scholarly journals Space-time variability of soil moisture droughts in the Himalayan region

2020 ◽  
Author(s):  
Santosh Nepal ◽  
Saurav Pradhananga ◽  
Narayan Kumar Shrestha ◽  
Sven Kralisch ◽  
Jayandra Shrestha ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Hydrological Model ◽  
Himalayan Region ◽  
Spatial And Temporal Variation ◽  
Four Seasons ◽  
Spatial Coverage ◽  
Drought Conditions ◽  
Koshi River Basin ◽  
J2000 Model

Abstract. Soil water is a major requirement for biomass production and therefore one of the most important factors for agriculture productivity. As agricultural droughts are related to declining soil moisture, this paper examines soil moisture drought in the transboundary Koshi River basin in the Central Himalayan region. By applying the J2000 hydrological model, daily spatially distributed soil moisture is derived for the entire basin over a 28-year period, 1980–2007. A multi-site and multi-variable approach – streamflow data at one station and evapotranspiration data at three stations – was used for the calibration and validation of the J2000 model. In order to identify drought conditions based on the simulated soil moisture, the Soil Moisture Deficit Index (SMDI) was then calculated, considering the derivation of actual from long-term soil moisture on a weekly timescale. To spatially sub-divide the variations in soil moisture, the river basin is partitioned into three distinct geographical areas, trans-Himalaya, the high and middle mountains, and the plains. Further, the SMDI is aggregated temporally to four seasons – winter, pre-monsoon, monsoon, and post-monsoon – based on wetness and dryness patterns observed in the study area. The results indicate that the J2000 model can simulate the hydrological cycle of the basin with good accuracy. Considerable variation in soil moisture was observed in the three physiographic regions and across the four seasons due to high variation in precipitation and temperature conditions. Droughts have been increasing in frequency in the later years of the period under study, most visibly in the pre-monsoon season. Comparing the SMDI with the standardized precipitation index (SPI) suggests that SMDI can reflect a higher variation of drought conditions than SPI. The novel contribution of this study is that a spatial and temporal variation of SMDI is calculated for the first time in the Central Himalayan region and for the Koshi River basin. This calculation is based on a high-resolution spatial representation of soil moisture, which was simulated using a fully distributed hydrological model. Our results suggest that both the occurrence and severity of droughts have increased in the Koshi River basin over the last three decades, especially in the winter and pre-monsoon seasons. The insights provided into the frequency, spatial coverage, and severity of drought conditions can provide valuable inputs towards an improved management of water resources and greater agricultural productivity in the region.

scholarly journals Space–time variability in soil moisture droughts in the Himalayan region

2021 ◽  
Vol 25 (4) ◽  
pp. 1761-1783
Author(s):  
Santosh Nepal ◽  
Saurav Pradhananga ◽  
Narayan Kumar Shrestha ◽  
Sven Kralisch ◽  
Jayandra P. Shrestha ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Himalayan Region ◽  
Four Seasons ◽  
Geographical Regions ◽  
Standardised Precipitation Index ◽  
Basin Scale ◽  
Drought Conditions ◽  
Koshi River Basin ◽  
J2000 Model

