Antecedent Moisture Conditions Research Articles

Drivers of Widespread Floods in Indian River Basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian subcontinental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian subcontinental river basins during the observed climate (1959–2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of the Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Land and Atmospheric Drivers of the 2023 Flood in India

AbstractFloods in India are recurring natural disasters resulting from extreme precipitation during the summer monsoon season (June–September). The recent flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructure. However, what led to the 2023 North India flood and the role of atmospheric and land drivers still need to be examined. Using in situ observations, satellite data, and ERA5 reanalysis combined with hydrological and hydrodynamical modeling, we examine the role of land and atmospheric drivers in flood occurrence and its impacts. Extreme precipitation in a large region during 7–10 July 2023 created favorable conditions for the flood in the hilly terrains and plains of north India. More than 300 mm of precipitation fell in just 4 days, which was eight times higher than the long‐term average (2001–2022). Anomalously high moisture transport over northern India was recorded on 7 July 2023, making atmospheric conditions favorable for intense landfall. Increased column water vapor and specific humidity at different pressure levels confirmed the continuous moisture presence before the extreme rainfall that caused floods in northern India from 7 to 12 July 2023. Atmospheric and land (high antecedent soil moisture) conditions contributed to a more than 200% rise in streamflow at several gauge stations. Satellite‐based flood extent shows a considerable flood inundation that caused damage in the Sutlej and Yamuna River basins. Our findings highlight the crucial role of the favorable land and atmospheric conditions that caused floods and flash floods in north India in July 2023.

Characterizing rapid infiltration processes on complex hillslopes: Insights from soil moisture response to rainfall events

Evaluating the soil moisture response characteristics to rainfall events is valuable for understanding eco-hydrological effects in the context of global climate change. However, the effects of soil thickness and rainfall characteristic indicators on soil moisture responses remain unclear, especially in the karst hillslopes characterized by highly complex topography. This study examined the soil moisture response to 35 rainfall events at 5-min intervals by deploying 20 sets of monitoring devices across two hillslope plots (5 × 20 m) with distinct mean soil thicknesses. The response metrics included the soil moisture response time (Tp2p) and wetting front velocity (Vwf), whereas indicator factors considered were soil thickness distribution, slope position, and rainfall characteristics. Overall, the results showed that Tp2p increased from downslope to upslope, as well as from the surface to deep layers. Surprisingly, the mean Vwf (1373 mm h−1) in this study site was significantly higher than that in non-karst regions (17–610 mm h−1). This suggests that rainwater can rapidly infiltrate the soil profile and contribute to subsurface runoff generation. Rainfall characteristics are the primary controlling factors influencing the soil moisture response to rainfall, with their contribution priority (44.3–63.9 %) higher than that of antecedent soil moisture conditions (26.1–35.2 %). The variation in soil moisture response metrics in shallow soil cover hillslopes was more affected by rainfall indicators (41–64 % vs. 31–47 %), thereby potentially weakening the potential influence of topography and soil properties. These findings highlight the effect of refined monitoring in characterizing soil moisture heterogeneity in response to rainfall and emphasize the potential impact of rapid responses on karst eco-hydrological processes.

Calculating Surface Runoff by Scs-Cn Model in the Sanjai River Basin, Jharkhand

Surface runoff prediction is a very intricate, evolving, and exponential phenomenon controlled by numerous interconnected components. Regulated by numerous types of related factors, surface runoff production is a highly complex, dynamic, and non-linear phenomena. Advancement and long-term control of water resources depend on run-off forecast. For runoff forecasts, many techniques and models are at hand; Among strategies, “Soil Conservation Services curve number(SCS- CN)” approach stands out. In order to determine potential outflow from a drainage system or region, the SCS implemented the Curve Number index. It is possible to figure out the curve number of a drainage basin by taking into account the antecedent moisture conditions(AMC), the soil, land surface use and land cover (LULC). Considered also is the Hydrological Soil Group (HSG). Four kinds of soils are offered at HSG: “A, B, C, and D”. Whereas soil type D stands for low penetration rates and greater surface flow capacity, soil category A comprises higher penetration rates below the surface and less runoff potentiality. The study's research site was the Sanjai river basin in Jharkhand. Arc Gis 10.1 and Erdas 14 software helped produce thematic layers like soil maps, LULC maps, and so on. The whole catchment was divided into sub-watersheds in order to more accurately measure runoff. The findings demonstrate that the SCS-CN simulation performs productive runoff estimates. The research demonstrates that more sustainable water use results from suitable surface runoff analysis.

