Sub-basin Scale Research Articles

The potential recording of North Ionian Gyre’s reversals as a decadal signal in sea level during the instrumental period

In recent decades, the north Ionian Sea, central Mediterranean Sea, has witnessed shifts in surface current circulation from cyclonic to anticyclonic and vice versa at the quasi-decadal scale, a phenomenon termed the North Ionian Gyre (NIG) reversal. This process impacts parameters such as sea level by altering thermohaline properties and redistributing water masses at the sub-basin scale. Previous studies have shown that during anticyclonic (cyclonic) phases, the Ionian sea-level trend is falling (rising), the opposite of what is observed in other Mediterranean sub-basins. Assuming that sea level records reversals, this study employed signal decomposition to analyze satellite altimetry data and tide gauge observations across the region, some dating back to the 1900s. A distinct quasi-decadal periodicity emerges as the second dominant oscillatory mode from all independent observations, aligning its peaks and troughs (i.e., changes in sea-level trend) with known NIG reversals and associated changes in the state of North Ionian vorticity. Furthermore, this mode from altimetry data clearly depicts the spatial variability of sea level attributed to the opposite NIG states. This periodicity appears coherent among different sub-basins within the domain, especially in the eastern Mediterranean, with a shared generalized out of phase and weakening occurred from the 1930s to the 1950s. This study presents a century-scale reconstruction of NIG reversals, contributing to the understanding of this phenomenon prior to 1987 using direct observations from sea-level data.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China.

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

Spatiotemporal pattern of landscape ecological risk in the Yangtze River Basin and its influence on NPP

The rapid development of the social economy and the continuous change in land use have greatly altered the ecological risk of the regional landscape. This study focused on the Yangtze River Basin in China and aimed to examine the temporal and spatial variation characteristics of landscape ecological risk (LER) over a period of 34 years (1982–2015), after determining the optimal sub-watershed scale. Based on the conditional probability framework, the non-linear response of NPP to LER was revealed. Finally, the primary driving factors of LER were explored, and additional potential causes for changes in NPP were discussed. The study findings indicated that the mean annual LER of the Yangtze River Basin exhibited a spatial distribution characterized by high values observed in the western regions and low values in the eastern regions at the optimal sub-basin scale. Specifically, 30.56 and 22.22% of the sub-basins demonstrated a significant upward and downward trend in annual LER, respectively (P < 0.05). The spatial distribution pattern of the mean annual NPP demonstrated high values in the middle region and low values in the western area, with annual NPP significantly increasing in 94.44% of the sub-basins (P < 0.05). The relationship between annual NPP and annual LER was found to be non-linear, indicating that higher annual LER results in a higher probability of median and high values of annual NPP from the perspective of watershed average. Furthermore, climate factors emerged as the main influencing factor of the NPP. Based on these discoveries, upcoming endeavors should concentrate on optimizing landscape formations and executing a judicious distribution of plant species.

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change.

Spatiotemporal changes in future water yield and the driving factors under the carbon neutrality target in Qinghai

The carbon neutrality target has prompted China to restructure its environmental governance system, influencing regional hydrological processes and ultimately affecting future water yield. Considering land use/land cover changes (LUCC) and climate changes, this study examines the impact of China's carbon neutrality target on future water yield. By integrating Future Land Use Simulation and Coupled Model Intercomparison Project datasets, the future water yield is characterized and analyzed under three distinct scenarios: the baseline scenario, carbon neutrality target scenario, and rapid development scenario. Furthermore, the study identifies the dominant driving factors of water yield under different scenarios at both regional and sub-basin scales from 2000 to 2060. Results reveal a clear increase in future water yield with a short-term lag effect of LUCC and climate change. Precipitation emerges as the dominant driving factor of water yield in Qinghai, though its contribution will decrease while vegetation's contribution will increase. The primary driving factors differ across various sub-basins, and the result indicates that maintaining the current land-use policy in Qaidam and Hexi corridor-Alxa Basins is conducive to the improvement of future water yield, while aligning land use with carbon neutrality targets benefits water yield in the Lancang River, Qangtan, Yellow River, and Yangtze River Basins. Notably, this underscores that the pursuit of carbon neutrality goals may not uniformly benefit water yield across all sub-basins. Instead, Qinghai can rely on higher quality and more extensive vegetation to enhance future water yield. This study offers valuable insights that can contribute to the effective management of water resources at the regional level under the carbon neutrality target.