Abstract. Soil water is a major requirement for biomass production and, therefore, one of the most important factors for agriculture productivity. As agricultural droughts are related to declining soil moisture, this paper examines soil moisture drought in the transboundary Koshi River basin (KRB) in the central Himalayan region. By applying the J2000 hydrological model, daily spatially distributed soil moisture is derived for the entire basin over a 28-year period (1980–2007). A multi-site and multi-variable approach – streamflow data at one station and evapotranspiration data at three stations – was used for the calibration and validation of the J2000 model. In order to identify drought conditions based on the simulated soil moisture, the soil moisture deficit index (SMDI) was then calculated, considering the derivation of actual soil moisture from long-term soil moisture on a weekly timescale. To spatially subdivide the variations in soil moisture, the river basin is partitioned into three distinct geographical regions, namely trans-Himalaya, the mountains, and the plains. Further, the SMDI is aggregated temporally to four seasons – winter, pre-monsoon, monsoon, and post-monsoon – based on wetness and dryness patterns observed in the study area. This has enabled us to look at the magnitude, extent, and duration of soil moisture drought. The results indicated that the J2000 model can simulate the hydrological processes of the basin with good accuracy. Considerable variation in soil moisture was observed in the three physiographic regions and across the four seasons due to high variation in precipitation and temperature conditions. The year 1992 was the driest year and 1998 was the wettest at the basin scale in both magnitude and duration. Similarly, the year 1992 also has the highest number of weeks under drought. Comparing the SMDI with the standardised precipitation index (SPI) suggested that SMDI can reflect a higher variation in drought conditions than SPI. Our results suggested that both the occurrence and severity of droughts have increased in the Koshi River basin over the last 3 decades, especially in the winter and pre-monsoon seasons. The insights provided into the frequency, spatial coverage, and severity of drought conditions can provide valuable contributions towards an improved management of water resources and greater agricultural productivity in the region.

Space-time variability of soil moisture based drought in the transboundary Koshi river basin of the Himalayan region

2020 ◽  
Author(s):  
Santosh Nepal ◽  
Saurav Pradhananga ◽  
Narayan Shrestha ◽  
Jayandra Shrestha ◽  
Manfred Fink ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
River Basin ◽  
Agricultural Productivity ◽  
Agricultural Drought ◽  
Himalayan Region ◽  
Severe Drought ◽  
Post Monsoon ◽  
Koshi River Basin ◽  
J2000 Model

<p>Soil moisture is an important part of the vegetation cycle and a controlling factor for agriculture. Withstanding the role of agricultural productivity in economic development of a nation, it is imperative that water resources planners and managers are able to assess and forecast agricultural drought. As agricultural drought is related to declining soil moisture, this paper studies the dynamics of soil moisture based drought in the transboundary Koshi river basin in the Himalayan region. By applying the J2000 hydrological model, the daily soil moisture is derived for the whole basin for a 28-year time frame (1980-2007). The soil moisture deficit index (SMDI) is calculated based on a fully distributed spatial representation by considering the derivation from the long term soil moisture on a weekly time scale. In order to analyze the variation of soil moisture drought spatially, the river basin is subdivided into three distinct geographical areas, i.e. Northern Tibet, High and Middle Mountains, and Southern Plain. Further, temporally the SMDI is calculated for four distinct seasons based on wetness and dryness patterns observed in the study area, i.e. monsoon, post-monsoon, winter and pre-monsoon. A multi-site and multi-variable (streamflow at one station and evapotranspiration at three stations) approach was used for the calibration and validation of the J2000 model. Results show that the J2000 model is able to simulate the hydrological cycle of the basin with high accuracy. The model properly represents the winter drought of 2005 and 2006 was the most severe drought in the 28-year time period. Results also show considerable increases in the frequency of pre-monsoon and post-monsoon soil moisture drought in recent years. Severe droughts have had a high frequency in recent years, which is also reflected by an increase of areas that were impacted. In summary, our results show that severity and occurrence of agricultural drought has increased in the Koshi river basin in the last three decades, especially in the winter and pre-monsoon. This will have serious implications for agricultural productivity and for water resources management of the basin.</p>

scholarly journals Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria

2019 ◽  
Vol 23 (2) ◽  
pp. 1113-1144 ◽  
Author(s):  
Abolanle E. Odusanya ◽  
Bano Mehdi ◽  
Christoph Schürz ◽  
Adebayo O. Oke ◽  
Olufiropo S. Awokola ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Model Calibration ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Potential Evapotranspiration ◽  
Swat Model ◽  
Water Balance Components ◽  
Southwestern Nigeria ◽  
Calibration And Validation

Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

Catchment scale prediction of soil moisture trends from Cosmic Ray Neutron Rover Surveys using machine learning