A simple analytical method to estimate runoff generation and accumulation

The study of runoff formation in semiarid regions relies generally on the solution of Richards equations using numerical approaches such as those provided by Hydrus-1D. We demonstrate here that a Simple Analytical Method (SAM) could be easily applied to estimate areas under flooding risk, under different soil and rainfall conditions, without the need to run burdensome numerical models. The impact of antecedent soil moisture condition was expressed quantitatively, conferring to SAM a predictive capability based on a-priori information on soil properties, rainfall characteristics, and initial conditions. The SAM was applied to the Shikma catchment, in the center of Israel, to illustrate its capability to estimate the spatial distribution of time of runoff occurrence and runoff coefficient for a prescribed rainfall event. The results suggest that SAM can be used as part of early warning systems of flood risks. Radars, carried by satellites and drones, can be used to provide refined data on rainfall intensity and soil surface initial conditions. SAM could be then coupled with these remotely sensed data to improve runoff initiation and accumulation estimates, thus reducing the time interval required to reach operational decisions regarding potential floods and inundations, and related water erosion hazards.

Monthly new water fractions and their relationships with climate and catchment properties across Alpine rivers

Abstract. The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. Thus, understanding runoff generation processes of Alpine streams is important for sustainable water management. It is currently unclear how much streamflow is derived from old water stored in the subsurface and how much stems from more recent precipitation that reaches the stream via near-surface quick flow processes. It is also unclear how this partitioning varies across different Alpine catchments in response to hydroclimatic forcing and catchment characteristics. Here, we use stable water isotope time series in precipitation and streamflow to quantify the young water fractions (Fyw; i.e., the fraction of water younger than approximately 2–3 months) and new water fractions (Fnew; here, the fraction of water younger than 1 month) in streamflow from 32 Alpine catchments. We contrast these measures of water age between summer and winter and between wet and dry periods and then correlate them with hydroclimatic variables and physical catchment properties. New water fractions varied between 3.5 % and 9.6 %, with values of 9.2 % in rainfall-dominated catchments, 9.6 % in hybrid catchments, and 3.5 % in snow-dominated catchments (mean across all catchments of 7.1 %). Young water fractions were approximately twice as large (reflecting their longer timescale) and ranged between 10.1 % and 17.6 %, with values of 17.6 % in rainfall-dominated catchments, 16.6 % in hybrid catchments, and 10.1 % in snow-dominated catchments (mean across all catchments of 14.3 %). New water fractions were negatively correlated with catchment size (Spearman rank correlation, rS, of −0.38), q95 baseflow (rS=-0.36), catchment elevation (rS=-0.37), total catchment relief (rS=-0.59), and the fraction of slopes steeper than 40° (rS=-0.48). Large new water fractions, implying faster transmission of precipitation to streamflow, are more prevalent in small catchments, at low elevations, with small elevation differences, and with large fractions of forest cover (rS=0.36). New water fractions averaged 3.3 % following dry antecedent conditions, compared with 9.3 % after wet antecedent conditions. Our results quantify how hydroclimatic and physical drivers shape the partitioning of old and new waters across the Alps, thus indicating which landscapes transmit recent precipitation more readily to streamflow and which landscapes tend to retain water over longer periods. Our results further illustrate how new water fractions may find relationships that remained invisible with young water fractions.

Empirical and physical modelling of soil erosion in agricultural hillslopes

Abstract Soil erosion is a complex and highly heterogeneous process with a wide range of environmental and economic impacts. Its estimation is particularly challenging and modelling is typically used for erosion estimation over large areas. The aim of this study was to compare the two leading empirical and physical erosion estimation models, i.e. the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP). The models were calibrated and validated using data collected from field experiments conducted in agricultural lands of Mexico. The simulated rainfall experiments involved measuring erosion from field plots subjected to four tillage systems (No crop, Conventional tillage, Conventional tillage + residues, and Handspike) under two antecedent soil moisture conditions (dry and wet). Different calibration approaches based on the factors K and C for RUSLE, and interrill erodibility and hydraulic conductivity in WEPP were tested. The best-performing methods in RUSLE involved measuring the K factor and adopting the recommended C factor by the National Forestry Commission of Mexico. In WEPP, the best results were obtained when interrill erodibility was estimated from experimental measurements. Overall, RUSLE outperformed WEPP in most of the treatments except for CT under WAMC.

