisely, thoroughly, sensitively, and consistently by combining the benefits of hydrological and meteorological drought indicators. Precipitation, evaporation, streamflow, the Pacific Decadal Oscillation (PDO), and the Indian Ocean Dipole (IOD) were the main contributing factors influencing the above 80% accuracy of the wet and dry forecast models. The average correlation between the outflow of each reservoir in LAD and CDI is 0.47, which is higher than the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI). Moreover, the delay in months of dry (wet) events based on SPI, SRI, and CDI are 0.45 (0.41), 1.07 (0.65), and 0.87 (0.60), respectively. It suggests reservoirs are less capable of adaptive scheduling for drought events than for wet events, and they respond most quickly to SPI defined events. The results can provide scientific and technological support for water safety and security in the study area.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132392"},"PeriodicalIF":5.9,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the temporal variability and predictability of streamflow signatures in the Colorado River Basin","authors":"Patricia Puente ,&nbsp;Balaji Rajagopalan ,&nbsp;Laura E. Condon","doi":"10.1016/j.jhydrol.2024.132386","DOIUrl":"10.1016/j.jhydrol.2024.132386","url":null,"abstract":"<div><div>It is well established that streamflow regimes evolve over decadal time scales (i.e., low frequency) leading to long term shifts in distributions. Similar low frequency variations have also been documented in streamflow predictability. Here we explore connections between streamflow distribution attributes and predictability regimes in the Upper Colorado River Basin. We employ nonlinear dynamical time series analysis methods on streamflow timeseries covering the period 762 – 2019 for six locations in the basin. First, a wavelet spectral analysis is performed to obtain the quasi-periodic ‘signal’ of the streamflow. The wavelet analysis also provides the temporal variability of the variance of the signal time series. The signal time series is embedded in a <span><math><mi>D</mi></math></span>-dimensional space with appropriate lag to reconstruct the phase space of the dynamics – i.e. the attractor. Overall predictability is assessed by quantifying the average divergence trajectories in the phase space using Global Lyapunov Exponents and the temporal variability of predictability via the Local Lyapunov Exponents. Results show clear oscillations in streamflow predictability with periods of both high and low predictability occurring throughout the study period at all gauges. Comparing predictability timeseries across the stream gauges we find that general consistency in high and low predictability periods, although they do not perfectly align temporally. In general, higher (lower) predictability periods are characterized by lower (higher) streamflow variance. While there is not a clear relationship between streamflow magnitude and predictability in general, modern high predictability epochs are characterized by a slightly greater likelihood of dry years and lower likelihood of wet years than other epochs. These findings indicate the potential for statistically significant differences in streamflow signatures between high and low predictability periods. Exploring these fundings further with potential connections to large-scale climate can be helpful in exploiting them for skillful short and medium term flow projections.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132386"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany","authors":"Yan Liu ,&nbsp;Yong Chang ,&nbsp;Ingo Haag ,&nbsp;Julia Krumm ,&nbsp;Visakh Sivaprasad ,&nbsp;Dirk Aigner ,&nbsp;Harry Vereecken ,&nbsp;Harrie-Jan Hendricks Franssen","doi":"10.1016/j.jhydrol.2024.132395","DOIUrl":"10.1016/j.jhydrol.2024.132395","url":null,"abstract":"<div><div>The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R² of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132395"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Occurance and pollution risk assessment of emerging contaminants in groundwater in the vicinity of a typical municipal landfill in northeastern China","authors":"Zhihao Zhang ,&nbsp;Nan Zhang ,&nbsp;Meichao Zhao ,&nbsp;Yiwu Zhang ,&nbsp;Weifei Yang ,&nbsp;Bo Liu","doi":"10.1016/j.jhydrol.2024.132408","DOIUrl":"10.1016/j.jhydrol.2024.132408","url":null,"abstract":"<div><div>Emerging contaminants (ECs) present a significant risk to both the ecological environment and human health. However, there is currently limited knowledge regarding the presence of ECs in leachate and the surrounding groundwater environment of landfills. The heterogeneity of aquifers introduces additional uncertainty into the transport of ECs, thereby impacting the accuracy of pollution risk prediction. In this study, the types and concentrations of ECs in the leachate and surrounding groundwater were firstly investigated in a typical landfill in northeastern China. Results show that 6 different ECs were detected in groundwater monitoring wells around the landfill, with concentrations ranging from 1.25-1471 ng/L, higher than most investigated landfills in China. Leachate contained 18 different ECs with concentrations ranging from 0.25-30414 ng/L. Based on the statistical characteristics of lithology reflected by borehole data, random lithology fields were generated and transformed into heterogeneity parameter fields using Markov chain analysis to facilitate the risk assessment of ECs. Following a simulation period of 100 years, it was observed that due to the low permeability of the aquifer, pollutants only spread up to 700 m northward. While pollution plumes may disperse towards residential areas, the probability of exposure to EC in these regions is minimal. Conversely, areas with high pollution risk are predominantly located on the eastern and northern sides of the landfill. This study contributes to a deeper understanding of the impact of landfills on surrounding groundwater environments, and our proposed pollution risk assessment model can serve as a valuable reference for controlling and treating ECs.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132408"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vegetation as a driver of shifts in rainfall-runoff relationship: Synthesising hydrological evidence with remote sensing","authors":"Hansini Gardiya Weligamage ,&nbsp;Keirnan Fowler ,&nbsp;Dongryeol Ryu ,&nbsp;Margarita Saft ,&nbsp;Tim Peterson ,&nbsp;Murray C Peel","doi":"10.1016/j.jhydrol.2024.132389","DOIUrl":"10.1016/j.jhydrol.2024.132389","url":null,"abstract":"<div><div>Drought-induced hydrological shifts and subsequent non-recovery have been reported globally, including in Australia. These phenomena involve changes in the rainfall-runoff relationship, so a year of given rainfall gives less streamflow than before. Some authors have indicated that vegetation dynamics played a key role in hydrological shifts during Australia’s Millennium Drought (MD, 1997–2009), but such interactions are complex and are yet to be fully examined. This study investigates vegetation responses before, during, and after the MD for the same set of catchments in southeast Australia where hydrological shifts and non-recovery have been reported. The characterisation of vegetation behaviour relies on remotely sensed vegetation indices (VIs), namely Normalised Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Enhanced Vegetation Index (EVI), and Vegetation Optical Depth (VOD). Despite the severe multi-year drought, in most locations, the results indicate increased or maintained VIs over the entire period spanning pre-drought to post-drought. However, the link with hydrological shifts is nuanced and depends on how data are analysed. Contrary to expectations, an initial analysis (focussing on raw VI values) indicated that VI shifts were not correlated with hydrological shifts. It was only when the data were reanalysed to better account for the meteorological conditions that the expected correlations emerged. Overall, the results suggest that vegetation was able to maintain indices such as greenness and, by extension, actual evapotranspiration, leaving less rainfall for streamflow. More broadly, this approach provides greater insights into how vegetation affects hydrological behaviour through matched catchments during this and other multi-year droughts.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132389"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}