Modeling hydrological processes under Multi-Model projections of climate change in a cold region of Hokkaido, Japan

Local hydrological systems are highly sensitive to global warming, especially in cold-climate-dominated basins. The integrated climate-hydrological model is a powerful tool to analyze and predict such effects. However, modeling hydrological processes contains considerable uncertainty in cold areas due to the complex dynamics of freeze–thaw cycles and requirements of lot inputs data. Here, we developed a fast and simple approach to modify the soil temperature module in soil and water assessment tool (SWAT) model in a cold region of the Ishikari River basin (IRB), Hokkaido, Japan. Water balance components and sediment yield simulations were explored by the modified SWAT model, outperforming the original SWAT model by PBIAS less than 15 % and NSE higher than 0.60 in monthly streamflow and sediment yield during both calibration (2011–2015) and validation phases (2016–2019). The future prediction was integrated with future climate datasets under four Shared Socioeconomic Pathways (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) derived from two Global Circulation Models (GCMs), in two future stages (2045–2069 and 2075–2099) with relative to the baseline stage (1990–2014). The varying climate with warmer temperatures (+3.05℃) and less precipitation (-7%) in the future would lead to that evapotranspiration (ET, 67 %) increased. Water yield (WY, −9%), surface runoff (SURQ, −18 %), groundwater flow (GWQ, −10 %), lateral flow (LATQ, −7%), percolation (PERC, −10 %), streamflow (-7%) and sediment yield (-14 %) decreased across the whole basin. However, due to climatic spatial variation, at the subbasin scale, hydrological components (e.g., WY, GWQ, LATQ, and PERC) would increase in the northern region, at the same time, SURQ and ET rise significantly in the central and southern areas. Our findings could offer evidence for future water and soil protection policymaking across the IRB.

A quantitative assessment of air–sea heat flux trends from ERA5 since 1950 in the North Atlantic basin

Abstract. This work aims to investigate the temporal stability and reliability of trends in air–sea heat fluxes from ERA5 forecasts over the North Atlantic basin for the period 1950–2019. Driving forces of the trends are investigated using analyzed state quantities from ERA5. Estimating trends from reanalysis data can be challenging as changes in the observing system may introduce temporal inconsistencies. To this end, the impact of analysis increments is discussed. For individual sub-regions in the North Atlantic basin, parametrization formulas for latent and sensible heat fluxes are linearized to quantitatively attribute trends to long-term changes in wind speed, moisture, and temperature. Our results suggest good temporal stability and reliability of air–sea heat fluxes from ERA5 forecasts on sub-basin scales and below. Regional averages show that trends are largely driven by changes in the skin temperature and atmospheric advection (e.g., of warmer or drier air masses). The influence of modes of climate variability, such as the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation, on the patterns found is discussed as well. Results indicate a significant impact on trends in the Irminger and Labrador seas associated with more positive NAO phases during the past 4 decades. Finally, we use basin-wide trends of air–sea heat fluxes in combination with an observational ocean heat content estimate to provide an energy-budget-based trend estimate of the Atlantic Meridional Overturning Circulation (AMOC). A decrease in area-averaged air–sea heat fluxes in the North Atlantic basin suggests a decline in the AMOC over the study period. However, basin-wide flux trends are deemed partially artificial, as indicated by temporally varying moisture increments. Thus, the exact magnitude of change is uncertain, but its sign appears robust and adds complementary evidence that the AMOC has weakened over the past 70 years.

Evaluation of 12 precipitation products and comparison of 8 multi-model averaging methods for estimating precipitation in the Qilian Mountains, Northwest China