2020 ◽  
Author(s):  
Erik Nixdorf ◽  
Marco Hannemann ◽  
Uta Ködel ◽  
Martin Schrön ◽  
Thomas Kalbacher
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Random Forest ◽  
River Basin ◽  
Hydrological Model ◽  
Cosmic Ray ◽  
Moisture Distribution ◽  
Catchment Scale ◽  
Forest Model ◽  
Soil Moisture Distribution

<p>Soil moisture is a critical hydrological component for determining hydrological state conditions and a crucial variable in controlling land-atmosphere interaction including evapotranspiration, infiltration and groundwater recharge.</p><p>At the catchment scale, spatial- temporal variations of soil moisture distribution are highly variable due to the influence of various factors such as soil heterogeneity, climate conditions, vegetation and geomorphology. Among the various existing soil moisture monitoring techniques, the application of vehicle-mounted Cosmic Ray Sensors (CRNS) allows monitoring soil moisture noninvasively by surveying larger regions within a reasonable time. However, measured data and their corresponding footprints are often allocated along the existing road network leaving inaccessible parts of a catchment unobserved and surveying larger areas in short intervals is often hindered by limited manpower.</p><p>In this study, data from more than 200 000 CRNS rover readings measured over different regions of Germany within the last 4 years have been employed to characterize the trends of soil moisture distribution in the 209 km<sup>2</sup> large Mueglitz River Basin in Eastern Germany. Subsets of the data have been used to train three different supervised machine learning algorithms (multiple linear regression, random forest and artificial neural network) based on 85 independent relevant dynamic and stationary features derived from public databases.  The Random Forest model outperforms the other models (R2= ~0.8), relying on day-of-year, altitude, air temperature, humidity, soil organic carbon content and soil temperature as the five most influencing predictors.</p><p>After test and training the models, CRNS records for each day of the last decade are predicted on a 250 × 250 m grid of Mueglitz River Basin using the same type of features. Derived CRNS record distributions are compared with both, spatial soil moisture estimates from a hydrological model and point estimates from a sensor network operated during spring 2019. After variable standardization, preliminary results show that the applied Random Forest model is able to resemble the spatio-temporal trends estimated by the hydrological model and the point measurements. These findings demonstrate that training machine learning models on domain-unspecific large datasets of CRNS records using spatial-temporally available predictors has the potential to fill measurement gaps and to improve soil moisture dynamics predictions on a catchment scale.</p>

scholarly journals ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yongwei Liu ◽  
Wen Wang ◽  
Yuanbo Liu
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Hydrological Model ◽  
European Space Agency ◽  
Low Flow ◽  
Observation Error ◽  
Rainfall Runoff ◽  
Huai River Basin ◽  
Huai River ◽  
Streamflow Simulation

The assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. This study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. The results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. The less-effective performance on large-flood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. This study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

scholarly journals Impact of Climate Change on the Snow Hydrology of Koshi River Basin

2016 ◽  
Vol 9 (1) ◽  
pp. 28-44 ◽  
Author(s):  
A. Khadka ◽  
L.P. Devkota ◽  
R.B. Kayastha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impacts ◽  
Baseline Period ◽  
Himalayan Region ◽  
Climate Change Scenarios ◽  
Snow Hydrology ◽  
Monsoon Flow ◽  
Change Impact ◽  
Koshi River Basin

Koshi river basin which is one of the largest river basins of Nepal has its headwaters in the northern Himalayan region of the country covered with perennial snow and glaciers. Increased warming due to climate change is most likely to impact snowpack of this Himalayan region. Snowmelt Runoff Model, a degree day based method, was used in this study to assess the snowmelt hydrology of the five sub-basins, viz. Tamor, Arun, Dudhkoshi, Tamakoshi and Sunkoshi of the Koshi river basin, with and without climate change impacts. The model has been fairly able to simulate the flow. Daily bias-corrected RCM data of PRECIS-ECHAM05 and PRECIS-HadCM3 for the period of 2041-2060 were used for future projection. A period of 2000-2008 was set as baseline period to evaluate changes in future flow. In climate change scenarios, magnitude and frequency of peak flows are expected to increase and snowmelt contribution to total river flows are likely to be more. Simulated flow results indicate that the annual flow would still be governed by monsoon flow even in the future under the climate change impact. A high probability of having more flows and snowmelt in 50’s decade than that in 40’s decade is seen. The estimated future flow by ECHAM05 is found more than those estimated by HadCM3 both seasonally and annually.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.28-44

scholarly journals Seasonality of hydrological model spin-up time: a case study using the Xinanjiang model

