Soil Moisture Storage Capacity Research Articles

LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction

The advancements in deep learning methods have presented new opportunities and challenges for predicting land surface variables (LSVs) due to their similarity with computer sciences tasks. However, few researchers focus on the benchmark datasets for LSVs predictions that hampers fair comparisons of different data-driven deep learning models. Hence, we propose a LSVs benchmark dataset and prediction toolbox to boost research in data-driven LSVs modeling and improve the consistency of data-driven deep learning models for LSVs. LSVs benchmark dataset contains a large number of hydrology-related variables, such as global soil moisture, runoff, etc., which can verify the simulation of hydrological processes. Various global data from European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5), ERA5-land, global gridded soil information (SoilGrid), soil moisture storage capacity (SMSC), and moderate-resolution imaging spectroradiometer (MODIS) datasets have been pre-processed into daily data at 0.5-, 1-, 2-, and 4-degree resolutions to facilitate their use in data-driven models. Simple statistical metrics, i.e., the root mean squared error and correlation coefficient, are chosen to evaluate the performance of different deep learning (DL) models, including convolutional neural network, long short-term memory and convolution long short-term memory models, with lead times of 1 and 5 days. A processed-based model serves as a physic baseline, soil moisture and surface sensible heat fluxes are taken as the target variables. The developed benchmark dataset and evaluation metrics for predicting LSVs using data-driven approaches, named as the LandBench toolbox, were implemented using Pytorch. This toolbox facilitates the reimplementation of existing methods, the development of novel predictive models, and the utilization of unified evaluation metrics. Additionally, the toolbox incorporates address mapping technology to enable high-resolution global predictions with constrained computing resources. We hope LandBench will not only serves as a standardized framework, fostering equitable model comparisons, but also provides indispensable data and a robust scientific foundation essential for advancing climate change research, disaster management, and sustainable development initiatives.

Drought Induced Dynamic Traits of Soil Water and Inorganic Carbon in Different Karst Habitats

Understanding the temporal variability of soil water and carbon is an important prerequisite for restoring the vegetation in fragile karst ecosystems. A systematic study of soil moisture and carbon storage capacity under drought conditions in different karst habitats is critical for cultivating suitable crops in karst regions. The hydrological characteristics of soil and changes in soil HCO3−, pH, and EC values under drought conditions were measured on simulated rock outcrops and non-outcrops in an indoor pot experiment. The results showed that the rock outcrops had less evaporation and significantly greater water retention capacity than the non-outcrops, which gave the retained water in the rock outcrops sufficient reaction time to dissolve atmospheric CO2, as well as to promote dissolution at the rock–soil interface. Therefore, the carbon sequestration capacity of the rock outcrops was higher than that of the non-outcrops. Due to the rock–soil–water interaction in the early stage of drought, the soil HCO3− concentration in the rock outcrops fluctuated with soil water content, but the soil HCO3− concentration tended to be stable in the whole drought period, showing a phenomenon of zero-carbon sink. No obvious change was observed in the soil HCO3− concentration in non-outcrops during the drought period, which indicated that the carbon sequestration of rock outcrops was mainly attributed to the dissolution of rocks. Therefore, rock outcrops were more effective for water and carbon storage, compared with non-outcrops, under drought, and could provide more available water and carbon resources for supporting the photosynthesis of plants in karst regions.

Assessing the Increase in Soil Moisture Storage Capacity and Nutrient Enhancement of Different Organic Amendments in Paddy Soil

Increasing soil moisture storage capacity is a strategy that can be implemented to minimize the use of water in paddy rice cultivation. Organic materials from different sources have the potential to increase soil moisture storage and nutrient enrichment. An incubation study was conducted to evaluate the incorporation of five selected organic amendments—as follows: rice husk biochar (RHB), oil palm empty fruit bunch biochar (EFBB), compost (COMP), rice husk ash (RHA), and oil palm bunch ash (PBA), with a control (no amendment) on soil moisture storage and some chemical properties of soil. The soil was incubated with five amendments for 60 days and sampled at 15-day intervals. After completion of the incubation, a greater extent of gravimetric water content was observed from RHB (0.46 g g−1) and EFBB (0.45 g g−1) followed by compost (0.40 g g−1). The addition of organic amendments significantly influenced soil chemical properties. Maximum soil pH was altered by PBA followed by EFBB compared to its initial value (5.01). The inclusion of EFBB finally contributed to the highest amount of total carbon (7.82%) and nitrogen (0.44%). The addition of PBA showed the highest available P and exchangeable K followed by RHB when compared with the amendments. The results indicated that RHB, EFBB, and compost retain more soil moisture compared to ash sources and added soil nutrients, indicating their potential to improve the chemical and hydrological properties of paddy soil.