Mountains are the water towers of the world, so it is critical to obtain accurate precipitation data for mountainous areas. Due to the complex topography of high mountainous areas, precipitation ground stations are sparse and unevenly distributed in such areas, so precipitation products such as remote sensing and reanalysis products are used to obtain gridded precipitation data for these areas. However, no single precipitation product performs best in all areas of mountainous regions. Therefore, this study first evaluated the performance of 12 precipitation products in estimating precipitation in the Qilian Mountains at the station scale and sub-basin scale, and then compared the performance of precipitation estimates for the Qilian Mountains generated by 8 multi-model averaging methods. The evaluation results for 29 meteorological stations in the Qilian Mountains showed that the China Meteorological Forcing Dataset product was the best-performing precipitation product, while the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record product was the worst-performing precipitation product. The evaluation results for 18 sub-basins showed that at these sub-basins, the WorldClim was the best-performing precipitation product, while the High Asia Refined analysis was the worst-performing precipitation product. Thus, station-scale evaluations may not necessarily be applicable to the basin scale. Multi-model averaging methods effectively improved the accuracy of precipitation estimates both at station scale and at sub-basin scale. The Granger-Ramanathan variant C was the best multi-model averaging method for estimating precipitation at station scale. As the Granger-Ramanathan methods allow negative weights, they are not recommended to interpolate the Granger-Ramanathan weight values of stations to grids. The Bayesian model averaging (BMA) was found to be the most suitable multi-model averaging method for estimating precipitation in the Qilian Mountains by interpolation of weight values of stations to grids. The precipitation estimates generated by BMA show that the mean annual precipitation in the Qilian Mountains from 2001 to 2018 was approximately 336.1 mm, and the annual precipitation during this period increased linearly by 2.4 mm per year.

Improving litter nutrients import/export modeling in a forested watershed through enhanced forest growth representation

Nutrient release from litter significantly contributes to the global nutrient cycle and the nutritional status of river systems. Simulating the diffuse nutrients from litterfall at the watershed scale is extremely uncertain, owing partly to a lack of measurements covering a variety of features and underlying terrain and insufficient modeling methods. We quantified annual litter production (ALP) simulation at the basin and sub-basin scales by improving the Soil and Water Assessment Tool (SWAT) for forest nutrient cycling, including the forest growth module, tree biomass accumulation, and parametrization scheme. The modified model was comprehensively constrained and calibrated for tree biomass, ALP, monthly dynamics of nutrient mineralization, monthly stream flow, sediment yield, nitrate nitrogen (NO3-N), dissolved organic nitrogen (DON), and total phosphorus (TP) in the Tumen River Basin (TRB). Results showed that the modified SWAT increases the ALP simulation from 132.13 ± 21.73 g/m2 to 351.88 ± 18.28 g/m2 and the relative error of spatial ALP decreased from −50.65 %∼66.59 % to −11.18 %∼27.62 %. The accuracy of nitrate improved notably (coefficient of determination (R2) increased by 0.2–0.25, Nash-Sutcliffe Efficiency (NSE) increased by 0.09–0.19), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE increased by 0.08 for validation period). A comparison with the original simulation of nutrient dynamics also indicated that the modified SWAT model could reasonably identify the dynamics and interactions between litter nutrients and climate factors. Increased mineralization caused by warming and snow melting in spring is the main factor contributing to nitrate accumulation and soluble phosphorus (soluble P) loss in the soil. Improved forest nutrient cycles are expected to help enhance future applications of SWAT for investigating the biogeochemical consequences of climate change.

Groundwater dominates terrestrial hydrological processes in the Amazon at the basin and subbasin scales

The hydrology of the Amazon River basin (ARB) has been extensively studied; however, critical scientific gaps remain regarding key processes that govern hydrologic dynamics and the resilience of the rainforest. This inhibits the understanding of hydrological considerations needed for sustainable forest management under climate change and growing human stressors. Here, using high resolution (∼2km), long-term simulations from a process-based hydrological model (LEAF-Hydro-Flood), we investigate the dominant hydrological processes across the ARB, their key roles in shaping basin functions, and the decadal evolution therein. Results indicate that shallow groundwater (<5m deep) strongly modulates the seasonality of the surface fluxes across the ARB and at least 34% of the Amazonian Forest is supported by groundwater during the dry season. A two-month lag between the seasonal peak of evapotranspiration (ET) and river discharge is a key mechanism that potentially prevents the rainforest from tipping into savanna. The dry season in the ARB is getting drier and the wet season is getting wetter, pointing to an accelerating hydrologic cycle. The ARB is dominantly energy limited, however, our results imply that in the absence of groundwater support, and with less than ∼125 mm/month of precipitation, the basin could have become water-limited over some regions. The long-term basin-averaged ET—dominated by transpiration—changed with a split pattern of ±9% in the past three decades. Similarly, water table depth (±19%) and runoff (±29%) changed with heterogenous patterns across the ARB. Further, the contribution of canopy interception loss and ground evaporation changed heterogeneously across the ARB in response to deforestation. River discharge did not change substantially due to the crucial buffering role of groundwater, but terrestrial water storage (TWS) decreased (increased) in the 2000s (2010s) compared to that in the 1990s. Although groundwater is the dominant contributor to total TWS, the dynamics of TWS over the major river channels are controlled by flood water, given relatively shallow groundwater. This study provides crucial insights on the dominant hydrological processes in the ARB to inform forest management practices.