2016 ◽  
Author(s):  
Mohammad M. Rahman ◽  
Minjiao Lu ◽  
Khin H. Kyi
Keyword(s):  
Soil Moisture ◽  
Equilibrium State ◽  
River Basin ◽  
Input Data ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Aridity Index ◽  
Adjustment Process ◽  
Data Sets ◽  
Xinanjiang Model

Abstract. The internal adjustment process of a hydrological model followed by an unusual initial condition is known as the model spin-up. And the time required for a complete adjustment is termed as the model spin-up time. Model results for the duration of this spin-up progression are greatly impacted by the initial conditions, and often impractical or erroneous. The speed of this adjustment process is affected by the characteristics of the input data sets and their persistence. This study discusses the variability and seasonality of hydrological model spin-up time against the aridity of the river basin using multi-year climatologies for 18 river basins distributed relatively snow-free regions of the USA. The Xinanjiang model was run with each of all available year input data sets with two extreme initial conditions (saturated and unsaturated) and thereafter detected the model equilibrium state based on the Mahalanobis distance between the soil moisture states of two model runs. The seasonality of model spin-up was investigated by conducting multiple simulations that start from different time of a year. The basin average soil moisture memory (SMM) timescale (Rahman et al., 2015) and basin aridity index was estimated and thereafter investigated their relationship with the average model spin-up time. Analysis suggests that the spin-up time highly varies with the simulation starting time and the dryness of the river basin. Overall, in all basins, model achieves the equilibrium state quickly while the simulation starts in late autumn (October–November). On the other hand, model equilibrates slowly while simulation starts in spring (March–May). Wet basin shows stronger variability of the model spin-up time (mean range 154 days) throughout the year as compared with that of dry basins (mean range 78 days). The mean spin-up time is shorter for wet basins (154 days) and longer for dry basins (233 days). The spin-up times are 3–7 times longer than the SMM timescale. The basin-wise mean spin-up time shows linear and exponential relationship with the SMM timescale and the basin aridity index respectively. The relationship offers predictability of model spin-up time from widely available potential evaporation and precipitation data sets.

scholarly journals Drought Assessment in the São Francisco River Basin Using Satellite-Based and Ground-Based Indices

2021 ◽  
Vol 13 (19) ◽  
pp. 3921
Author(s):  
Franklin Paredes-Trejo ◽  
Humberto Alves Barbosa ◽  
Jason Giovannettone ◽  
T. V. Lakshmi Kumar ◽  
Manoj Kumar Thakur ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Extreme Drought ◽  
Severe Drought ◽  
Sao Francisco River ◽  
São Francisco River ◽  
Groundwater Drought ◽  
Drought Conditions

The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). Historically, in the low part of the SFRB, people have to cope with strong periods of drought. However, there are incipient signs of increasing drought conditions in the upper and middle parts of the SFRB, where its main reservoirs (i.e., Três Marias, Sobradinho, and Luiz Gonzaga) and croplands are located. Therefore, the assessment of the impacts of extreme drought events in the SFRB is of vital importance to develop appropriate drought mitigation strategies. These events are characterized by widespread and persistent dry conditions with long-term impacts on water resources and rain-fed agriculture. The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12-month time scales derived from ground data were used as benchmark drought indices. The self-calibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB. A drying trend at an annual time scale in the middle and south regions of the SFRB was evidenced. An expansion of the area under drought conditions was observed only during the southern hemisphere winter months (i.e., JJA). A marked depletion of groundwater levels concurrent with an increase in soil moisture content was observed during the most severe drought conditions, indicating an intensification of groundwater abstraction for irrigation. These results could be useful to guide social, economic, and water resource policy decision-making processes.

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