Increased colluvial hollow discharge and subsequent recovery after a low intensity wildfire in the Blue Ridge Mountains, USA

AbstractWildfires in mountainous regions have been documented to enhance water repellent soils which can increase runoff, erosion, and sedimentation during subsequent rain events. However, the extent of soil hydrophobicity and water repellency varies significantly with burn severity and between ecosystems, and the southern Appalachians remain an understudied region. Here we examine the impact of the low severity Chestnut Knob Fire, which occurred in the fall 2016, on soil properties and runoff in South Mountains State Park. To examine these impacts, we installed crest‐stage gauges in burned (n = 10) and unburned (n = 8) colluvial hollows to compare peak runoff. Results from the 2017 field season indicated that burned locations produced significantly higher peak discharges than unburned sites. From July 2019 to January 2020, we repeated the experiment and found that burned areas produced runoff comparable to unburned areas. Examination of soil profiles during the summer of 2017 found high variability in hydrophobicity in both the burned (n = 10) and unburned (n = 2) soils. Further, we found that burned soils had significantly deflated organic surface horizons compared with unburned soils. We interpret the differences in runoff in 2017 to be the result of a combination of increased hydrophobicity and decreased soil moisture storage capacity in organic rich surface soils. While the recovery we observed here was relatively fast, it is important to understand that increased runoff immediately after a fire likely increases the chances of sediment mobilization and debris flow occurrence.

A coupled atmospheric–hydrologic modeling system with variable grid sizes for rainfall–runoff simulation in semi-humid and semi-arid watersheds: how does the coupling scale affects the results?

Abstract. The coupled atmospheric–hydrologic modeling system is an effective way to improve the accuracy of rainfall–runoff modeling and extend the lead time in real-time flood forecasting. The aim of this study is to explore the appropriate coupling scale of the coupled atmospheric–hydrologic modeling system, which is established by the Weather Research and Forecasting (WRF) model and the gridded Hebei model with three different sizes (1 km×1 km, 3 km×3 km and 9 km×9 km). The Hebei model is a conceptual rainfall–runoff model designed to describe a mixed runoff generation mechanism, including both storage excess and infiltration excess, in the semi-humid and semi-dry area of northern China. The soil moisture storage capacity and infiltration capacity of different grids in the gridded Hebei model are obtained and dispersed using the topographic index. The lumped Hebei model is also used to establish the lumped atmospheric–hydrologic coupled system as a reference system. Four 24 h storm events occurring at two small- and medium-scale sub-watersheds in northern China are selected as case studies. Contrastive analyses of the flood process simulations from the gridded and lumped systems are carried out. The results show that the flood simulation results may not always be improved with higher-dimension precision and more complicated system, and the grid size selection has a strong relationship with the rainfall evenness. For the storm events with uniform spatial distribution, the coupling scale has less impact on flood simulation results, and the lumped system also performs well. For the storm events with uneven spatiotemporal distribution, the corrected rainfall can improve the simulation results significantly, and higher resolution leads to better flood process simulation. The results can help to establish the appropriate coupled atmospheric–hydrologic modeling system to improve the flood forecasting accuracy.

A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data

Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter β to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter β was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter β are proposed, i.e., one is to derive a priori distribution of β , and the other is to derive a fixed value for β . The simulations of the Hymod models employing the two ways to estimate β are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter β , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter β of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed β value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the β value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter β .

Pros and cons of no-till technology in a long-term field experiment on sod-podzolic soil

In a ten-year field experiment the technologies of conventional tillage (Plowing) and resource- and soil-saving treatment (No-till and Mini-till) were compared. The pros and cons of the No-tillage in the condition of temperate humid zone on the sod-podzolic soil are shown. The cons are: significant increase of soil density, lower crop biomass, higher weed infestation, an increase of pesticides application. The pros are increased soil moisture storage capacity, potential stability of cereal crop yield and economic benefits on grain production.