Divergent runoff regime revealed by hydrological simulations with corrected precipitation in the upper Indus

The upper Indus basin (UIB) supplies water for the largest irrigation system in Asia and for the livelihoods of millions of people downstream. Yet a large gap still remains in the current knowledge of the hydrological processes in this region due to data scarcity, especially at sub-basin scales. In this work, we evaluated eight precipitation datasets and screened out an optimized (MERRA-2) to be inversely corrected through the Variable Infiltration Capacity (VIC)-glacier hydrological model across the UIB. Divergent runoff regimes, changes, and their attributions were quantified at sub-basin scales based on corrected MERRA-2 precipitation and the well-constrained VIC-glacier model against observed streamflow and glacier/snow changes. Spatial variations exist in the corrected precipitation over the UIB with the lowest mean annual average of 414 mm in the Kharmong and the highest of 1318 mm in the Astore, and a mean of 646 mm over the entire basin in 1980–2013. Three runoff regimes were identified across the UIB basins, including the glacial regime of Shigar, Hunza, and Shyok with glacier runoff dominating (41.6%–53.4%), nival-pluvial regime of Astore and Gilgit with rainfall (46.9%–51.9%) and snowmelt (33.1%–40.7%) runoff dominating, and pluvial regime of UIB-M and Kharmong with rainfall runoff (56.7%–60.9%) dominating. Total runoff generally exhibited stable or insignificant positive tendencies over the UIB basins during 1980–2013. Runoff increases in the glacial regime basins were mainly controlled by the increased glacier runoff, while runoff changes in the non-glacial regime basins were mostly attributed to the changes of precipitation-induced runoff from non-glacierized areas. The increased glacier runoff served as a buffer against hydrological drought by offsetting the non-glacierized runoff decrease caused by the overall decreased precipitation. This study is expected to further the understanding of divergent hydrological regimes in the UIB, and provide essential information for policy decisions on sustainable water resource management in this water-stressed area.

Assessing nitrogen sources in Lake Erhai: A spatially explicit modelling approach

Due to the vulnerable ecosystem environments, plateau lakes in the southwest of China face high eutrophication risks. Excessive nitrogen input is an important driver of eutrophication in plateau lakes. However, it is lacked of accurate analysis of spatial and temporal distribution characteristics of nitrogen sources for plateau lakes, which restricts local water environment management. Taking a typical plateau lake, Erhai Lake, as an example, this study applied the MARINA (Model to Assess River Inputs of Nutrients to seAs)-Lakes model to 26 sub-basins of Erhai Lake to analyze the temporal (1998–2016) characteristics of nitrogen inputs and identify the main pollution sources in different sub-basins. The results showed that the most obvious decreasing trends in river export of nitrogen occurred in 2016. It decreased from 2.9 in 2010–2.3 Mg in 2016, which is associated with the series of water quality protection measures implemented. By distinguish nitrogen forms, we found that dissolved inorganic nitrogen was mainly from diffuse source (synthetic fertilizer) whereas point source (directly discharge of animal manure) was the dominant source for dissolved organic nitrogen. The highest river export of nitrogen was from Zhonghe sub-basin in the western areas with high population density and intensive agricultural system, and the lowest was from Mijuhe sub-basin in the northern areas. Tourism was the dominant source of river export of nitrogen in several sub-basins, including Zhonghe, Meitao, Haidongqin, Boluo, and Jinli. Our study developed an explicit spatial analysis approach to quantify river export of nitrogen by sources at sub-basin scale. Based on the primary sources of nitrogen within each sub-basin, we could divide Erhai Lake basin into different management zones. These results can not only help decision-makers formulate accurate and effective lake protection policies for Erhai Lake, but also provide new insights for other plateau lakes.