Divergent rainfall infiltration patterns on the Chinese Loess Plateau between growing and non-growing seasons after long-term revegetation

Rainfall infiltration into hillslopes on the Chinese Loess Plateau (CLP) undergoes noticeable changes during the growing season following long-term revegetation. However, infiltration patterns during the non-growing season have been understudied. In this study, we monitored soil moisture over three years (May 2016-December 2018) at 10-minute intervals, spanning five soil depths (10, 20, 40, 60, and 100 cm). Monitoring was conducted in both a forestland catchment and an adjacent grassland catchment on the CLP, with two monitoring sites per catchment: one downhill in the gully, and the other uphill on the slope. Our findings reveal pronounced seasonal variations in meteorological conditions and vegetation index. We observed divergent rainfall infiltration patterns and their governing factors between the growing (May to October) and non-growing seasons (November to April). During the growing season, rainfall facilitated a quicker response with shorter lag time (23.34 h vs. 56.84 h in the non-growing season) and noticeable soil moisture increments (up to 35.7 % cm3/cm3 vs. only up to 6.4 % cm3/cm3). The wetting depth often extended to 40–100 cm (compared to 10–40 cm in the non-growing season), with a higher wetting front velocity (17.44 mm/h vs. 6.24 mm/h) and an increased occurrence frequency of preferential flow (19.77 % vs. 9.72 %). The impact of rainfall amount and peak intensity on infiltration was more pronounced during the non-growing season, whereas, in the growing season, infiltration was additionally influenced by antecedent moisture conditions and vegetation type. Notably, in the growing season, the enhanced soil moisture recharge due to rainfall infiltration exceeded the rise in evapotranspiration, resulting in higher soil water storage. The findings of this study shed new light on seasonal infiltration dynamics on the greening CLP, contributing valuable insights into hillslope hydropedological processes post-revegetation.

Implementation of a slope stability method in the CRITERIA-1D agro-hydrological modeling scheme

AbstractThis paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

The Contradictory Issue of the Impact of Antecedent Soil Moisture to Interrill Erosion in Clay Soil: A Two-Year Field Study

The impact of antecedent soil moisture content on soil erosion has been a contradictory issue in erosion research, as well as process-based soil loss estimation models. The objective of this study was to investigate the impact of antecedent soil moisture content on the loss of clay soil through two-year runoff plot experiments under natural rainfall. Volumetric soil moisture sensors were used to monitor soil moisture changes, and readings were used along with rainfall records to quantify the antecedent soil moisture conditions. The results of this study show that the impact of antecedent soil moisture on interrill erosion is conditional, and the impact only exists in erosion events with a low Rainfall–Runoff Erosivity Index. The coefficient of determination between antecedent soil moisture content and soil loss per the Rainfall–Runoff Erosivity Index (Soil Loss/EI30) varies from 0.222 to 0.758, depending on the rainfall duration and Rainfall–Runoff Erosivity. The results of this study also suggest that accumulative rainfall within 48 h (Pp48) prior to an effective erosion event is strongly correlated with Soil Loss/EI30, particularly when the duration of an effective erosion event is either 3~7 h or 10~30 h. Hence, Pp48 can be considered as a replacement for antecedent soil moisture in process-based soil loss simulation models.

SIMULATION OF SPATIO-TEMPORAL DYNAMICS OF STREAM FLOW TO CLIMATE VARIABILITY IN NJORO RIVER CATCHMENT, KENYA

Climate variability continues to alter hydrological regimes and the response of many catchments globally thus threatening water security. Njoro River Catchment has not been an exception. There is a steady recognition of climate variability and adverse influences like deterioration of ecosystems, surface, and groundwater sources. Therefore, the study focused on modelling the impact of climate variability on the spatial and temporal distribution of stream flow in the Njoro River Catchment, Kenya. Modelling of stream flow response to climate variability using SWAT was carried out based on USGS/NASA downloaded Digital Elevation Model, FAO soil data, Landsat (MSS 1-5) LULC of 1978, and meteorological data for the period (1978 - 2017). Simulation of spatial and temporal impacts of climate variability was then carried out. Based on the analysis, SUFI-2 algorithm results revealed that the most sensitive parameter in Njoro River Catchment is SCS curve number for antecedent moisture condition (II) (CN2), ranked according to the highest sensitivity tested at p < 0.05. The least sensitive parameter is maximum canopy storage (CANMX). Overall uncertainty analysis results showed a good performance value of P-factor (0.72) and R-factor (0.38), which indicated that SWAT reproduced the dynamics of the catchment hydrologic response relatively well. The values of R2, NSE, and PBIAS for calibration and validation of monthly stream flow were 0.88 and 0.77, 0.86 and 0.74, and 5.51 % and -15.42 % respectively. From the spatio-temporal impacts of climate variability on hydrologic variables, actual evapotranspiration increased by about 5.6 %, 3 %, and 6.62 % in the 2nd, 3rd, and 4th decades. Surface runoff decreased in the 2nd decade by 34.56 % and increased by 4.55 % and 34.02 % in the 3rd and 4th decades. Annual water yield decreased in the 2nd decade by 29.69 % and increased by 4.25 % and 60.18 % in the 3rd and 4th decades respectively. On average, stream flow reduced by 30.91 % in the 2nd decade and increased by 5.47 % and 63.63 % in the 3rd and 4th decades respectively. These findings provide pertinent insights, which may perhaps enlighten decision-making in designing adaptable mitigation measures, catchment rehabilitation, and strategic initiatives for the integrated management of water resources.