On the use of mean monthly runoff to predict the flow–duration curve in ungauged catchments

ABSTRACTWe examine the applicability of predicting the daily flow–duration curve (FDC) using mean monthly runoff represented in its stochastic form (MM_FDC) to aid in predictions in ungauged basins, using long-term hydroclimatic data at 73 catchments of humid climate, in the eastern USA. The analysis uses soil hydrological properties, soil moisture storage capacity and the predominant runoff generation mechanism. The results show that MM_FDC did not distinguish the shapes of the upper and lower thirds of the FDC. The upper third is where the precipitation pattern and the antecedent moisture conditions are dominant, while the lower third is where drought-induced low flows and the evapotranspiration effect are prevalent. It is possible to use the MM_FDC to predict the middle third of the FDC (exceedence probabilities between 33% and 66%). The method is constrained by the catchment flow variability (slope of FDC), which changes in accordance with landscape properties and the predominant runoff generation mechanism.

Comparison of Satellite Soil Moisture Products in Mongolia and Their Relation to Grassland Condition

Monitoring of soil moisture dynamics provides valuable information about grassland degradation, since soil moisture directly affects vegetation cover. While the Mongolian soil moisture monitoring network is limited to the urban and protected natural areas, remote sensing data can be used to determine the soil moisture status elsewhere. In this paper, we determine whether in situ and remotely sensed data in the unaccounted areas of Southwestern Mongolia are consistent with each other, by comparing Soil Moisture and Ocean Salinity (SMOS) first passive L-band satellite data with in situ measurements. To evaluate the soil moisture products, we calculated the temporal, seasonal, and monthly average soil moisture content. We corrected the bias of SMOS soil moisture (SM) data using the in situ measured soil moisture with both the simple ratio and gamma methods. We verified the bias-corrected SMOS data with Nash–Sutcliffe method. The comparison results suggest that bias correction (of the simple ratio and gamma methods) enhances the reliability of the SMOS data, resulting in a higher correlation coefficient. We then examined the correlation between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems. Analysis of the SMOS and in situ measured soil moisture data revealed that spatial soil moisture distribution matches the rainfall events in Southwestern Mongolia for the period 2010 to 2015. The results illustrate that the bias-corrected, monthly-averaged SMOS data has a high correlation with the monthly-averaged NDVI (R2 > 0.81). Both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. During 2015, we detected decreasing soil moisture in approximately 30% of the forest-steppe and steppe areas. We assume that the current ecosystem of land is changing rapidly from forest to steppe and also from steppe to desert. The rainfall rate is the most critical factor influencing the soil moisture storage capacity in this region. The collected SMOS data reflects in situ conditions, making it an option for grassland studies.

Predicting soil types and soil properties with limited data in the Uasin Gishu Plateau, Kenya

Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only general soil maps are available. In this study, we utilized a knowledge-based inference soil mapping approach to develop a first-generation digital soil map for part of the Uasin Gishu Plateau in western Kenya. We calculated the following environmental covariates from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM): slope gradient, multi-resolution valley bottom flatness index (MrVBF), multi-resolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were then used along with existing soil information and expert knowledge from soil scientists familiar with the area to produce new raster-based maps of soil types, effective soil depth, soil moisture storage capacity and soil drainage class. The soil type maps predicted using clustering analysis and fuzzy logic methods showed good agreement with field observations based on the overall accuracy values. The fuzzy logic map performed slightly better (kappa coefficient (k) = 0.68; overall accuracy = 0.76) than the map based on clustering analysis (k = 0.59; overall accuracy = 0.68). The accuracy for the effective soil depth fuzzy logic map was higher (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). Seven major soil types occur in the study area: Ferralsols, Nitisols, Gleysols, Luvisols, Acrisols, Cambisols and Regosols, according to the World Reference Base soil classification system. This study generated detailed and improved predictions of soil types and properties at 30 m grid resolution. These maps should be more useful for soil, crop and land use management decisions than existing maps.