The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis

AbstractRecent extreme events in the Middle‐Lower reaches of the Yangtze River basin (MLYRB) are proven to be possibly linked to the El Niño‐Southern Oscillation (ENSO) events as indicated by terrestrial water storage anomaly (TWSA). But the relatively short observation time of Gravity Recovery and Climate Experiment series missions (2002–2017; 2018–present) affects the robustness of the evaluation of TWSA. Here, the applicability of four long‐term TWSA data sets (since 1979) in the MLYRB is evaluated first using an evaluation framework including two completely independent tests. After selecting the optimal one, we investigate the effects of ENSO on TWSA in the MLYRB at the basin, subbasin, and grid cell scales, respectively. Results show that ENSO, especially the Eastern Pacific type ENSO has had a significant impact on TWSA variations in the MLYRB and its two subbasins (the Dongting Lake basin and the Poyang Lake basin) since 1979 with correlation coefficients at 0.56–0.65 and time lags at 5–6 months. However, TWSAs in the other two subbasins (the Hanjiang River basin and the Mainstream River basin) have almost no correlation with ENSO. Further analysis reveals that compared with human activity that has a limited impact on TWSA, precipitation is one of the key inducements for regional water storage changes in these two subbasins, and the no correlation between ENSO and TWSA is mainly caused by the weak link between ENSO and precipitation.

Determination of the maximum run‐off sediment transport capacity during a rainstorm flood in a small watershed on the Loess Plateau

AbstractThe channel sediment transport capacity is an important parameter in soil erosion models, and it determines the proportion of soil erosion that is sent outside the watershed. However, the calculation and judgment methods for the sediment transport capacity of channel are still incomplete, and further research is needed. Therefore, this paper takes Chabagou, a typical watershed in the hilly and gully region of the Loess Plateau, as an example and, based on the measured hydrological and sediment data collected during rainstorm floods in 1961–1969 at three hydrological stations, Caoping, Shejiagou, and Tuanshangou, studies the sediment transport capacity of channels in a small watershed from the perspective of energy dissipation. Clearly, the sediment transport capacity is better determined by using a single flood and sediment concentration peak process. The discriminant conditions are determined for the sediment concentration to reach sediment transport capacity during the flood rising and falling sections of the sediment process of the small watershed storm flood. When determining sediment transport capacity based on the characteristics of the flood and sediment concentration peak, we find that sediment transport capacity is reached in the flood discharge peak when the flood discharge peak occurs earlier than the sediment concentration peak. When determining sediment transport capacity based on the flow discharge and sediment concentration characteristics of the sediment fall section, we find that when the flood event exhibits a counterclockwise hysteresis curve and a time‐lagged effect occurs, the sediment concentration at that time reaches sediment transport capacity. In this study, the sediment concentration as a function of run‐off amount is logarithmic at different subbasin scales when run‐off reaches its maximum capacity to transport sediment. The R2 is high, ranging from 0.78 to 0.92. The results of this research provide a reference for the study of sediment transport capacity.

Navigating the ecosystem flood regulation service flows from subbasin to catchment in the Han River Basin

With the increasing cross-regional impact of climate change increasing in recent years, the Han River Basin, as a vital water resource supply and densely populated area in China, faces severe cross-regional flood threats and challenges. The systematic consideration of flood regulation throughout the entire upstream and downstream of the basin has become imminent. Our research aims to gain a deeper understanding of the ecosystem flood regulation service flows from upstream to downstream within subbasins and catchments scales of the Han River Basin. The results showed that the overflow path from upstream to downstream of the flood was basically consistent with the trend of the stream and the main tributaries of the Han River. It emerged that subbasins c, e, g, h and k were the key areas for the overall regulation of the upstream and downstream floods at subbasin scale. A total of 11 catchments overflow into their adjacent downstream catchments across the subbasins, a fact which is critical for catchment scale flood regulation. What’s more, there is evident interaction not only between adjacent subbasin and catchment units but also substantial exchange of service flows between non-adjacent units. Notably, catchment c’s flood regulation service flow was primarily contributed by its non-adjacent units. These findings not only contribute to filling the current knowledge gap in cross-watershed flood overflow and flood regulation service flows, but also provide support for the integrated response of upstream and downstream flood disaster risk management at the whole basin scale.