How Much Does Land–Atmosphere Coupling Influence Summertime Temperature Variability in the Western United States?

Abstract Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading empirical orthogonal function that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture–temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature–soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of 3 on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land–atmosphere interactions play an important role in interannual summertime temperature variability in the western United States; soil moisture variations cause distal temperature anomalies and impart predictability at time scales longer than one season. Our results indicate that 40% of the observed warming trend across the western United States since 1981 has been driven by wintertime precipitation trends in the U.S. southwest. Significance Statement Year-to-year fluctuations in summertime temperatures have a large impact on drought, wildfire, and extreme heat across the Western United States. We find strong evidence that soil moisture deficits in the preceding spring may be the primary driver of higher-than-average summer temperatures in this region. Our results suggest that memory in the water cycle may lead to greater predictability in the climate system from season to season and that trends in southwest precipitation have exerted a considerable influence on observed warming over the past 40 years.

Seasonal meteorological forcing controls runoff generation at multiple scales in a Mediterranean forested mountain catchment

Understanding hydrological processes during dry periods in Mediterranean mountain catchments is critical due to the increasing frequency of drought episodes. In this work, we aimed at characterizing the effect of the seasonal variability of meteorological forcing on the hydrological response of a small mountain forested catchment in the Mediterranean region. We analyzed the hydrological response and its timing based on hydrometric and electrical conductivity (EC) data for a year in the nested (0.31–2 km2) Re della Pietra catchment, in Central Italy. We used a soil moisture-based metric to distinguish between wet and dry periods and performed EC-based hydrograph separations during these two periods. The results revealed the important role of seasonality as a meteorological forcing affecting soil moisture, groundwater, streamflow response, and stream event water fractions. Wet and dry periods were distinctly characterized by different streamflow, soil moisture, and groundwater responses. Event water fractions in streamflow also highlight the relevance of the seasonality in the meteorological forcing on runoff generation. Particularly, at the rainfall-runoff event scale, the combination of antecedent soil moisture and precipitation depth controlled the non-linear response of streamflow, groundwater, and different event water fractions in the wet and dry periods.Stream stages and event water fractions also varied across nested spatial scales. Antecedent moisture conditions triggered a faster streamflow response due to higher connectivity along the hillslope in the wet period, with higher event water fractions in the upper sub-catchments (25 %) compared to the lower sub-catchments (15 %). Conversely, in the dry period, higher event water fractions were registered at the outlet (11 %) than at the headwaters (7 %). Time lags between peak flows observed across the nested catchment showed a complex pattern, suggesting the interaction of multiple factors controlling the timing of streamflow peaks in the study area. These findings contributed to improve our mechanistic insights into the elusive seasonal hydrological patterns observed in Mediterranean mountain forested catchments.

Rainfall-Runoff Estimation in the North-Eastern Region of Bangladesh Using SCS-CN Method