Effect of subsoiling depths on soil physical characters and sugarcane yield

We investigated the physical properties of the plough soil and the components of sugarcane yield in the depth of mechanized subsoiling of sugarcane field, along with the clarification on the specific soil location and obstacle factors of subsoiling, with the aim to provide scientific basis for the construction of a good sugarcane cultivation layer and the development of soil improvement strategies. Three depths of subsoiling operation (35, 40 and 45 cm) were set up, with nosubsoiling as control. Soil physical properties, including compactness, bulk density, water content, porosity, three-phase volume ratio, and yield components and cane yield of sugarcane in the fields were investigated. The results showed that subsoiling depth was significantly correlated with the soil structure characteristics and the improvement of sugarcane yield in sugarcane field. Subsoiling broke down the plow bottom, significantly reduced soil compaction, bulk density, and the corresponding penetration resistance and shear strength during mechanical operation, especially for the above factors in 20-30 cm soil layer, with positive consequences for sugarcane yield. Moreover, subsoiling significantly increased the liquid volume rate of the soil layer within 30 cm and soil moisture storage capacity, and thus significantly improved the water index of the 10-30 cm soil layer. The 10-30 cm soil layer was the location for the most significant effect of subsoiling on the improvement of solid volume rate in the plough soil. The effective stem number, plant height, cane yield and sucrose content of sugarcane were significantly promoted by subsoiling. In view of the common equipment level in the sugarcane planting area, we suggested that the operating depth standard of mechanized subsoiling should not be less than 40 cm.

Research on soil quality under traditional drainage and ecological water storage models of saline and alkaline land

This paper aims to study halide under traditional drainage and ecological water treatment of saline-alkali soil through bulk density, porosity, moisture content, soluble and nutrient characteristics in Shaanxi, to analyze the different governance mode of different soil physical and chemical characteristics to provide scientific basis for saline-alkali land management. Saline-alkali land model test was set in Shaanxi Fuping, respectively set up traditional drainage and ecological water treatment, analysis soil bulk density, porosity, electric conductivity, total salt, organic matter, total N, available K, available P and fractal dimension of 0-30 cm soil layer; Analysis soil moisture content and reservoir capacity of 0-160 cm soil layer, and comprehensive analysis the average fractal dimension of soil and soil nutrient average correlation in 0-30 cm soil depth. Results show that: (1) in 0-30 cm soil depth, two kinds of mode, the soil bulk density and porosity appear consistent, the soil bulk density can be reduced, and ecological water storage mode can effectively increase the soil porousness compare with traditional drainage mode. (2) Under the same amount of water, in the 0-160 cm soil layer, the average soil moisture and average water storage capacity under the ecological water storage treatment were 4.47% and 2.57% higher than the traditional drainage treatment. (3) In the 0-30 cm soil layer, the traditional drainage and ecological water storage treatment significantly reduced the soil pH, conductivity and total salt content before the test, and the ecological water storage treatment decreased significantly; (4) In the 0-30 cm soil layer, the nutrient content of each treatment showed the same trend, which decreased with the increase of soil layer, and the average content of organic matter, total nitrogen, available potassium and available phosphorus under ecological storage treatment was higher than that of traditional drainage treatment. The height was 18.96%, 4.76%, 10.67% and 9.35%, and the difference between treatments was significant (P<0.05). (5) In the 0-30 cm soil layer, the fractal dimension of soil aggregates in dry and wet sieves showed opposite trend, and there was a good linear relationship with soil average bulk density, organic matter, total nitrogen, available potassium and available phosphorus (R2 = 0.8006∼0.9499), the difference was significant (P<0.05). In summary, the ecological water storage treatment can effectively improve soil physical and chemical properties, improve soil stability and soil quality, and achieve good salinity treatment.

Regional variation of flow duration curves in the eastern United States: Process-based analyses of the interaction between climate and landscape properties

This paper advances the physical understanding of the flow duration curve (FDC) regional variation. It provides a process-based analysis of the interaction between climate and landscape properties to explain disparities in FDC shapes. We used (i) long term measured flow and precipitation data over 73 catchments from the eastern US. (ii) We calibrated the Sacramento model (SAC-SMA) to simulate soil moisture and flow components FDCs. The catchments classification based on storm characteristics pointed to the effect of catchments landscape properties on the precipitation variability and consequently on the FDC shapes. The landscape properties effect was pronounce such that low value of the slope of FDC (SFDC)—hinting at limited flow variability—were present in regions of high precipitation variability. Whereas, in regions with low precipitation variability the SFDCs were of larger values. The topographic index distribution, at the catchment scale, indicated that saturation excess overland flow mitigated the flow variability under conditions of low elevations with large soil moisture storage capacity and high infiltration rates. The SFDCs increased due to the predominant subsurface stormflow in catchments at high elevations with limited soil moisture storage capacity and low infiltration rates. Our analyses also highlighted the major role of soil infiltration rates on the FDC despite the impact of the predominant runoff generation mechanism and catchment elevation. In conditions of slow infiltration rates in soils of large moisture storage capacity (at low elevations) and predominant saturation excess, the SFDCs were of larger values. On the other hand, the SFDCs decreased in catchments of prevalent subsurface stormflow and poorly drained soils of small soil moisture storage capacity. The analysis of the flow components FDCs demonstrated that the interflow contribution to the response was the higher in catchments with large value of slope of the FDC. The surface flow FDC was the most affected by the precipitation as it tracked the precipitation duration curve (PDC). In catchments with low SFDCs, this became less applicable as surface flow FDC diverged from PDC at the upper tail (> 40% of the flow percentile). The interflow and baseflow FDCs illustrated most the filtering effect on the precipitation. The process understanding we achieved in this study is key for flow simulation and assessment in addition to future works focusing on process-based FDC predictions.