Recent advances in seasonal and multi-annual tropical cyclone forecasting

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products.

Spatial scale effects of integrated landscape indicators on river water quality in Chaohu Lake basin, China.

Climate change and rapid urbanization have changed the characteristics of basin landscapes. Non-point-source (NPS) pollution affects river water quality. Exploring the impact of landscapes on river water quality is crucial for the control of water pollution in a basin. Current researchers focus on the impact of landscape pattern change on NPS pollution in the basin, but few consider climate, terrain, soil, and other geographical factors. In this study, we selected a subtropical agricultural basin in China named Chaohu Lake basin as the study area, added precipitation, soil erosion resistance, and slope to the original landscape pattern indicators. We quantified the spatial scale effect and seasonal dependence of integrated landscape indicators on water quality and comprehensively analyzed the optimal spatial scale and key landscape indicators. According to the nonlinear relationship between the key landscape indicators and river nutrients, we also determined the Type-1 threshold values of key landscape indicators for water quality protection in the basin. The results showed that the rivers in Chaohu Lake basin were mainly polluted by nitrogen and phosphorus. The strength of interpretation of the integrated landscape indicators of river water quality increased with riparian zone width. We determined the subbasin scale to be the optimal spatial scale. The key landscape indicators affecting water quality in the wet season at the optimal scale were precipitation and aggregation index of construction land (AIbul), whereas those in the dry season were AIbul and COHESION. The interpretation of the key landscape indicators in the wet season was slightly higher than that in the dry season. The above conclusions provide a scientific reference for NPS pollution control and water quality protection in subtropical agricultural basins.

Assessing environmental, economic, and social impacts of inter-basin water transfer in China

Inter-basin water transfer (IBWT) has been widely implemented to address water deficit problems in many parts of the world. IBWT projects can pose various environmental, economic, and social impacts on both water recipient and source basins. This study presents an analysis of multi-dimensional impacts of China’s IBWT at the sub-basin scale, using three newly developed indicators. The analysis is based on China’s operating IBWT projects in 2016, which had a collective capacity of transferring ∼48 billion m3/yr water across sub-basins. Results indicate that IBWT helped improve environmental flow conditions in 20 out of 26 recipient sub-basins, with 6 out of 17 source sub-basins experiencing critical environmental flow conditions. IBWT generated net economic benefits of approximately 4.2 billion US dollars and net social benefits of providing domestic water use for 170 million people. IBWT posed overall positive environmental, economic, and social impacts, with the most benefits delivered to the North China Plain (i.e., the Huai and Hai River Basins). However, a few water source basins with relatively low local water availability were faced with a high risk of low environmental flows and high socio-economic costs, especially under low water resources conditions. Large spatial variations of IBWT impacts were found across sub-basins, which cannot be known by assessment at the major basin level. This highlights the need to assess IBWT impacts at finer spatial scales, considering regional differences in local water resources and social-economic development conditions. This study for the first time reveals multi-dimensional impacts of IBWT in China at the sub-basin scale. The results can be used by policymakers to inform water transfer and water management policies.

A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

Abstract. The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992–2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient &gt;0.5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event (∼40 % deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellite-derived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo.7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).

Estimation of Surface-Subsurface Water Balance in Lower Tapi River Basin Using Gridded Data and Station-Based Observed Data

ABSTRACT Water is an essential resource. An understanding of catchment water balance is crucial to ensure water availability in the region. Hydrological models are credible tools for water balance assessment of a region. These models are data intensive. The traditional hydrological modeling approach relies on station-based observed data. The availability of sufficient long-term consistent observed data is often limited. On the other hand, climate reanalysis data for a long timeperiod is available from multiple data sources. This study estimates runoff characteristics for Lower Tapi River Basin in western India, with the help of a Soil Conservation Service Curve Number method. The catchment water balance in terms of runoff, baseflow, and recharge at sub-basin scale is worked out with the help of station-based and climate reanalysis datasets. The model output are calibrated against observed river gauging data. A reasonable fit between model-simulated and observed runoff is obtained in terms of duration and magnitude of runoff peak. Tested model is applied to future climate scenarios. The simulations show an increase in surface runoff, recharge, baseflow and total runoff for future time period. Results reveal that climate changes have significant impact on hydrology of the basin.