Search Results

You are looking at 1 - 7 of 7 items for

  • Author or Editor: Guo-Yue Niu x
  • Refine by Access: All Content x
Clear All Modify Search
Li-Ling Chang
and
Guo-Yue Niu

Abstract

The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions.

Significance Statement

The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.

Free access
Guo-Yue Niu
and
Zong-Liang Yang

Abstract

The presence of ice in soil dramatically alters soil hydrologic and thermal properties. Despite this important role, many recent studies show that explicitly including the hydrologic effects of soil ice in land surface models degrades the simulation of runoff in cold regions. This paper addresses this dilemma by employing the Community Land Model version 2.0 (CLM2.0) developed at the National Center for Atmospheric Research (NCAR) and a simple TOPMODEL-based runoff scheme (SIMTOP). CLM2.0/SIMTOP explicitly computes soil ice content and its modifications to soil hydrologic and thermal properties. However, the frozen soil scheme has a tendency to produce a completely frozen soil (100% ice content) whenever the soil temperature is below 0°C. The frozen ground prevents infiltration of snowmelt or rainfall, thereby resulting in earlier- and higher-than-observed springtime runoff. This paper presents modifications to the above-mentioned frozen soil scheme that produce more accurate magnitude and seasonality of runoff and soil water storage. These modifications include 1) allowing liquid water to coexist with ice in the soil over a wide range of temperatures below 0°C by using the freezing-point depression equation, 2) computing the vertical water fluxes by introducing the concept of a fractional permeable area, which partitions the model grid into an impermeable part (no vertical water flow) and a permeable part, and 3) using the total soil moisture (liquid water and ice) to calculate the soil matric potential and hydraulic conductivity. The performance of CLM2.0/SIMTOP with these changes has been tested using observed data in cold-region river basins of various spatial scales. Compared to the CLM2.0/SIMTOP frozen soil scheme, the modified scheme produces monthly runoff that compares more favorably with that estimated by the University of New Hampshire–Global Runoff Data Center and a terrestrial water storage change that is in closer agreement with that measured by the Gravity Recovery and Climate Experiment (GRACE) satellites.

Full access
Enrique Rosero
,
Zong-Liang Yang
,
Lindsey E. Gulden
,
Guo-Yue Niu
, and
David J. Gochis

Abstract

The authors introduce and compare the performance of the unified Noah land surface model (LSM) and its augments with physically based, more conceptually realistic hydrologic parameterizations. Forty-five days of 30-min data collected over nine sites in transition zones are used to evaluate (i) their benchmark, the standard Noah LSM release 2.7 (STD); (ii) a version equipped with a short-term phenology module (DV); and (iii) one that couples a lumped, unconfined aquifer model to the model soil column (GW). Their model intercomparison, enhanced by multiobjective calibration and model sensitivity analysis, shows that, under the evaluation conditions, the current set of enhancements to Noah fails to yield significant improvement in the accuracy of simulated, high-frequency, warm-season turbulent fluxes, and near-surface states across these sites. Qualitatively, the versions of DV and GW implemented degrade model robustness, as defined by the sensitivity of model performance to uncertain parameters. Quantitatively, calibrated DV and GW show only slight improvement in the skill of the model over calibrated STD. Then, multiple model realizations are compared to explicitly account for parameter uncertainty. Model performance, robustness, and fitness are quantified for use across varied sites. The authors show that the least complex benchmark LSM (STD) remains as the most fit version of the model for broad application. Although GW typically performs best when simulating evaporative fraction (EF), 24-h change in soil wetness (ΔW 30), and soil wetness, it is only about half as robust as STD, which also performs relatively well for all three criteria. GW’s superior performance results from bias correction, not from improved soil moisture dynamics. DV performs better than STD in simulating EF and ΔW 30 at the wettest site, because DV tends to enhance transpiration and canopy evaporation at the expense of direct soil evaporation. This same model structure limits performance at the driest site, where STD performs best. This dichotomous performance suggests that the formulations that determine the partitioning of LE flux need to be modified for broader applicability. Thus, this work poses a caveat for simple “plug and play” of functional modules between LSMs and showcases the utility of rigorous testing during model development.

Full access
Cédric H. David
,
David R. Maidment
,
Guo-Yue Niu
,
Zong-Liang Yang
,
Florence Habets
, and
Victor Eijkhout

Abstract

The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin.

Full access
Enrique Rosero
,
Lindsey E. Gulden
,
Zong-Liang Yang
,
Luis G. De Goncalves
,
Guo-Yue Niu
, and
Yasir H. Kaheil

Abstract

The ability of two versions of the Noah land surface model (LSM) to simulate the water cycle of the Little Washita River experimental watershed is evaluated. One version that uses the standard hydrological parameterizations of Noah 2.7 (STD) is compared another version that replaces STD’s subsurface hydrology with a simple aquifer model and topography-related surface and subsurface runoff parameterizations (GW). Simulations on a distributed grid at fine resolution are compared to the long-term distribution of observed daily-mean runoff, the spatial statistics of observed soil moisture, and locally observed latent heat flux. The evaluation targets the typical behavior of ensembles of models that use realistic, near-optimal sets of parameters important to runoff. STD and GW overestimate the ratio of runoff to evapotranspiration. In the subset of STD and GW runs that best reproduce the timing and the volume of streamflow, the surface-to-subsurface runoff ratio is overestimated and simulated streamflow is much flashier than observations. Both models’ soil columns wet and dry too quickly, implying that there are structural shortcomings in the formulation of STD that cannot be overcome by adding GW’s increased complexity to the model. In its current formulation, GW extremely underestimates baseflow’s contribution to total runoff and requires a shallow water table to function realistically. In the catchment (depth to water table >10 m), GW functions as a simple bucket model. Because model parameters are likely scale and site dependent, the need for even “physically based” models to be extensively calibrated for all domains on which they are applied is underscored.

Full access
Michael A. Brunke
,
Patrick Broxton
,
Jon Pelletier
,
David Gochis
,
Pieter Hazenberg
,
David M. Lawrence
,
L. Ruby Leung
,
Guo-Yue Niu
,
Peter A. Troch
, and
Xubin Zeng

Abstract

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.

Full access
Cenlin He
,
Fei Chen
,
Michael Barlage
,
Zong-Liang Yang
,
Jerry W. Wegiel
,
Guo-Yue Niu
,
David Gochis
,
David M. Mocko
,
Ronnie Abolafia-Rosenzweig
,
Zhe Zhang
,
Tzu-Shun Lin
,
Prasanth Valayamkunnath
,
Michael Ek
, and
Dev Niyogi
Open access