Integrating Topography and Soil Properties for Spatial Soil Moisture Storage Modeling

The understanding of the temporal and spatial dynamics of soil moisture and hydraulic property of soil is crucial to the study of hydrological and ecological processes. The purpose of this study was to derive equations that describe spatial soil water storage deficit based on topography and soil properties. This storage deficit together with the topographical index can be used to conclude the spatial distribution curve of storage capacity in a (sub-) basin for developing hydrological model. The established model was able to match spatial and temporal variations of water balance components (i.e., soil moisture content (SMC), evapotranspiration, and runoff) over the Ziluoshan basin. Explicit expression of the soil moisture storage capacity (SMSC) in the model reduced parameters, which provides a method for hydrological simulation in ungauged basins.

The impact of land use/land cover changes and hydraulic structures on flood recession process

Intensified human activities have brought about great changes in runoff generation and convergence. As a significant part of the hydrological process, recession flows represent the capacity of a river basin to store rain and drain it during dry periods; therefore study of the influence of human-induced factors on flood flow recession is of great importance. The Fuping sub-catchment was selected as the study area. Comparisons of land use/land cover and soil moisture storage capacity changes were made between reference and impaired periods. In addition, 64 recession flows during 1958–2005 were simulated using the linear and non-linear reservoir recession models. Then the influence of land use/land cover changes and hydraulic structures on recession flows was identified. Results showed that grassland and cultivated land declined in area while forests increased. At the same time, there was a sharp increase in the soil moisture storage capacity. The non-linear recession model, being more accurate than the linear recession model, was used to simulate the recession process. Compared with recession curves before 1980, the initial outflow from the basin declined while the power law coefficient and recession duration increased; the power law exponent was relatively constant. Furthermore, the shapes of recession curves were flattened.

Flood forecasting that considers the impact of hydraulic projects by an improved TOPMODEL model in the Wudaogou basin, Northeast China

Many hydraulic projects such as reservoirs, ponds and paddy fields as well as soil and water conservation engineering projects have been constructed to improve utilization of water resources upstream of the Wudaogou station basin in Northeast China in recent years. As a result, the local hydrological characteristics of the basin and the flood runoff and process have been changed. These changes in the basin characteristics make basin hydrological forecasting more difficult. In order to model and assess this situation, the TOPMODEL, which includes the dynamic soil moisture storage capacity (DSMSC-TOPMODEL), is used in this study to simulate the flood impact of hydraulic projects. Furthermore, the Bayesian method is used to evaluate model parameter uncertainty and assess the TOPMODEL's performance over the basin. Flood simulation results show that accuracy is significantly improved when the stock version of TOPMODEL is replaced with DSMSC-TOPMODEL, with the qualified ratio of forecasting runoff yield increasing from 65% in the former to 88% in the latter. Moreover, these flood simulations are more suitable for helping observers visualize the process.

Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM) resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin.