Measuring discharge in a developing nation like Bangladesh is very important for flood prediction, land management, and sustainable development. Rainfall-induced runoff is a part of the cycle of hydrology and is required for efficient water resource planning. The most significant debatable procedure in hydrology is calculating and determining catchment surface runoff. The study aims to estimate runoff using the GIS-based Soil Conservation Service-Curve Number method (SCS – CN) method, where this hydrological model has a physical foundation and spatial distribution, and the curve number plays a key role in this model's runoff computation. The Hydrologic Soil Group (HSG) present in the study area and the land use pattern are used to find the Curve Number (CN). The calculated weighted CN for Antecedent Moisture Condition (AMC) I, II, and III for the study area was roughly 74.78, 87.12, and 94.06 respectively with the incorporation of Land use and land cover (LULC) and HSG. For total average rainfall of 35163.8mm, the SCS-CN approach determined the total average runoff 16077.55 mm for the period of 2009-2018. With a correlation coefficient of 0.966, the average rainfall and SCS-CN discharge have a significant linear relationship. The runoff regulates the amount of water that enters stream systems and returns extra precipitation to the ocean benefitting the hydrological cycle. With this runoff estimation, the quality and amount of water resources can be better understood, managed, and tracked. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 83-96

Can precipitation intermittency predict flooding?

A mystery has emerged as to why patterns of increasing extreme rainfall have not been accompanied by similar levels of flooding, garnering growing attention given concerns over future flood risks. Antecedent moisture conditions have been proposed as the missing explanatory factor. Yet, reasons for moisture variability prior to flooding remain largely unstudied. Here, we evaluate the potential utility of precipitation intermittency, defined as the dry spell length prior to a flood, to explain the variability of flooding over 108 watersheds from 1950 to 2022. Flood magnitude is shown to be sensitive to intermittency, particularly in arid and semi-arid regions (PET/P > 0.84) and for basins with low soil field capacity (<0.31 m3/m3). Following extended dry spells >20 days, floods are only possible from the most intense storms, whereas a wider range of storms can produce flooding for shorter intermittency. The flood probability decreases by approximately 0.5 % for each additional day of dry spell, with overall flood probabilities being up to 30 % lower following extended dry periods. These results underscore the potential utility of precipitation intermittency for diagnosing current and future flood risks.

Rainfall-runoff modelling using GIS based SCS-CN method in umiam catchment region, Meghalaya, India

Rainfall-runoff is a crucial parameter and is often applied in assessing water resources. It is a significant constituent for recharging groundwater and plays an important role in understanding the hydrological characteristics of a basin for watershed management practices. In the current work, the soil conservation service-curve number (SCS–CN), coupled with GIS and remote sensing techniques, has been utilized to estimate the runoff and discharge in the Umiam Catchment area of Meghalaya for the years 2011 and 2020. This area receives heavy rainfall throughout the year, which contributes to the degradation of topsoil and the initiation of landslides. The rainfall-runoff modelling incorporates various thematic layers prepared in the GIS platform to calculate the weighted CN value. The antecedent moisture condition (AMC) was applied for CN correction. Both annual and monthly runoff and discharge were calculated for a total of four sub-watersheds. The result showed that land alteration and an increase in rainfall have been the predominant reasons for an increase in runoff in 2020 compared to 2011. Comparing both years, agricultural land, built-up, and open scrub have increased followed by shrinking open and dense forests. Sub-watersheds 2 and 4 had critical discharge changes of 75.95 and 75.82 cubic meters per second (cumec) from year 2011–2020 respectively. This study emphasizes the efficacy of SCS-CN method for simulating and estimating the runoff of a basin for better watershed planning and sustainable management at the regional level. It may also help in recognizing and examining the characteristics and volume of water resources.

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

Isotopic evidence for seasonal water sources in tree xylem and forest soils

AbstractForest trees greatly influence both the routing of water downward into the subsurface and the re‐routing of water upward through water uptake and transpiration. To reveal how the subsurface soil water pools used by trees change across seasons, we analysed 2 years of stable isotope ratios of precipitation, soil water from different depths (using both bulk sampling and suction‐cup lysimeters), and xylem in a mixed beech and spruce forest. Precipitation as well as mobile and bulk soil waters all showed a distinct seasonal signature; the seasonal amplitude decreased with depth, and mobile soil waters fluctuated less than bulk soil waters. Xylem water signatures in both tree species were similar to the bulk soil water signatures and rather different from the mobile soil water signatures. The beech and spruce trees had different isotope ratios, suggesting the use of different water sources, and these differences were larger under dry antecedent conditions than wet antecedent conditions. Despite these differences, both species predominantly transpired waters with a winter‐precipitation isotopic signature throughout the summer, including during wet conditions when more recent precipitation was available. Over most of the sampling dates, the fraction of recent precipitation (i.e. from the preceding 30 days) in xylem water was low, despite both species typically demonstrating the use of both shallow and deeper soil waters. These results provide evidence that the soil water storages used by these trees are largely filled in winter and bypassed by recent precipitation, implying long residence times.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.