Effects of Irrigation in India on the Atmospheric Water Budget

Abstract The effect of large-scale irrigation in India on the moisture budget of the atmosphere was investigated using three regional climate models and one global climate model, all of which performed an irrigated run and a natural run without irrigation. Using a common irrigation map, year-round irrigation was represented by adding water to the soil moisture to keep it at 90% of the maximum soil moisture storage capacity, regardless of water availability. For two focus regions, the seasonal cycle of irrigation matched that of the reference dataset, but irrigation application varied between the models by up to 0.8 mm day−1. Because of the irrigation, evaporation increased in all models, but precipitation decreased because of a strong decrease in atmospheric moisture convergence. A moisture tracking scheme was used to track individual evaporated moisture parcels through the atmosphere to determine where these lead to precipitation. Up to 35% of the evaporation moisture from the Ganges basin is recycling within the river basin. However, because of a decreased moisture convergence into the river basin, the total amount of precipitation in the Ganges basin decreases. Although a significant fraction of the evaporation moisture recycles within the river basin, the changes in large-scale wind patterns due to irrigation shift the precipitation from the eastern parts of India and Nepal to the northern and western parts of India and Pakistan. In these areas where precipitation increases, the relative precipitation increase is larger than the relative decrease in the areas where precipitation decreases. It is concluded 1) that the direct effects of irrigation on precipitation are small and are not uniform across the models; 2) that a fraction of up to 35% of any marginal evaporation increase (for example, due to irrigation) will recycle within the river basin; and 3) that when irrigation is applied on a large scale, the dominant effect will be a change in large-scale atmospheric flow that decreases precipitation in eastern India and increases it in western and northern India.

Mapping the role of tropical cyclones on the hydroclimate of the southeast United States: 2002–2011

ABSTRACTThe role of tropical cyclones (TCs) in the water budget of 3211 watersheds in the southeast United States (SE US) over the last decade (2002–2011) is examined in detail at daily and longer time‐scales towards mapping regional hydro‐climatic vulnerabilities. The work documents highly heterogeneous spatial and inter‐annual patterns of variability in the contribution of TC events to the annual cycle of rainfall from below 5% (e.g. 2011) to 65–70% (e.g. 2004 and 2005) at the watershed scale. The data indicate that, with average return periods of 1–3 years, singular TCs are key agents of drought demise redressing watershed precipitation deficits by 30–100%, and a key mechanism of regional meteorological drought mitigation at seasonal and inter‐annual time‐scales. Flood response and drought mitigation impacts of TCs are stronger for storms with terrestrial tracks predominantly on the Atlantic region and, or aligned with the Appalachian Mountains (AM) where orographic effects are evident in high precipitation amounts and high runoff ratios. Locally, rainfall‐runoff response along the terrestrial tracks of TCs increases when inter‐storm arrivals are short (&lt;2 weeks), but evidence of the cumulative impact of antecedent precipitation on available soil moisture storage capacity at the seasonal scale is lacking except where groundwater‐tables are shallow in the Piedmont and in the Coastal Plain. Flood peaks do not exceed the 2‐year flood magnitude event at over 90% of gauged locations for any one storm. Thus, whereas increases in intensity and frequency of TCs should have a positive impact on regional water resources by reducing or interrupting and eliminating severe drought, especially in the Piedmont and Coastal Plains, the impact on river discharge should mostly affect the magnitude of the bank‐full and low hydroperiodicity flooding (≤5‐year events) that are tightly linked to the functional resiliency of natural and developed landscapes.

Numerical simulation and evaluation of a new hydrological model coupled with GRAPES

Hydrological processes exert enormous influences on the land surface water and energy balance, and have a close relationship with human society. We have developed a new hydrological runoff parameterization called XXT to improve the performance of a coupled land surface-atmosphere modeling system. The XXT parameterization, which is based upon the Xinanjiang hydrological model and TOPMODEL, includes an optimized function of runoff calculation with a new soil moisture storage capacity distribution curve (SMSCC). We then couple XXT with the Global/Regional Assimilation Prediction System (GRAPES) and compare it to GRAPES coupled with a simple water balance model (SWB). For the model evaluation and comparison, we perform 72-h online simulations using GRAPES-XXT and GRAPES-SWB during two torrential events in August 2007 and July 2008, respectively. The results show that GRAPES can reproduce the rainfall distribution and intensity fairly well in both cases. Differences in the representation of feedback processes between surface hydrology and the atmosphere result in differences in the distributions and amounts of precipitation simulated by GRAPES-XXT and GRAPES-SWB. The runoff simulations are greatly improved by the use of XXT in place of SWB, particularly with respect to the distribution and amount of runoff. The average runoff depth is nearly doubled in the rainbelt area, and unreasonable runoff distributions simulated by GRAPES-SWB are made more realistic by the introduction of XXT. Differences in surface soil moisture between GRAPES-XXT and GRAPES-SWB show that the XXT model changes infiltration and increases surface runoff. We also evaluate river flood discharge in the Yishu River basin. The peak values of flood discharge calculated from the output of GRAPES-XXT agree more closely with observations than those calculated from the output of GRAPES-SWB.